# Dataset papers used in the position paper

This is the **fixed snapshot of 647 dataset papers** used in *Autonomous Driving Research Requires a Community-Driven Data Paradigm*. It is not an actively or continuously updated catalog.

Entries are grouped by publication year. IDs preserve the original survey row order. Every paper title is shown; dataset names are included when supplied. Links lead to papers, not dataset downloads.

[Download CSV](source.csv) · [Snapshot provenance](README.md) · [Position paper](https://openreview.net/forum?id=HavgE17TFU)

## 2026 (41)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0444 | TruckDrive | TruckDrive: Long-Range Autonomous Highway Driving Dataset | [Paper](https://www.semanticscholar.org/paper/c0450e289c15cc517d7363e755a0d01440d1c3d9) |
| d0445 | Boreas Road Trip (Boreas-RT) | Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads | [Paper](https://doi.org/10.48550/arXiv.2602.16870) |
| d0466 | ADAS-TO | ADAS-TO: A Large-Scale Multimodal Naturalistic Dataset and Empirical Characterization of Human Takeovers during ADAS Engagement | [Paper](https://www.semanticscholar.org/paper/877ac592a0745cb28d375f005b901d162be954c3) |
| d0467 | doScenes | Natural Language Instructions for Scene-Responsive Human-in-the-Loop Motion Planning in Autonomous Driving using Vision-Language-Action Models | [Paper](https://doi.org/10.48550/arXiv.2602.04184) |
| d0511 | DSERT-RoLL | DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR | [Paper](https://www.semanticscholar.org/paper/63c2606baf40b29041c4783ad05a54a8e8f0169b) |
| d0512 | African Road Object Detection Dataset | Region-Specific Road Object Detection in African Environments: Dataset Curation, Benchmarking, and Edge Deployment | [Paper](https://doi.org/10.48084/etasr.17251) |
| d0513 | V2U4Real | V2U4Real: A Real-world Large-scale Dataset for Vehicle-to-UAV Cooperative Perception | [Paper](https://www.semanticscholar.org/paper/621f8f9fee9de69ede3fbf0844f57e9b4d1b5909) |
| d0514 | KITScenes LongTail | LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset | [Paper](https://www.semanticscholar.org/paper/d7723f67698bd59c4a0ac72558545b0c9149e56a) |
| d0515 | eAP (event-enhanced autonomous perception) | Toward Deep Representation Learning for Event-Enhanced Visual Autonomous Perception: The eAP Dataset | [Paper](https://doi.org/10.1109/TRO.2026.3677912) |
| d0516 | RAID | Towards Driver Behavior Understanding: Weakly-Supervised Risk Perception in Driving Scenes | [Paper](https://www.semanticscholar.org/paper/6a77f78ac16641ea3c610edf78f9d62e787ef8a0) |
| d0517 | CampusSyn | CampusSyn: A Real World Complex Environment Dataset for Vehicle-to-Vehicle Collaborative Perception | [Paper](https://doi.org/10.1109/ICICIP67436.2026.11417530) |
| d0518 | Dense Depth Map Dataset (author-collected) | Dense Depth Map Estimation Based on Camera–LiDAR Sensor Fusion | [Paper](https://doi.org/10.1109/JSEN.2025.3649237) |
| d0519 | Roadscapes | RoadscapesQA: A Multitask, Multimodal Dataset for Visual Question Answering on Indian Roads | [Paper](https://doi.org/10.48550/arXiv.2602.12877) |
| d0520 | BikeActions (FUSE-Bike platform) | BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition | [Paper](https://doi.org/10.48550/arXiv.2601.10521) |
| d0521 | WHU-PCPR | WHU-PCPR: A cross-platform heterogeneous point cloud dataset for place recognition in complex urban scenes | [Paper](https://doi.org/10.48550/arXiv.2601.06442) |
| d0522 | Campus Driving Dataset | ML-Based Perception and Control for Semi-Autonomous Electric Buggy in Smart Campus Environments | [Paper](https://doi.org/10.1109/COMSNETS67989.2026.11418235) |
| d0555 | ANAID (Autonomous Naturalistic Obstacle-Avoidance Interaction Dataset) | ANAID: Autonomous Naturalistic Obstacle-Avoidance Interaction Dataset | [Paper](https://doi.org/10.3390/data11040077) |
| d0556 | ArticuSurDepth dataset | Cross-Vehicle 3D Geometric Consistency for Self-Supervised Surround Depth Estimation on Articulated Vehicles | [Paper](https://www.semanticscholar.org/paper/6aa3e94e7a4c1464d73e57007b85eeb81203ef7d) |
| d0557 | VRUD (Vehicle-Vulnerable Road User Interaction Dataset) | VRUD: A Drone Dataset for Complex Vehicle-VRU Interactions within Mixed Traffic | [Paper](https://www.semanticscholar.org/paper/1b834dfadd641854b24aca0886634533ff7a87b0) |
| d0558 | Ghost-FWL | Ghost-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Ghost Detection and Removal | [Paper](https://www.semanticscholar.org/paper/8a1014b72db0792f9c2bfda2adc343cc005287c6) |
| d0559 | CustomizedSpeedDataset | Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving | [Paper](https://www.semanticscholar.org/paper/e449bdad7955b4dc3a55a7bcc8c95906ee94c13b) |
| d0560 | DarkDriving | DarkDriving: A Real-World Day and Night Aligned Dataset for Autonomous Driving in the Dark Environment | [Paper](https://www.semanticscholar.org/paper/610442edcdbca9ea08acde398acdfc0b4d1666d8) |
| d0561 | DriveXQA | DriveXQA: Cross-modal Visual Question Answering for Adverse Driving Scene Understanding | [Paper](https://www.semanticscholar.org/paper/ab1ccf656a9d9dd874284007d51579827e61f28d) |
| d0562 | SWIFTraj | The Swarm Intelligence Freeway-Urban Trajectories (SWIFTraj) Dataset -- Part I: Dataset Description and Applications | [Paper](https://www.semanticscholar.org/paper/15378b66652a73388e50e7d6ba5b4c766149a7d4) |
| d0563 | L-LIO (Looking-and-Listening Inside-and-Outside) dataset | Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making | [Paper](https://doi.org/10.48550/arXiv.2602.07668) |
| d0564 | DrivIng | DrivIng: A Large-Scale Multimodal Driving Dataset with Full Digital Twin Integration | [Paper](https://doi.org/10.48550/arXiv.2601.15260) |
| d0565 | UMAD (University of Malaya Autonomous Driving) | DeepFusion encoder for unsupervised monocular metric depth estimation | [Paper](https://doi.org/10.7717/peerj-cs.3299) |
| d0610 | G-MIND | G-MIND: Galway Multimodal Infrastructure Node Dataset for Intelligent Transportation Systems | [Paper](https://doi.org/10.1109/OJVT.2025.3648251) |
| d0611 | Roadside LiDAR Merging Zone Dataset | A Driving Trajectory and Intention Prediction Framework for Vehicle in Merging Zones Using Roadside LiDAR | [Paper](https://doi.org/10.1109/TASE.2026.3662294) |
| d0612 | MicroVision | MicroVision: An Open Dataset and Benchmark Models for Detecting Vulnerable Road Users and Micromobility Vehicles | [Paper](https://www.semanticscholar.org/paper/93d707b45bc4fd9e4d0ff01dfa6d297350a0ac8f) |
| d0613 | TSBOW | TSBOW: Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions | [Paper](https://doi.org/10.48550/arXiv.2602.05414) |
| d0614 | SparseLaneSTP dataset | SparseLaneSTP: Leveraging Spatio-Temporal Priors with Sparse Transformers for 3D Lane Detection | [Paper](https://doi.org/10.48550/arXiv.2601.04968) |
| d0621 | DesertFormer Off-Road Desert Terrain Dataset | DesertFormer: Transformer-Based Semantic Segmentation for Off-Road Desert Terrain Classification in Autonomous Navigation Systems | [Paper](https://www.semanticscholar.org/paper/ed9b738117d630d8f912e960b4bd9ce653691d0e) |
| d0622 | ABLDataset | A 360-degree Multi-camera System for Blue Emergency Light Detection Using Color Attention RT-DETR and the ABLDataset | [Paper](https://www.semanticscholar.org/paper/a95b0476e52f49b288be90a01fad340815e46c5d) |
| d0623 | TWVD-XMUT | Multi-view detection of two-wheeled vehicles on urban roads: a novel dataset and a tailored detector | [Paper](https://doi.org/10.1088/1361-6501/ae4d73) |
| d0624 | Off-Road Autonomous Vehicle Dataset | Off-Road Autonomous Vehicle Semantic Segmentation and Spatial Overlay Video Assembly | [Paper](https://doi.org/10.3390/s26061944) |
| d0625 | Verizon Connect Traffic Light Dataset (VZC-TLD) | Color Is Not Enough: Dataset and Method for Identifying Relevant Traffic Lights in Driving Scenes | [Paper](https://doi.org/10.1109/TITS.2025.3626165) |
| d0638 | Berlin Roadwork Dataset | Deep Neural Network Based Roadwork Detection for Autonomous Driving | [Paper](https://www.semanticscholar.org/paper/7cd9a0a8d957622c3e4742c568df501a6c09001f) |
| d0639 | C-TAO | COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm | [Paper](https://www.semanticscholar.org/paper/29dfa4d164088964981d8b69ac88eca58f4df146) |
| d0640 | LER (Left-Ego-Right) dataset | Flexible multitask deep learning for lane-aware panoptic segmentation in autonomous driving | [Paper](https://doi.org/10.7717/peerj-cs.3487) |
| d0647 | — | A Framework for Vehicular Instrumentation and Dataset Creation for Designing ADAS and Driver Intention Prediction Based on Eye Data | [Paper](https://doi.org/10.5220/0014263700004084) |

