  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.
