ML Anthology

LiDAR-Video Driving Dataset: Learning Driving Policies Effectively

Yiping Chen, Jingkang Wang, Jonathan Li, Cewu Lu, Zhipeng Luo, Han Xue, Cheng Wang
CVPR 2018
doi:10.1109/CVPR.2018.00615 /cvpr/2018/chen2018cvpr-lidarvideo/

Abstract

Learning autonomous-driving policies is one of the most challenging but promising tasks for computer vision. Most researchers believe that future research and applications should combine cameras, video recorders and laser scanners to obtain comprehensive semantic understanding of real traffic. However, current approaches only learn from large-scale videos, due to the lack of benchmarks that consist of precise laser-scanner data. In this paper, we are the first to propose a LiDAR-Video dataset, which provides large-scale high-quality point clouds scanned by a Velodyne laser, videos recorded by a dashboard camera and standard drivers' behaviors. Extensive experiments demonstrate that extra depth information help networks to determine driving policies indeed.

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Cite

Text

Chen et al. "LiDAR-Video Driving Dataset: Learning Driving Policies Effectively." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00615

Markdown

[Chen et al. "LiDAR-Video Driving Dataset: Learning Driving Policies Effectively." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/chen2018cvpr-lidarvideo/) doi:10.1109/CVPR.2018.00615

BibTeX

@inproceedings{chen2018cvpr-lidarvideo,
  title     = {{LiDAR-Video Driving Dataset: Learning Driving Policies Effectively}},
  author    = {Chen, Yiping and Wang, Jingkang and Li, Jonathan and Lu, Cewu and Luo, Zhipeng and Xue, Han and Wang, Cheng},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00615},
  url       = {https://mlanthology.org/cvpr/2018/chen2018cvpr-lidarvideo/}
}