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
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)
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)