驾驶次任务沉浸等级对接管行为的影响分析

王彦峰; 陈浩林; 赵晓华; 李海舰; 李振龙; 付强

doi:10.3963/j.jssn.1674-4861.2022.01.016

驾驶次任务沉浸等级对接管行为的影响分析

doi: 10.3963/j.jssn.1674-4861.2022.01.016

1.
北京交通运输职业学院汽车经济管理系北京 102618
2.
北京工业大学城市交通学院北京 100124

基金项目:

国家自然科学基金项目 52072012

详细信息

作者简介:
王彦峰（1975—），硕士，副教授.研究方向：车辆工程.E-mail: jtxxwyf@163.com

通讯作者:
赵晓华（1971—），博士，教授.研究方向：驾驶行为与交通安全.E-mail: zhaoxiaohua@bjut.edu.cn

中图分类号: U491.25
计量
- 文章访问数: 926
- HTML全文浏览量: 481
- PDF下载量: 54
- 被引次数: 0
出版历程
- 收稿日期: 2021-01-14
- 网络出版日期: 2022-03-31

A Study on the Impact of Immersion Levels of Non-driving-related Tasks on Takeover Behavior

1.
Department of Automotive Economic Management, Beijing Vocational Transportation College, Beijing 102618, China
2.
College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China

摘要

摘要:
为探究自动驾驶中驾驶次任务沉浸等级对接管行为的影响，基于驾驶模拟器搭建自动驾驶接管行为测试平台，设计事故接管场景，基于驾驶次任务（娱乐任务和工作任务）和接管请求时间（5 s和10 s）因素组合开发4个事故接管情景，招募被试参与驾驶模拟实验并采集驾驶人的接管行为数据，选择速度、横向偏移、接管反应时间和接管正确时间4个指标衡量接管行为。研究结果表明：①速度随着驾驶次任务沉浸等级的降低而降低，接管车辆后的降速幅度随之增大；接管请求时间为5 s时，驾驶次任务沉浸等级对横向偏移具有显著影响；②接管请求时间为10 s时，驾驶次任务沉浸等级对接管反应时间具有弱显著性（p = 0.056 < 0.1）, 接管反应时间随着驾驶次任务沉浸等级的增加而逐级降低（低沉浸等级=3.94 s；中沉浸等级=3.45 s；高沉浸等级=3.21 s）；驾驶次任务沉浸等级对接管正确时间均具有统计学差异（5 s时：p =0.031 < 0.05；10 s时：p =0.019 < 0.05），接管正确时间随着驾驶次任务沉浸等级的上升而降低；③在相同驾驶次任务条件下，接管反应时间随着驾驶次任务沉浸等级的升高而降低，统计结果表明驾驶次任务与驾驶次任务沉浸等级的交互作用对接管反应时间无统计学差异，而对接管正确时间具有显著影响。
- 事故场景 /
- 自动驾驶 /
- 接管行为 /
- 驾驶次任务沉浸等级 /
- 影响分析 /
- 驾驶模拟器
Abstract:
This study aims to study the effects of high, medium, and low levels of immersion of non-driving-related (NDR)tasks on the takeover behavior under automated driving. Test scenarios are developed using a driving simulator. There are four takeover scenarios developed based on the task of non-driving-related activity(entertainment and work task)and time to collision(5 s and 10 s). To collect data on takeover behavior, volunteer drivers are recruited for a driving simulation. In order to measure takeover behavior, four metrics are used: speed, lateral deviation, responding time, and takeover correct time(TCT). The results show that: ①the vehicle speed decreases as the immersion level of NDR tasks decreases, and the deceleration rate increases after taking over the vehicle. When the time to collision is 5 s, the level of immersion of NDR tasks significantly affects lateral deviation. ②When the time to collision is 10s, the level of immersion of NDR tasks has a weaker significance on the takeover response time (p =0.056 < 0.1), and it decreases as the immersion level increases(low immersion level=3.94 s; medium immersion level=3.45 s; high immersion level=3.21 s). A significant difference between the level of immersion of NDR task and the time of takeover has been found(time to collision 5s: p =0.031 < 0.05;time to collision 10 s: p =0.019 < 0.05). That is, the time for takeover decreases as the level of immersionofNDR tasks increases. ③ With the same NDR task, the takeover responding time decreases as the immersion level increases. According to the results of statistical analysis, the interaction between NDR tasks and their immersion level does not affect the response time for takeover significantly, however, it does affect the TCT.
- accident scene /
- automated driving /
- takeover behavior /
- non-driving-related task immersion levels /
- impact analysis /
- driving simulator

