B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving
This addresses navigation challenges for autonomous vehicles in unpredictable, dense traffic environments, representing an incremental improvement by combining existing simulation and reinforcement learning techniques.
The paper tackles ego-vehicle navigation in dense simulated traffic with varying driver behaviors by enriching simulators with behavior-rich trajectories and training a deep reinforcement learning policy, resulting in a policy that computes safe trajectories accounting for aggressive maneuvers like overtaking and sudden lane changes.
We address the problem of ego-vehicle navigation in dense simulated traffic environments populated by road agents with varying driver behaviors. Navigation in such environments is challenging due to unpredictability in agents' actions caused by their heterogeneous behaviors. We present a new simulation technique consisting of enriching existing traffic simulators with behavior-rich trajectories corresponding to varying levels of aggressiveness. We generate these trajectories with the help of a driver behavior modeling algorithm. We then use the enriched simulator to train a deep reinforcement learning (DRL) policy that consists of a set of high-level vehicle control commands and use this policy at test time to perform local navigation in dense traffic. Our policy implicitly models the interactions between traffic agents and computes safe trajectories for the ego-vehicle accounting for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes. Our enhanced behavior-rich simulator can be used for generating datasets that consist of trajectories corresponding to diverse driver behaviors and traffic densities, and our behavior-based navigation scheme can be combined with state-of-the-art navigation algorithms.