## 2025 (126)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0214 | Pi3DET | Perspective-Invariant 3D Object Detection | [Paper](https://doi.org/10.48550/arXiv.2507.17665) |
| d0240 | Para-Lane | Para-Lane: Multi-Lane Dataset Registering Parallel Scans for Benchmarking Novel View Synthesis | [Paper](https://doi.org/10.1109/3DV66043.2025.00034) |
| d0245 | DriveAction | DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models | [Paper](https://doi.org/10.48550/arXiv.2506.05667) |
| d0248 | R-LiViT | R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception | [Paper](https://doi.org/10.48550/arXiv.2503.17122) |
| d0251 | HiMo Highway Multi-LiDAR Dataset | HiMo: High-Speed Objects Motion Compensation in Point Clouds | [Paper](https://doi.org/10.1109/TRO.2025.3619042) |
| d0261 | DeepScenario Open 3D Dataset (DSC3D) | Highly Accurate and Diverse Traffic Data: The DeepScenario Open 3D Dataset | [Paper](https://doi.org/10.1109/IV64158.2025.11097484) |
| d0272 | UMH-Gardens, Coimbra-Liv | Ground Segmentation for LiDAR Point Clouds in Structured and Unstructured Environments Using a Hybrid Neural–Geometric Approach | [Paper](https://doi.org/10.3390/technologies13040162) |
| d0296 | PDB (Personalized Driving Behavior) | PDB: Not All Drivers Are the Same - A Personalized Dataset for Understanding Driving Behavior | [Paper](https://doi.org/10.48550/arXiv.2503.06477) |
| d0308 | Spotting the Unexpected (STU) | Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving | [Paper](https://doi.org/10.1109/CVPR52734.2025.01109) |
| d0316 | CS-WildPlaces | HOTFormerLoc: Hierarchical Octree Transformer for Versatile Lidar Place Recognition Across Ground and Aerial Views | [Paper](https://doi.org/10.1109/CVPR52734.2025.00623) |
| d0326 | Large-scale mmWave Radar Dataset (1M samples) | Towards Foundational Models for Single-Chip Radar | [Paper](https://doi.org/10.48550/arXiv.2509.12482) |
| d0327 | L-RadSet | L-RadSet: A Long-Range Multimodal Dataset With 4D Radar for Autonomous Driving and Its Application | [Paper](https://doi.org/10.1109/TIV.2024.3424942) |
| d0336 | SUS (Snowy Urban Scene) | Transferring Prior Thermal Knowledge for Snowy Urban Scene Semantic Segmentation | [Paper](https://doi.org/10.1109/TITS.2025.3555617) |
| d0340 | NightVehicle | HighlightNet: Learning Highlight-Guided Attention Network for Nighttime Vehicle Detection | [Paper](https://doi.org/10.1109/TITS.2025.3539095) |
| d0342 | RASMD (RGB and SWIR Multispectral Driving Dataset) | RASMD: RGB And SWIR Multispectral Driving Dataset for Robust Perception in Adverse Conditions | [Paper](https://doi.org/10.48550/arXiv.2504.07603) |
| d0343 | Dur360BEV | Dur360BEV: A Real-World 360-Degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving | [Paper](https://doi.org/10.1109/ICRA55743.2025.11128609) |
| d0351 | DORIE | DORIE: Dataset of Road Infrastructure Elements—A Benchmark of YOLO Architectures for Real-Time Patrol Vehicle Monitoring | [Paper](https://doi.org/10.3390/s25216653) |
| d0352 | UrbanIng-V2X | UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception | [Paper](https://doi.org/10.48550/arXiv.2510.23478) |
| d0353 | Mixed Signals | Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration | [Paper](https://doi.org/10.48550/arXiv.2502.14156) |
| d0359 | DLR Urban Traffic Dataset (DLR-UT) | The DLR Urban Traffic Dataset (DLR-UT): A Comprehensive Traffic Dataset from an Urban Research Intersection | [Paper](https://doi.org/10.1109/IV64158.2025.11097400) |
| d0367 | FastTracker-Benchmark | FastTracker: Real-Time and Accurate Visual Tracking | [Paper](https://doi.org/10.48550/arXiv.2508.14370) |
| d0368 | STRIDE-QA | STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes | [Paper](https://doi.org/10.48550/arXiv.2508.10427) |
| d0369 | MSD-VMMS-HK | Multimodal sensor dataset from vehicle-mounted mobile mapping system for comprehensive urban scenes | [Paper](https://doi.org/10.1038/s41597-025-05471-1) |
| d0370 | UniSense | UniSense: Spatial-Uncertainty-Aware Collaborative Sensing for Autonomous Driving | [Paper](https://doi.org/10.1145/3711875.3729130) |
| d0371 | OpenLKA | OpenLKA: An Open Dataset of Lane Keeping Assist from Recent Car Models under Real-world Driving Conditions | [Paper](https://doi.org/10.48550/arXiv.2505.09092) |
| d0372 | ATLAS | The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving | [Paper](https://doi.org/10.1109/IV64158.2025.11097347) |
| d0383 | TOMD (Trail-based Off-road Multimodal Dataset) | TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions | [Paper](https://doi.org/10.1109/IJCNN64981.2025.11229344) |
| d0384 | S2R-Bench | S2R-Bench: A Sim-to-Real Evaluation Benchmark for Autonomous Driving | [Paper](https://doi.org/10.1038/s41597-025-06255-3) |
| d0385 | FOV Ground Truth Dataset for Autonomous Vehicles | Probabilistic Segmentation for Robust Field of View Estimation | [Paper](https://doi.org/10.48550/arXiv.2503.07375) |
| d0396 | SR-SPECNet Radar Dataset | Model-Based Knowledge-Driven Learning Approach for Enhanced High-Resolution Automotive Radar Imaging | [Paper](https://doi.org/10.1109/TRS.2025.3563492) |
| d0399 | NVHD-20K | Toward Large-Scale Non-Motorized Vehicle Helmet Wearing Detection: A New Benchmark and Beyond | [Paper](https://doi.org/10.1109/TCE.2025.3527678) |
| d0402 | DOROS | DOROS: A multilevel traffic dataset for dynamic urban scene understanding | [Paper](https://doi.org/10.4218/etrij.2025-0063) |
| d0403 | DriveIndia | Driveindia: an Object Detection Dataset for Diverse Indian Traffic Scenes | [Paper](https://doi.org/10.1109/ITSC60802.2025.11423478) |
| d0404 | PHE3D | Accurate 3D Multi-Object Detection and Tracking on Vietnamese Street Scenes Based on Sparse Point Cloud Data | [Paper](https://doi.org/10.1109/TITS.2024.3483953) |
| d0417 | BikeScenes-lidarseg | BikeScenes: Online LiDAR Semantic Segmentation for Bicycles | [Paper](https://doi.org/10.48550/arXiv.2510.25901) |
| d0418 | ZJUSSet | RaSS: 4D mm-Wave Radar Point Cloud Semantic Segmentation with Cross-Modal Knowledge Distillation | [Paper](https://doi.org/10.3390/s25175345) |
| d0419 | GSN | WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver Assistance Systems | [Paper](https://doi.org/10.1109/TIM.2026.3655940) |
| d0420 | Crowded Pedestrian Multi-view LiDAR-Camera 3D MOT Benchmark | Learning Better Representations for Crowded Pedestrians in Offboard LiDAR-Camera 3D Tracking-by-detection | [Paper](https://doi.org/10.1109/ICRA55743.2025.11128508) |
| d0430 | SemanticLiDAR | Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans | [Paper](https://doi.org/10.1109/ITSC60802.2025.11423654) |
| d0442 | FNTVD | Enhanced Nighttime Vehicle Detection for On-Board Processing | [Paper](https://doi.org/10.1109/ACCESS.2025.3548837) |
| d0446 | Multi-Vehicle Dataset with Camera, LiDAR, Radar, and Scanned 3D Models | A Multi-Vehicle Dataset with Camera, LiDAR, and Radar Sensors and Scanned 3D Models for Custom Auto-Annotation using RTK-GNSS | [Paper](https://doi.org/10.1109/SDF67080.2025.11331266) |
| d0447 | GRiN-Drive | GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions | [Paper](https://doi.org/10.48550/arXiv.2508.21102) |
| d0448 | CaSSeD (CAVS Semantic Segmentation Dataset) | CAVS semantic segmentation dataset for off-road autonomous vehicles | [Paper](https://doi.org/10.1117/12.3053940) |
| d0449 | LiDARDustX | LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments | [Paper](https://doi.org/10.1109/ICRA55743.2025.11127917) |
| d0450 | M3DSS | M3DSS: A Multi-Platform, Multi-Sensor, and Multi-Scenario Dataset for SLAM System | [Paper](https://doi.org/10.1109/ICRA55743.2025.11128631) |
| d0451 | TUM Traffic Accid3nD (TUMTraf-Accid3nD) | Towards Vision Zero: The TUM Traffic Accid3nD Dataset | [Paper](https://doi.org/10.1109/ICCVW69036.2025.00093) |
| d0452 | Emirates Multi-Task (EMT) Dataset | EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region | [Paper](https://doi.org/10.48550/arXiv.2502.19260) |
| d0468 | Nigerian Roads Object Detection Dataset | Real-Time YOLOv5-Based Object Detection for Autonomous Vehicles on Nigerian Roads | [Paper](https://doi.org/10.65138/ijramt.2025.v6i12.3168) |
| d0469 | NavControl | Closing the Navigation Compliance Gap in End-to-end Autonomous Driving | [Paper](https://www.semanticscholar.org/paper/3b68b5d8a5599febd5d0f137ca44203d78405585) |
| d0470 | E-Scooter Trajectory Dataset | Multi-Trajectory Prediction for E-Scooter Riders with Multi-Modal Inputs | [Paper](https://doi.org/10.1109/ITSC60802.2025.11423003) |
| d0471 | Multimodal Light Field Dataset | A Collaborative Error-Tolerant Synchronization Framework With its Application in Multimodal Light Field System Development | [Paper](https://doi.org/10.1109/JSEN.2025.3611499) |
| d0472 | City Crossings Dataset (CiCross) | IntersectioNDE: Learning Complex Urban Traffic Dynamics Based on Interaction Decoupling Strategy | [Paper](https://doi.org/10.1109/ITSC60802.2025.11423034) |
| d0473 | I-24 MOTION Scenario Dataset (I24-MSD) | Noise-Aware Generative Microscopic Traffic Simulation | [Paper](https://doi.org/10.48550/arXiv.2508.07453) |
| d0474 | Unstructured Scene Understanding Dataset (inferred) | Scene as Occupancy and Reconstruction: A Comprehensive Dataset for Unstructured Scene Understanding | [Paper](https://doi.org/10.1038/s41597-025-05532-5) |
| d0475 | Perth CBD High-Resolution LiDAR Map with GNSS and IMU Calibration | LiDAR, GNSS and IMU Sensor Fine Alignment through Dynamic Time Warping to Construct 3D City Maps | [Paper](https://www.semanticscholar.org/paper/9d7de28f31082e48ea55524cb9a6fd2fc7933b4c) |
| d0476 | Drive-Gaze | Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis | [Paper](https://doi.org/10.48550/arXiv.2506.23263) |
| d0477 | HiLO dataset | HiLO: High-Level Object Fusion for Autonomous Driving Using Transformers | [Paper](https://doi.org/10.1109/IV64158.2025.11097611) |
| d0478 | BETTY Dataset | BETTY Dataset: A Multi-Modal Dataset for Full-Stack Autonomy | [Paper](https://doi.org/10.1109/ICRA55743.2025.11127350) |
| d0479 | CMHT autonomous dataset | CMHT autonomous dataset: A multi-sensor dataset including radar and IR for autonomous driving | [Paper](https://doi.org/10.1016/j.dib.2025.111552) |
| d0480 | Multi-Spectral Stereo (MS²) Dataset | Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges | [Paper](https://doi.org/10.48550/arXiv.2503.22060) |
| d0481 | Panoptic-CUDAL | Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions | [Paper](https://doi.org/10.1109/ITSC60802.2025.11423256) |
| d0482 | FreeWorld Dataset | Bench2FreeAD: A Benchmark for Vision-based End-to-end Navigation in Unstructured Robotic Environments | [Paper](https://doi.org/10.48550/arXiv.2503.12180) |
| d0483 | Hazard QA | Hazardnet: A Small-Scale Vision Language Model for Real-Time Traffic Safety Detection at Edge Devices | [Paper](https://doi.org/10.1109/ICMI65310.2025.11141337) |
| d0495 | FLUID | FLUID: A Fine-Grained Lightweight Urban Signalized-Intersection Dataset of Dense Conflict Trajectories | [Paper](https://doi.org/10.48550/arXiv.2509.00497) |
| d0499 | RLMDAC | RMSeg-UDA: Unsupervised Domain Adaptation for Road Marking Segmentation Under Adverse Conditions | [Paper](https://doi.org/10.1109/ICRA55743.2025.11128761) |
| d0500 | DenseOoS and QuadOoS | Panoramic Out-of-Distribution Segmentation | [Paper](https://doi.org/10.48550/arXiv.2505.03539) |
| d0501 | ComsAmy | Open-set Anomaly Segmentation in Complex Scenarios | [Paper](https://doi.org/10.48550/arXiv.2504.19706) |
| d0506 | SL (Surface-Lane) | SL-Seg: A CNN-Transformer Fusion Network for Road Surface and Lane Segmentation in Complex Scenarios | [Paper](https://doi.org/10.1109/TITS.2025.3615568) |
| d0523 | DARTS | Semi-Automated Data Annotation in Multisensor Datasets for Autonomous Vehicle Testing | [Paper](https://doi.org/10.48550/arXiv.2512.24896) |
| d0524 | HDSVT (High-Density Semantic Vehicle Trajectory Dataset) | HDSVT: High-Density Semantic Vehicle Trajectory Dataset Based on a Cosmopolitan City Bridge | [Paper](https://doi.org/10.1038/s41597-025-05603-7) |
| d0525 | — | Estimation of Kinematic Motion from Dashcam Footage | [Paper](https://doi.org/10.48550/arXiv.2512.01104) |
| d0526 | DA4D | DetAny4D: Detect Anything 4D Temporally in a Streaming RGB Video | [Paper](https://doi.org/10.48550/arXiv.2511.18814) |
| d0527 | — | From City Streets to Country Roads: Can Automated Vehicles Generalize Lane Detection? | [Paper](https://doi.org/10.1109/IDST67888.2025.11469998) |
| d0528 | CADD (Chinese Accident Duty-determination Dataset) | CADD: A Chinese Traffic Accident Dataset for Statute-Based Liability Attribution | [Paper](https://doi.org/10.48550/arXiv.2511.11715) |
| d0529 | RoboTerra | RoboTerra: Multi-Sensor Dataset from Mobile Robot Navigation in Challenging Off-Road Terrains | [Paper](https://doi.org/10.23919/ICCAS66577.2025.11301146) |
| d0530 | VIL-PPGen Dataset | VIL-PPGen: A Novel Pseudo Point Generator Based on Visible Light Camera, Infrared Camera and Lidar | [Paper](https://doi.org/10.1109/TIV.2024.3511923) |
| d0531 | SDIRF (Stereo Driving In Real Fog) | Estimating Fog Parameters From a Sequence of Stereo Images | [Paper](https://doi.org/10.1109/TPAMI.2025.3626275) |
| d0532 | WoodScape iToF NFL LiDAR dataset | The WoodScape iToF NFL LiDAR dataset | [Paper](https://doi.org/10.1109/ICVES65691.2025.11376416) |
| d0533 | Tram Pedestrian Trajectory Dataset | Towards Pedestrian Trajectory Prediction in Tram Environments for Safe and Efficient Autonomous Tram Systems: Dataset Development | [Paper](https://doi.org/10.1109/ICIRT66379.2025.11216747) |
| d0534 | Chinese Expressway Stereo Dataset (implied from B-BEV evaluation) | B-BEV: A Cost-Effective Framework for Geometric Map Construction in Highway Environments | [Paper](https://doi.org/10.1109/BDAI66031.2025.11325576) |
| d0535 | Non-conventional Local Vehicle Detection Dataset | Deep Learning for Non-Conventional Local Vehicle Detection and Classification using YOLOv11 | [Paper](https://doi.org/10.1109/INTERCON67304.2025.11244705) |
| d0536 | ROVR | ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving | [Paper](https://doi.org/10.48550/arXiv.2508.13977) |
| d0537 | CoVeRaP | CoVeRaP: Cooperative Vehicular Perception through mmWave FMCW Radars | [Paper](https://doi.org/10.1109/ICCCN65249.2025.11133916) |
| d0538 | AIRCloud | AIRCloud: Um Dataset Segmentado de Nuvens de Pontos 3D no Brasil | [Paper](https://doi.org/10.5753/semish.2025.8245) |
| d0539 | NSTRain | Practicality of Translation-Assisted Monocular Depth Estimation for Rainy Weather | [Paper](https://doi.org/10.1109/AIRC64931.2025.11077516) |
| d0540 | Highway10K | RevPv8: Reverse Information for Road Space and Lane Line Segmentation in Highway Surveillance Scene | [Paper](https://doi.org/10.1109/ICASSP49660.2025.10889465) |
| d0541 | Event-Based Crossing Dataset (EBCD) | Event-Based Crossing Dataset (EBCD) | [Paper](https://doi.org/10.48550/arXiv.2503.17499) |
| d0542 | CADC-SOT | Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions | [Paper](https://doi.org/10.48550/arXiv.2501.07133) |
| d0566 | HK-MEMS Dataset | HK‐MEMS, a Multi‐Sensor Data Set With MEMS LiDAR on Degenerate and Dynamic Urban Scenarios | [Paper](https://doi.org/10.1002/rob.70136) |
| d0567 | Intention-Drive | From Human Intention to Action Prediction: Intention-Driven End-to-End Autonomous Driving | [Paper](https://www.semanticscholar.org/paper/ac765fefe6b590ddd7ad41272982cfed1dff6286) |
| d0568 | VibV | A VibV Dataset Integrating Vibration and Vision for Enhanced Safety in Self-Driving Tasks | [Paper](https://doi.org/10.1038/s41597-025-06382-x) |
| d0569 | SceneEdited | SceneEdited: A City-Scale Benchmark for 3D HD Map Updating via Image-Guided Change Detection | [Paper](https://doi.org/10.48550/arXiv.2511.15153) |
| d0570 | PAVE | PAVE: An End-to-End Dataset for Production Autonomous Vehicle Evaluation | [Paper](https://doi.org/10.48550/arXiv.2511.14185) |
| d0571 | UrbanV2X | UrbanV2X: A Multisensory Vehicle-Infrastructure Dataset for Cooperative Navigation in Urban Areas | [Paper](https://doi.org/10.1109/ITSC60802.2025.11423762) |
| d0572 | UT-NLOS Dataset | SG-NLOSTrack: Semantic-Guided Non-Line-of-Sight Target Tracking | [Paper](https://doi.org/10.1109/ITSC60802.2025.11423163) |
| d0573 | AutoMine-Benchmark | A Visual Benchmark for Autonomous Driving in Open-Pit Mines | [Paper](https://doi.org/10.1109/TPAMI.2025.3632357) |
| d0574 | Tesla TLSSC Field Dataset | Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights | [Paper](https://doi.org/10.48550/arXiv.2512.11802) |
| d0575 | SIGBench | Towards Physics-informed Spatial Intelligence with Human Priors: An Autonomous Driving Pilot Study | [Paper](https://doi.org/10.48550/arXiv.2510.21160) |
| d0576 | BlendCLIP object-level triplets dataset | BlendCLIP: Bridging Synthetic and Real Domains for Zero-Shot 3D Object Classification with Multimodal Pretraining | [Paper](https://doi.org/10.48550/arXiv.2510.18244) |
| d0577 | CAM-based trajectory prediction dataset | CAMNet: Leveraging Cooperative Awareness Messages for Vehicle Trajectory Prediction | [Paper](https://doi.org/10.1109/CCNC65079.2026.11366398) |
| d0578 | Light Field-LiDAR Semantic Segmentation Dataset | Geometry-Aware Cross Modal Alignment for Light Field-LiDAR Semantic Segmentation | [Paper](https://www.semanticscholar.org/paper/d6982f87720cd10e24e13bd0d3cc445ca10154bd) |
| d0579 | PIPD | FUSION-IGV: Design and Implementation of a Multi-Sensor Fusion Framework for Port IGVs | [Paper](https://doi.org/10.1109/ICUS66297.2025.11294063) |
| d0580 | AMR Multispectral Dataset (thermal and visible spectrum) | Safety in Outdoor Applications: Multispectral Deep Fusion Approach with Distance Estimation | [Paper](https://doi.org/10.1109/ETFA65518.2025.11205654) |
| d0581 | Intersection-Flow-5k | FlowDet: Overcoming Perspective and Scale Challenges in Real-Time End-to-End Traffic Detection | [Paper](https://doi.org/10.48550/arXiv.2508.19565) |
| d0582 | RoadSight | I2V-GS: Infrastructure-to-Vehicle View Transformation with Gaussian Splatting for Autonomous Driving Data Generation | [Paper](https://doi.org/10.48550/arXiv.2507.23683) |
| d0583 | SmartPNT-MSF | SmartPNT-MSF: A Multi-Sensor Fusion Dataset for Positioning and Navigation Research | [Paper](https://doi.org/10.48550/arXiv.2507.19079) |
| d0584 | MRC-VMap dataset (4,000 intersections across 4 major metropolitan areas in China) | Learning to Generate Vectorized Maps at Intersections with Multiple Roadside Cameras | [Paper](https://doi.org/10.1109/IROS60139.2025.11246548) |
| d0585 | IPRPAS | IPRPAS: A Dataset of Physical Adversarial Samples for Assessing Object Detection in Intelligent Vehicles | [Paper](https://doi.org/10.1109/JIOT.2025.3550048) |
| d0586 | STRIDE | TARDIS STRIDE: A Spatio-Temporal Road Image Dataset and World Model for Autonomy | [Paper](https://doi.org/10.48550/arXiv.2506.11302) |
| d0587 | SensorRainFall | Perception Characteristics Distance: Measuring Stability and Robustness of Perception System in Dynamic Conditions under a Certain Decision Rule | [Paper](https://doi.org/10.48550/arXiv.2506.09217) |
| d0588 | IDD-PeD (Indian Driving Pedestrian Dataset) | Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeD | [Paper](https://doi.org/10.1109/ICRA55743.2025.11128395) |
| d0589 | SILM dataset | SILM: A Subjective Intent Based Low-Latency Framework for Multiple Traffic Participants Joint Trajectory Prediction | [Paper](https://doi.org/10.1109/IROS60139.2025.11246264) |
| d0590 | SydneyScapes | SydneyScapes: Image Segmentation for Australian Environments | [Paper](https://doi.org/10.48550/arXiv.2504.07542) |
| d0591 | V2I-HD Mapping Dataset | A Benchmark for Vision-Centric HD Mapping by V2I Systems | [Paper](https://doi.org/10.1109/IV64158.2025.11097695) |
| d0592 | Tractor Road Scene Dataset | FrustumFusionNets: A Three-Dimensional Object Detection Network Based on Tractor Road Scene | [Paper](https://doi.org/10.48550/arXiv.2503.13951) |
| d0593 | PFSD (Pedestrian-Focused Scene Dataset) | PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments | [Paper](https://doi.org/10.48550/arXiv.2502.15342) |
| d0594 | RoRaTrack | A Racing Dataset and Baseline Model for Track Detection in Autonomous Racing | [Paper](https://doi.org/10.48550/arXiv.2502.14068) |
| d0615 | Pandar128 | Pandar128 dataset for lane line detection | [Paper](https://doi.org/10.48550/arXiv.2511.07084) |
| d0626 | Text2Loc++ dataset | Text2Loc++: Generalizing 3D Point Cloud Localization from Natural Language | [Paper](https://doi.org/10.48550/arXiv.2511.15308) |
| d0627 | TerraVerse | TerraVerse: A Multimodal Sensor Dataset for Unstructured Scenes Spanning Multiple Regions in China | [Paper](https://doi.org/10.1109/ICUS66297.2025.11295498) |
| d0628 | Racetrack Rolling-shutter Stereo Visual Odometry Dataset | Racetrack Rolling-shutter Stereo Visual Odometry and Dataset | [Paper](https://doi.org/10.1109/ECMR65884.2025.11163197) |
| d0629 | TRoad and TLane | Incorporating Onboard Visual Sensing and Geographic Information for Lane-Level Vehicle Localization | [Paper](https://doi.org/10.1109/JSEN.2025.3557355) |
| d0641 | VBR-D (Vehicle Driving Behavior Recognition Dataset) | A Lightweight Hybrid Network for Vehicle Driving Behavior Recognition | [Paper](https://doi.org/10.1109/SMC58881.2025.11342639) |
| d0642 | Osijek Lane Detection Dataset | A New Tool for Lane Line Annotation, a New Dataset and a Comparison of Existing Deep Learning-based Lane Line Detection Models | [Paper](https://doi.org/10.1109/ELMAR66948.2025.11194029) |
| d0643 | IRDA (Intention-Related Driving Attention Dataset) | CGVA: Cognitive Guided Visual Attention Selection in Traffic Driving Environment | [Paper](https://doi.org/10.1109/IV64158.2025.11097518) |
| d0644 | UNFLAPSet | Dataset for unflappable driving: UNFLAPSet | [Paper](https://doi.org/10.1186/s40537-025-01101-0) |

