mlsanityData to 14 Oct 2026

Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks

arXiv 2404.06860 ↗First posted 10 Apr 2024cs.CVcited by 9

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Abstract (arXiv)

3D lane detection is essential in autonomous driving as it extracts structural and traffic information from the road in three-dimensional space, aiding self-driving cars in logical, safe, and comfortable path planning and motion control. Given the cost of sensors and the advantages of visual data in color information, 3D lane detection based on monocular vision is an important research direction in the realm of autonomous driving, increasingly gaining attention in both industry and academia. Regrettably, recent advancements in visual perception seem inadequate for the development of fully reliable 3D lane detection algorithms, which also hampers the progress of vision-based fully autonomous vehicles. We believe that there is still considerable room for improvement in 3D lane detection algorithms for autonomous vehicles using visual sensors, and significant enhancements are needed. This review looks back and analyzes the current state of achievements in the field of 3D lane detection research. It covers all current monocular-based 3D lane detection processes, discusses the performance of these cutting-edge algorithms, analyzes the time complexity of various algorithms, and highlights the main achievements and limitations of ongoing research efforts. The survey also includes a comprehensive discussion of available 3D lane detection datasets and the challenges that researchers face but have not yet resolved. Finally, our work outlines future research directions and invites researchers and practitioners to join this exciting field.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 85 of its 132 references that are in the map. First posted on arXiv 10 Apr 2024.

Builds on

85 of 132 references.

References · 85

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50 most cited of 85 All 85 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 2.

9 papers.

  1. 2024Monocular Lane Detection Based on Deep Learning: A Surveycited by 4
  2. 2024CLRKDNet: Speeding up Lane Detection with Knowledge Distillationcited by 3
  3. 2025Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detectioncited by 2
  4. 2026Annotation-Free Detection of Drivable Areas and Curbs Leveraging LiDAR Point Cloud Mapscited by 2
  5. 2024Erase, then Redraw: A Novel Data Augmentation Approach for Free Space Detection Using Diffusion Modelcited by 1
  6. 2025Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillationcited by 1
  7. 2025Breaking Down Monocular Ambiguity: Exploiting Temporal Evolution for 3D Lane Detectioncited by 0
  8. 2025Depth3DLane: Fusing Monocular 3D Lane Detection with Self-Supervised Monocular Depth Estimationcited by 0
  9. 20263D Lane Detection with Odometry for High-Speed Vehicle Racingcited by 0