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

• URL: http://arxiv.org/abs/2008.05930v1
• Date: Thu, 13 Aug 2020 14:40:46 GMT
• Title: Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
• Authors: Abbas Sadat, Sergio Casas, Mengye Ren, Xinyu Wu, Pranaab Dhawan, Raquel Urtasun
• Abstract summary: We propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles. Our network is learned end-to-end from human demonstrations.
• Score: 81.05412704590707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 demonstrations. The 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.

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