Prediction Model for Driver Reaction Time Based on PSO-BP Neural Network Model. (10th August 2022)

• Record Type: Journal Article
• Authors: Wang, Dan; Hong, Liang; Zhang, Ce; Bai, Yajie; Bi, Zhen Zhen; Lin, Yier
• Other Names: Khan Mohammad Ayoub Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-08-10
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/6100702
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : A critical element of accident reconstruction technology is the driver reaction time, which is also a key aspect of evaluating driver takeover time in autonomous driving. There are various factors affecting driver reaction time, including driver psychological factors, nondriving tasks, external environment, and other reasons. It is necessary to record and predict the driver reaction time to support the takeover time in the degraded takeover study because this paper is designed to conduct a degraded takeover study in the human-machine codriving stage with safety of the intended functionality. Therefore, a model based on PSO-BP neural network algorithm to predict driver reaction time is developed. A wavelet transform algorithm is used to denoise the signal first in order to improve the convergence speed and prediction accuracy of the model. Meanwhile, the BP neural network prediction model based on the PSO is established to optimize the weights and thresholds of the BP neural network to achieve the prediction of the driver reaction time. A total of six main feature parameters of driver's HRV in the time and frequency domains were selected as input indicators and substituted into the input signal of PSO-BP neural network model for training and testing. The prediction results obtained from the PSO-BP neural network model were compared with that of the BP neural network prediction, and it demonstrated that the prediction results obtained in this paper have smaller error values, verifying the reasonableness and validity of the model.