Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety

The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Chen, Jianyu, Bodi Yuan, Tomizuka, Masayoshi
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.10.2019
Subjects
Computer Science - Robotics
Computer simulation
Data acquisition
Learning
Neural networks
Safety
Urban environments
Online AccessGet full text

Cover

More Information
Summary:The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.
ISSN:2331-8422
DOI:10.48550/arxiv.1903.00640