@inproceedings{b61f8e84306d41459770b4d904145e98,
title = "Modeling driver risk perception and response mechanism based on psychological field theory",
abstract = "To enhance human-like behavior in advanced driver assistance systems (ADAS) and improve driver satisfaction, this paper proposes a driver risk perception and response modeling approach based on psychological field theory. First, a driver risk perception model is constructed, quantifying perceived risk intensity by incorporating both driver characteristics and environmental risk factors. Second, a human-like risk response mechanism is developed, linking quantified risk perception to behavioral responses. Using naturalistic driving data from the HighD dataset, a genetic algorithm is applied for personalized parameter optimization, improving the model{\textquoteright}s predictive accuracy. Experimental results show that the proposed Driver Risk Perception and Response Model (DRRM) more accurately captures acceleration trends across driving styles, reducing mean squared error (MSE) by 25.29\% and 37.11\% compared to the IDM and FVD models, respectively. This work offers theoretical support for developing intelligent, driver-adaptive assistance systems.",
keywords = "Driver Modeling, Driver Risk Perception, Driving Style, Psychological Field Theory, Risk Response",
author = "Xiaoran Feng and Ying Cheng",
note = "Publisher Copyright: {\textcopyright} 2025 SPIE.; 10th International Conference on Electromechanical Control Technology and Transportation, ICECTT 2025 ; Conference date: 06-06-2025 Through 08-06-2025",
year = "2025",
month = sep,
day = "10",
doi = "10.1117/12.3079109",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Goh, \{Hui Hwang\} and Jinsong Wu and Jinsong Wu",
booktitle = "Tenth International Conference on Electromechanical Control Technology and Transportation, ICECTT 2025",
address = "United States",
}