Research License — MotionNet

MotionNet for Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps.

The ability to reliably perceive the environmental states, particularly the existence of objects and their motion behavior, is crucial for autonomous driving. In this work, we propose an efficient deep model, called MotionNet, to jointly perform perception and motion prediction from 3D point clouds. MotionNet takes a sequence of LiDAR sweeps as input and outputs a bird's eye view (BEV) map, which encodes the object category and motion information in each grid cell. The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses. Extensive experiments show that the proposed method overall outperforms the state-of-the-arts, including the latest scene-flow- and 3D-object-detection-based methods. This indicates the potential value of the proposed method serving as a backup to the bounding-box-based system, and providing complementary information to the motion planner in autonomous driving.

  •  Wu, P., Chen, S., "MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
    BibTeX TR2020-068 PDF Software
    • @inproceedings{Wu2020jun,
    • author = {Wu, Pengxiang and Chen, Siheng},
    • title = {MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps},
    • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    • year = 2020,
    • month = jun,
    • url = {https://www.merl.com/publications/TR2020-068}
    • }

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