驾驶人跟车风险接受水平对其接管绩效的影响

doi:10.19562/j.chinasae.qcgc.2021.06.003

Abstract

Abstract:

Takeover in automated driving is a kind of manual driving operation with high safety requirements， which may be affected by the driver’s manual driving safety habits. Based on a driving simulator，the manual car?following and an automated driving takeover experiments are designed in this paper to study the influence of the driver’ acceptance level of car?following risk on the takeover performance. Meanwhile， the impact of takeover time budget and visual non?driving related task is also investigated. The results indicate that drivers with a low acceptable level of car?following risk in daily driving have shorter takeover reaction time， and show lower longitudinal collision risk after taking over from the condition of monitoring automated driving. In addition， when drivers are in the state of visual distraction， the 5 s takeover time budget leads to poor lateral and longitudinal stability and high longitudinal collision risk. The results of this study can provide a theoretical basis for the design of personalized automated driving system.

Key words: takeover in automated driving, acceptance level of car?following risk, takeover time budget, visual non?driving related task, takeover performance

Guangquan Lu,Facheng Chen,Penghui Li,Junda Zhai,Haitian Tan,Pengyun Zhao. Effect of Drivers’ Acceptance Level of Car⁃following Risk on the Takeover Performance[J].Automotive Engineering, 2021, 43(6): 808-814.

Figures/Tables 11

References 23

1	FAGNANT D J， KOCKELMAN K. Preparing a nation for autonomous vehicles： opportunities， barriers and policy recommendations［J］. Transportation Research Part A： Policy and Practice， 2015， 77： 167-181.
2	SAE. Taxonomy and definitions for terms related to driving automation systems for on‐road motor vehicles［S］. J3016， June 2018.
3	MERAT N， SEPPELT B， LOUW T， et al. The “out⁃of⁃the⁃loop” concept in automated driving： proposed definition， measures and implications［J］. Cognition Technology & Work， 2019， 21（3）.
4	MCDONALD A D， ALAMBEIGI H， ENGSTRÖM 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.
5	GOLD C， DAMBÖCK D， LORENZ L， et al. “Take over！” how long does it take to get the driver back into the loop？［C］.Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Sage CA： Los Angeles， CA： Sage Publications， 2013， 57（1）： 1938-1942.
6	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.
7	ITO T， TAKATA A， OOSAWA K. Time required for take⁃over from automated to manual driving［C］. SAE World Congress & Exhibition. 2016.
8	ERIKSSON A， STANTON N A. Takeover time in highly automated vehicles： noncritical transitions to and from manual control［J］. Human Factors， 2017， 59（4）： 689-705.
9	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 & Prevention， 2016， 92： 230-239.
10	NAUJOKS F， PURUCKER C， WIEDEMANN K， et al. Noncritical state transitions during conditionally automated driving on german freeways： effects of non⁃driving related tasks on takeover time and takeover quality［J］. Human Factors， 2019， 61（4）： 596-613.
11	WANDTNER B， SCHÖMIG N， SCHMIDT G. Effects of non⁃driving related task modalities on takeover performance in highly automated driving［J］. Human Factors， 2018， 60（6）： 870-881.
12	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 & Prevention， 2015， 78： 212-221.
13	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 and Behaviour， 2016， 39： 19-32.
14	CHEN F， LU G， LIN Q， et al. Take⁃over performance by novice and experienced drivers in Level 3 automation［C］. Transportation Research Board Annual Meeting2020， Washington D C（No. 20-05478.
15	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 Q F， WANG Z J， LU G Q. Takeover safety evaluation model for level 3 automated vehicles［J］. Automotive Engineering， 2019，41（11）：1258-1264.
16	（11.2012）. Road vehicles⁃ergonomic aspects of transport information and control systems⁃calibration tasks for methods which assess driver demand due to the use of in⁃vehicle systems［S］. ISO International Organization for Standardization ，TS 14198， 2012.
17	HERGETH S， LORENZ L， KREMS J F. Prior familiarization with takeover requests affects drivers’ takeover performance and automation trust［J］. Human Factors， 2017， 59（3）： 457-470.
18	KÖRBER M， BASELER E， BENGLER K. Introduction matters： manipulating trust in automation and reliance in automated driving［J］. Applied Ergonomics， 2018， 66： 18-31.
19	VOGEL K. A comparison of headway and time to collision as safety indicators［J］. Accident Analysis & Prevention， 2003， 35（3）： 427-433.
20	ZHANG B， DE WINTER J， VAROTTO S， et al. Determinants of take⁃over time from automated driving： a meta⁃analysis of 129 studies［J］. Transportation Research Part F： Traffic Psychology and Behaviour， 2019， 64： 285-307.
21	LUCIDI F， GIANNINI A M， SGALLA R， et al. Young novice driver subtypes： relationship to driving violations， errors and lapses［J］. Accident Analysis & Prevention， 2010， 42（6）： 1689-1696.
22	LOEB H， BELWADI A， MAHESHWARI J， et al. Age and gender differences in emergency takeover from automated to manual driving on simulator［J］. Traffic Injury Prevention， 2019， 20（sup2）： S163-S165.
23	WAN J， WU C. The effects of lead time of take⁃over request and nondriving tasks on taking⁃over control of automated vehicles［J］. IEEE Transactions on Human⁃Machine Systems， 2018， 48（6）： 582-591.

