Autonomous Highway Driving using Deep Reinforcement Learning

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. Due to this, formulating a rule based decision maker for selecting driving maneuvers may not be ideal. Similarly, it may not be efficient to solve optimal control problem in real-time for a predefined cost function. In order to address these issues and to avoid peculiar behaviors when encountering unforeseen scenario, we propose a reinforcement learning (RL) based method, where the ego car, i.e., an autonomous vehicle, learns to make decisions by directly interacting with the simulated traffic. Here the decision maker is a deep neural network that provides an action choice for a given system state. We demonstrate the performance of the developed algorithm in highway driving scenario where the trained AV encounters varying traffic density.