Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
Guardado en:
| Publicado en: | arXiv.org (Oct 28, 2024), p. n/a |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , , , , |
| Publicado: |
Cornell University Library, arXiv.org
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3037192806 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3037192806 | ||
| 045 | 0 | |b d20241028 | |
| 100 | 1 | |a Ma, Fulong | |
| 245 | 1 | |a Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks | |
| 260 | |b Cornell University Library, arXiv.org |c Oct 28, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Autonomous cars | ||
| 653 | |a Monocular vision | ||
| 653 | |a Algorithms | ||
| 653 | |a Traffic information | ||
| 653 | |a Complexity | ||
| 653 | |a Visual perception | ||
| 653 | |a Motion control | ||
| 653 | |a Visual perception driven algorithms | ||
| 700 | 1 | |a Qi, Weiqing | |
| 700 | 1 | |a Zhao, Guoyang | |
| 700 | 1 | |a Zheng, Linwei | |
| 700 | 1 | |a Wang, Sheng | |
| 700 | 1 | |a Liu, Yuxuan | |
| 700 | 1 | |a Liu, Ming | |
| 700 | 1 | |a Ma, Jun | |
| 773 | 0 | |t arXiv.org |g (Oct 28, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3037192806/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2404.06860 |