[1]	WANG Dan, LIU Zhong-Chang, TIAN Jing, HAN Yong-Qiang, TAN Man-Zhi. [J]. , 2016, 38(12): 1415 -1419 .
[2]	KONG Xiang-Dong, ZHANG Zhen-Dong, XIE Nai-Liu, CHENG Qiang, YIN Cong-Bo. [J]. , 2016, 38(12): 1471 -1476 .
[3]	LI Ling, MA Li, MOU Yu, XU Chao, LI Wen-Ru, SHI Shu-Ming. [J]. , 2016, 38(12): 1508 -1514 .
[4]	GAO Ji-Dong, GAO Bo-Lin, XIE Shu-Gang. [J]. , 2016, 38(12): 1515 -1520 .
[5]	QIN Xiao-Hui, WANG Jian-Qiang, XIE Bo-Yuan, HU Man-Jiang, LI Ke-Qiang. [J]. , 2017, 39(1): 73 -78 .
[6]	SU Chang, FU Li-Ming, WEI Jun, CAO Yue, HUANG Lei, LI Shuo. [J]. , 2017, 39(1): 35 -40 .
[7]	ZHANG Jian, ZHOU Dao-Kui, TONG Rui, QI Hao-Chen. [J]. , 2017, 39(1): 47 -51 .
[8]	ZHENG Wei. [J]. , 2017, 39(1): 107 -112 .
[9]	SHAO Shi-Ping, HUANG Ju-Hua, CAO Ming. [J]. , 2017, 39(2): 200 -205 .
[10]	Chen-Xin, FENG Xiao, SHEN Chuan-Liang, WANG Shuo, HU Cui-Song, LI Yan-Yang, YANG Chang-Hai-　. [J]. , 2017, 39(2): 206 -213 .

指标		描述
跟车风险接受水平	最小TTC	跟车中，自车距离前车的碰撞时间的最小值，TTC计算为两车车间距除以两车速度差。
跟车风险接受水平	最小TH	跟车中，自车和前车经过同一地点的时间差的最小值，TH计算为两车车头距除以自车速度。
接管绩效	接管反应时间	系统发出接管请求时刻和驾驶人按下接管按钮时刻之间的时间长度。
	接管后最小TTC	驾驶人接管车辆控制权后，自车距离抛锚车辆的碰撞时间最小值，常被用于评价接管安全性。
	最大纵向减速度	驾驶人接管车辆控制权后，自车的纵向减速度最大值，常被用于评价接管纵向稳定性。
	最大横向加速度	驾驶人接管车辆控制权后，自车的横向加速度最大值，常被用于评价接管横向稳定性。

风险接受水平（人数）	年龄（标准差）	性别（男/女）	驾龄（标准差）
低（11）	31.91（8.408）	64%/36%	9.55（8.454）
高（14）	31.14（10.302）	57%/43%	7.79（8.622）
非参数检验	χ²=0.193，	χ²=0.311，	χ²=0.366，
非参数检验	p=0.660	p=0.386	p=0.545

Effect of Drivers’ Acceptance Level of Car⁃following Risk on the Takeover Performance

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