Learning Interactive Driving Policies via Data-driven Simulation

• URL: http://arxiv.org/abs/2111.12137v1
• Date: Tue, 23 Nov 2021 20:14:02 GMT
• Title: Learning Interactive Driving Policies via Data-driven Simulation
• Authors: Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting, Igor Gilitschenski, Sertac Karaman, Daniela Rus
• Abstract summary: Data-driven simulators promise high data-efficiency for driving policy learning. Small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We propose a simulation method that uses in-painted ado vehicles for learning robust driving policies.
• Score: 125.97811179463542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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