B-GAP: Behavior-Guided Action Prediction and Navigation for Autonomous Driving

We present an algorithm for behaviorally-guided action prediction and local navigation for autonomous driving in dense traffic scenarios. Our approach classifies the driver behavior of other vehicles or road-agents (aggressive or conservative) and considers that information for decision-making and safe driving. We present a behavior-driven simulator that can generate trajectories corresponding to different levels of aggressive behaviors, and we use our simulator to train a reinforcement learning policy using a multilayer perceptron neural network. We use our reinforcement learning-based navigation scheme to compute safe trajectories for the ego-vehicle accounting for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes. We have integrated our algorithm with the OpenAI gym-based ``Highway-Env'' simulator and demonstrate the benefits of our navigation algorithm in terms of reducing collisions by $3.25 - 26.90$% and handling scenarios with $2.5 \times$ higher traffic density.