mlsanityData to 7 Oct 2026

Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review

arXiv 2504.08540 ↗First posted 11 Apr 2025cs.CVcited by 1

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Abstract (arXiv)

Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation across a variety of road scenarios. Numerous datasets have been introduced to support the development and evaluation of lane detection algorithms, each differing in terms of the amount of data, sensor types, annotation granularity, environmental conditions, and scenario diversity. This paper provides a comprehensive review of 20 publicly available lane detection datasets, systematically analyzing their characteristics, advantages, and limitations. We classify these datasets based on key performance indicators such as sensor resolution, annotation types and diversity of road and weather conditions using a novel multidimensional metric for dataset quality. By identifying existing challenges and research gaps, we highlight opportunities for future dataset improvements that can further drive innovation in robust lane detection. This review serves as a resource for researchers seeking appropriate datasets for robust lane detection and contributes to the broader goal of advancing autonomous driving.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 61 references that are in the map. First posted on arXiv 11 Apr 2025.

Builds on

32 of 61 references.

References · 32

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  3. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  4. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  5. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  6. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  7. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  8. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  9. 2017Spatial As Deep: Spatial CNN for Traffic Scene Understandingcited by 187
  10. 2022PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmarkcited by 140
  11. 20183D-LaneNet: End-to-End 3D Multiple Lane Detectioncited by 106
  12. 2020CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blendingcited by 80
20 more
  1. 2020Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detectioncited by 75
  2. 2017VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognitioncited by 56
  3. 2022ONCE-3DLanes: Building Monocular 3D Lane Detectioncited by 44
  4. 2014Real time Detection of Lane Markers in Urban Streetscited by 41
  5. 2019Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networkscited by 41
  6. 2018A Commute in Data: The comma2k19 Datasetcited by 33
  7. 2022Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanescited by 32
  8. 2019Deep Multi-Sensor Lane Detectioncited by 30
  9. 2021VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detectioncited by 21
  10. 2022Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Drivingcited by 20
  11. 2024LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusionscited by 18
  12. 2020Synthetic-to-Real Domain Adaptation for Lane Detectioncited by 11
  13. 2021K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highwayscited by 11
  14. 2020Semi-Local 3D Lane Detection and Uncertainty Estimationcited by 10
  15. 2022CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domainscited by 9
  16. 2024SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditionscited by 8
  17. 2022Towards Driving-Oriented Metric for Lane Detection Modelscited by 7
  18. 2020FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networkscited by 5
  19. 2018Ego-Lane Analysis System (ELAS): Dataset and Algorithmscited by 4
  20. 2024Monocular Lane Detection Based on Deep Learning: A Surveycited by 4

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Data Augmentation Strategies for Robust Lane Marking Detectioncited by 0