弯道工况下驾驶员主观风险感知的量化研究

doi:10.19562/j.chinasae.qcgc.2022.09.016

Abstract

Abstract:

In order to in-depth understand driver’s behavior， a quantitative exploration on the subjective risk perception of driver in curve driving condition is conducted in this paper. Firstly， a method to quantify drivers’ subjective risk perception in curve driving is proposed based on 1/CTLC， 1/STLC， and 1/TAD indicators. Then， the subjective evaluation experiments are devised for latent and overt risk perception characteristics respectively， the driver’s risk estimate is obtained by using the magnitude estimation method， and the results are fitted according to Stevens’ power law to construct a driver’s subjective risk perception model in curve driving. Finally， the driver’s natural driving data are analyzed based on the model. The results show that the risk perception model constructed can reflect the risk perception characteristics of drivers in curve driving condition.

Key words: drivers, risk perception model, subjective evaluation, magnitude estimation method

Zibin Guo,Hui Chen,Taokai Xia,Wei Ran,Yosuke Nishimura,Jianzhen Wang. Study on Quantification of Driver’s Subjective Risk Perception in Curve Driving Condition[J].Automotive Engineering, 2022, 44(9): 1447-1455.

Figures/Tables 12

场景	①自变量为横向偏移距离				场景	②自变量为速度
场景	$1 T A D$	$1 S T L C$	D/m		场景	$1 T A D$	$1 S T L C$	v/（m·s^-1）
1	46	0.50	向内侧偏离	0.3	1	9	0.4	6
2	20	0.53		0.7	2	14	0.6	10
3	11	0.61		1.3	3	19	0.8	13
4	9	0.63	居中	1.5	4	24	1.0	17
5	11	0.66	向外侧偏离	1.1	5	29	1.2	20
6	20	0.81		0.7	6	34	1.4	24
7	46	1.00		0.3	7	39	1.6	27

References 17

1	BECKER C， YOUNT L， ROSEN-LEVY S， et al. Functional safety assessment of a generic automated lane centering system and related foundational vehicle systems［R］. Washington DC： National Highway Traffic Safety Administration， 2018.
2	FLEMING J M， ALLISON C K， YAN X D， et al. Adaptive driver modelling in ADAS to improve user acceptance： a study using naturalistic data［J］. Safety Science， 2019， 119： 76-83.
3	SPACEK P. Track behavior in curve areas： attempt at typology［J］. Journal of Transportation Engineering， 2005， 131（9）： 669-676.
4	赵斌，陈慧，冉巍，等. 车道对中控制系统的驾驶员自适应需求验证［J］.汽车技术， 2021（3）： 1-6.
	ZHAO B， CHEN H， RAN W， et al. Verification of adaptive demand of lane centering control system［J］. Automobile Technology， 2021（3）： 1-6.
5	SIVAK M. The information that drivers use： is it indeed 90% visual ［J］. Perception， 1996， 25（9）： 1081-1089.
6	LIU A， SALVUCCI D. Modeling and prediction of human driver behavior［C］. Intl. Conference on HCI， 2001： 1479-1483.
7	WANG J， ZHANG L， ZHANG D， et al. An adaptive longitudinal driving assistance system based on driver characteristics［J］. IEEE Transactions on Intelligent Transportation Systems， 2013， 14（1）： 1-12.
8	BARENDSWAARD S， POOL D M， BOER E R， et al. A classification method for driver trajectories during curve-negotiation［C］. IEEE International Conference on Systems， Man and Cybernetics， 2019： 3729-3734.
9	KONDOH T， YAMAMURA T， KITAZAKI S， et al. Identification of visual cues and quantification of drivers’ perception of proximity risk to the lead vehicle in car-following situations［J］. Journal of Mechanical Systems for Transportation and Logistics， 2008， 1（2）： 170-180.
10	EBOLI L， MAZZULLA G， PUNGILLO G. How to define the accident risk level of car drivers by combining objective and subjective measures of driving style［J］. Transportation Research Part F： Traffic Psychology and Behaviour， 2017， 49： 29-38．
11	BOROWSKY A， ORON-GILAD T. Exploring the effects of driving experience on hazard awareness and hazard perception via real-time hazard identification， hazard classification， and rating tasks［J］. Accident Analysis and Prevention， 2013， 59： 548-565.
12	BOER E R. Satisficing curve negotiation： explaining drivers’ situated lateral position variability［J］. IFAC-PapersOnLine， 2016， 49（19）： 183-188.
13	BARENDSWAARD S， VAN BREUGEL L， SCHELFAUT B， et al. Effect of velocity and curve radius on driver steering behaviour before curve entry［C］. IEEE International Conference on Systems， Man and Cybernetics， 2019： 3866-3871.
14	BIAN Y G， DING J Y， HU M J， et al. An advanced lane-keeping assistance system with switchable assistance modes［J］. IEEE Transactions on Intelligent Transportation Systems， 2020， 21（1）： 385-396.
15	KONDOH T， FURUYAMA N， HIROSE T， et al. Direct evidence of the inverse of TTC hypothesis for driver’s perception in car-closing situations［J］. International Journal of Automotive and Mechanical Engineering， 2014， 5（4）： 121-128.
16	STEVENS S. On the psychophysical law［J］. Psychological Review， 1957， 64（3）： 153-181.
17	MUTOH S. Psychology metrics （in Japanese）［M］. Tokyo， Asakura Shoten Co. Ltd， 1982.

道路半径=60 m		道路半径=120 m
场景	1/CTLC/m^-1	场景	1/CTLC/m^-1
1	0.22	1	0.34
2	0.45	2	0.50
3	0.57	3	0.69
4	0.66	4	0.88
5	0.74	5	1.07
6	0.88	6	1.29
7	1.00	7	1.47
1/TAD=20 m^-1		1/TAD=30 m^-1
1/STLC=1.5 m^-1		1/STLC=1.0 m^-1

[1]	Kaizhan Gao,Qiao Luo,Zhifei Zhang,Zhongming Xu. Vibration Comfort Evaluation of Vehicle Seat Based on Body Pressure Distribution [J]. Automotive Engineering, 2022, 44(12): 1936-1943.
[2]	Bi Fengrong, Huang Yu, Zhang Lipeng, Shen Pengfei, Lü Dali. Research on Subjective Evaluation Method of Vehicle Sound Quality Based on Interval Grey Number Theory [J]. Automotive Engineering, 2020, 42(7): 933-940.
[3]	Pei Yeqing, Jin Xiaoping, Song Zhenghe, Liu Longcan. Design and Implementation of Mental Workload Calculation System for Drivers [J]. Automotive Engineering, 2019, 41(11): 1265-1272.
[4]	Yuan Xiayi, Chen Lin, Li Shuai, Wang Wenyuan. Simulation and Evaluation on the Crosswind Stability of Vehicles [J]. Automotive Engineering, 2019, 41(11): 1286-1293.

Study on Quantification of Driver’s Subjective Risk Perception in Curve Driving Condition

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 17

Related Articles 4

Metrics

Comments

Recommended 10