接管场景驱动下非驾驶任务对驾驶人视觉与生理特征的影响

doi:10.16097/j.cnki.1009-6744.2024.03.028

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (3): 290-298.DOI: 10.16097/j.cnki.1009-6744.2024.03.028

接管场景驱动下非驾驶任务对驾驶人视觉与生理特征的影响

张磊¹ ，彭金栓^*1，陈晓利^{1, 2}

1. 重庆交通大学，交通运输学院，重庆 400074；2. 招商局重庆交通科研设计院有限公司，重庆 400067

收稿日期:2024-01-11 修回日期:2024-03-04 接受日期:2024-03-21 出版日期:2024-06-25 发布日期:2024-06-24
作者简介:张磊(1992- )，男，河北邢台人，博士生
基金资助:
国家重点研发计划(2018YFB1600501)；重庆市高校创新研究群体项目(CXQT21022)；重庆市自然科学基金(CSTB2022NSCQ-MSX0549)

Impact of Non-driving Tasks on Drivers' Visual and Physiological Characteristics Under Take-over Scenario

ZHANG Lei¹ , PENG Jinshuan^*1 , CHEN Xiaoli^{1, 2}

1. College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China; 2. China Merchants Chongqing Communications Technology Research & Design Institute Co Ltd, Chongqing 400067, China

Received:2024-01-11 Revised:2024-03-04 Accepted:2024-03-21 Online:2024-06-25 Published:2024-06-24
Supported by:
National Key Research and Development Program of China (2018YFB1600501)；University Creative Research Group Project of Chongqing (CXQT21022)；Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0549)

摘要/Abstract

摘要： 为研究驾驶人在接管车辆控制权过程中视觉与生理特征的变化规律，本文构建面向城市道路与高速公路工况下的6类接管场景，开展模拟驾驶试验，提取视觉特征、心电特征、皮电特征等维度的参数，利用双因素方差分析确定影响驾驶人状态参数的因素。研究结果表明：接管场景类型和非驾驶任务对驾驶人瞳孔面积变化率、注视熵、兴趣区域注视概率、心率增长率、心率变异性、皮电增长率等具有显著影响 (p < 0.05) ；在城市道路工况下，驾驶人瞳孔面积变化率 (M = 26.91，SD = 10.17) 高于高速公路 (M = 21.32, SD = 7.69) ；认知分心状态的注视熵 (M = 3.84, SD = 1.53) 小于基线状态 (M = 4.46, SD = 1.87) 且视觉搜索范围显著减少(p < 0.05) ；与基线和认知分心状态相比，复合分心状态下驾驶人心率增长率提高了34.69%，而心率变异性则降低了10.55%；此外，在复合分心状态下，驾驶人皮电增长率最高，较另外两类非驾驶任务状态高出82.43%。研究结果可为驾驶人接管绩效的评估与提升，自动驾驶人机交互方式的优化等提供重要参考。

关键词: 智能交通, 接管特征, 双因素方差分析, 接管场景, 视觉特征, 生理特征

Abstract: To investigate the changes in drivers' visual and physiological characteristics during the process of take-over vehicle control, this paper analyzes six types of take-over scenarios for urban roads and highways conditions. The driving experiments were conducted and the parameters were extracted from various dimensions, such as visual features, electro cardio graphic features, and electro dermal activity features. The two-way ANOVA was used to identify the factors affecting drivers' state parameters. The results show that the type of take-over scenario and nondriving tasks significantly affect drivers' pupil area changing rate, fixation entropy, fixation probability in areas of interest, heart rate growth, heart rate variability, and electro dermal activity growth (p < 0.05) . In urban road conditions, the drivers' pupil area changing rate (M = 26.91, SD = 10.17) is higher than on highways (M = 21.32, SD = 7.69) . The fixation entropy (M = 3.84, SD = 1.53) during cognitive distraction state is lower than in baseline state (M = 4.46 , SD = 1.87) , and there is a significant reduction in visual search range (p < 0.05) . Compared to baseline and cognitive distraction states, drivers' heart rate growth increased by 34.69% in composite distraction state, while heart rate variability is reduced by 10.55%. Additionally, in composite distraction state, drivers exhibit the highest electro dermal activity growth, surpassing the other two non-driving task by 82.43%. The research results provide important references for the evaluation and improvement of driver take-over performance and the optimization of humancomputer interaction mode of automated driving.

