A User Satisfaction Evaluation Method for Autonomous Vehicle Towing and Following Strategies Based on Statistical Features and Data-Driven Approach

With the rapid development of computer technology and Artificial Intelligence (AI), unmanned systems have been widely applied and studied in various fields. For unmanned systems based on Reinforcement Learning (RL) principles, user satisfaction evaluation of their performance can provide accurate reward functions, which deeply influence the optimization of system control strategies. This paper takes the towing and following system of autonomous vehicles as the research object, and proposes a user satisfaction evaluation method for autonomous vehicle behavior based on statistical characteristics and data-driven approach, realizing the mapping from raw sequence data of towing and following to user satisfaction. The core innovation of this paper lies in the first attempt to use the dynamic state of the tow rope (length, angular velocity, etc.) as a key feature source, which is fused with vehicle kinematic data to evaluate user subjective experience; and a self-supervised feedback mechanism is designed to optimize model parameters, enabling the model to have certain self-learning ability. Experimental results show that this method can effectively reflect user satisfaction with autonomous vehicle driving strategies, and achieve progressive optimization of satisfaction evaluation through self-supervised feedback mechanism. This study lays a theoretical foundation for building more intelligent and human-oriented human-machine collaborative following systems, and can be embedded as a reward function in reinforcement learning frameworks for training and optimizing towing and following strategies.