Deep Reinforcement Learning for Autonomous Driving Systems

Abstract

Autonomous driving systems (ADS) are poised to revolutionize the future of transportation, promising increased safety, efficiency, and convenience. Deep Reinforcement Learning (DRL) has emerged as a powerful approach to solving complex decision-making tasks in dynamic environments, making it a promising candidate for the development of intelligent autonomous vehicles. This paper explores the application of DRL techniques in autonomous driving, focusing on the integration of perception, planning, and control. We review state-of-the-art DRL algorithms, including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC), and examine their roles in enabling end-to-end learning for driving policies. Furthermore, we discuss the challenges inherent in deploying DRL in real-world autonomous driving scenarios, including sample inefficiency, safety constraints, and the sim-to-real gap. Finally, the paper presents case studies and experimental results that highlight the potential of DRL to improve autonomous vehicle performance in complex environments, while identifying future research directions to address open problems in the field.