HTML全文

图 1 自动驾驶接管行为测试平台

Figure 1. The test platform of takeover behavior in automated driving

下载: 全尺寸图片幻灯片

图 2 HMI设计

Figure 2. HMI design

下载: 全尺寸图片幻灯片

图 3 前方事故接管场景

Figure 3. Scene of accident takeover ahead

下载: 全尺寸图片幻灯片

图 4 横向稳定状态判别

Figure 4. Lateral stability state discrimination

下载: 全尺寸图片幻灯片

图 5 不同驾驶次任务沉浸等级条件下的速度和横向偏移指标分析

Figure 5. V and LD indicators analysis of NDRT immersion level

下载: 全尺寸图片幻灯片

图 6 不同驾驶次任务沉浸等级条件下的接管反应时间和接管正确时间指标分析

注：^*表示具有显著性（p < 0.05）

Figure 6. TRT and TCT indicators analysis of NDRT immersion level

下载: 全尺寸图片幻灯片

图 7 不同驾驶次任务条件下沉浸等级对接管反应时间和接管正确时间的影响差异分析

注：^*表示具有显著性（p < 0.05）

Figure 7. Influence of immersion level on TRT and TCT under different NDRT

下载: 全尺寸图片幻灯片

表 1 接管情景

Table 1. Takeover scenario

接管情景		接管请求时间/s
接管情景		5	10
驾驶次任务	工作任务	S1	S2
驾驶次任务	娱乐任务	S3	S4

下载: 导出CSV

表 2 驾驶次任务沉浸等级聚类结果

Table 2. Clustering results of NDRT immersion level 单位: s

沉浸等级	娱乐任务沉浸时长	工作任务沉浸时长
低沉浸等级	(19.35，49.22]	(15.60，50.00]
中沉浸等级	(49.22，76.00]	(50.00，79.63]
高沉浸等级	(76.00，134.95]	(79.63，143.63]

下载: 导出CSV

参考文献(21)