## 2024 (140)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0019 | SUP-AD | DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models | [Paper](https://doi.org/10.48550/arXiv.2402.12289) |
| d0055 | TUMTraf V2X | TUMTraf V2X Cooperative Perception Dataset | [Paper](https://doi.org/10.1109/CVPR52733.2024.02139) |
| d0078 | RCooper | RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception | [Paper](https://doi.org/10.1109/CVPR52733.2024.02109) |
| d0081 | CoVLA | CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving | [Paper](https://doi.org/10.1109/WACV61041.2025.00195) |
| d0108 | MAN TruckScenes | MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions | [Paper](https://doi.org/10.48550/arXiv.2407.07462) |
| d0118 | Road Surface Reconstruction Dataset | A road surface reconstruction dataset for autonomous driving | [Paper](https://doi.org/10.1038/s41597-024-03261-9) |
| d0124 | TartanDrive 2.0 | TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks | [Paper](https://doi.org/10.1109/ICRA57147.2024.10611265) |
| d0131 | Fixated Object Detection Dataset | Fixated Object Detection Based on Saliency Prior in Traffic Scenes | [Paper](https://doi.org/10.1109/TCSVT.2023.3295058) |
| d0136 | LMOT (Low-light Multi-Object Tracking) | Multi-Object Tracking in the Dark | [Paper](https://doi.org/10.1109/CVPR52733.2024.00044) |
| d0140 | MUSES | MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty | [Paper](https://doi.org/10.1007/978-3-031-73202-7_2) |
| d0142 | ZJU-4DRadarCam | RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale | [Paper](https://doi.org/10.1109/ICRA57147.2024.10610929) |
| d0145 | HoloVic | HoloVic:Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative | [Paper](https://doi.org/10.1109/CVPR52733.2024.02089) |
| d0146 | RSUD20K | RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving | [Paper](https://doi.org/10.48550/arXiv.2401.07322) |
| d0152 | V2X-Radar | V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception | [Paper](https://doi.org/10.48550/arXiv.2411.10962) |
| d0156 | WTS | WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding | [Paper](https://doi.org/10.48550/arXiv.2407.15350) |
| d0157 | DrivingDojo | DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model | [Paper](https://doi.org/10.48550/arXiv.2410.10738) |
| d0161 | eTraM | eTraM: Event-Based Traffic Monitoring Dataset | [Paper](https://doi.org/10.1109/CVPR52733.2024.02136) |
| d0162 | V2XPnP Sequential Dataset | V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction | [Paper](https://doi.org/10.48550/arXiv.2412.01812) |
| d0167 | MARS | Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset | [Paper](https://doi.org/10.1109/CVPR52733.2024.02081) |
| d0168 | VBR (Vision Benchmark in Rome) | VBR: A Vision Benchmark in Rome | [Paper](https://doi.org/10.1109/ICRA57147.2024.10611395) |
| d0176 | OORD: The Oxford Offroad Radar Dataset | OORD: The Oxford Offroad Radar Dataset | [Paper](https://doi.org/10.1109/TITS.2024.3424984) |
| d0177 | MIAS-LCEC datasets | Online, Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching | [Paper](https://doi.org/10.1109/TIV.2024.3456299) |
| d0180 | V2AIX | V2AIX: A Multi-Modal Real-World Dataset of ETSI ITS V2X Messages in Public Road Traffic | [Paper](https://doi.org/10.1109/ITSC58415.2024.10920150) |
| d0196 | Complex Road Scene (CRS) dataset | Enhanced Scene Understanding and Situation Awareness for Autonomous Vehicles Based on Semantic Segmentation | [Paper](https://doi.org/10.1109/TSMC.2024.3403859) |
| d0197 | OmniHD-Scenes | OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous Driving | [Paper](https://doi.org/10.48550/arXiv.2412.10734) |
| d0205 | Third Generation Simulation Dataset | Introduction to the Third Generation Simulation Dataset: Data Collection and Trajectory Extraction | [Paper](https://doi.org/10.1177/03611981241257257) |
| d0209 | DrivingContexts | ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving Using Vision Language Models | [Paper](https://doi.org/10.1109/ITSC58415.2024.10920066) |
| d0213 | VBLane | TCLaneNet: Task-Conditioned Lane Detection Network Driven by Vibration Information | [Paper](https://doi.org/10.1109/TIV.2024.3356813) |
| d0215 | HeLiMOS | HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors | [Paper](https://doi.org/10.1109/IROS58592.2024.10801938) |
| d0216 | WayveScenes101 | WayveScenes101: A Dataset and Benchmark for Novel View Synthesis in Autonomous Driving | [Paper](https://doi.org/10.48550/arXiv.2407.08280) |
| d0217 | ROADWork | ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones | [Paper](https://www.semanticscholar.org/paper/a25157011d4a8a1dbc64bb19fac1ba95cc7af9e8) |
| d0218 | MSP dataset | Understanding LiDAR Performance for Autonomous Vehicles Under Snowfall Conditions | [Paper](https://doi.org/10.1109/TITS.2024.3409907) |
| d0219 | SNAIL radar | SNAIL radar: A large-scale diverse benchmark for evaluating 4D-radar-based SLAM | [Paper](https://doi.org/10.1177/02783649251329048) |
| d0223 | NJFU | Research on multitask model of object detection and road segmentation in unstructured road scenes | [Paper](https://doi.org/10.1088/1361-6501/ad35dd) |
| d0224 | DeepSense-V2V | DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset | [Paper](https://doi.org/10.1109/TVT.2025.3578349) |
| d0230 | ITD (Indian Traffic Dataset) | ITD: Indian Traffic Dataset for Intelligent Transportation Systems | [Paper](https://doi.org/10.1109/COMSNETS59351.2024.10427394) |
| d0241 | RoScenes | RoScenes: A Large-scale Multi-view 3D Dataset for Roadside Perception | [Paper](https://doi.org/10.48550/arXiv.2405.09883) |
| d0249 | SID (Stereo Image Dataset) | SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions | [Paper](https://doi.org/10.1109/NAECON61878.2024.10670659) |
| d0252 | WildOcc | WildOcc: A Benchmark for Off-Road 3D Semantic Occupancy Prediction | [Paper](https://doi.org/10.48550/arXiv.2410.15792) |
| d0253 | Roadside Radar and Video Dataset (RRVD) and Traffic flow Parameter Estimation Dataset (TPED) | Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception | [Paper](https://doi.org/10.1080/15472450.2024.2390844) |
| d0254 | TIAND (TiHAN-IITH Autonomous Navigation Dataset) | TIAND: A Multimodal Dataset for Autonomy on Indian Roads | [Paper](https://doi.org/10.1109/IV55156.2024.10588583) |
| d0255 | ZJU-Multispectrum | RIDERS: Radar-Infrared Depth Estimation for Robust Sensing | [Paper](https://doi.org/10.1109/TITS.2024.3432996) |
| d0257 | ML3DOP | 3DOPFormer: 3D Occupancy Perception From Multi-Camera Images With Directional and Distance Enhancement | [Paper](https://doi.org/10.1109/TIV.2023.3343749) |
| d0267 | MapDR | Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map | [Paper](https://doi.org/10.1109/CVPR52734.2025.00640) |
| d0268 | milliSonic | mmPlace: Robust Place Recognition With Intermediate Frequency Signal of Low-Cost Single-Chip Millimeter Wave Radar | [Paper](https://doi.org/10.1109/LRA.2024.3377562) |
| d0284 | 4D Radar-Camera-LiDAR Dataset | Semantic-Guided Depth Completion From Monocular Images and 4D Radar Data | [Paper](https://doi.org/10.1109/TIV.2024.3355125) |
| d0285 | Bosch Street Dataset (BSD) | Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving | [Paper](https://doi.org/10.48550/arXiv.2407.12803) |
| d0286 | View-of-Delft Prediction | Multi-Class Trajectory Prediction in Urban Traffic Using the View-of-Delft Prediction Dataset | [Paper](https://doi.org/10.1109/LRA.2024.3385693) |
| d0287 | IAMCV (Interaction of Autonomous and Manually Controlled Vehicles) | Interaction of Autonomous and Manually Controlled Vehicles Multiscenario Vehicle Interaction Dataset | [Paper](https://doi.org/10.1109/MITS.2024.3378114) |
| d0295 | Novel raw image dataset with pedestrians and cars | Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection | [Paper](https://doi.org/10.1109/ACCESS.2023.3348663) |
| d0297 | MIPD | MIPD: A Multi-Sensory Interactive Perception Dataset for Embodied Intelligent Driving | [Paper](https://doi.org/10.1109/TITS.2025.3593298) |
| d0314 | Bangladesh Native Vehicle Dataset (BNVD) | Bangladeshi Native Vehicle Detection in Wild | [Paper](https://doi.org/10.48550/arXiv.2405.12150) |
| d0317 | Not explicitly named in abstract | Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology | [Paper](https://doi.org/10.1109/TIV.2024.3360273) |
| d0318 | D2E (Driver to Evaluation dataset) | D2E: An Autonomous Decision-Making Dataset involving Driver States and Human Evaluation of Driving Behavior | [Paper](https://doi.org/10.1109/ITSC58415.2024.10919714) |
| d0328 | SR-SPECNet radar dataset | Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance | [Paper](https://doi.org/10.48550/arXiv.2406.07399) |
| d0329 | RSU-SF | FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units | [Paper](https://doi.org/10.1109/TITS.2024.3442861) |
| d0335 | LIVOX-Road | Scene-Aware Online Calibration of LiDAR and Cameras for Driving Systems | [Paper](https://doi.org/10.1109/TIM.2023.3342241) |
| d0341 | RAOD (Road Abandoned Object Detection) | RAOD: A Benchmark for Road Abandoned Object Detection From Video Surveillance | [Paper](https://doi.org/10.1109/ACCESS.2024.3407955) |
| d0344 | PanKyo | Domain-Invariant 3D Structural Convolutional Network for Autonomous Driving Point Cloud Dataset | [Paper](https://doi.org/10.1109/IV55156.2024.10588622) |
| d0345 | ESP-dataset | ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in Emergency Scenarios | [Paper](https://doi.org/10.1109/ICRA57147.2024.10610002) |
| d0354 | MPRC (Monitoring Perspective for Radar and Camera) | RVIFNet: Radar–Visual Information Fusion Network for All-Weather Vehicle Perception in Roadside Monitoring Scenarios | [Paper](https://doi.org/10.1109/JSEN.2024.3481492) |
| d0355 | DeepUrban | DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery | [Paper](https://doi.org/10.1109/ITSC58415.2024.10919949) |
| d0362 | RobotCycle | RobotCycle: Assessing Cycling Safety in Urban Environments | [Paper](https://doi.org/10.1109/IV55156.2024.10588375) |
| d0365 | — | Advancements in Lane Marking Detection: An Extensive Evaluation of Current Methods and Future Research Direction | [Paper](https://doi.org/10.1109/TIV.2024.3369733) |
| d0366 | — | Infrastructure-based Perception with Cameras and Radars for Cooperative Driving Scenarios | [Paper](https://doi.org/10.1109/IV55156.2024.10588604) |
| d0373 | Heterogeneous Traffic Labeled Dataset (HTLD) | Multi-Class Vehicle Detection Using VDnet in Heterogeneous Traffic | [Paper](https://doi.org/10.1109/TITS.2024.3476122) |
| d0374 | MLDAS (Multi-LiDARs Domain Adaptation Segmentation) | Bridging LiDAR Gaps: A Multi-LiDARs Domain Adaptation Dataset for 3D Semantic Segmentation | [Paper](https://doi.org/10.24963/ijcai.2024/72) |
| d0375 | RPTU-Forest dataset | Exploring Image Fusion Techniques for Off-Road Semantic Segmentation in Harsh Lighting Conditions. A Multispectral Imagery Analysis | [Paper](https://doi.org/10.1109/UR61395.2024.10597528) |
| d0386 | ROAD-Almaty | Domain Generalization in Autonomous Driving: Evaluating YOLOv8s, RT-DETR, and YOLO-NAS with the ROAD-Almaty Dataset | [Paper](https://doi.org/10.48550/arXiv.2412.12349) |
| d0387 | Winter Driving Dataset | Trajectory-Based Road Autolabeling With Lidar-Camera Fusion in Winter Conditions | [Paper](https://doi.org/10.1109/ACCESS.2025.3582061) |
| d0388 | fisheye Dominant dataset | GenerOcc: Self-supervised Framework of Real-time 3D Occupancy Prediction for Monocular Generic Cameras | [Paper](https://doi.org/10.1109/IROS58592.2024.10802337) |
| d0389 | ADUULM-360 | The ADUULM-360 Dataset - A Multi-Modal Dataset for Depth Estimation in Adverse Weather | [Paper](https://doi.org/10.1109/ITSC58415.2024.10920201) |
| d0390 | NeRF2Points Urban Street Dataset | NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization | [Paper](https://doi.org/10.48550/arXiv.2404.04875) |
| d0397 | Micromobility Lane Recognition Dataset (MLRD) | Using Attention Mechanisms in Compact CNN Models for Improved Micromobility Safety Through Lane Recognition | [Paper](https://doi.org/10.5220/0012600300003702) |
| d0398 | Driver Attention Tracking Dataset | Driver Attention Tracking and Analysis | [Paper](https://doi.org/10.48550/arXiv.2404.07122) |
| d0401 | DivNEDS (Diverse Naturalistic Edge Driving Scene Dataset) | DivNEDS: Diverse Naturalistic Edge Driving Scene Dataset for Autonomous Vehicle Scene Understanding | [Paper](https://doi.org/10.1109/ACCESS.2024.3394530) |
| d0405 | OccluRoads | Prediction of Occluded Pedestrians in Road Scenes Using Human-Like Reasoning: Insights from the OccluRoads Dataset | [Paper](https://doi.org/10.1109/IV64158.2025.11097510) |
| d0406 | FishOBB | Toward Oriented Fisheye Object Detection: Dataset and Baseline | [Paper](https://doi.org/10.1145/3702640) |
| d0407 | AUTONOMOS-LABS | Impact of 3D LiDAR Resolution in Graph-Based SLAM Approaches: A Comparative Study | [Paper](https://doi.org/10.1109/ROBOT61475.2024.10796891) |
| d0408 | M3-GMN | M3-GMN: A Multi-environment, Multi-LiDAR, Multi-task dataset for Grid Map based Navigation | [Paper](https://doi.org/10.1109/IROS58592.2024.10802553) |
| d0409 | EuroCity Persons 2.0 | EuroCity Persons 2.0: A Large and Diverse Dataset of Persons in Traffic | [Paper](https://doi.org/10.1109/TPAMI.2024.3471170) |
| d0410 | InScope | InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios | [Paper](https://doi.org/10.1016/j.inffus.2025.103951) |
| d0411 | Campus Map | Campus Map: A Large-Scale Dataset to Support Multi-View VO, SLAM and BEV Estimation | [Paper](https://doi.org/10.1109/ICRA57147.2024.10610656) |
| d0412 | DIDLM | DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LiDAR, 4D Radar, and Others Under Adverse Weather, Low Light Conditions, and Rough Roads | [Paper](https://doi.org/10.1109/TITS.2025.3632411) |
| d0413 | VRUNet | VRUFinder: A Real-Time, Infrastructure-Sensor- Enabled Framework for Recognizing Vulnerable Road Users | [Paper](https://doi.org/10.1109/JSEN.2024.3356528) |
| d0421 | — | Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving | [Paper](https://doi.org/10.48550/arXiv.2411.09971) |
| d0422 | OTVIC | OTVIC: A Dataset with Online Transmission for Vehicle-to-Infrastructure Cooperative 3D Object Detection | [Paper](https://doi.org/10.1109/IROS58592.2024.10802656) |
| d0423 | SemanticSpray++ | SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions | [Paper](https://doi.org/10.1109/IV55156.2024.10588458) |
| d0424 | Salzburg Bicycle LiDAR Data Set (SBLD) | Transformer based 3D semantic segmentation of urban bicycle infrastructure | [Paper](https://doi.org/10.1080/17489725.2024.2307969) |
| d0425 | ANNA | ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for Autonomous Vehicles | [Paper](https://doi.org/10.48550/arXiv.2401.11358) |
| d0431 | Constellation | Constellation Dataset: Benchmarking High-Altitude Object Detection for an Urban Intersection | [Paper](https://doi.org/10.48550/arXiv.2404.16944) |
| d0432 | BadODD (Bangladeshi Autonomous Driving Object Detection Dataset) | BadODD: Bangladeshi Autonomous Driving Object Detection Dataset | [Paper](https://doi.org/10.48550/arXiv.2401.10659) |
| d0433 | TrafficScene | TrafficScene: A Multi-modal Dataset including Light Field for Semantic Segmentation of Traffic Scenes | [Paper](https://doi.org/10.1109/ICME57554.2024.10687943) |
| d0453 | — | Point-Cloud Instance Segmentation for Spinning Laser Sensors | [Paper](https://doi.org/10.3390/jimaging10120325) |
| d0454 | Traffic Object Importance (TOI) | On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance | [Paper](https://doi.org/10.48550/arXiv.2411.17152) |
| d0455 | ViewpointDepth | ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts | [Paper](https://doi.org/10.1109/IV64158.2025.11097590) |
| d0456 | DBSPRY | Navigating on Adverse Weather: Enhancing LiDAR-Based Detection with the DBSPRY Dataset | [Paper](https://doi.org/10.1109/ITSC58415.2024.10920224) |
| d0457 | — | Learning Scene Semantics From Vehicle-Centric Data for City-Scale Digital Twins | [Paper](https://doi.org/10.1109/CBMI62980.2024.10859207) |
| d0458 | SaBi3d (Salzburg Bicycle 3d) | SaBi3d - A LiDAR Point Cloud Data Set of Car-to-Bicycle Overtaking Maneuvers | [Paper](https://doi.org/10.3390/data9080090) |
| d0459 | LidarCODA | Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentation | [Paper](https://doi.org/10.1109/IV64158.2025.11163519) |
| d0460 | Multi-class 3D LiDAR dataset (name not specified) | Power of Cooperative Supervision: Multiple Teachers Framework for Enhanced 3D Semi-Supervised Object Detection | [Paper](https://doi.org/10.48550/arXiv.2405.20720) |
| d0461 | Autoshuttle | Autoshuttle: A Novel Dataset for Advancing Autonomous Driving in Shuttle-Specific Environments | [Paper](https://doi.org/10.1109/SAMI60510.2024.10432901) |
| d0484 | IRisPath Day-Night Thermal-RGB Dataset | IRisPath: Enhancing Costmap for Off-Road Navigation with Robust IR-RGB Fusion for Improved Day and Night Traversability | [Paper](https://www.semanticscholar.org/paper/3cba2217c302c2448957d5b514e1e2ee58d04661) |
| d0485 | Multi-Scenario Road Vehicle and Pedestrian Detection Dataset | Construction and Performance Evaluation of a Comprehensive Multi-Scenario Road Vehicle and Pedestrian Detection Dataset | [Paper](https://doi.org/10.1109/AIHCIR65563.2024.00043) |
| d0486 | SceNDD++ | SceNDD++: An Enhanced Scenario-based Naturalistic Driving Dataset | [Paper](https://doi.org/10.1109/ICCSI62669.2024.10799423) |
| d0487 | iseAuto dataset | Camera-LiDAR Fusion based Object Segmentation in Adverse Weather Conditions for Autonomous Driving | [Paper](https://doi.org/10.1109/BEC61458.2024.10737955) |
| d0488 | CoopScenes | CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving | [Paper](https://doi.org/10.1109/IV64158.2025.11097591) |
| d0489 | TLID (Thermal camera and LiDAR in Infrastructure Dataset) | Open-Set Object Detection for the Identification and Localization of Dissimilar Novel Classes by means of Infrastructure Sensors | [Paper](https://doi.org/10.1109/IV55156.2024.10588872) |
| d0490 | Freiburg Panoptic Driving dataset | uPLAM: Robust Panoptic Localization and Mapping Leveraging Perception Uncertainties | [Paper](https://doi.org/10.1109/LRA.2024.3419648) |
| d0494 | Pedestrian-Vehicle Collision Pose (PVCP) | Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective | [Paper](https://doi.org/10.52202/079017-0784) |
| d0496 | Multi-LiDAR Stitching dataset | Enhancing Roadside 3D Object Detection Using Dynamic Pseudo HD Map | [Paper](https://doi.org/10.1109/ICTC62082.2024.10827502) |
| d0502 | PANIC (Panoptic ANomalies In Context) | Open-World Panoptic Segmentation | [Paper](https://doi.org/10.48550/arXiv.2412.12740) |
| d0503 | Darwick | Darwick: A Paired Dataset in Low-Light Driving Scenarios for Advanced Perceptual Enhancement and Benchmarking Assessment | [Paper](https://doi.org/10.1109/ITSC58415.2024.11414063) |
| d0504 | — | Real-Time Semantic Segmentation for Autonomous Scale Cars using Mixed Real and Synthetic Data | [Paper](https://doi.org/10.1109/ICMRE60776.2024.10532162) |
| d0507 | Vehicle Positioning Dataset with Satellite Maps | High-Precision Vehicle Positioning Technology by Combining Vehicle Images and Satellite Maps | [Paper](https://doi.org/10.1109/SMC54092.2024.10831076) |
| d0509 | Parking Lane Dataset (unnamed in abstract) | PAINet: Toward Fast and Efficient Parking Lot Lane Detection | [Paper](https://doi.org/10.1109/ACCESS.2024.3381488) |
| d0510 | YouTube Driving Light Detection dataset (YDLD) | A New Multi-Source Light Detection Benchmark and Semi-Supervised Focal Light Detection | [Paper](https://doi.org/10.52202/079017-2225) |
| d0543 | Padik | Padik - Pedestrian-Driver Interaction Dataset in Heterogeneous, Unstructured Traffic Environments | [Paper](https://doi.org/10.1109/ICVES61986.2024.10928019) |
| d0544 | — | Explainable Dataset for Ground Based Drones in Urban Environments | [Paper](https://doi.org/10.1109/ROBIO64047.2024.10907672) |
| d0545 | Australian Botanic Garden Mount Annan LiDAR Dataset | Performance Assessment of Lidar Odometry Frameworks: A Case Study at the Australian Botanic Garden Mount Annan | [Paper](https://doi.org/10.48550/arXiv.2411.16931) |
| d0546 | AgriDrive | AgriDrive: Data, Benchmarks and Analysis | [Paper](https://doi.org/10.1109/ICUS61736.2024.10840115) |
| d0547 | Fuzhou DashCam (FZDC) | Learning a Memory-Enhanced Multi-Stage Goal-Driven Network for Egocentric Trajectory Prediction | [Paper](https://doi.org/10.3390/biomimetics9080462) |
| d0548 | VWise | VWise: A novel benchmark for evaluating scene classification for vehicular applications | [Paper](https://doi.org/10.48550/arXiv.2406.03273) |
| d0549 | Visual Positioning Dataset in Sand-Dust Weather | Construction of computer visual dataset for autonomous driving in sand‐dust weather | [Paper](https://doi.org/10.1049/ell2.13171) |
| d0595 | Beacon | Beacon: A Naturalistic Driving Dataset During Blackouts for Benchmarking Traffic Reconstruction and Control | [Paper](https://doi.org/10.1109/IROS60139.2025.11246136) |
| d0596 | RELLIS-OCC | 3DTTNet: Multimodal Fusion-Based 3D Traversable Terrain Modeling for Off-Road Environments | [Paper](https://www.semanticscholar.org/paper/fc190f5cc482ece8211eb41926948fef08cd974b) |
| d0597 | AdScenes | AdWeatherNet: Adverse Weather Denoising with Point Cloud Spatiotemporal Attention | [Paper](https://doi.org/10.1109/VCIP63160.2024.10849938) |
| d0598 | Surround-View Fisheye Dataset for 3D Object Detection | 3D Object Detection based on Surround-View Fisheye Cameras | [Paper](https://doi.org/10.1109/CVCI63518.2024.10830142) |
| d0599 | Indian Driving VPR Dataset (IDDVPR) | Visual Place Recognition in Unstructured Driving Environments | [Paper](https://doi.org/10.1109/IROS58592.2024.10802708) |
| d0600 | AvOID (Avoiding Obstacles In unstructured Driving) | Two Teachers Are Better Than One: Leveraging Depth In Training Only For Unsupervised Obstacle Segmentation | [Paper](https://doi.org/10.1109/IROS58592.2024.10801483) |
| d0601 | MOE | MOE: A Dense LiDAR MOving Event Dataset, Detection Benchmark and LeaderBoard | [Paper](https://doi.org/10.1109/IROS58592.2024.10802513) |
| d0602 | WATonoBus radar dataset | Robust Radar Object Detection Using HD Map Likelihoods and Information | [Paper](https://doi.org/10.1109/ITSC58415.2024.10920054) |
| d0603 | MSDAD | MSDAD:A Multi-Sensor Dataset for Autonomous Driving | [Paper](https://doi.org/10.1109/ITSC58415.2024.10919939) |
| d0604 | Mining Road Dataset | A Robust Camera-LiDAR Fusion Framework for 3D Object Detection in High-Dust Environments | [Paper](https://doi.org/10.1109/INDIN58382.2024.10774485) |
| d0605 | Port dataset | Anchor-Based Transformer for Temporal LiDAR 3D Object Detection | [Paper](https://doi.org/10.1109/ICARM62033.2024.10715948) |
| d0606 | ETFOD-v2 | Matching strategy and skip-scale head configuration guideline based traffic object detection | [Paper](https://doi.org/10.1088/1361-6501/ad3296) |
| d0630 | UrbanME | UrbanME: A New Benchmark for HD Map Elements Extraction in Urban Traffic Scenes | [Paper](https://doi.org/10.1109/QRS-C63300.2024.00134) |
| d0631 | WeatherProof | WeatherProof: Leveraging Language Guidance for Semantic Segmentation in Adverse Weather | [Paper](https://doi.org/10.48550/arXiv.2403.14874) |
| d0645 | Lane-level Congestion Detection Dataset | A Convenient Approach for Lane-Level Congestion Detection with On-Board Camera Images and Vehicle Data | [Paper](https://doi.org/10.1109/ITSC58415.2024.10919951) |
| d0646 | PVDN (Provident Vehicle Detection at Night) | Combining Visual Saliency Methods and Sparse Keypoint Annotations to Create Object Representations for Providently Detecting Vehicles at Night | [Paper](https://doi.org/10.1109/IV55156.2024.10588455) |

