Construction of Driving Risk Field Model Considering Driver Cognitive Processing in Edge Scenes

doi:10.3969/j.issn.1004-132X.2026.01.023

Abstract

Abstract:

During the human-vehicle co-driving processes， driver's emotional changes would lead to cognitive changes， which in turn altered the vehicle risk field. Therefore， a human factor risk field model was constructed considering driver's cognitive-emotional state. Firstly， vehicle driving data and driver physiological signals were collected and analyzed through driving simulator experiments. Then， the driver factors in the human factor risk field were calibrated. Finally， experimental data were collected through a 6-DOF driving simulator， and the risk indicators of the human factor risk field were compared with multiple traditional risk indicators. The results show that the human factor risk field model is more effective and may evaluate the driving risks of drivers stably with different emotions in edge scenarios.

Key words: driving risk field, edge scene, cognitive processing, human factor risk field

摘要：

人-车共驾过程中，驾驶员的情绪变化会导致认知变化，进而改变车辆风险场，为此构建了一种考虑驾驶员认知-情绪状态的人因风险场模型。首先通过驾驶模拟器实验收集并分析车辆行驶数据和驾驶员生理信号；随后标定人因风险场中的驾驶员因子；最后通过六自由度驾驶模拟器采集实验数据并对人因风险场风险指标与多传统风险指标进行对比。人因风险场模型在边缘场景下能更有效和稳定评估不同情绪驾驶员的行车风险。

关键词: 行车风险场, 边缘场景, 认知处理过程, 人因行为场

CLC Number:

TP182

ZHOU Bin, YANG Zhifeng, ZHANG Junning, DONG Yuanfa, PENG Wei. Construction of Driving Risk Field Model Considering Driver Cognitive Processing in Edge Scenes[J]. China Mechanical Engineering, 2026, 37(1): 223-232.

周彬, 杨志峰, 张峻宁, 董元发, 彭巍. 边缘场景下计及驾驶员认知处理过程的驾驶风险场模型构建[J]. 中国机械工程, 2026, 37(1): 223-232.

Figures/Tables 16

Fig.1 Expressway edge scene diagram

Fig.2 Urban road edge scene diagram

Fig.3 Experimental design for simulated driving

Fig.4 Driving range division

Tab.1 Calibration results of risk field model parameters

参数	$μ$	$K 1$	$K 2$	RMSE
标定结果	0.0379	$-$ 0.0390	0.6741	0.5762

Tab.1 Calibration results of risk field model parameters

参数	$μ$	$K 1$	$K 2$	RMSE
标定结果	0.0379	$-$ 0.0390	0.6741	0.5762

Fig.5 Heat maps of the driver's gaze area under different emotional states

Fig.6 Trend chart of driver’s gaze characteristics index changes under different emotional states

Fig.7 Brain region maps of drivers in different emotional states

Fig.8 The power of brain waves at different frequencies of drivers under various emotional states

Tab.2 Cognitive risk factor index weight calibration

指标	$I θ$	$I α$	$I β$	$I γ$	$I D$	$I T$
权重	0.0689	0.1659	0.1389	0.2436	0.1934	0.1893

Tab.2 Cognitive risk factor index weight calibration

指标	$I θ$	$I α$	$I β$	$I γ$	$I D$	$I T$
权重	0.0689	0.1659	0.1389	0.2436	0.1934	0.1893

Fig.9 Cognitive factor map of drivers under different emotional states

Fig.10 Comparison of ECG indexes of drivers under different emotional states

Fig.11 Driving ability of drivers in different driving ranges under different emotional states

Tab.3 The frequency and proportion of speeding behavior of drivers under different emotional states

	合规车速驾驶		轻微超速驾驶		严重超速驾驶
情绪状态	频数	占比/%	频数	占比/%	频数	占比/%
中性情绪	2893	96.40	60	2.00	48	1.60
正性情绪	2794	93.54	135	4.52	58	1.94
负性情绪	2428	82.00	448	14.93	92	3.07%

Fig.12 Three-dimensional diagram of HF-CRF under different emotions （ED represents the field strength of the human behavior field）

Fig.13 HF-CRF and commonly used risk quantification indicators

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