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Motion planning

Given the observations and the predictions of the dynamic actors around the self-driving vehicle, as well as the static constructs provided by HD maps, the problem of motion planning is to efficiently solve for the trajectory the self-driving vehicle will follow.

Recent ATG R&D publications

DSDNet: Deep Structured self-Driving Network

Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun (ECCV 2020)

Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction

Kelvin Wong, Qiang Zhang, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat, Raquel Urtasun (ECCV 2020)

Learning Lane Graph Representations for Motion Forecasting

Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun (ECCV 2020, oral)

Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

Abbas Sadat*, Sergio Casas*, Mengye Ren, Xinyu Wu, Pranaab Dhawan, Raquel Urtasun (ECCV 2020)

Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles

Abbas Sadat*, Mengye Ren*, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, Raquel Urtasun (IROS 2019, oral)

End-to-End Interpretable Neural Motion Planner

Wenyuan Zeng*, Wenjie Luo*, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun (CVPR 2019, oral)

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