## 2023 (108)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0007 | Argoverse 2 | Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting | [Paper](https://doi.org/10.48550/arXiv.2301.00493) |
| d0029 | V2V4Real | V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception | [Paper](https://doi.org/10.1109/CVPR52729.2023.01318) |
| d0038 | V2X-Seq | V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting | [Paper](https://doi.org/10.1109/CVPR52729.2023.00531) |
| d0056 | Zenseact Open Dataset (ZOD) | Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving | [Paper](https://doi.org/10.1109/ICCV51070.2023.01846) |
| d0061 | WaterScenes | WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces | [Paper](https://doi.org/10.1109/TITS.2024.3415772) |
| d0070 | Road Surface Classification Dataset | A Comprehensive Implementation of Road Surface Classification for Vehicle Driving Assistance: Dataset, Models, and Deployment | [Paper](https://doi.org/10.1109/TITS.2023.3264588) |
| d0074 | RTSS | Mitigating Modality Discrepancies for RGB-T Semantic Segmentation | [Paper](https://doi.org/10.1109/TNNLS.2022.3233089) |
| d0077 | MS2 (Multi-Spectral Stereo) | Deep Depth Estimation from Thermal Image | [Paper](https://doi.org/10.1109/CVPR52729.2023.00107) |
| d0079 | HeLiPR | HeLiPR: Heterogeneous LiDAR dataset for inter-LiDAR place recognition under spatiotemporal variations | [Paper](https://doi.org/10.1177/02783649241242136) |
| d0084 | — | 4D Radar-Based Pose Graph SLAM With Ego-Velocity Pre-Integration Factor | [Paper](https://doi.org/10.1109/LRA.2023.3292574) |
| d0086 | Dual Radar | Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autononous Driving | [Paper](https://doi.org/10.1038/s41597-025-04698-2) |
| d0088 | NeRF-MVL | LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields | [Paper](https://doi.org/10.1145/3664647.3681482) |
| d0090 | FishEye8K | FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection | [Paper](https://doi.org/10.1109/CVPRW59228.2023.00559) |
| d0094 | A9 infrastructure dataset | InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors | [Paper](https://doi.org/10.1109/IV55152.2023.10186723) |
| d0095 | TUMTraf Intersection Dataset | TUMTraf Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422289) |
| d0103 | ScatterNeRF In-the-Wild and Fog Chamber Dataset | ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering | [Paper](https://doi.org/10.1109/ICCV51070.2023.01646) |
| d0107 | Multimodal High-Resolution Radar Dataset (with Doppler Unfolding subdataset) | Deep-Neural-Network-Enabled Vehicle Detection Using High-Resolution Automotive Radar Imaging | [Paper](https://doi.org/10.1109/TAES.2023.3275887) |
| d0113 | SurMine | MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck | [Paper](https://doi.org/10.1109/TIV.2022.3221767) |
| d0115 | UT Campus Object Dataset (CODa) | Toward Robust Robot 3-D Perception in Urban Environments: The UT Campus Object Dataset | [Paper](https://doi.org/10.1109/TRO.2024.3400831) |
| d0117 | SWAN | SAT3D: Slot Attention Transformer for 3D Point Cloud Semantic Segmentation | [Paper](https://doi.org/10.1109/TITS.2023.3243643) |
| d0119 | DSEC-Det | Enhancing Traffic Object Detection in Variable Illumination With RGB-Event Fusion | [Paper](https://doi.org/10.1109/TITS.2024.3456108) |
| d0128 | scenario-identical real-world and simulated LiDAR dataset | Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level | [Paper](https://doi.org/10.1109/TIV.2023.3251650) |
| d0144 | — | Safe Autonomous Driving in Adverse Weather: Sensor Evaluation and Performance Monitoring | [Paper](https://doi.org/10.1109/IV55152.2023.10186596) |
| d0147 | LiDAR-MOS dataset | MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation With Bird's Eye View Based Appearance and Motion Features | [Paper](https://doi.org/10.1109/LRA.2023.3325687) |
| d0150 | SemanticSpray | Energy-Based Detection of Adverse Weather Effects in LiDAR Data | [Paper](https://doi.org/10.1109/LRA.2023.3282382) |
| d0163 | Pitt-Radar | Azimuth Super-Resolution for FMCW Radar in Autonomous Driving | [Paper](https://doi.org/10.1109/CVPR52729.2023.01679) |
| d0166 | ICV22 (Inha Computer Vision 2022) | Joint Multiclass Object Detection and Semantic Segmentation for Autonomous Driving | [Paper](https://doi.org/10.1109/ACCESS.2023.3266284) |
| d0169 | i24motion | So you think you can track? | [Paper](https://doi.org/10.1109/WACV57701.2024.00447) |
| d0170 | Occlusion Aware Pedestrian Detection Dataset (inferred) | Detecting Darting Out Pedestrians With Occlusion Aware Sensor Fusion of Radar and Stereo Camera | [Paper](https://doi.org/10.1109/TIV.2022.3220435) |
| d0171 | LiDARnet-sim 1.0 (simulated) and unnamed real-world roadside LiDAR dataset | TrajMatch: Toward Automatic Spatio-Temporal Calibration for Roadside LiDARs Through Trajectory Matching | [Paper](https://doi.org/10.1109/TITS.2023.3295757) |
| d0174 | IDD-AW | IDD-AW: A Benchmark for Safe and Robust Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather | [Paper](https://doi.org/10.1109/WACV57701.2024.00455) |
| d0181 | INT2 | INT2: Interactive Trajectory Prediction at Intersections | [Paper](https://doi.org/10.1109/ICCV51070.2023.00784) |
| d0182 | LUCOOP | LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset | [Paper](https://doi.org/10.1109/IV55152.2023.10186693) |
| d0190 | RACECAR | RACECAR - The Dataset for High-Speed Autonomous Racing | [Paper](https://doi.org/10.1109/IROS55552.2023.10342053) |
| d0192 | Interstate-24 3D Dataset | The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking | [Paper](https://doi.org/10.48550/arXiv.2308.14833) |
| d0195 | DAPS-2 | DAPS3D: Domain Adaptive Projective Segmentation of 3D LiDAR Point Clouds | [Paper](https://doi.org/10.1109/ACCESS.2023.3298706) |
| d0198 | Adverse Weather Object Detection Datasets (three datasets for rain, fog, snow) | I Had a Bad Day: Challenges of Object Detection in Bad Visibility Conditions | [Paper](https://doi.org/10.1109/IV55152.2023.10186674) |
| d0201 | FLORIDA | An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation | [Paper](https://doi.org/10.1109/TITS.2024.3399708) |
| d0203 | Indonesian Dense Traffic Dataset | Object Detection in Dense and Mixed Traffic for Autonomous Vehicles With Modified Yolo | [Paper](https://doi.org/10.1109/ACCESS.2023.3335826) |
| d0204 | InfraParis | InfraParis: A multi-modal and multi-task autonomous driving dataset | [Paper](https://doi.org/10.1109/WACV57701.2024.00295) |
| d0206 | GROUNDED | GROUNDED: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather | [Paper](https://doi.org/10.1177/02783649231183460) |
| d0225 | RSRD (Road Surface Reconstruction Dataset) | RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving | [Paper](https://doi.org/10.48550/arXiv.2310.02262) |
| d0227 | Winter Adverse Driving Dataset | Winter adverse driving dataset for autonomy in inclement winter weather | [Paper](https://doi.org/10.1117/1.OE.62.3.031207) |
| d0233 | LVLane | LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422704) |
| d0234 | Gated Stereo Dataset | Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues | [Paper](https://doi.org/10.1109/CVPR52729.2023.01273) |
| d0237 | Navya3DSeg | Navya3DSeg - Navya 3D Semantic Segmentation Dataset Design & Split Generation for Autonomous Vehicles | [Paper](https://doi.org/10.1109/LRA.2023.3290516) |
| d0246 | Carolinas Highway Dataset (CHD) | A POV-Based Highway Vehicle Trajectory Dataset and Prediction Architecture | [Paper](https://doi.org/10.1109/TITS.2024.3435775) |
| d0247 | UPCT dataset | Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data | [Paper](https://doi.org/10.3390/s23042009) |
| d0258 | Realistic Driving Scenes under Bad Weather (RDSBW) | Framework for Generation and Removal of Multiple Types of Adverse Weather from Driving Scene Images | [Paper](https://doi.org/10.3390/s23031548) |
| d0262 | CrossSP\_Depth (Cross-Spectrum Depth Dataset) | Unsupervised Cross-Spectrum Depth Estimation by Visible-Light and Thermal Cameras | [Paper](https://doi.org/10.1109/TITS.2023.3279559) |
| d0263 | ADD (Autopilot Desensitization Dataset) | ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving | [Paper](https://doi.org/10.1016/j.engappai.2023.106766) |
| d0273 | RoadSC | Camera-Based Road Snow Coverage Estimation | [Paper](https://doi.org/10.1109/ICCVW60793.2023.00433) |
| d0274 | OATS1 | Ordered Atomic Activity for Fine-grained Interactive Traffic Scenario Understanding | [Paper](https://doi.org/10.1109/ICCV51070.2023.00792) |
| d0275 | Doppler-Aware Odometry FMCW Radar Dataset | Doppler-Aware Odometry from FMCW Scanning Radar | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422412) |
| d0276 | SimoSet | SimoSet: A 3D Object Detection Dataset Collected from Vehicle Hybrid Solid-State LiDAR | [Paper](https://doi.org/10.3390/electronics12112424) |
| d0288 | TWICE Dataset | TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment | [Paper](https://doi.org/10.48550/arXiv.2310.03895) |
| d0292 | Local driving dataset (name not specified) | An intelligent modular real-time vision-based system for environment perception | [Paper](https://doi.org/10.48550/arXiv.2303.16710) |
| d0293 | Austrian Highway Traffic Sign Data Set (ATSD) | Traffic Sign Detection and Classification on the Austrian Highway Traffic Sign Data Set | [Paper](https://doi.org/10.3390/data8010016) |
| d0294 | RLMD | RLMD: A Dataset for Road Marking Segmentation | [Paper](https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226935) |
| d0298 | CRUW3D | Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving | [Paper](https://doi.org/10.1109/IV55156.2024.10588620) |
| d0299 | SMART-Rain | SMART-Rain: A Degradation Evaluation Dataset for Autonomous Driving in Rain | [Paper](https://doi.org/10.1109/IROS55552.2023.10342015) |
| d0300 | FlexSense | FlexSense: Flexible Infrastructure Sensors for Traffic Perception | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422616) |
| d0301 | Stereo Visual Localization Dataset with Event Cameras | Stereo Visual Localization Dataset Featuring Event Cameras | [Paper](https://doi.org/10.1109/ECMR59166.2023.10256407) |
| d0302 | YUTO (York University and Teledyne Optech) MMS dataset | HDPV-SLAM: Hybrid Depth-Augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera | [Paper](https://doi.org/10.1109/CASE56687.2023.10260361) |
| d0309 | AVstack MSMA Dataset | Datasets, Models, and Algorithms for Multi-Sensor, Multi-agent Autonomy Using AVstack | [Paper](https://doi.org/10.48550/arXiv.2312.04970) |
| d0310 | Radar Scattering Characteristics of Vehicles Dataset | A Dataset for Radar Scattering Characteristics of Vehicles Under Real-World Driving Conditions: Major Findings for Sensor Simulation | [Paper](https://doi.org/10.1109/JSEN.2023.3238015) |
| d0319 | Underground Parking Lot Driving Dataset | Scale-Aware Monocular Visual Odometry and Extrinsic Calibration Using Vehicle Kinematics | [Paper](https://doi.org/10.1109/TITS.2023.3309833) |
| d0320 | ParisLuco3D | ParisLuco3D: A High-Quality Target Dataset for Domain Generalization of LiDAR Perception | [Paper](https://doi.org/10.1109/LRA.2024.3393209) |
| d0321 | WiDEVIEW | WiDEVIEW: An UltraWideBand and Vision Dataset for Deciphering Pedestrian-Vehicle Interactions | [Paper](https://doi.org/10.48550/arXiv.2309.16057) |
| d0322 | DTLD (Diverse Traffic Labeled Dataset) | Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation | [Paper](https://doi.org/10.1080/19427867.2023.2250622) |
| d0323 | Perth-WA | Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps | [Paper](https://doi.org/10.1109/ICRA48891.2023.10161128) |
| d0330 | Long-Range Acoustic Beamforming Dataset | Seeing With Sound: Long-Range Acoustic Beamforming for Multimodal Scene Understanding | [Paper](https://doi.org/10.1109/CVPR52729.2023.00101) |
| d0331 | RGB-Polarimetric Driving Dataset | Polarimetric Imaging for Perception | [Paper](https://doi.org/10.48550/arXiv.2305.14787) |
| d0334 | RSI-V2X Cooperative Navigation Dataset (Hong Kong C-V2X Testbed) | Roadside Infrastructure assisted LiDAR/Inertial-based Mapping for Intelligent Vehicles in Urban Areas | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422552) |
| d0346 | PAVIN Fog and Rain Platform | Qualification of the PAVIN Fog and Rain Platform and Its Digital Twin for the Evaluation of a Pedestrian Detector in Fog | [Paper](https://doi.org/10.3390/jimaging9100211) |
| d0347 | BorealHDR | Exposing the Unseen: Exposure Time Emulation for Offline Benchmarking of Vision Algorithms | [Paper](https://doi.org/10.1109/IROS58592.2024.10803057) |
| d0356 | Multi-sensor Risk Assessment Dataset for ADAS Parking Scenarios | Dynamic Risk Assessment Methodology with an LDM-Based System for Parking Scenarios | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422385) |
| d0357 | IMPTC (Infrastructural Multi-Person Trajectory and Context Dataset) | The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset | [Paper](https://doi.org/10.1109/IV55152.2023.10186776) |
| d0363 | TVSS | Semantic Segmentation in Thermal Videos: A New Benchmark and Multi-Granularity Contrastive Learning-Based Framework | [Paper](https://doi.org/10.1109/TITS.2023.3300038) |
| d0376 | RGB-FIR Pedestrian Detection Dataset (under various ambient light and temperature conditions) | Multispectral Pedestrian Detection with Visible and Far-infrared Images Under Drifting Ambient Light and Temperature | [Paper](https://doi.org/10.1109/SENSORS56945.2023.10325231) |
| d0377 | RealStreet | Creation and Testing of Synthetic Datasets for Training Road Scenes Algorithms | [Paper](https://doi.org/10.1109/RO-MAN57019.2023.10309307) |
| d0378 | R2S100K | Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild | [Paper](https://doi.org/10.1007/s11263-024-02207-3) |
| d0391 | R2D2 (Rural Road Detection Dataset) | R2D2: Rural Road Detection Dataset | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422185) |
| d0392 | LS2N | Uncertainty-Aware Adaptive Semidirect LiDAR Scan Matching for Ground Vehicle Positioning | [Paper](https://doi.org/10.1109/JSEN.2023.3260473) |
| d0393 | Cyc-CP | A benchmark for cycling close pass detection from video streams | [Paper](https://doi.org/10.1016/j.trc.2025.105112) |
| d0414 | TSTTC | TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios | [Paper](https://doi.org/10.48550/arXiv.2309.01539) |
| d0426 | GEM | Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction | [Paper](https://www.semanticscholar.org/paper/fa3322c076ea114b86e682ee58a47203c602aef1) |
| d0427 | ViF-GTAD | ViF-GTAD: A new automotive dataset with ground truth for ADAS/AD development, testing, and validation | [Paper](https://doi.org/10.1177/02783649231188146) |
| d0438 | Carolinas Anomaly Dataset (CAD) | VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications | [Paper](https://doi.org/10.48550/arXiv.2311.07880) |
| d0439 | Beyond Image-Plane-Level Line Detection Dataset | Beyond Image-Plane-Level: A Dataset for Validating End-to-End Line Detection Algorithms for Autonomous Vehicles | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422065) |
| d0443 | South Asian Vehicle Detection Dataset (undisciplined traffic) | Towards the next generation intelligent transportation system: A vehicle detection and counting framework for undisciplined traffic conditions | [Paper](https://doi.org/10.14311/nnw.2023.33.011) |
| d0462 | VDD4TS | VDD4TS: A Vehicle Detection Dataset for Traffic Survey of Road | [Paper](https://doi.org/10.1109/ICICML60161.2023.10424891) |
| d0463 | LISA Vehicle Lights Dataset | Patterns of Vehicle Lights: Addressing Complexities in Curation and Annotation of Camera-Based Vehicle Light Datasets and Metrics | [Paper](https://doi.org/10.48550/arXiv.2307.14521) |
| d0491 | Multimodal automotive sensor dataset (unnamed) | Multimodal Early Fusion of Automotive Sensors based on Autoencoder Network: An anchor-free approach for Vehicle 3D Detection | [Paper](https://doi.org/10.23919/FUSION52260.2023.10224140) |
| d0492 | Automotive Radar Dataset | An Automotive Radar Dataset For Object Classification | [Paper](https://doi.org/10.1109/ICASSP49357.2023.10097078) |
| d0497 | AV and Roadside LiDAR Dataset | Transfer-Learning of a LiDAR Detection Algorithm Pre-Trained on Autonomous Driving Data for Roadside Infrastructure Application | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422207) |
| d0505 | CityIntensified | Enhancing Visual Domain Adaptation with Source Preparation | [Paper](https://doi.org/10.48550/arXiv.2306.10142) |
| d0508 | LTDR | Traffic Risk Assessment from Driving Scene Images | [Paper](https://doi.org/10.1109/SMC53992.2023.10393985) |
| d0550 | — | Collaborative Grid Mapping for Moving Object Tracking Evaluation | [Paper](https://doi.org/10.1109/ITSC57777.2023.10422035) |
| d0551 | PPD (Parking Pedestrian Dataset) | PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving | [Paper](https://doi.org/10.48550/arXiv.2309.11002) |
| d0607 | HuanYu | Surround-View Road Scene Layout Estimation via IPM-Transformer for Autonomous Driving | [Paper](https://doi.org/10.1109/RCAE59706.2023.10398860) |
| d0616 | — | Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via Automatic Label Generation | [Paper](https://doi.org/10.48550/arXiv.2311.02044) |
| d0617 | TunnelTrack | TunnelTrack: A Dataset for Multi-Object Tracking in Tunnel Roads | [Paper](https://doi.org/10.1109/IST59124.2023.10355705) |
| d0618 | FollowMe | FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings | [Paper](https://doi.org/10.48550/arXiv.2304.06121) |
| d0619 | — | Design of an automotive platform for computer vision research | [Paper](https://doi.org/10.2352/ei.2023.35.16.avm-119) |
| d0632 | Winter Driving Dataset (TADAP) | TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised features in winter driving conditions | [Paper](https://doi.org/10.48550/arXiv.2312.12954) |
| d0633 | — | LineMarkNet: Line Landmark Detection for Valet Parking | [Paper](https://doi.org/10.48550/arXiv.2309.10475) |
| d0634 | Missing Traffic Signs Video Dataset (MTSVD) | CueCAn: Cue-driven Contextual Attention for Identifying Missing Traffic Signs on Unconstrained Roads | [Paper](https://doi.org/10.1109/ICRA48891.2023.10161576) |

