@inproceedings{ecb575cdf43547b3adefd6bb7ff9014c,
title = "DSDNet: Deep Structured Self-driving Network",
abstract = "In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network. Towards this goal, we develop a deep structured energy based model which considers the interactions between actors and produces socially consistent multimodal future predictions. Furthermore, DSDNet explicitly exploits the predicted future distributions of actors to plan a safe maneuver by using a structured planning cost. Our sample-based formulation allows us to overcome the difficulty in probabilistic inference of continuous random variables. Experiments on a number of large-scale self driving datasets demonstrate that our model significantly outperforms the state-of-the-art.",
keywords = "Autonomous driving, Motion planning, Motion prediction",
author = "Wenyuan Zeng and Shenlong Wang and Renjie Liao and Yun Chen and Bin Yang 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 = "10.1007/978-3-030-58589-1_10",
language = "English (US)",
isbn = "9783030585884",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "156--172",
editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",
booktitle = "Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings",
address = "Germany",
}