Key words: intelligent transportation, takeover characteristic, two-way ANOVA, takeover scenario, visual feature; physiological feature

中图分类号:

U461.6

张磊, 彭金栓, 陈晓利. 接管场景驱动下非驾驶任务对驾驶人视觉与生理特征的影响[J]. 交通运输系统工程与信息, 2024, 24(3): 290-298.

ZHANG Lei , PENG Jinshuan , CHEN Xiaoli. Impact of Non-driving Tasks on Drivers' Visual and Physiological Characteristics Under Take-over Scenario[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 290-298.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2024.03.028

http://www.tseit.org.cn/CN/Y2024/V24/I3/290

参考文献

[1] MARCANO M, DIAZ S, PEREZ J, et al. A review of shared control for automated vehicles: Theory and applications[J]. IEEE Transactions on Human-Machine Systems, 2020, 50(6): 475-491.

[2] DU N, ZHOU F, PULVER E M, et al. Predicting driver takeover performance in conditionally automated driving [J]. Accident Analysis & Prevention, 2020, 148: 105748.

[3] 郭烈, 胥林立, 秦增科, 等. 自动驾驶接管影响因素分析与研究进展[J]. 交通运输系统工程与信息, 2022, 22 (2): 72-90. [GUO L, XU L L, QIN Z K, et al. Analysis and overview of influencing factors on autonomous driving takeover[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 72-90.]

[4] 宗长富, 代昌华, 张东. 智能汽车的人机共驾技术研究现状和发展趋势[J]. 中国公路学报, 2021, 34(6): 214- 237. [ZONG C F, DAI C H, ZHANG D. Human-machine interaction technology of intelligent vehicles: Current development trends and future directions[J]. China Journal of Highway and Transport, 2021, 34(6): 214- 237.]

[5] WADA T, SONODA K, OKASAKA T, et al. Authority transfer method from automated to manual driving via haptic shared control[C]. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, 2016: 2659-2664.

[6] NILSSON J, FALCONE P, VINTER J. Safe transitions from automated to manual driving using driver controllability estimation[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 1806- 1816.

[7] KO S, SANGHAVI H, ZHANG Y, et al. Modeling the effects of perceived intuitiveness and urgency of various auditory warnings on driver takeover performance in automated vehicles[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 90: 70-83.

[8] KUTCHEK K, JEON M. Takeover and handover requests using non-speech auditory displays in semi-automated vehicles[C]//Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 2019: 1-6.

[9] BAZILINSKYY P, PETERMEIJER S M, PETROVYCH V, et al. Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 56: 82-98.

[10] 王丹, 林业. 警告刺激对驾驶员接管性能的影响机理研究[J]. 交通运输系统工程与信息, 2024, 24(1): 106- 114. [WANG D, LIN Y. Research on the mechanisms of the effect of warning stimuli on driver takeover performance[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 106-114.]

[11] 胡宏宇, 张慧珺, 姚荣涵, 等. L3级自动驾驶接管过程驾驶员情景意识研究[J]. 吉林大学学报(工学版), 2024, 54(2): 410-418. [HU H Y, ZHANG H J, YAO R H, et al. Driver's situational awareness in takeover process of L3 automated vehicles[J]. Journal of Jilin University (Engineering and Technology Edition), 2024, 54(2): 410-418.]

[12] MELNICUK V, THOMPSON S, JENNINGS P, et al. Effect of cognitive load on drivers' state and task performance during automated driving: Introducing a novel method for determining stabilisation time following take-over of control[J]. Accident Analysis & Prevention, 2021, 151: 105967.

[13] ZHANG B, WINTER J D, VAROTTO S, et al. Determinants of take-over time from automated driving: A meta-analysis of 129 studies[J]. Transportation Research, 2019, 64: 285-307.

