A comprehensive review on safe reinforcement learning for autonomous vehicle control in dynamic environments

Abstract

To operate safely in a dynamic environment, autonomous vehicles must possess the same level of predictive driving abilities as human drivers and must be capable of anticipating the future actions of other dynamic objects in the environment, especially those of neighboring vehicles. The development of safe autonomous vehicles (AVs) poses a challenging task as it requires algorithms that can make real-time decisions in unpredictable circumstances. Reinforcement learning (RL) presents a promising approach for AV control, as it utilizes trial and error to enable optimal decision-making. However, traditional RL algorithms are unsuitable for safety-critical applications, as they may explore unsafe actions, potentially resulting in accidents. Safe reinforcement learning (SRL) algorithms have been developed to address this issue, prioritizing safe and reliable decisions. These algorithms incorporate constraints to prevent unsafe actions or utilize techniques to estimate action risk and avoid actions deemed excessively risky. Despite computational challenges, SRL holds significant promise for AV control, and is likely to play a crucial role in developing safe and reliable systems. SRL methods are critical for the general adoption of autonomous vehicles by guaranteeing their safety and reliability. These algorithms utilize methods like uncertainty and risk estimation along with penalty functions, to avoid excessively risky actions and have the potential to significantly reduce accidents and build public trust in autonomous driving. However, there are challenges that need to be addressed, such as the dynamic nature of real-world traffic, high computational costs, and the diversity of road design; and these varying conditions make the designing, testing, and validating of SRL algorithms difficult. Despite these challenges, SRL presents a promising solution, through integrating new sensing technologies and machine learning techniques, to develop safe, efficient, and environmentally friendly transportation systems.