Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness. (January 2022) - Li, Guofa; Yang, Yifan; Li, Shen; Qu, Xingda; Lyu, Nengchao; Li, Shengbo Eben
Highlights: A lane change decision making framework based on deep reinforcement learning is proposed. A probabilistic-model based risk assessment method is proposed to assess the driving risk. A risk-aware strategy with the minimum expected risk is developed for autonomous driving. Our proposed methods have superior lane change driving performance in both static and moving scenarios. Our proposed methods can be applied for safe autonomous driving in dangerous situations. Abstract: Driving safety is the most important element that needs to be considered for autonomous vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making framework based on deep reinforcement learning to find a risk-aware driving decision strategy with the minimum expected risk for autonomous driving. Firstly, a probabilistic-model based risk assessment method was proposed to assess the driving risk using position uncertainty and distance-based safety metrics. Then, a risk aware decision making algorithm was proposed to find a strategy with the minimum expected risk using deep reinforcement learning. Finally, our proposed methods were evaluated in CARLA in two scenarios (one with static obstacles and one with dynamically moving vehicles). The results show that our proposed methods can generate robust safe driving strategies and achieve better driving performances than previous methods.