L3级自动驾驶汽车的接管安全性评价模型*

doi:10.19562/j.chinasae.qcgc.2019.011.005

摘要/Abstract

摘要： 为评价L3级自动驾驶车辆接管的安全性,基于驾驶模拟器设计了双向六车道高速公路环境下的接管场景并进行驾驶模拟实验,驾驶人在自动驾驶过程中始终执行视觉次任务操作,次任务为观看3种难度等级的箭头图,接管场景为自车行驶中遇到同车道前方的一辆抛锚车辆,接管请求时间设为7 s,自动驾驶车辆的速度为110 km/h。实验共计招募了49名被试(男性30名,女性19名),平均年龄为31.06岁(标准差为7.10岁)。当车辆发出听觉+视觉的接管请求信号后,被试应通过按下转向盘上的切换按钮来获取车辆的控制权。以最小TTC的组别为因变量,设定最小TTC小于等于1 s为危险组,大于1 s为安全组,利用二元logistics回归建立接管安全性评价模型。研究结果表明:7 s的接管请求时间条件下,影响接管安全性的因素主要是接管反应时间和次任务,本文中建立的接管安全性评价模型的预测准确率达85.5%。

关键词: 自动驾驶, 驾驶人行为, 接管, 建模, 次任务

Abstract: To evaluate the safety of takeover in Level 3 automated vehicles, the takeover scenario on a two-way six-lane highway is designed to conduct driving simulation experiments based on driving simulator. Drivers carry out the secondary tasks in vision all along during automated driving. The secondary tasks are watching arrowhead charts with three different-degrees of difficulty and the takeover scenario is that ago vehicle comes across a broken-down vehicle in front on the same lane, with a take-over request time of 7 s and an automated vehicle speed of 110 km/h. A total of 49 testees (30 males, 19 females) are recruited for experiment with an average age of 31.06 years (standard deviation: 7.10 years). After the vehicle sends auditory and visual request signals for takeover, testee should push the switch button on steering wheel to take back vehicle control. Bivariate logistic regression is used to build the evaluation model for takeover safety, with the group of minimum TTC taken as dependent variable: minimum TTC ≤ 1s is set as dangerous group and minimum TTC > 1s is set as safe group. The results show that in the condition of 7s takeover request time, the factors affecting takeover safety are takeover reaction time and secondary task, and the prediction accuracy of the takeover safety evaluation model built reaches 85.5%.

Key words: automated driving, driver behavior, takeover, modeling, secondary task

林庆峰, 王兆杰, 鲁光泉. L3级自动驾驶汽车的接管安全性评价模型^*[J]. 汽车工程, 2019, 41(11): 1258-1264.

Lin Qingfeng, Wang Zhaojie, Lu Guangquan. Takeover Safety Evaluation Model for Level 3 Automated Vehicles[J]. Automotive Engineering, 2019, 41(11): 1258-1264.

参考文献

[1] SAE International. Taxonomy and definitions for terms related to on-road motor vehicle automated driving Systems: J3016[R]. Warrendale, PA,2018.
[2] GOLD C, DAMBÖCK D, LORENZ L, et al. Take over! how long does it take to get the driver back into the loop?[J]. Proceedings of the Human Factors & Ergonomics Society Annual Meeting,2013,57(1):1938-1942.
[3] ITO T, TAKATA A, OOSAWA K. Time required for take-over from automated to manual driving[C]. SAE Paper 2016-01-0158.
[4] ERIKSSON A, STANTON N A. Takeover time in highly automated vehicles: noncritical transitions to and from manual control[J]. Human Factors the Journal of the Human Factors & Ergonomics Society,2017,59(4):689-705.
[5] ZEEB K, BUCHNER A, SCHRAUF M. What determines the take-over time? an integrated model approach of driver take-over after automated driving[J]. Accident Analysis and Prevention,2015,78:212-221.
[6] KÖRBER M, GOLD C, LECHNER D, et al. The influence of age on the take-over of vehicle control in highly automated driving[J]. Transportation Research Part F Traffic Psychology & Behaviour,2016,39:19-32.
[7] RADLMAYR J, GOLD C, LORENZ L, et al. How traffic situations and secondary tasks affect the take-over quality in highly automated driving [J]. Proceedings of the Human Factors & Ergonomics Society Annual Meeting,2014,58(1):2063-2067.
[8] ZEEB K, BUCHNER A, SCHRAUF M. Is take-over time all that matters? the impact of visual-cognitive load on driver take-over quality after conditionally automated driving[J]. Accident Analysis and Prevention,2016,92:230-239.
[9] HAPPEE R, GOLD C, RADLMAYR J, et al. Take-over performance in evasive manoeuvres[J]. Accident; Analysis and Prevention,2017,106:211.
[10] GOLD C, HAPPEE R, BENGLER K. Modeling take-over performance in level 3 conditionally automated vehicles[J]. Accident Analysis & Prevention,2017.
[11] 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.
[12] 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.
[13] KIRCHER K, AHLSTRÖM C, RYDSTRÖM A, et al. Secondary task workload test bench-2TB: final report[R]. Human-Vehicle-Transport System Interaction,2014.
[14] 林庆峰,成波,徐少兵,等.基于Logistic回归的危险认知模型与避撞时间模型的对比[J].中国公路学报,2012,25(6):123-128.
[15] YOUNG W, SOBHANI A, LENNÉ M G, et al. Simulation of safety: a review of the state of the art in road safety simulation modelling[J]. Accident Analysis & Prevention, 2014, 66(66C):89-103.

L3级自动驾驶汽车的接管安全性评价模型^*

Takeover Safety Evaluation Model for Level 3 Automated Vehicles

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