Invited Speaker丨特邀报告

 Assoc. Prof. Tingru ZHANG, Shenzhen University

Tingru Zhang is an associate professor at the Institute of Human Factors and Ergonomics, Shenzhen University, and Guest Researcher at the CGN National Key Laboratory. Her main research focuses on intelligent transportation, human-AI interaction, and smart interaction & user experience. She has led over 10 research projects. With more than 50 publications, her work includes 1 ESI Hot Paper and 1 Highly Cited Paper. She currently serves as an editorial board member for Transportation Research Part F and International Journal of Ergonomics, and has been recognized with honors such as the Top 2% Most-Cited Scientists Worldwide (2023, 2024) and the Outstanding Early Career Award from the International Ergonomics Association (2023).

Speech Topic: EEG-based assessment of driver trust

Abstract: Effective collaboration between automated vehicles (AVs) and human drivers relies on maintaining an appropriate level of trust. However, real-time assessment of human trust remains a significant challenge. While initial efforts have delved into the potential use of physiological signals, such as skin conductance and heart rate, to evaluate trust, limited attention has been given to the feasibility of assessing trust through electroencephalogram (EEG) signals. This study aimed to address this issue by using EEG signals to objectively assess driver trust towards AVs. A total of 420 time- and frequency-domain EEG features were extracted, and nine machine learning algorithms were applied to construct driver trust assessment models. Additionally, to explore the potential of developing cost-effective models with reduced feature inputs, this study developed trust models using features solely from single brain regions: frontal, parietal, occipital, or temporal. The results showed that the best-performing model, utilizing features from the whole brain and employing the Light Gradient Boosting Machine (LightGBM) algorithm, achieved an accuracy of 88.44% and an F1-score of 78.31%. In comparison, models based on single brain regions did not achieve comparable performance to the comprehensive model. However, the frontal and parietal regions showed important potentials for developing costeffective trust assessment models. This study also performed feature analysis on the best-performing model to identify features highly responsive to changes in trust. The results showed that an increased power of beta waves tended to indicate a lower level of trust in AVs. These findings contribute to our understanding of the neural correlates of trust in AVs and hold practical implications for the development of trust-aware AV technologies capable of adapting and responding to driver’s trust levels effectively.