Learning Interactive Driving Policies via Data-driven Simulation
This addresses the bottleneck of data-efficiency in simulation for autonomous driving, enabling robust policy learning in multi-agent scenarios.
The paper tackles the problem of learning interactive driving policies in data-driven simulators by proposing a method that uses in-painted ado vehicles to generate challenging edge cases, resulting in policies that can be directly transferred to a full-scale autonomous vehicle without traditional sim-to-real techniques.
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.