## 2022 (75)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0012 | DAIR-V2X | DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection | [Paper](https://doi.org/10.1109/CVPR52688.2022.02067) |
| d0025 | View-of-Delft (VoD) | Multi-class Road User Detection with 3+1D Radar in the View-of-Delft Dataset | [Paper](https://doi.org/10.1109/LRA.2022.3147324) |
| d0035 | K-Radar (KAIST-Radar) | K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions | [Paper](https://doi.org/10.52202/068431-0276) |
| d0039 | Boreas | Boreas: A multi-season autonomous driving dataset | [Paper](https://doi.org/10.1177/02783649231160195) |
| d0041 | DRAMA | DRAMA: Joint Risk Localization and Captioning in Driving | [Paper](https://doi.org/10.1109/WACV56688.2023.00110) |
| d0044 | Rope3D | Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task | [Paper](https://doi.org/10.1109/CVPR52688.2022.02065) |
| d0049 | TJ4DRadSet | TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving | [Paper](https://doi.org/10.1109/ITSC55140.2022.9922539) |
| d0060 | ViViD++ | ViViD++ : Vision for Visibility Dataset | [Paper](https://doi.org/10.48550/arXiv.2204.06183) |
| d0062 | ORFD | ORFD: A Dataset and Benchmark for Off-Road Freespace Detection | [Paper](https://doi.org/10.48550/arXiv.2206.09907) |
| d0068 | A9-Dataset | A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research | [Paper](https://doi.org/10.48550/arXiv.2204.06527) |
| d0075 | TartanDrive | TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models | [Paper](https://doi.org/10.48550/arXiv.2205.01791) |
| d0082 | Ithaca365 | Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions | [Paper](https://doi.org/10.1109/CVPR52688.2022.02069) |
| d0089 | AutoMine | AutoMine: An Unmanned Mine Dataset | [Paper](https://doi.org/10.1109/CVPR52688.2022.02062) |
| d0093 | CaT (CAVS Traversability Dataset) | CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving | [Paper](https://doi.org/10.1109/ACCESS.2022.3154419) |
| d0100 | aiMotive Dataset | aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception | [Paper](https://doi.org/10.48550/arXiv.2211.09445) |
| d0101 | Trust, but Verify (TbV) dataset | Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection | [Paper](https://doi.org/10.48550/arXiv.2212.07312) |
| d0105 | Rainy WCity | Rainy WCity: A Real Rainfall Dataset with Diverse Conditions for Semantic Driving Scene Understanding | [Paper](https://doi.org/10.24963/ijcai.2022/243) |
| d0120 | Urban LiDAR Change Detection Benchmark | Point cloud registration and change detection in urban environment using an onboard Lidar sensor and MLS reference data | [Paper](https://doi.org/10.1016/j.jag.2022.102767) |
| d0129 | C3I Thermal Automotive dataset | Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems | [Paper](https://doi.org/10.1109/TIV.2022.3158094) |
| d0134 | Las Vegas Driving Dataset (DriveIRL) | Driving in Real Life with Inverse Reinforcement Learning | [Paper](https://doi.org/10.48550/arXiv.2206.03004) |
| d0141 | STheReO | STheReO: Stereo Thermal Dataset for Research in Odometry and Mapping | [Paper](https://doi.org/10.1109/IROS47612.2022.9981857) |
| d0148 | MONA (Munich Motion Dataset of Natural Driving) | MONA: The Munich Motion Dataset of Natural Driving | [Paper](https://doi.org/10.1109/ITSC55140.2022.9922263) |
| d0164 | SOS | Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects | [Paper](https://doi.org/10.1007/978-3-031-26348-4_28) |
| d0178 | AutoMatch Multi-View Traffic Image Dataset | AutoMatch: Leveraging Traffic Camera to Improve Perception and Localization of Autonomous Vehicles | [Paper](https://doi.org/10.1145/3560905.3568519) |
| d0185 | IDD-3D | IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes | [Paper](https://doi.org/10.1109/WACV56688.2023.00446) |
| d0186 | SVLD-3D | CenterLoc3D: monocular 3D vehicle localization network for roadside surveillance cameras | [Paper](https://doi.org/10.1007/s40747-022-00962-9) |
| d0187 | HelixNet | Online Segmentation of LiDAR Sequences: Dataset and Algorithm | [Paper](https://doi.org/10.48550/arXiv.2206.08194) |
| d0189 | RoadSaW | RoadSaW: A Large-Scale Dataset for Camera-Based Road Surface and Wetness Estimation | [Paper](https://doi.org/10.1109/CVPRW56347.2022.00490) |
| d0207 | PeSOTIF | PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios | [Paper](https://doi.org/10.1109/IV55152.2023.10186651) |
| d0210 | Fisheye Parking Dataset (FPD) | Surround-View Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-Insensitive Multi-Task Framework | [Paper](https://doi.org/10.1109/TIV.2022.3218594) |
| d0211 | Occluded Objects Dataset (Bangladesh Road Scenes) | Occluded Object Detection for Autonomous Vehicles Employing YOLOv5, YOLOX and Faster R-CNN | [Paper](https://doi.org/10.1109/IEMCON56893.2022.9946565) |
| d0220 | Dtrail | Self-Supervised 3D Traversability Estimation With Proxy Bank Guidance | [Paper](https://doi.org/10.1109/ACCESS.2023.3279711) |
| d0221 | SceNDD | SceNDD: A Scenario-based Naturalistic Driving Dataset | [Paper](https://doi.org/10.1109/ITSC55140.2022.9921953) |
| d0222 | RMD (Road Marking Dataset) | Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset | [Paper](https://doi.org/10.3390/rs14184508) |
| d0228 | ADS Data Acquisition and Processing Platform Dataset | Automated Driving Systems Data Acquisition and Processing Platform | [Paper](https://doi.org/10.48550/arXiv.2211.13425) |
| d0229 | VTP-TL | D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights | [Paper](https://doi.org/10.48550/arXiv.2207.10398) |
| d0232 | Bangkok Urbanscapes Dataset | The Bangkok Urbanscapes Dataset for Semantic Urban Scene Understanding Using Enhanced Encoder-Decoder With Atrous Depthwise Separable A1 Convolutional Neural Networks | [Paper](https://doi.org/10.1109/ACCESS.2022.3176712) |
| d0235 | WHUVID | WHUVID: A Large-Scale Stereo-IMU Dataset for Visual-Inertial Odometry and Autonomous Driving in Chinese Urban Scenarios | [Paper](https://doi.org/10.3390/rs14092033) |
| d0242 | Vehicle Yaw Angle Dataset (from YAEN paper) | A Deep Learning Framework for Accurate Vehicle Yaw Angle Estimation from a Monocular Camera Based on Part Arrangement | [Paper](https://doi.org/10.3390/s22208027) |
| d0243 | ETRIDriving | PathGAN: Local path planning with attentive generative adversarial networks | [Paper](https://doi.org/10.4218/etrij.2021-0192) |
| d0250 | 4Seasons | 4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions | [Paper](https://doi.org/10.1007/s11263-024-02230-4) |
| d0256 | Surface Mine Semantic Segmentation Dataset | A real-time semantic segmentation method for autonomous driving in surface mine | [Paper](https://doi.org/10.1109/ITSC55140.2022.9922492) |
| d0264 | — | Conception of a High-Level Perception and Localization System for Autonomous Driving | [Paper](https://doi.org/10.3390/s22249661) |
| d0269 | ESRORAD | Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset | [Paper](https://doi.org/10.3390/s22103922) |
| d0271 | CMR1K | Unsupervised Domain Adaptation for Semantic Segmentation of Urban Street Scenes Reflected by Convex Mirrors | [Paper](https://doi.org/10.1109/TITS.2022.3208334) |
| d0277 | Road Spray LiDAR Dataset | Simulating Road Spray Effects in Automotive Lidar Sensor Models | [Paper](https://doi.org/10.1109/IV55156.2024.10588834) |
| d0278 | — | Spectranet: A High Resolution Imaging Radar Deep Neural Network for Autonomous Vehicles | [Paper](https://doi.org/10.1109/sam53842.2022.9827798) |
| d0279 | Driver Monitoring Dataset (DMD) | Challenges of Large-Scale Multi-Camera Datasets for Driver Monitoring Systems | [Paper](https://doi.org/10.3390/s22072554) |
| d0289 | iseAuto | Object Segmentation for Autonomous Driving Using iseAuto Data | [Paper](https://doi.org/10.3390/electronics11071119) |
| d0291 | DORA (Drone Over Roads) | The JKU DORA Traffic Dataset | [Paper](https://doi.org/10.1109/ACCESS.2022.3203188) |
| d0303 | Singapore Autonomous Bus (SGAB) dataset | Online Obstacle Trajectory Prediction for Autonomous Buses | [Paper](https://doi.org/10.3390/machines10030202) |
| d0311 | 3DHD CityScenes | 3DHD CityScenes: High-Definition Maps in High-Density Point Clouds | [Paper](https://doi.org/10.1109/ITSC55140.2022.9921866) |
| d0324 | Self-captured crossing pedestrian dataset | Efficient detection of crossing pedestrians from a moving vehicle with an array of cameras | [Paper](https://doi.org/10.1117/1.OE.62.3.031210) |
| d0325 | iVS dataset | iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications | [Paper](https://doi.org/10.3390/rs14040833) |
| d0337 | TrackletMapper Dataset | TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories | [Paper](https://doi.org/10.48550/arXiv.2209.05247) |
| d0338 | WatchPed | WatchPed: Pedestrian Crossing Intention Prediction Using Embedded Sensors of Smartwatch | [Paper](https://doi.org/10.1109/IROS55552.2023.10341607) |
| d0358 | Batara Kresna Railbus Dataset | Panoptic Segmentation Datasets for Rail-based Autonomous Vehicle Under Mixed-traffic Scenario | [Paper](https://doi.org/10.1109/ICSET57543.2022.10010755) |
| d0360 | Fine-Grained Vehicle Detection (FGVD) Dataset | A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads✱ | [Paper](https://doi.org/10.1145/3571600.3571626) |
| d0379 | ZMDepth | Monocular Fisheye Depth Estimation for Automated Valet Parking: Dataset, Baseline and Deep Optimizers | [Paper](https://doi.org/10.1109/ITSC55140.2022.9922174) |
| d0415 | New Delhi Pedestrian Detection Dataset | On the efficacy of Pedestrian Detection in Indian Road Scenario | [Paper](https://doi.org/10.1109/ICI53355.2022.9786893) |
| d0428 | VLS (Vehicle Tail Light Signal) Dataset | VLS: Vehicle Tail Light Signal Detection Benchmark | [Paper](https://doi.org/10.1145/3579654.3579770) |
| d0429 | Monza Eni Circuit Dataset | Object tracking with low resolution Lidar and Radar fusion, a comparison | [Paper](https://doi.org/10.23919/AEIT56783.2022.9951730) |
| d0434 | JARI dataset | LiDAR Translation Based on Empirical Approach between Sunny and Foggy for Driving Simulation | [Paper](https://doi.org/10.1109/WPMC55625.2022.10014784) |
| d0435 | DualCam | DualCam: A Novel Benchmark Dataset for Fine-Grained Real-Time Traffic Light Detection | [Paper](https://doi.org/10.1109/ICMLA58977.2023.00270) |
| d0440 | iRAP-BH | Dynamic Loss Balancing and Sequential Enhancement for Road-Safety Assessment and Traffic Scene Classification | [Paper](https://doi.org/10.1109/TITS.2024.3456214) |
| d0441 | LanePainter Dataset (rural lane marks from U.S.) | LanePainter: Lane Marks Enhancement via Generative Adversarial Network | [Paper](https://doi.org/10.1109/ICPR56361.2022.9956446) |
| d0464 | VIS-TIR-Datasets | Unsupervised Visible-light Images Guided Cross-Spectrum Depth Estimation from Dual-Modality Cameras | [Paper](https://doi.org/10.48550/arXiv.2205.00257) |
| d0465 | — | Quantity over Quality: Training an AV Motion Planner with Large Scale Commodity Vision Data | [Paper](https://doi.org/10.1109/IROS47612.2022.9982116) |
| d0493 | — | Over 60,000 km in a year: remotely collecting large-volume high-quality data from a logistics truck | [Paper](https://doi.org/10.1007/s42452-022-05159-w) |
| d0552 | Dataset for Robust and Accurate Leading Vehicle Velocity Recognition | Dataset for Robust and Accurate Leading Vehicle Velocity Recognition | [Paper](https://doi.org/10.48550/arXiv.2204.12717) |
| d0608 | Anonymous Driving Scene Perception (ADSP) Dataset | Contextual Driving Scene Perception from Anonymous Vehicle Bus Data for Automotive Applications | [Paper](https://doi.org/10.1109/IROS47612.2022.9981063) |
| d0609 | Radar-Visual Surround View Dataset | The Construction of Radar-Visual Surround View Dataset and Intrinsic and Extrinsic Calibration Method for the System | [Paper](https://doi.org/10.1109/APEMC53576.2022.9888304) |
| d0635 | PRD (Paired Rainy Dataset) | High-quality rainy image generation method for autonomous driving based on few-shot learning | [Paper](https://doi.org/10.1145/3579654.3579673) |
| d0636 | Bandung Cityscapes | Application of Convolutional Neural Network for Semantic Segmentation of Bandung Urban Scenes | [Paper](https://doi.org/10.1109/ICoDSE56892.2022.9972006) |
| d0637 | Small Object Segmentation in Road Scenes Dataset | Context-aware Method for Small Object Segmentation in Road Scenes | [Paper](https://doi.org/10.1109/ICARM54641.2022.9959713) |

