Driver cognitive state recognition in autonomous driving takeover decision making

doi:10.16265/j.cnki.issn1003-3033.2026.03.0912

Abstract

Abstract:

In order to address the issue of human-machine collaboration failure in the takeover decision-making of autonomous driving and achieve precise recognition of the driver's cognitive state, this paper simulates three typical scenarios, night high-speed curves, mobile phone distracted driving, and combined scenarios of strong light and heavy rain, collecting and analyzing the dynamic interaction data of driving behavior and eye movement features construct a dynamic weight allocation(DWA)distribution feature fusion framework for multimodal perception and cognition collaboration, constructed DWA-RF model, and explore the dynamic mechanism of the driver's cognitive state and takeover decision-making behavior in complex environments. The results show that the distracted state significantly prolongs the takeover time. In scenes of strong light and heavy rain, the superimposition of distraction and environmental pressure leads to a sharp reduction in the range of scanning. Fatigue causes an increase in lane departure distance, accompanied by a reduction in pupil diameter and abnormal scanning behavior. The cognitive state classification accuracy of DWA-RF constructed in this paper has reached 93.6%, verifying the effectiveness of this model in identifying the driver's cognitive state for autonomous driving takeover decisions.

Key words: driver, autonomous driving, takeover decision, multimodal perception, dynamic weight allocation random forest (DWA-RF)

CLC Number:

X910安全人体学

SHAO Shuyu, LI Yanping, HAN Jiaqi. Driver cognitive state recognition in autonomous driving takeover decision making[J]. China Safety Science Journal, 2026, 36(3): 255-263.

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认知状态	接管时间/s	转向角速度/((°)·s^-1)	车道偏离/m	ACC跟车距离/m	预警响应延迟/s
警觉	1.12 ± 0.25	28.4 ± 4.2	0.18 ± 0.05	1.82 ± 0.35	0.45 ± 0.12
疲劳	1.58 ± 0.31^*	22.1 ± 3.8^*	0.32 ± 0.12^*	2.45 ± 0.41^*	0.62 ± 0.18^*
分心	1.82 ± 0.37^*	19.7 ± 4.5^*	0.42 ± 0.15^*	2.89 ± 0.53^*	0.71 ± 0.21^*

认知状态	接管时间/s	转向角速度/((°)·s^-1)	车道偏离/m	ACC跟车距离/m	预警响应延迟/s
警觉	0.95 ± 0.28	32.1 ± 5.1	0.15 ± 0.04	1.67 ± 0.32	0.38 ± 0.10
疲劳	1.32 ± 0.34^*	26.8 ± 4.9^*	0.38 ± 0.14^*	2.21 ± 0.47^*	0.55 ± 0.16^*
分心	1.67 ± 0.42^*	18.4 ± 4.1^*	0.45 ± 0.18^*	2.95 ± 0.58^*	0.82 ± 0.24^*

认知状态	接管时间/s	转向角速度/((°)·s^-1)	车道偏离/m	ACC跟车距离/m	预警响应延迟/s
警觉	1.05 ± 0.22	25.7 ± 4.7	0.12 ± 0.03	1.52 ± 0.28	0.48 ± 0.15
疲劳	1.45 ± 0.35^*	21.3 ± 4.3^*	0.28 ± 0.11^*	2.15 ± 0.39^*	0.68 ± 0.20^*
分心	1.72 ± 0.40^*	17.9 ± 4.0^*	0.35 ± 0.16^*	2.78 ± 0.45^*	0.95 ± 0.27^*

认知状态	平均注视时长/ms	瞳孔直径/ mm	扫视幅度/(°)	眨眼频率/ (次· min^-1)
警觉	250 ± 30	4.2 ± 0.5	3.2 ± 0.8	8 ± 2
疲劳	320 ± 40^*	3.8 ± 0.6^*	4.5 ± 1.2^*	15 ± 3^*
分心	180 ± 25^*	3.5 ± 0.4^*	2.1 ± 0.6^*	25 ± 4^*

认知状态	平均注视时长/ms	瞳孔直径/mm	扫视幅度/(°)	眨眼频率/ (次· min^-1)
警觉	220 ± 28	4.5 ± 0.6	4.8 ± 1.0	10 ± 3
疲劳	280 ± 35^*	4.1 ± 0.7^*	6.2 ± 1.5^*	18 ± 4^*
分心	150 ± 20^*	3.2 ± 0.5^*	1.8 ± 0.4^*	35 ± 5^*