Take-over Performance Prediction Under Different Cognitive Loads of Non-driving Tasks in Highly Automated Driving

被引：0
|
作者：
Ma Y. [1 ]
Lu J. [1 ]
Zhu J. [1 ]
Han X. [1 ]
机构：
[1] School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin
来源：
Qiche Gongcheng/Automotive Engineering | 2023年 / 45卷 / 12期
关键词：
cognitive DNRT; highly automated driving; random forest; takeover performance prediction; traffic engineering;
D O I：
10.19562/j.chinasae.qcgc.2023.12.015
中图分类号：
学科分类号：
摘要：
In highly automated driving，accurate prediction of takeover performance is of great significance to improve the safety of automated driving takeover. Based on the design of driving take-over scenarios under different cognitive load non driving-related tasks（NDRT），the significance of takeover performance indicators and EEG indicators under different cognitive load NDRT of automated driving is analyzed. Using eSense value and brainwave data of the driver as input，a prediction model of take-over performance based on random forest is constructed to analyze the prediction effect of the model within time windows of 3，5，7 and 9 s，and the validity of the model is verified. The results show that there are significant differences in take-over time，maximum lateral acceleration，minimum TTC and driver’s eSense under different loads of NDRT. It is found that the random forest has the best prediction performance within 9 s time window，with the accuracy of 0.94. The prediction accuracy and micro-AUC area of random forest are higher than the results of support vector machine，naive Bayes and logistic regression. The proposed method can effectively predict the take-over performance and provide a theoretical basis for the interaction design between the driver and the autonomous vehicle. © 2023 SAE-China. All rights reserved.
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页码：2330 / 2337and2329
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