## 2021 (63)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0008 | KITTI-360 | KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D | [Paper](https://doi.org/10.1109/TPAMI.2022.3179507) |
| d0009 | Waymo Open Motion Dataset | Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset | [Paper](https://doi.org/10.1109/ICCV48922.2021.00957) |
| d0011 | ACDC | ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding | [Paper](https://doi.org/10.1109/ICCV48922.2021.01059) |
| d0014 | nuPlan | nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles | [Paper](https://www.semanticscholar.org/paper/b88b38ec61a4881173ab94647d1e97500f4af15b) |
| d0015 | DSEC | DSEC: A Stereo Event Camera Dataset for Driving Scenarios | [Paper](https://doi.org/10.1109/LRA.2021.3068942) |
| d0020 | ONCE | One Million Scenes for Autonomous Driving: ONCE Dataset | [Paper](https://www.semanticscholar.org/paper/ddbd9baf007d86f6174b2d41c36fda1c5ec64b58) |
| d0026 | PandaSet | PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving | [Paper](https://doi.org/10.1109/itsc48978.2021.9565009) |
| d0032 | CRUW | RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization | [Paper](https://doi.org/10.1109/JSTSP.2021.3058895) |
| d0043 | RadarScenes | RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications | [Paper](https://doi.org/10.5281/ZENODO.4559821) |
| d0051 | RADIal | Raw High-Definition Radar for Multi-Task Learning | [Paper](https://doi.org/10.1109/CVPR52688.2022.01651) |
| d0054 | SODA10M | SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving | [Paper](https://www.semanticscholar.org/paper/99a25cf80bcc1b0d8c89bf458c376f3e0f5a1298) |
| d0057 | Urban dataset with lidar and images from 20 cities across 5 continents | Learning 3D Semantic Segmentation with only 2D Image Supervision | [Paper](https://doi.org/10.1109/3DV53792.2021.00046) |
| d0058 | LOKI | LOKI: Long Term and Key Intentions for Trajectory Prediction | [Paper](https://doi.org/10.1109/ICCV48922.2021.00966) |
| d0063 | Winter Adverse Driving dataSet (WADS) | DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather | [Paper](https://www.semanticscholar.org/paper/d801fad379b57e2e176a3507d4b0e14ad7c1ae70) |
| d0071 | VI-Eye Indoor Autonomous Driving Testbed Dataset and Campus Smart Lamppost Dataset | VI-eye: semantic-based 3D point cloud registration for infrastructure-assisted autonomous driving | [Paper](https://doi.org/10.1145/3447993.3483276) |
| d0072 | Drivable Area and Road Anomaly Detection Benchmark for Ground Mobile Robots | Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms | [Paper](https://doi.org/10.1109/TCYB.2021.3064089) |
| d0076 | RaDICaL | RaDICaL: A Synchronized FMCW Radar, Depth, IMU and RGB Camera Data Dataset With Low-Level FMCW Radar Signals | [Paper](https://doi.org/10.1109/JSTSP.2021.3061270) |
| d0080 | Paris-CARLA-3D | Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping | [Paper](https://doi.org/10.3390/rs13224713) |
| d0083 | VIL-100 | VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection | [Paper](https://doi.org/10.1109/ICCV48922.2021.01539) |
| d0091 | SCUT-Seg | MCNet: Multi-level Correction Network for thermal image semantic segmentation of nighttime driving scene | [Paper](https://doi.org/10.1016/J.INFRARED.2020.103628) |
| d0097 | IPS300+ | IPS300+: a Challenging Multimodal Dataset for Intersection Perception System | [Paper](https://www.semanticscholar.org/paper/ebe5bb169c539cf84c2a38e9f01863c9984a1e35) |
| d0102 | CeyMo | CeyMo: See More on Roads - A Novel Benchmark Dataset for Road Marking Detection | [Paper](https://doi.org/10.1109/WACV51458.2022.00344) |
| d0104 | PUP | Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty | [Paper](https://doi.org/10.1109/IROS47612.2022.9982283) |
| d0106 | Snow driving dataset (name not specified) | Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios | [Paper](https://doi.org/10.1109/JSEN.2021.3077029) |
| d0109 | DGL-MOTS | DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception | [Paper](https://doi.org/10.1109/WACV51458.2022.00347) |
| d0110 | PSI (IUPUI-CSRC Pedestrian Situated Intent) | PSI: A Pedestrian Behavior Dataset for Socially Intelligent Autonomous Car | [Paper](https://www.semanticscholar.org/paper/479cc564ed66a19098609db9eb8f6dd833b1c678) |
| d0121 | Euro-PVI | Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers | [Paper](https://doi.org/10.1109/CVPR46437.2021.00634) |
| d0122 | AMV-Bench | Asynchronous Multi-View SLAM | [Paper](https://doi.org/10.1109/ICRA48506.2021.9561481) |
| d0130 | BaoLi Mine RSU LiDAR Dataset | 3D Vehicle Detection With RSU LiDAR for Autonomous Mine | [Paper](https://doi.org/10.1109/TVT.2020.3048985) |
| d0132 | The Radar Ghost Dataset | The Radar Ghost Dataset – An Evaluation of Ghost Objects in Automotive Radar Data | [Paper](https://doi.org/10.1109/IROS51168.2021.9636338) |
| d0135 | BAAI-VANJEE Roadside Dataset | BAAI-VANJEE Roadside Dataset: Towards the Connected Automated Vehicle Highway technologies in Challenging Environments of China | [Paper](https://www.semanticscholar.org/paper/217d7bd0307e397e490bb655ae092b7082016f70) |
| d0137 | 2D LiDAR vehicle dataset | Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods | [Paper](https://doi.org/10.1109/TITS.2020.3007631) |
| d0138 | Motorway Measurement Campaign Dataset | Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies | [Paper](https://doi.org/10.3390/s21062169) |
| d0149 | RaidaR | RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes | [Paper](https://doi.org/10.1109/ICCVW54120.2021.00330) |
| d0153 | METEOR | METEOR: A Dense, Heterogeneous, and Unstructured Traffic Dataset with Rare Behaviors | [Paper](https://doi.org/10.1109/ICRA48891.2023.10161281) |
| d0155 | TAS500 | A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios | [Paper](https://doi.org/10.1109/ICPR48806.2021.9411987) |
| d0158 | DurLAR | DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications | [Paper](https://doi.org/10.1109/3DV53792.2021.00130) |
| d0165 | Hannover Mobile Mapping LiDAR Dataset | Classification and Change Detection in Mobile Mapping LiDAR Point Clouds | [Paper](https://doi.org/10.1007/s41064-021-00148-x) |
| d0173 | ARTSv2 | Object Tracking and Geo-localization from Street Images | [Paper](https://doi.org/10.3390/rs14112575) |
| d0179 | Traffic Camera Intersection Dataset | Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data | [Paper](https://doi.org/10.5244/c.35.269) |
| d0188 | PC-Urban | Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation | [Paper](https://doi.org/10.1109/ACCESS.2021.3062547) |
| d0193 | Sign Salience Dataset (autonomous driving) | On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset | [Paper](https://doi.org/10.1109/WACVW54805.2022.00070) |
| d0194 | EPOSH | Bird's Eye View Segmentation Using Lifted 2D Semantic Features | [Paper](https://doi.org/10.5244/c.35.227) |
| d0199 | — | Traversability mapping in off-road environment using semantic segmentation | [Paper](https://doi.org/10.1117/12.2587661) |
| d0200 | Gated3D Dataset | Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues | [Paper](https://doi.org/10.1109/ICCV48922.2021.00293) |
| d0259 | Smartphone driving dataset (urban environment) | Driven by Vision: Learning Navigation by Visual Localization and Trajectory Prediction | [Paper](https://doi.org/10.3390/s21030852) |
| d0265 | ETLi | ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation | [Paper](https://doi.org/10.4218/etrij.2021-0055) |
| d0270 | ROOAD (RELLIS Off-road Odometry Analysis Dataset) | ROOAD: RELLIS Off-road Odometry Analysis Dataset | [Paper](https://doi.org/10.1109/iv51971.2022.9827133) |
| d0280 | FieldSafePedestrian | 3D Pedestrian Detection in Farmland by Monocular RGB Image and Far-Infrared Sensing | [Paper](https://doi.org/10.3390/rs13152896) |
| d0281 | Ceit-TSR and Ceit-Foggy | Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads | [Paper](https://doi.org/10.3390/s21041254) |
| d0304 | — | Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads | [Paper](https://www.semanticscholar.org/paper/6cc33061643eceb65064c8d4abc2fd533c4a7d71) |
| d0305 | BHDE-v1 | SFA-MDEN: Semantic-Feature-Aided Monocular Depth Estimation Network Using Dual Branches | [Paper](https://doi.org/10.3390/s21165476) |
| d0312 | R3 Driving Dataset | Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualities | [Paper](https://doi.org/10.1109/IROS47612.2022.9981110) |
| d0313 | Off-road dataset (unnamed) | Multi-Resolution and Multi-Domain Analysis of Off-Road Datasets for Autonomous Driving | [Paper](https://doi.org/10.1109/CRV52889.2021.00030) |
| d0348 | JLU campus dataset | LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information | [Paper](https://doi.org/10.1371/journal.pone.0261053) |
| d0349 | Vehicle Lane Merge Visual Benchmark | Vehicle Lane Merge Visual Benchmark | [Paper](https://doi.org/10.1109/ICPR48806.2021.9412960) |
| d0361 | HDV-Mess | Highly accurate digital traffic recording as a basis for future mobility research: Methods and concepts of the research project HDV-Mess | [Paper](https://www.semanticscholar.org/paper/ecad2f2607a73368b452da7a490b35d4058e43ee) |
| d0364 | RGB-Thermal Snowy Road Dataset | An Evaluation of RGB-Thermal Image Segmentation for Snowy Road Environment | [Paper](https://doi.org/10.1109/ICMA52036.2021.9512708) |
| d0380 | RoboBus | RoboBus: A Diverse and Cross-Border Public Transport Dataset | [Paper](https://doi.org/10.1109/PerComWorkshops51409.2021.9431129) |
| d0394 | CARISSMA Pre-Crash Scenario Dataset | A Fusion Approach for Pre-Crash Scenarios based on Lidar and Camera Sensors | [Paper](https://doi.org/10.1109/VTC2021-Spring51267.2021.9449039) |
| d0436 | CVIS dataset | Robust 2D/3D Vehicle Parsing in Arbitrary Camera Views for CVIS | [Paper](https://doi.org/10.1109/ICCV48922.2021.01534) |
| d0498 | IPLT (Institut Pascal Long-Term Dataset) | Over Two Years of Challenging Environmental Conditions for Localization: The IPLT Dataset | [Paper](https://doi.org/10.5220/0010518303830387) |
| d0620 | CarCam | Video Super-Resolution with Long-Term Self-Exemplars | [Paper](https://www.semanticscholar.org/paper/a242b709db7d62b84fe1ee427e05dfc6a090063c) |