[1]	NOY I Y, SHINAR D, HORREY W J. Automated driving: Safety blind spots[J]. Safety Science, 2018, 102(2): 68-78.
[2]	MCDONALD A D, ALAMBEIGI H, ENGSTROM J, et al. Toward computational simulations of behavior during automated driving takeovers: A review of the empirical and modeling literatures[J]. Human Factors, 2019, 61(4): 642-688. doi: 10.1177/0018720819829572
[3]	张艺竞, 常若松, 马锦飞, 等. L3等级自动驾驶条件下驾驶员接管过程及心理模型的构建[J]. 心理科学, 2019, 42(2): 415-421. https://www.cnki.com.cn/Article/CJFDTOTAL-XLKX201902023.htm ZHANG Y J, CHANG R S, MA J F, et al. Take-over process and the construction of drivers'psychological model under conditionally automated driving[J]. Journal of Psychological Science, 2019, 42(2): 415-421. (in chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XLKX201902023.htm
[4]	CLARK H, FENG J. Age differences in the takeover of vehicle control and engagement in non-driving-related activities in simulated driving with conditional automation[J]. Accident Analysis & Prevention, 2017(106): 468-479.
[5]	PURUCKER C, BERGHFER F, NAUJOKS F, et al. Prediction of take-over time demand in highly automated driving results of a naturalistic driving study[C]. Human Factors and Ergonomics Society Europe Chapter Annual Meeting, Berlin, Germany: Human Factors and Ergonomics Society Europe Chapter, 2018.
[6]	KREUZMAIR C, GOLD C, MEYER M. The influence of driver fatigue on take-over performance in highly automated vehicles[C]. 25^th International Technical Conference on the Enhanced Safety of Vehicles(ESV), Detroit Michigan, United States: National Highway Traffic Safety Administration, 2017.
[7]	林庆峰, 王兆杰, 鲁光泉. 城市道路环境下自动驾驶车辆接管绩效分析[J]. 中国公路学报, 2019, 32(6): 240-247. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906025.htm LIN Q F, WANG Z J, LU G Q. Analysis of take-over performance for automated vehicles in urban road environments[J]. China Journal of Highway and Transport, 2019, 32(6): 240-247. (in chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906025.htm
[8]	FORSTER Y, NAUJOKS F, NEUKUM A, et al. Driver compliance to take-over requests with different auditory outputs in conditional automation[J]. Accident Analysis & Prevention, 2017(109): 18-28.
[9]	鲁光泉, 赵鹏云, 王兆杰, 等. 自动驾驶中视觉次任务对年轻驾驶人接管时间的影响[J]. 中国公路学报, 2018, 31(4): 165-171. doi: 10.3969/j.issn.1001-7372.2018.04.020 LU G Q, ZHAO P Y, WANG Z J, et al. Impact of visual secondary task on young drivers'take-over time in automated driving[J]. China Journal of Highway and Transport, 2018, 31 (4): 165-171.(in chinese) doi: 10.3969/j.issn.1001-7372.2018.04.020
[10]	LIN R, LIU N, MA L, et al. Exploring the self-regulation of secondary task engagement in the context of partially automated driving: A pilot study[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019(64): 147-160.
[11]	JAROSCH O, KUHNT M, BENGLER K, et al. It's out of our hands now! effects of non-driving related tasks during highly automated driving on drivers' fatigue[C]. International Driving Symposium on Human Factors in Driver Assessment, Manchester Village, Vermont. Iowa City: Public Policy Center, University of Iowa, 2017.
[12]	BUENO M, DOGAN E, HADJ SELEM F, et al. How different mental workload levels affect the take-over control after automated driving[C]. 2016 IEEE 19^th International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil: IEEE, 2016.
[13]	ERIKSSON A, STANTON N A. Takeover time in highly automated vehicles: noncritical transitions to and Ffrom manual control[J]. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2017, 59(4): 689-705. doi: 10.1177/0018720816685832
[14]	DOGAN E, HONNET V, MASFRAND S, et al. Effects of non-driving-related tasks on takeover performance in different takeover situations in conditionally automated driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019(62): 494-504.
[15]	ZEEB K, BUCHNER A, SCHRAUF M. What determines the take-over time? An integrated model approach of driver take-over after automated driving[J]. AccidentAnalysis & Prevention, 2015, 78(5): 212-22. http://www.researchgate.net/profile/Michael_Schrauf/publication/273833386_What_determines_the_take-over_time_An_integrated_model_approach_of_driver_take-over_after_automated_driving/links/5593b12408ae5af2b0eb99db.pdf
[16]	KIM H J, YANG J H. Takeover requests in simulated partially autonomous vehicles considering human factors[J]. IEEE Transactions on Human-Machine Systems, 2017, 47(5): 735-740. doi: 10.1109/THMS.2017.2674998
[17]	BURNETT G, LARGE D R, SALANITRI D. How will drivers interact with vehicles of the future[R]. London, England: University of Nottingham, 2019.
[18]	周荣贵, 钟连德. 公路通行能力手册[M]. 北京: 人民交通出版社股份有限公司, 2017. ZHOU R G, ZHONG L D. Highway capacity manual[M]. Beijing: China Communications Publishing & Media Management Co., Ltd, 2017. (in chinese)
[19]	王积伟. 控制理论与控制工程[M]. 北京: 机械工业出版社, 2011. WANG J W. Control theory and control engineering[M]. Beijing: China Machine Press, 2011. (in chinese)
[20]	任福田, 刘小明, 孙立山, 等. 交通工程学[M]. 北京: 人民交通出版社股份有限公司, 2017. REN F T, LIU X M, SUN L S, et al. Traffic engineering[M]. Beijing: China Communications Publishing & Media Management Co., Ltd, 2017.
[21]	PRITSCHET L, POWELL D, HORNE Z. Marginally significant effects as evidence for hypotheses: Changing attitudes over four decades[J]. Psychological Science, 2016, 27(7): 1036-1042. doi: 10.1177/0956797616645672