Impact-Aware Maneuver Decision With Driving Style Tuning for Autonomous Vehicle

Autonomous driving is an emerging technology that has developed rapidly over the last decade, with decision-making remaining a critical challenge, particularly due to its significant role in traffic congestion. In this paper, we propose a novel perception-and-decision framework, calledHEAD, which consists of an enHanced pErception module and a mAneuverDecision module to address this challenge. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to address incomplete vehicle features and predict future states in parallel. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a driving policy based on a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. To make our framework applicable to more scenarios, we further propose an improvedHEAD(HEAD++) framework that makes the autonomous vehicle adapt to various road structures, such as lane merging and diverging scenarios. Besides, we develop a style tuning module inHEAD++, which supports personalized driving style tuning. To mitigate high training costs, an efficient style tuning method with approximate gradient descent is proposed to reduce the number of training iterations. Extensive experiments demonstrate the effectiveness of our framework. Compared to state-of-the-art methods,HEAD++ reduces overall traffic disturbance by 23.3%-40.9%, lowers collision risk by 4.5%-17.8%, and improves passenger comfort by 13.1%-30.5%, while maintaining high traffic efficiency.