## 2020 (67)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0016 | A2D2 (Audi Autonomous Driving Dataset) | A2D2: Audi Autonomous Driving Dataset | [Paper](https://www.semanticscholar.org/paper/07e3db9c2034b522c3cf04399602cc7124964306) |
| d0017 | One Thousand and One Hours | One Thousand and One Hours: Self-driving Motion Prediction Dataset | [Paper](https://www.semanticscholar.org/paper/5ff9429efe7b6c4e7c039064920600e2b7664828) |
| d0021 | MulRan | MulRan: Multimodal Range Dataset for Urban Place Recognition | [Paper](https://doi.org/10.1109/ICRA40945.2020.9197298) |
| d0024 | RELLIS-3D | RELLIS-3D Dataset: Data, Benchmarks and Analysis | [Paper](https://doi.org/10.1109/ICRA48506.2021.9561251) |
| d0027 | Canadian Adverse Driving Conditions (CADC) | Canadian Adverse Driving Conditions dataset | [Paper](https://doi.org/10.1177/0278364920979368) |
| d0028 | Toronto-3D | Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways | [Paper](https://doi.org/10.1109/CVPRW50498.2020.00109) |
| d0030 | RADIATE | RADIATE: A Radar Dataset for Automotive Perception in Bad Weather | [Paper](https://doi.org/10.1109/ICRA48506.2021.9562089) |
| d0031 | SemanticPOSS | SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances | [Paper](https://doi.org/10.1109/IV47402.2020.9304596) |
| d0033 | Dark Zurich | Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation | [Paper](https://doi.org/10.1109/TPAMI.2020.3045882) |
| d0034 | rounD Dataset | The rounD Dataset: A Drone Dataset of Road User Trajectories at Roundabouts in Germany | [Paper](https://doi.org/10.1109/ITSC45102.2020.9294728) |
| d0037 | Stanford-TRI Intent Prediction (STIP) | Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction | [Paper](https://doi.org/10.1109/LRA.2020.2976305) |
| d0042 | CARRADA | CARRADA Dataset: Camera and Automotive Radar with Range- Angle- Doppler Annotations | [Paper](https://doi.org/10.1109/ICPR48806.2021.9413181) |
| d0046 | Ford Multi-AV Seasonal Dataset | Ford Multi-AV Seasonal Dataset | [Paper](https://doi.org/10.1177/0278364920961451) |
| d0047 | TITAN | TITAN: Future Forecast Using Action Priors | [Paper](https://doi.org/10.1109/cvpr42600.2020.01120) |
| d0048 | LIBRE | LIBRE: The Multiple 3D LiDAR Dataset | [Paper](https://doi.org/10.1109/IV47402.2020.9304681) |
| d0050 | DDD20 | DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction | [Paper](https://doi.org/10.1109/ITSC45102.2020.9294515) |
| d0059 | 4Seasons | 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving | [Paper](https://doi.org/10.1007/978-3-030-71278-5_29) |
| d0066 | RGB-P dataset | Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion | [Paper](https://doi.org/10.1364/OE.416130) |
| d0067 | ZUT (Zachodniopomorski Uniwersytet Technologiczny) | Pedestrian Detection in Severe Weather Conditions | [Paper](https://doi.org/10.1109/ACCESS.2020.2982539) |
| d0069 | HeatNet RGB-Thermal Dataset | HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images | [Paper](https://doi.org/10.1109/IROS45743.2020.9341192) |
| d0073 | TJU-DHD | TJU-DHD: A Diverse High-Resolution Dataset for Object Detection | [Paper](https://doi.org/10.1109/TIP.2020.3034487) |
| d0085 | High Resolution Radar Dataset for Autonomous Driving | High Resolution Radar Dataset for Semi-Supervised Learning of Dynamic Objects | [Paper](https://doi.org/10.1109/CVPRW50498.2020.00058) |
| d0096 | PED (Parallel End-to-End Driving Dataset) | Learning Driving Models From Parallel End-to-End Driving Data Set | [Paper](https://doi.org/10.1109/JPROC.2019.2952735) |
| d0098 | I2MAP | Proximity based automatic data annotation for autonomous driving | [Paper](https://doi.org/10.1109/JAS.2020.1003033) |
| d0111 | FinnForest | FinnForest dataset: A forest landscape for visual SLAM | [Paper](https://doi.org/10.1016/j.robot.2020.103610) |
| d0114 | HSI Road | Hsi Road: A Hyper Spectral Image Dataset For Road Segmentation | [Paper](https://doi.org/10.1109/icme46284.2020.9102890) |
| d0116 | OLIMP | OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception | [Paper](https://doi.org/10.3390/electronics9040560) |
| d0123 | Cirrus | Cirrus: A Long-range Bi-pattern LiDAR Dataset | [Paper](https://doi.org/10.1109/ICRA48506.2021.9561267) |
| d0125 | Multimodal Driver Monitoring (MDM) Dataset | The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention | [Paper](https://doi.org/10.1109/TITS.2021.3095462) |
| d0126 | SCORP | Deep Open Space Segmentation using Automotive Radar | [Paper](https://doi.org/10.1109/ICMIM48759.2020.9299052) |
| d0127 | Attentional-GCNN Pedestrian Dataset | Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases | [Paper](https://doi.org/10.1109/ICRA48506.2021.9561480) |
| d0133 | Hong Kong Urban Canyon Datasets | Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas | [Paper](https://doi.org/10.1109/ACCESS.2020.3006210) |
| d0139 | Snowy Driving Dataset | Semantic Image Segmentation on Snow Driving Scenarios | [Paper](https://doi.org/10.1109/ICMA49215.2020.9233538) |
| d0143 | Small Obstacle Segmentation Dataset | LiDAR guided Small obstacle Segmentation | [Paper](https://doi.org/10.1109/IROS45743.2020.9341465) |
| d0151 | — | Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification? | [Paper](https://doi.org/10.23919/FUSION45008.2020.9190338) |
| d0154 | University of Sydney Campus Data Set | Developing and Testing Robust Autonomy: The University of Sydney Campus Data Set | [Paper](https://doi.org/10.1109/MITS.2020.2990183) |
| d0172 | Expressway Vehicle Detection Dataset | Video-Based Vehicle Counting for Expressway: A Novel Approach Based on Vehicle Detection and Correlation-Matched Tracking Using Image Data from PTZ Cameras | [Paper](https://doi.org/10.1155/2020/1969408) |
| d0175 | Highway Driving Dataset | Highway Driving Dataset for Semantic Video Segmentation | [Paper](https://www.semanticscholar.org/paper/622949b1aacd316c60a7034c44121c698a3fb6a4) |
| d0183 | FSOCO | FSOCO: The Formula Student Objects in Context Dataset | [Paper](https://doi.org/10.4271/12-05-01-0003) |
| d0191 | TAF-BW Dataset (Test Area Autonomous Driving Baden-Württemberg Dataset) | From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset) | [Paper](https://doi.org/10.1109/ITSC45102.2020.9294539) |
| d0202 | Warthog Off-Road and Urban LiDAR Dataset | LiDARNet: A Boundary-Aware Domain Adaptation Model for Lidar Point Cloud Semantic Segmentation | [Paper](https://www.semanticscholar.org/paper/4c1f62bf534b443fdd93329a07dda830174d2e36) |
| d0208 | Multi-View Region of Interest Dataset | Multi-View Region of Interest Prediction for Autonomous Driving Using Semi-Supervised Labeling | [Paper](https://doi.org/10.1109/ITSC45102.2020.9294387) |
| d0212 | Pit30M | Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars | [Paper](https://doi.org/10.1109/IROS45743.2020.9340924) |
| d0226 | MIT-AVT Clustered Driving Scene Dataset | MIT-AVT Clustered Driving Scene Dataset: Evaluating Perception Systems in Real-World Naturalistic Driving Scenarios | [Paper](https://doi.org/10.1109/IV47402.2020.9304677) |
| d0231 | Cars in Uncommon States (CUS) dataset | 3D Part Guided Image Editing for Fine-Grained Object Understanding | [Paper](https://doi.org/10.1109/CVPR42600.2020.01135) |
| d0236 | Off-Road Perception Dataset | Low-latency Perception in Off-Road Dynamical Low Visibility Environments | [Paper](https://doi.org/10.1016/j.eswa.2022.117010) |
| d0238 | SDVScenes | Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs | [Paper](https://www.semanticscholar.org/paper/781da932cceb0e69e5471e018c41fb95224e2e4c) |
| d0239 | Mobile Mapping System Point Cloud-Image Dataset | Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud Rendering | [Paper](https://doi.org/10.1007/s41064-020-00114-z) |
| d0244 | Blind Spot Vehicle Prediction Dataset | Predicting Vehicles Appearing from Blind Spots Based on Pedestrian Behaviors | [Paper](https://doi.org/10.1109/ITSC45102.2020.9294443) |
| d0260 | Off-road Dataset | DEEP SEMANTIC SEGMENTATION FOR THE OFF-ROAD AUTONOMOUS DRIVING | [Paper](https://doi.org/10.5194/isprs-archives-xliii-b2-2020-617-2020) |
| d0266 | LSCUT (Large-Scale China Urban Traffic) | Deeply Supervised Z-Style Residual Network Devotes to Real-Time Environment Perception for Autonomous Driving | [Paper](https://doi.org/10.1109/TITS.2019.2918227) |
| d0282 | KAIST RGBD-scan dataset | MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks | [Paper](https://doi.org/10.1109/IROS45743.2020.9340767) |
| d0283 | Pedestrian Near-Miss (PNM) dataset | Joint Pedestrian Detection and Risk-level Prediction with Motion-Representation-by-Detection | [Paper](https://doi.org/10.1109/ICRA40945.2020.9197399) |
| d0306 | Eindhoven Crowdsourced Mapping Dataset | Pose-graph based Crowdsourced Mapping Framework | [Paper](https://doi.org/10.1109/CAVS51000.2020.9334622) |
| d0307 | — | Camera-Radar Fusion for 3-D Depth Reconstruction | [Paper](https://doi.org/10.1109/IV47402.2020.9304559) |
| d0332 | — | Dataset Construction from Naturalistic Driving in Roundabouts | [Paper](https://doi.org/10.3390/s20247151) |
| d0333 | — | Extraction and Assessment of Naturalistic Human Driving Trajectories from Infrastructure Camera and Radar Sensors | [Paper](https://doi.org/10.1109/CASE48305.2020.9216992) |
| d0339 | Vehicle Matching Dataset with Large Viewpoint Differences | Machine Learning Techniques for Vehicle Matching with Non-Overlapping Visual Features | [Paper](https://doi.org/10.1109/CAVS51000.2020.9334562) |
| d0350 | NEOLIX | The NEOLIX Open Dataset for AutonomousDriving | [Paper](https://www.semanticscholar.org/paper/6156f74a31383b4437a8f7a9ab0f6b63e1f99554) |
| d0381 | UrTra2D | UrTra2D – Urban Traffic 2D Object Detection Dataset | [Paper](https://doi.org/10.1109/ICCE-Berlin50680.2020.9352154) |
| d0382 | CUHK-AHU Dataset | CUHK-AHU Dataset: Promoting Practical Self-Driving Applications in the Complex Airport Logistics, Hill and Urban Environments | [Paper](https://doi.org/10.1109/IROS45743.2020.9341317) |
| d0395 | — | Vulnerable Road Users Detection based on Convolutional Neural Networks | [Paper](https://doi.org/10.1109/ISNCC49221.2020.9297332) |
| d0400 | Nighttime Object Detection Datasets from Australia and China | A Comparative Study of Nighttime Object Detection With Datasets From Australia and China | [Paper](https://doi.org/10.1109/CAC51589.2020.9327278) |
| d0416 | Baidu RAL Trajectory and 3D Perception Dataset | CVPR 2019 WAD Challenge on Trajectory Prediction and 3D Perception | [Paper](https://www.semanticscholar.org/paper/7c6679c14efde44d6fe17502549591a4c6f5dd57) |
| d0437 | Road semantic segmentation dataset (university campus) | Neural road semantic segmentation in driving scenarios | [Paper](https://doi.org/10.1109/ECAI50035.2020.9223197) |
| d0553 | Starlight-RGB Dataset | A Novel Starlight-RGB Colorization Method Based on Image Pair Generation for Autonomous Driving | [Paper](https://doi.org/10.1109/CVCI51460.2020.9338603) |
| d0554 | Risky Driving Behaviors Dataset | Learning to Predict Risky Driving Behaviors for Autonomous Driving | [Paper](https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258163) |

