Evaluation of driver’s situation awareness in freeway exit using backpropagation neural network

• Published in: Transportation research. Part F, Traffic psychology and behaviour Vol. 105; pp. 42 - 57
• Main Authors: Yang, Yanqun, Chen, Yue, Easa, Said M., Lin, Jie, Chen, Meifeng, Zheng, Xinyi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Backpropagation neural network; Electroencephalogram; Eye movement; Situation awareness; Situation awareness; Backpropagation neural network; Electroencephalogram; Eye movement
• ISSN: 1369-8478
• DOI: 10.1016/j.trf.2024.06.018

•The indicators were collected based on three levels of situation awareness (SA).•The drivers’ SA assessment system was built using grey correlation analysis.•The constructed backpropagation neural network model can effectively predict SA. Based on combining the relevant studies on situation awareness (SA), this paper integrated multiple indicators, including eye movement, electroencephalogram (EEG), and driving behavior, to evaluate SA. SA is typically divided into three stages: perception, understanding and prediction. This paper used eye movement indicators to represent perception, EEG indicators to represent understanding, and driving behavior indicators to represent prediction. After identifying indicators for evaluating SA, a driving simulation experiment was designed to collect data on the indicators. 41 subjects were recruited to participate in the investigation, and the experimenter collected data from each subject in a total of 9 groups. After removing 4 groups of invalid data, 365 groups of valid data were finally obtained. The grey correlation analysis was used to optimize the SA indicators, and 10 SA evaluation indicators were finally determined. There were the average fixation duration, the nearest neighbor index, pupil area, the percentage power spectral density values of the 3 rhythmic waves (θ, α, β), rhythmic wave energy combination parameters (α / θ), mean speed, SD of speed and acceleration. Taking the optimized 10 indicators as input and the SA scores as output, a backpropagation neural network model with a topological structure of 10-8-1 was constructed. 75% of the data were randomly selected for model training, and the final network training’s mean square error was 0.0025. Using the remaining 25% of data for verification, the average absolute error and average relative error of the predicted results are 0.248 and 0.046, respectively. This showed that the model was effective, and it was feasible to evaluate the SA by using the data of eye movement, EEG and driving behavior parameters.