Continuous Decision-Making in Lane Changing and Overtaking Maneuvers for Unmanned Vehicles: A Risk-Aware Reinforcement Learning Approach With Task Decomposition

Reinforcement learning methods have shown the ability to solve challenging scenarios in unmanned systems. However, solving long-time decision-making sequences in a highly complex environment, such as continuous lane change and overtaking in dense scenarios, remains challenging. Although existing unmanned vehicle systems have made considerable progress, minimizing driving risk is the first consideration. Risk-aware reinforcement learning is crucial for addressing potential driving risks. However, the variability of the risks posed by several risk sources is not considered by existing reinforcement learning algorithms applied in unmanned vehicles. Based on the above analysis, this study proposes a risk-aware reinforcement learning method with driving task decomposition to minimize the risk of various sources. Specifically, risk potential fields are constructed and combined with reinforcement learning to decompose the driving task. The proposed reinforcement learning framework uses different risk-branching networks to learn the driving task. Furthermore, a low-risk episodic sampling augmentation method for different risk branches is proposed to solve the shortage of high-quality samples and further improve sampling efficiency. Also, an intervention training strategy is employed wherein the artificial potential field (APF) is combined with reinforcement learning to speed up training and further ensure safety. Finally, the complete intervention risk classification twin delayed deep deterministic policy gradient-task decompose (IDRCTD3-TD) algorithm is proposed. Two scenarios with different difficulties are designed to validate the superiority of this framework. Results show that the proposed framework has remarkable improvements in performance.