Learning Interactive Driving Policies via Data-driven Simulation¶
Authors: Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting, Igor Gilitschenski, Sertac Karaman, Daniela Rus
Published: 2022 (Conference Paper)
Source: IEEE International Conference on Robotics and Automation (ICRA)
Algorithm: Inpainted Data-Driven Simulation
arXiv: 2111.12137
DOI: 10.1109/ICRA46639.2022.9812407
Summary¶
Companion paper to VISTA 2.0 that extends data-driven simulation to multi-agent scenarios by inpainting other vehicles into real-world footage, enabling interactive driving policy learning that transfers directly to a full-scale autonomous vehicle.
Abstract¶
Data-driven simulators promise high data-efficiency for driving policy learning. When used for modelling interactions, this data-efficiency becomes a bottleneck: Small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We address this challenge by proposing a simulation method that uses in-painted ado vehicles for learning robust driving policies. Thus, our approach can be used to learn policies that involve multi-agent interactions and allows for training via state-of-the-art policy learning methods. We evaluate the approach for learning standard interaction scenarios in driving. In extensive experiments, our work demonstrates that the resulting policies can be directly transferred to a full-scale autonomous vehicle without making use of any traditional sim-to-real transfer techniques such as domain randomization.
Links¶
Primary
Standard
Alternate
Tags¶
-
Autonomous Driving
-
Data-driven Simulation
-
Multi-agent Interaction
-
Policy Learning
-
Sim-to-Real Transfer
-
Inpainting