## 2019 (23)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0003 | nuScenes | nuScenes: A Multimodal Dataset for Autonomous Driving | [Paper](https://doi.org/10.1109/cvpr42600.2020.01164) |
| d0004 | Waymo Open Dataset | Scalability in Perception for Autonomous Driving: Waymo Open Dataset | [Paper](https://doi.org/10.1109/CVPR42600.2020.00252) |
| d0006 | Argoverse | Argoverse: 3D Tracking and Forecasting With Rich Maps | [Paper](https://doi.org/10.1109/CVPR.2019.00895) |
| d0010 | DDAD (Dense Depth for Automated Driving) | 3D Packing for Self-Supervised Monocular Depth Estimation | [Paper](https://doi.org/10.1109/cvpr42600.2020.00256) |
| d0013 | Seeing Through Fog | Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather | [Paper](https://doi.org/10.1109/cvpr42600.2020.01170) |
| d0018 | The Oxford Radar RobotCar Dataset | The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset | [Paper](https://doi.org/10.1109/ICRA40945.2020.9196884) |
| d0022 | Highway Vehicle Dataset | Vision-based vehicle detection and counting system using deep learning in highway scenes | [Paper](https://doi.org/10.1186/s12544-019-0390-4) |
| d0023 | WoodScape | WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving | [Paper](https://doi.org/10.1109/ICCV.2019.00940) |
| d0036 | PointCloudDeNoising | CNN-Based Lidar Point Cloud De-Noising in Adverse Weather | [Paper](https://doi.org/10.1109/LRA.2020.2972865) |
| d0040 | A\*3D | A\*3D Dataset: Towards Autonomous Driving in Challenging Environments | [Paper](https://doi.org/10.1109/ICRA40945.2020.9197385) |
| d0045 | UrbanLoco | UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes | [Paper](https://doi.org/10.1109/ICRA40945.2020.9196526) |
| d0052 | Unsupervised LLAMAS | Unsupervised Labeled Lane Markers Using Maps | [Paper](https://doi.org/10.1109/ICCVW.2019.00111) |
| d0053 | UTBM Robocar Dataset | EU Long-term Dataset with Multiple Sensors for Autonomous Driving | [Paper](https://doi.org/10.1109/IROS45743.2020.9341406) |
| d0065 | D2-City | D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios | [Paper](https://www.semanticscholar.org/paper/85e3a8a6c46788bd01ded879bcca8f8eed0ad4f7) |
| d0087 | Road Traversing Knowledge (RTK) Dataset | Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset | [Paper](https://doi.org/10.22456/2175-2745.91522) |
| d0092 | Brno Urban Dataset | Brno Urban Dataset - The New Data for Self-Driving Agents and Mapping Tasks | [Paper](https://doi.org/10.1109/ICRA40945.2020.9197277) |
| d0099 | — | Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning | [Paper](https://doi.org/10.1109/ITSC.2019.8916853) |
| d0112 | Boxy | Boxy Vehicle Detection in Large Images | [Paper](https://doi.org/10.1109/ICCVW.2019.00112) |
| d0159 | Mcity Dataset | Mcity Data Collection for Automated Vehicles Study | [Paper](https://www.semanticscholar.org/paper/02eb1b9cc14996713d1e653ada100ea14201b3e6) |
| d0160 | MIT DriveSeg | Value of Temporal Dynamics Information in Driving Scene Segmentation | [Paper](https://doi.org/10.1109/tiv.2021.3094836) |
| d0184 | Multi-lane Detection Dataset (unnamed in abstract) | Multi-lane Detection Using Instance Segmentation and Attentive Voting | [Paper](https://doi.org/10.23919/ICCAS47443.2019.8971488) |
| d0290 | — | Large Scale Multimodal Data Capture, Evaluation and Maintenance Framework for Autonomous Driving Datasets | [Paper](https://doi.org/10.1109/ICCVW.2019.00530) |
| d0315 | UrbanFlow | Human Driver Behavior Prediction based on UrbanFlow\* | [Paper](https://doi.org/10.1109/ICRA40945.2020.9196918) |

## 2018 (2)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0005 | BDD100K | BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning | [Paper](https://doi.org/10.1109/cvpr42600.2020.00271) |
| d0064 | comma2k19 | A Commute in Data: The comma2k19 Dataset | [Paper](https://www.semanticscholar.org/paper/50c1ab22f442470efbe3198f0b338fb699416bc5) |

## 2016 (1)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0002 | Cityscapes | The Cityscapes Dataset for Semantic Urban Scene Understanding | [Paper](https://doi.org/10.1109/CVPR.2016.350) |

## 2012 (1)

| ID | Dataset | Paper title | Link |
| --- | --- | --- | --- |
| d0001 | KITTI | Are we ready for autonomous driving? The KITTI vision benchmark suite | [Paper](https://doi.org/10.1109/CVPR.2012.6248074) |