LiDAR-Video Driving Dataset: Learning Driving Policies Effectively

By Yiping Chen, Jingkang Wang, Jonathan Li, Cewu Lu, Zhipeng Luo, Han Xue, Cheng Wang. 2018

122 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Cityscapes [270] X X Vehicle Berkley DeepDrive [271] X X Vehicle X X Mapillary [272] X X Vehicle X X Oxford RobotCar [49] X X Vehicle X X X KITTI [174] X X X X Vehicle H3D [273] X X X Vehicle ApolloScape [274] X X X X Vehicle nuScenes [175] X X X X Vehicle X X Udacity [275] X X X X Vehicle DDD17 [85”
nuScenes: A Multimodal Dataset for Autonomous Driving (2019, background): “Other notable multimodal datasets include [15] providing driving behavior labels, [43] providing place categorization labels and [6, 55] providing raw data without semantic labels.”
A Survey of End-to-End Driving: Architectures and Training Methods (2020, methodology): “0 HDD [144] 3 X X X X X X X X X 104h Academic Brain4Cars [145] 1 X X X X 1180 miles Academic Li-Vi [95], [146] 1 X X X X X 10h Academic DDD17 [147] 1* X X X X X X 12h CC-BY-NC-SA-4.”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, background): “Other reviewed datasets such as [59], [77], [78], [81],”
End-to-End Autonomous Driving: Challenges and Frontiers (2023, background): “Naive concatenation is also frequently adopted [15], [22], [30], [142], [143], [144], [145], [146].”
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners (2018, background): “Autonomous driving has seen dramatic advances in recent years, for instance for road scene parsing [1,2,3,4], lane following [5,6,7], path planning [8,9,10,11], and end-toend driving models [12,13,14,15].”
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection (2019, background): “Scene understanding in 3D plays a principal role in designing effective real-world systems such as in urban autonomous driving [2, 10, 15] and robotics [17, 36].”
Review: deep learning on 3D point clouds (2020, unknown): “DBNet [77].”