We present a novel learning algorithm for action prediction and local navigation for autonomous driving  . Our approach classifies the driver behavior of other vehicles or road-agents (aggressive or conservative) and takes that into account for decision making and safe driving. We present a behavior-driven simulator that can generate trajectories corresponding to different levels of aggressive behaviors and use our simulator to train a policy using graph convolutional networks . We use a reinforcement learning-based navigation scheme that uses a proximity graph of traffic agents and computes a safe trajectory for the ego-vehicle that accounts for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes. We have integrated our algorithm with  OpenAI gym-based " Highway-Env " simulator and demonstrate the benefits in terms of improved navigation in different scenarios .
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 .
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