Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
In this paper, we develop a safe decision making method for self driving cars in a multi lane, single agent setting. The proposed approach utilizes deep reinforcement learning (RL) to achieve a high level policy for safe tactical decision making. We address two major challenges that arise solely in autonomous navigation. First, the proposed algorithm ensures that collisions never happen, and therefore accelerate the learning process. Second, the proposed algorithm takes into account the unobservable states in the environment. These states appear mainly due to the unpredictable behavior of other agents, such as cars, and pedestrians, and make the Markov Decision Process (MDP) problematic when dealing with autonomous navigation. Simulations from a well known self driving car simulator demonstrate the applicability of the proposed method
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