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
@article{arxiv.2105.06517,
title = {Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving},
author = {Arash Mohammadhasani and Hamed Mehrivash and Alan Lynch and Zhan Shu},
journal= {arXiv preprint arXiv:2105.06517},
year = {2021}
}