ADAPS: Autonomous Driving Via Principled Simulations

Weizi Li1, David Wolinski1, and Ming C. Lin1,2
1University of North Carolina at Chapel Hill
2University of Maryland at College Park


Autonomous driving has gained significant advancements in recent years. However, obtaining a robust control policy for driving remains challenging as it requires training data from a variety of scenarios, including rare situations (e.g., accidents), an effective policy architecture, and an efficient learning mechanism. We propose ADAPS for producing robust control policies for autonomous vehicles. ADAPS consists of two simulation platforms in generating and analyzing accidents to automatically produce labeled training data, and a memoryenabled hierarchical control policy. Additionally, ADAPS offers a more efficient online learning mechanism that reduces the number of iterations required in learning compared to existing methods such as DAGGER. We present both theoretical and experimental results. The latter are produced in simulated environments, where qualitative and quantitative results are generated to demonstrate the benefits of ADAPS.


DOI, IEEE International Conference on Robotics and Automation (ICRA), 2019
Preprint (with Appendix)


The authors would like to thank US Army Research Office and UNC Arts & Science Foundation, and Dr. Feng "Bill" Shi for insightful discussions.

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