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

Abbas Sadat; Sergio Casas; Mengye Ren; Xinyu Wu; Pranaab Dhawan; Raquel Urtasun

ECCV 2020poster229 citations

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

Abbas Sadat, Sergio Casas, Mengye Ren, Xinyu Wu, Pranaab Dhawan, Raquel Urtasun

Abstract

Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations","In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable intermediate representations. Unlike existing neural motion planners, our motion planning costs are consistent with our perception and prediction estimates. This is achieved by a novel differentiable semantic occupancy representation that is explicitly used as cost by the motion planning process. Our network is learned end-to-end from human demonstration. Our experiments in a large-scale manual-driving dataset and closed-loop simulation show that the proposed model significantly outperforms state-of-the-art planners in imitating the human behaviors while producing much safer trajectories."

BibTeX

@inproceedings{eccv2020_perceivepredicta,
  title = {Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations},
  author = {Abbas Sadat and Sergio Casas and Mengye Ren and Xinyu Wu and Pranaab Dhawan and Raquel Urtasun},
  booktitle = {ECCV 2020},
  year = {2020}
}