Yao, Mingxi (Southeast University), Fang, Zhenwu (National University of Singapore), Gong, Junjie (Southeast University), Wang, Jinxiang (Southeast University), Liu, Mingchun (Higer Bus Company Limited), Yin, Guodong (Southeast University)

A Dynamic Driver Trust Model for Freeway On-Ramp Merging Considering Driving Style

Scheduled for presentation during the Invited Session "S23b-Trustworthy AI for Traffic Sensing and Control" (TH-EA-T23), Thursday, November 20, 2025, 14:10−14:30, Coolangata 2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

In freeway on-ramp merging scenarios, human–machine cooperative driving systems face a significant risk of misoperation due to imbalances in driver trust. This paper proposes a personalized dynamic trust modeling framework based on Kalman filtering for on-ramp merging. Simulation results from 72 participants reveal substantial differences in trust levels among aggressive, normal, and conservative drivers. Incorporating driving style parameters yields dynamic trust estimation with RMSE between 2% and 5%, demonstrating robust predictive performance.