汽车工程师

融合驾驶员状态与环境感知的自动驾驶动态预警模型研究

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朱乃宣 ¹^,² , 罗俊杰 ¹ , 高镇海 ² , 杨益 ² , 郑竹安 ¹

汽车工程师 | 2026中国汽车工程学会年会优秀论文（选登） 2026,(10): 1-10

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汽车工程师 |2026中国汽车工程学会年会优秀论文（选登） 2026 , (10) : 1 -10

融合驾驶员状态与环境感知的自动驾驶动态预警模型研究

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朱乃宣¹^,², 罗俊杰¹, 高镇海², 杨益², 郑竹安¹

作者信息

^1．盐城工学院，盐城　224051

^2．吉林大学汽车底盘集成与仿生全国重点实验室，长春　130012

Research on a Dynamic Warning Model for Autonomous Driving Integrating Driver State and Environmental Perception

Naixuan Zhu¹^,², Junjie Luo¹, Zhenhai Gao², Yi Yang², Zhu’an Zheng¹

Affiliations

^1.Yancheng Institute of Technology, Yancheng 224051

^2.State Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130012

出版时间: 2026-10-15 doi: 10.20104/j.cnki.1674-6546.20260261

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摘要

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针对人机共驾场景下传统碰撞预警系统主要依赖车外环境参数，难以适应驾驶员疲劳或分心导致的反应时间延长等问题，提出双流联合感知网络（DSCP-Net）。车外分支引入动态蛇形卷积（DSC）和内部交并比（Inner-IoU）损失，以改善目标特征提取和边界框定位精度；车内分支构建轻量化行为认知模块（BCM），基于面部特征对驾驶员状态进行量化；决策层融合驾驶员状态与环境感知信息，自适应调整碰撞时间（TTC）阈值。试验结果表明，BCM对驾驶员状态的识别准确率达97.6%，在100 km/h重度疲劳紧急制动工况下，TTC阈值由3.0 s扩展至4.5 s，提供了41.7 m的安全缓冲区间，有效提高了人机共驾场景下的主动安全预警能力。

关键词

自动驾驶 / 目标识别 / 疲劳检测 / 动态预警

Abstract

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To address the limitation that conventional collision warning systems in human-machine co-driving scenarios mainly rely on external environmental parameters and cannot adequately account for prolonged driver reaction time caused by fatigue or distraction, a Dual-Stream Joint Perception Network (DSCP-Net) is proposed. In the external perception branch, Dynamic Snake Convolution (DSC) and Inner-IoU loss are introduced to improve target feature extraction and bounding-box localization. In the in-cabin branch, a lightweight Behavioral Cognitive Module (BCM) is developed to quantify driver states based on facial features. At the decision-making level, driver-state and environmental-perception information are fused to adaptively adjust the Time-to-Collision (TTC) threshold. Experimental results show that the BCM achieves a driver-state recognition accuracy of 97.6%. Under a severe-fatigue emergency-braking condition at 100 km/h, the TTC threshold is adaptively increased from 3.0 s to 4.5 s, providing an additional safety buffer of 41.7 m. The results demonstrate that the proposed method improves active safety warning performance in human-machine co-driving scenarios.

Key words

Autonomous driving / Object recognition / Fatigue detection / Dynamic early-warning

引用本文

朱乃宣, 罗俊杰, 高镇海, 杨益, 郑竹安. 融合驾驶员状态与环境感知的自动驾驶动态预警模型研究. 汽车工程师, 2026 , (10) : 1 -10 . DOI: 10.20104/j.cnki.1674-6546.20260261

Naixuan Zhu, Junjie Luo, Zhenhai Gao, Yi Yang, Zhu’an Zheng. Research on a Dynamic Warning Model for Autonomous Driving Integrating Driver State and Environmental Perception[J]. Automotive Engineer, 2026 , (10) : 1 -10 . DOI: 10.20104/j.cnki.1674-6546.20260261

基金

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汽车底盘集成与仿生全国重点实验室开放基金项目(20250206)

参考文献

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参考文献引证文献

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2026年第卷第10期

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文章信息

doi: 10.20104/j.cnki.1674-6546.20260261

首发时间：2026-10-29
出版时间：2026-10-15

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修回日期：2026-08-30

基金

汽车底盘集成与仿生全国重点实验室开放基金项目(20250206)

作者信息

^1．盐城工学院，盐城　224051

^2．吉林大学汽车底盘集成与仿生全国重点实验室，长春　130012

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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