[14] 姚荣涵, 祁文彦, 郭伟伟. 自动驾驶环境下驾驶人接管行为结构方程模型[J]. 交通运输工程学报, 2021, 21 (2): 209-221. [YAO R H, QI W Y, GUO W W. A structural equation model of driver takeover behavior in automatic driving environment[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 209-221.]

[15] BROOK A, SHIFERA W, DAVID P, et al. Gaze entropy measures detect alcohol-induced driver impairment[J]. Drug and Alcohol Dependence, 2019, 204(5): 1-8.

接管场景驱动下非驾驶任务对驾驶人视觉与生理特征的影响

Impact of Non-driving Tasks on Drivers' Visual and Physiological Characteristics Under Take-over Scenario

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	刘美岐, 金楷然, 李雅澜, 郭戈. 城市道路智能网联车辆轨迹鲁棒控制方法[J]. 交通运输系统工程与信息, 2024, 24(4): 31-40.
[2]	温惠英, 张昕怡, 黄俊达, 许鹏鹏. 考虑动态交互作用的智能车辆轨迹预测[J]. 交通运输系统工程与信息, 2024, 24(4): 60-68.
[3]	赵朔, 奇格奇, 李培豪, 关伟. 基于脑电通道注意力机制的驾驶行为识别研究[J]. 交通运输系统工程与信息, 2024, 24(4): 283-291.
[4]	马千里, 王通, 邵帅, 贾鹏. 路测环境下无人驾驶小巴复杂场景运行评价[J]. 交通运输系统工程与信息, 2024, 24(4): 300-310.
[5]	董红召, 杨嘉炜, 全程. 车联网下公交专用道复用的动态清空控制方法[J]. 交通运输系统工程与信息, 2024, 24(3): 12-20.
[6]	刘江, 张楚, 蔡伯根, 王剑, 陆德彪. 基于样本增强的列车卫星定位伪距欺骗检测方法[J]. 交通运输系统工程与信息, 2024, 24(3): 32-42.
[7]	李建市, 徐友春, 齐尧, 谢德胜, 李华. 起伏道路条件下无人车纵向速度控制方法研究[J]. 交通运输系统工程与信息, 2024, 24(3): 43-52.
[8]	张金雷, 杨健, 刘晓冰, 陈瑶, 杨立兴, 高自友. 基于计算机视觉的轨道交通站内火灾检测与定位[J]. 交通运输系统工程与信息, 2024, 24(3): 53-63.
[9]	陈喜群, 朱奕璋, 谢宁珂, 耿茂思, 吕朝锋. 基于异构多智能体自注意力网络的路网信号协调顺序优化方法[J]. 交通运输系统工程与信息, 2024, 24(3): 114-126.
[10]	辛琪, 王嘉琪, 杨文科, 徐猛, 袁伟. 信控路段混行交通生态驾驶深度强化学习模型[J]. 交通运输系统工程与信息, 2024, 24(3): 127-139.
[11]	倪浩原, 余贵珍, 李涵, 陈鹏, 刘喜, 王文达. 露天矿作业区无人矿车协同通行决策方法研究[J]. 交通运输系统工程与信息, 2024, 24(3): 277-289.
[12]	张河山, 谭鑫, 范梦伟, 潘存书, 徐进, 张羽. 无人机高空航拍视角下小尺度车辆精确检测方法[J]. 交通运输系统工程与信息, 2024, 24(3): 299-309.
[13]	魏丽英, 吴润泽. 基于鱼群效应的智能网联车队形成与演化机理研究[J]. 交通运输系统工程与信息, 2024, 24(2): 76-85.
[14]	黄玮, 李世昌, 曾海鹏. 基于非冲突合流策略的交叉口灵活信号控制方法[J]. 交通运输系统工程与信息, 2024, 24(2): 86-95.
[15]	张玺君, 聂生元, 李喆, 张红. 基于自注意力机制的深度强化学习交通信号控制[J]. 交通运输系统工程与信息, 2024, 24(2): 96-104.