Citation: | ZHAI J D,LU G Q,CHEN F C. Effect analysis of automation levels on stabilization time of driving behaviors[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(11):3477-3483 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0863 |
Despite an absence of pertinent studies, the stabilization of drivers’ states following crucial incidents at varied automation degrees has a significant impact on traffic safety. In order to investigate the effects of automation levels on the stabilization time of drivers’ workload and driving performance, a method for acquiring signal stabilization time was proposed based on the confidence interval of sample mean value. A driving simulation was carried out that featured two crucial events (lead vehicle deceleration and lead vehicle lane changing) and three automation levels manual driving (MD), adaptive cruise control (ACC), and highly automated (HAD) driving. The effective data was collected from 53 participants. The results showed that compared to MD, heart rate stabilization time after the two critical events significantly increased 4.56 s. Moreover, automation levels did not show significant differences in speed, resultant acceleration and standard deviation of lane position of the vehicle after the lead vehicle deceleration. Following the lead vehicle’s lane changing event, the driving performance stabilization time increased by an average of 3.2 s and 5 s in the ACC and HAD conditions when compared to MD. The results provide a theoretical basis for the design of mode and time during the control transition of automated driving.
[1] |
SAE International. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles: SAE J3016-202104[S]. Warrendale: SAE International, 2021.
|
[2] |
吴超仲, 吴浩然, 吕能超. 人机共驾智能汽车的控制权切换与安全性综述[J]. 交通运输工程学报, 2018, 18(6): 131-141. doi: 10.3969/j.issn.1671-1637.2018.06.014
WU C Z, WU H R, LYU N C. Review of control switch and safety of human-computer driving intelligent vehicle[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 131-141(in Chinese). doi: 10.3969/j.issn.1671-1637.2018.06.014
|
[3] |
黄晶, 韦伟, 邹德飚. 基于个性化间距策略的自适应巡航系统模式切换策略研究[J]. 汽车工程, 2020, 42(10): 1302-1311.
HUANG J, WEI W, ZOU D B. Research on multi-mode switching strategy of adaptive cruise control system based on personalized spacing strategy[J]. Automotive Engineering, 2020, 42(10): 1302-1311(in Chinese).
|
[4] |
NOY I Y, SHINAR D, HORREY W J. Automated driving: Safety blind spots[J]. Safety Science, 2018, 102: 68-78. doi: 10.1016/j.ssci.2017.07.018
|
[5] |
鲁光泉, 陈发城, 李鹏辉, 等. 驾驶人跟车风险接受水平对其接管绩效的影响[J]. 汽车工程, 2021, 43(6): 808-814.
LU G Q, CHEN F C, LI P H, et al. Effect of drivers’ acceptance level of car-following risk on the takeover performance[J]. Automotive Engineering, 2021, 43(6): 808-814(in Chinese).
|
[6] |
LARSSON A F L, KIRCHER K, HULTGREN J A. Learning from experience: Familiarity with ACC and responding to a cut-in situation in automated driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 27: 229-237. doi: 10.1016/j.trf.2014.05.008
|
[7] |
DAMBÖCK D, WEIßGERBER T, KIENLE M, et al. Requirements for cooperative vehicle guidance[C]//Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems. Piscataway: IEEE Press, 2013: 1656-1661.
|
[8] |
RADLMAYR J, GOLD C, LORENZ L, et al. How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving[J]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2014, 58(1): 2063-2067. doi: 10.1177/1541931214581434
|
[9] |
STANTON N A, YOUNG M S, WALKER G H, et al. Automating the driver’s control tasks[J]. International Journal of Cognitive Ergonomics, 2001, 5(3): 221-236. doi: 10.1207/S15327566IJCE0503_5
|
[10] |
DE WINTER J C F, HAPPEE R, MARTENS M H, et al. Effects of adaptive cruise control and highly automated driving on workload and situation awareness: a review of the empirical evidence[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 27: 196-217. doi: 10.1016/j.trf.2014.06.016
|
[11] |
LU G Q, ZHAI J D, LI P H, et al. Measuring drivers’ takeover performance in varying levels of automation: Considering the influence of cognitive secondary task[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 82: 96-110. doi: 10.1016/j.trf.2021.08.005
|
[12] |
STRAND N, NILSSON J, KARLSSON I C M, et al. Semi-automated versus highly automated driving in critical situations caused by automation failures[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 27: 218-228. doi: 10.1016/j.trf.2014.04.005
|
[13] |
VOGELPOHL T, KÜHN M, HUMMEL T, et al. Transitioning to manual driving requires additional time after automation deactivation[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 55: 464-482. doi: 10.1016/j.trf.2018.03.019
|
[14] |
MERAT N, JAMSON A H, LAI F C H, et al. Transition to manual: Driver behaviour when resuming control from a highly automated vehicle[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 27: 274-282. doi: 10.1016/j.trf.2014.09.005
|
[15] |
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 and Prevention, 2021, 151: 105967. doi: 10.1016/j.aap.2020.105967
|
[16] |
PAMPEL S M, LARGE D R, BURNETT G, et al. Getting the driver back into the loop: The quality of manual vehicle control following long and short non-critical transfer-of-control requests: TI: NS[J]. Theoretical Issues in Ergonomics Science, 2019, 20(3): 265-283. doi: 10.1080/1463922X.2018.1463412
|
[17] |
DOGAN E, RAHAL M C, DEBORNE R, et al. Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2017, 46: 205-215. doi: 10.1016/j.trf.2017.01.012
|
[18] |
ULAHANNAN A, JENNINGS P, OLIVEIRA L, et al. Designing an adaptive interface: Using eye tracking to classify how information usage changes over time in partially automated vehicles[J]. IEEE Access, 2020, 8: 16865-16875. doi: 10.1109/ACCESS.2020.2966928
|
[19] |
INAGAKI T. Adaptive automation: Sharing and trading of control[M]//HOLLNAGEL E. Handbook of cognitive task design. Mahwah: Lawrence Erlbaum Associates Publishers, 2003: 147-169.
|
[20] |
YOUNG M S, BROOKHUIS K A, WICKENS C D, et al. State of science: Mental workload in ergonomics[J]. Ergonomics, 2015, 58(1): 1-17. doi: 10.1080/00140139.2014.956151
|
[21] |
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. doi: 10.3969/j.issn.1001-7372.2018.11.018
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(in Chinese). doi: 10.3969/j.issn.1001-7372.2018.11.018
|
[22] |
汪旭. 基于心率增长率的山区复杂公路纵坡路段驾驶负荷试验研究[D]. 重庆: 重庆交通大学, 2016.
WANG X. Experimental research on the driving load of longitudinal section road in complex mountain highway based on the growth rate of heart[D]. Chongqing: Chongqing Jiaotong University, 2016(in Chinese).
|
[23] |
翟俊达, 鲁光泉, 陈发城, 等. 城市交叉口车路网联信息对青年驾驶人驾驶行为的影响分析[J]. 交通信息与安全, 2022, 40(1): 126-134. doi: 10.3963/j.jssn.1674-4861.2022.01.015
ZHAI J D, LU G Q, CHEN F C, et al. Effects of information from connected vehicles and infrastructure on driving behavior of young drivers at urban intersections[J]. Journal of Transport Information and Safety, 2022, 40(1): 126-134(in Chinese). doi: 10.3963/j.jssn.1674-4861.2022.01.015
|
[24] |
YERKES R M, DODSON J D. The relation of strength of stimulus to rapidity of habit-formation[J]. Journal of Comparative Neurology and Psychology, 1908, 18(5): 459-482. doi: 10.1002/cne.920180503
|