Talk-to-Drive: A Voice-Driven Framework for Driving Style Adjustment in Motion Planning*

This study proposes a personalized motion planning framework for autonomous driving, driven by driver voice commands. The method begins with the recognition and parsing of voice commands to identify personalized driving preferences. An artificial potential field(APF) model is then constructed and dynamically adjusted according to the parsed requirements. A vehicle motion planning model is subsequently developed, integrating the personalized artificial potential field with Model Predictive Control (MPC) to achieve individualized driving behavior. The effectiveness of the method is validated through MATLAB/SCANeR co-simulations, confirming its capability to adapt motion planning to driver-specific requirements.