@inproceedings{bea06ea686f142fd884c8fcbe4b6dd84,
title = "Research on Driver Trust Prediction based on the Attention-CNN Model",
abstract = "Driver trust in Automated Driving Systems (ADS) is a key factor for ensuring human-vehicle-cooperative driving safety. This study focuses on this aspect and conducts driving simulation experiments on a static driving platform. Under various scenarios involving different driving takeover events, vehicle styles, and takeover warning types, the study uses driver eye-tracking data and vehicle status data to predict driver trust. An Attention-CNN model, combining multi-scale convolution and attention mechanisms, is employed for trust prediction, and Shapley values are used to determine the importance of each feature to optimize the model. The experimental results show that the model performs well in predicting driver trust, with an accuracy of 80.6\% and an F1 score of 81.4\%, representing a significant improvement over the baseline model. This provides effective methodological support for driver trust evaluation in Automated Driving Systems.",
keywords = "Attention-CNN, Automated Driving, Shapley values, Trust",
author = "Xiaobei Jiang and Tao Zhou and Guanyu Li and Chun Hu and Hanlin Wang and Lihui Li and Hongwei Guo and Wuhong Wang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 8th International Conference on Transportation Information and Safety, ICTIS 2025 ; Conference date: 16-07-2025 Through 19-07-2025",
year = "2025",
doi = "10.1109/ICTIS68762.2025.11214955",
language = "English",
series = "8th International Conference on Transportation Information and Safety: Transportation + Artificial Intelligence and Green Energy: Making a Sustainable World, ICTIS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1521--1525",
booktitle = "8th International Conference on Transportation Information and Safety",
address = "United States",
}