@inproceedings{1315fe32de87436b8b57a775599ec61e,
title = "Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations",
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.",
author = "Abbas Sadat and Sergio Casas and Mengye Ren and Xinyu Wu and Pranaab Dhawan and Raquel Urtasun",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2020",
doi = "10.1007/978-3-030-58592-1_25",
language = "English (US)",
isbn = "9783030585914",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "414--430",
editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",
booktitle = "Computer Vision – ECCV 2020 - 16th European Conference, Glasgow, 2020, Proceedings",
address = "Germany",
}