Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
This addresses safety challenges in autonomous driving for self-driving cars, but it is incremental as it builds on existing RL methods for a specific domain.
The paper tackles safe decision-making for autonomous driving on highways by developing a deep reinforcement learning method that ensures no collisions and handles unobservable states from unpredictable agents, with simulations in a self-driving car simulator demonstrating applicability.
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