Trajectory tracking and collision avoidance are key functions of vehicle intelligence. For the singular control style limitation of existing control methods in the same scene， a novel multi-style reinforcement learning （RL） method is proposed in this paper. To achieve diversity in control styles， style indicators are innovatively incorporated into value and policy networks to establish a multi-style tracking and collision avoidance policy network. Alongside this， a multi-style policy iteration framework is developed combining the distributional RL theory. Based on the framework， a multi-style distributional soft actor-critic algorithm （M-DSAC） is put forward. Through simulation and real vehicle tests， it is validated that the proposed method is capable of executing trajectory tracking and collision avoidance tasks across various driving styles， such as aggressive， neutral， and conservative， with the real vehicle’s steady-state trajectory tracking error less than 5 cm， with high control accuracy. The average single-step decision-making time for the real vehicle is merely 6.07 ms， meeting real-time requirements.
