自然驾驶状态下城市地下环道驾驶人心理负荷研究

doi:10.16097/j.cnki.1009-6744.2024.04.026

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (4): 274-282.DOI: 10.16097/j.cnki.1009-6744.2024.04.026

自然驾驶状态下城市地下环道驾驶人心理负荷研究

尚婷^*1a,1b，易爱强^1a，何军²，刘祥²

1. 重庆交通大学，a.交通运输学院，b.智能综合立体交通重庆市重点实验室，重庆400074； 2. 重庆市气象服务中心，重庆401147

收稿日期:2024-04-23 修回日期:2024-05-30 接受日期:2024-06-05 出版日期:2024-08-25 发布日期:2024-08-22
作者简介:尚婷(1983- )，女，重庆人，副教授，博士。
基金资助:
重庆市技术创新与应用发展专项重点项目 (CSTB2022TIAD-KPX0123)；重庆市自然科学基金(CSTB2023NSCQ-MSX0742)；重庆市研究生科研创新项目 (CYS240477)。

Mental Workload of Drivers in Urban Underground Ring Roads Under Naturalistic Driving Conditions

SHANGTing^*1a,1b,YI Aiqiang^1a,HE Jun²,LIU Xiang²

1a. School of Traffic & Transportation, 1b. Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, Chongqing Jiaotong University, Chongqing 400074, China; 2. Chongqing Meteorological Bureau, Chongqing 401147, China

Received:2024-04-23 Revised:2024-05-30 Accepted:2024-06-05 Online:2024-08-25 Published:2024-08-22
Supported by:
Chongqing Municipal Special KeyProjectforTechnologicalInnovation and Application Development (CSTB2022TIAD-KPX0123)；Natural Science Foundation Project of Chongqing (CSTB2023NSCQ-MSX0742)；Chongqing Postgraduate Research Innovation Project (CYS240477)。

摘要/Abstract

摘要： 为探究城市地下环道驾驶人心理负荷特性，组织驾驶人自然驾驶试验，使用生理仪获取自然驾驶状态下驾驶人心电信号数据。以驾驶人心率、心率增长率和心率变异性指标为关键变量，基于因子分析法构建心理负荷量化模型，揭示地下环道平曲线半径、曲线类型、出入口以及熟练程度对驾驶人心理负荷的影响。研究表明：心率指标和心率变异性指标分别与地下环道平曲线半径呈负相关和正相关关系；驾驶人心理负荷与平曲线半径呈负相关，在相同半径条件下，连续曲线场景下的心理负荷显著高于单个曲线场景，且连续曲线间负荷累积作用超过单一曲线半径对驾驶人心理负荷的影响；入口段驾驶人心理负荷比出口段高21.39%；非熟练驾驶人心理负荷比熟练驾驶人高30.75%；建议在城市地下环道出入口和曲率变化区段增设视线诱导设施，设计车速为20~40 km·h-1时，平曲线半径不宜小于30m。

关键词: 交通工程, 驾驶人心理负荷, 因子分析, 城市地下环道, 心率变异性, 自然驾驶试验

Abstract: To explore the mental workload characteristics of drivers on urban underground ring roads, a natural driving test was conducted, and physiological instruments were used to obtain drivers' electrocardiogram data in their natural driving state. A quantitative model of mental workload was constructed based on the factor analysis, using drivers' heart rate, heart rate growth rate, and heart rate variability indicators as key variables, to reveal the effects of underground ring road flat curve radius, the curve type, entrance and exit, and the proficiency level on drivers' mental workload. Results show that heart rate index and heart rate variability index are negatively and positively correlated with the radius of the underground ring road flat curve, respectively. The mental workload of drivers is negatively correlated with the radius of the flat curve. Under the same radius conditions, the mental workload of consecutive curve scenes is significantly higher than that of single curve scenes. Furthermore, the cumulative effect of consecutive curves exceeds that of single curve radius on drivers' mental workload. The mental workload of drivers at the entrance segment is 21.39% higher than that at the exit segment. The mental workload of non-proficient drivers is 30.75% higher than that of proficient drivers. It is recommended that sight-inducing facilities be added at the entrances and exits of urban underground ring roads and sections with curvature changes, and the flat curve radius should not be less than 30 m when the design speed is 20~40 km·h^-1.

Key words: traffic engineering, drivers' mental workload, factor analysis, urban underground ring roads, heart rate variability, natural driving test

中图分类号:

U491

尚婷, 易爱强, 何军, 刘祥. 自然驾驶状态下城市地下环道驾驶人心理负荷研究[J]. 交通运输系统工程与信息, 2024, 24(4): 274-282.

SHANGTing, YI Aiqiang, HE Jun, LIU Xiang. Mental Workload of Drivers in Urban Underground Ring Roads Under Naturalistic Driving Conditions[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(4): 274-282.

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http://www.tseit.org.cn/CN/Y2024/V24/I4/274

参考文献

[1]毛建民,徐达明,吴翱翔.城市中央商务区地下交通规划研究[J]. 地下空间与工程学报,2020, 16(S1): 1-6, 23. [MAO J M, XU D M, WU A X. Study on underground transportation planning of urban central business district Chinese Journal of Underground Space and Engineering, 2020, 16(S1): 1-6, 23.] [J].

[2]HUANG Y N, CHEN F, SONG M T, et al. Effect evaluation of traffic guidance in urban underground road diverging and merging areas: A simulator study[J]. Accident Analysis & Prevention, 2023, 186: 107036.

[3]YOUNG M S, BROOKHUISK A, WICKENS C D, et al. State of science: Mental workload in ergonomics[J]. Ergonomics, 2015, 58(1): 1-17.

[4]HEINE T, LENIS G, REICHENSPERGER P, et al. Electrocardiographic features for the measurement of drivers' mental workload[J]. Applied Ergonomics, 2017, 61: 31-43.

[5]YANG S Y, KUO J, LENNE M G, et al. The impacts of temporal variation and individual differences in driver cognitive workload on ECG-based detection[J]. Human Factors, 2021, 63(5): 772-787.

[6] CHEN Y, WANG X, ZHANG X, et al. Experimental study on driver' s mental load in hairpin curves of mountainous highway[J]. Transport, 2023, 38(3): 127 138.

[7] 阎莹,王虹婷,万利,等.基于因子分析与熵值法的不同隧道侧向宽度下驾驶负荷模型[J].中国公路学报, 2023, 36(2): 190-202. [YAN Y, WANG H T, WAN L, et al. Driver load model under different tunnel lateral widths based on factor analysis and entropy method[J]. China Journal of Highway and Transport, 2023, 36(2): 190-202.]

[8]刘卓凡,丁涛,吴付威,等.平直高速公路低交通量下L2自动驾驶对驾驶人心理负荷的影响[J].中国公路学报, 2022, 35(4): 256-266. [LIU Z F, DING T, WU F W, et al. Impact of level-2 autonomous driving mode on drivers' mental workload under low traffic volume of straight motorway[J]. China Journal of Highway and Transport, 2022, 35(4): 256-266.]

[9] 徐进, 刘小明,胡静.基于自然驾驶数据的螺旋匝道(桥)驾驶人心理负荷分析[J].交通运输系统工程与信息, 2020, 20(3): 212-218. [XU J, LIU X M, HU J. Analysis of driver mental load on helical ramps and helical bridges based on naturalistic driving data[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(3): 212-218.]

[10] 朱彤, 吴玲, 胡月琦,等.基于因子模型的高速公路特长隧道驾驶人心理负荷特性研究[J].中国公路学报, 2018, 31(11): 165-175. [ZHU T, WU L, HU Y Q, et al. Research on characteristics of drivers' mental workload in extra-long expressway tunnels based on the factor model[J]. China Journal of Highway and Transport, 2018, 31(11): 165-175.]

[11] 白婧荣, 唐伯明,孙宗元,等.毗邻互通立交特长隧道驾驶负荷研究[J].交通运输系统工程与信息,2022,22 (1): 301-310. [BAI J R, TANG B M, SUN Z Y, et al. On driving load of super-long tunnel adjacent to interchanges [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 301-310.]

[12] DU J W, REN G, LIU W, et al. How is the visual working memory load of driver influenced by information density of traffic signs?[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 86: 65-83.

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自然驾驶状态下城市地下环道驾驶人心理负荷研究

Mental Workload of Drivers in Urban Underground Ring Roads Under Naturalistic Driving Conditions

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