基于极值理论与智能网联信息的超车风险评估

doi:10.13229/j.cnki.jdxbgxb.20230576

Abstract

Abstract:

In order to assess the overtaking risk level of intelligent networked vehicles when different networked information was provided. To make up for the neglect of driver factors in traditional risk assessment and the insufficient evaluation capability of a single traffic conflict indicator for complex traffic scenarios， the block maxima （BM） and peak over threshold （POT） methods were introduced to fit the extreme value distributions for the two types of conflict scenarios （follow-me and frontal oncoming conflict） involved in overtaking events， so as to assess the risk of following and frontal collision accidents， respectively. In each conflict scenario， a bivariate extreme value model was proposed to integrate different traffic conflict indicators and a non-stationary extreme value model was proposed to take the driver into account for road safety estimation， and the models were validated with the intelligent and connected vehicle overtaking test data. Extracted the overtaking event from the original test data and calculated the conflict indicators： including the time to collision between the ego vehicle and a preceding vehicle GAP， the time to collision between the ego vehicle and an oncoming vehicle TTC_t₁， the deceleration DRAC， the time to collision TTC， headway with the preceding vehicle THW. The degree of collision risk was characterized by the event probability that the time conflict index is negative or the DRAC is greater than the MADR. The results show that the error results of the binary extremum model constructed by different conflict indicators are different in the head-on collisions， and the binary extremum model constructed by THW&DRAC has the most accurate evaluation results （standard error MAE=0.000 28）. The binary extreme value model constructed by TTC&DRAC is the most accurate （MAE=0.006） in the frontal oncoming collisions. In different conflict scenarios， the non-stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy （the AIC and BIC values are small） compared with the model that does not consider the driver factor. In addition， different intelligent network information （real-time distance， overtaking advice， speed advice） brings different passing maneuvers risks， and when the intelligent network information is speed advice， the overtaking risk of the car is the smallest. Therefore， the non-stationary extreme value model considering the driver factor and binary extreme value model proposed can effectively evaluate the driving risk through the traffic conflict index. Secondly， the experimental data of intelligent and connected vehicles show that the proposed model can accurately assess the overtaking risk level of intelligent and connected vehicles when they provide different intelligent network information.

Key words: engineering of transportation safety, overtaking risk assessment, extreme value theory, intelligent and connected vehicle, human risks

CLC Number:

U491.3

Zhao-xia LIU,Fui FU,Shi-feng NIU. Risk assessment in overtaking scenarios using extreme value theory and intelligent and connected information[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 925-937.

Figures/Tables 18

Fig.1

Fig.2

Table 1

Fig.3

Table 2

Table 3

Table 4

parameter estimation of the non- stationary BM model for following collisions"

参数		模型#0	模型#1	模型#2	模型#3	模型#4
参数		估计值（误差）	估计值（误差）	估计值（误差）	估计值（误差）	估计值（误差）
$μ ?$	$μ ? 0$	-2.39（0.12）	-2.41（0.14）	-2.44（0.11）	-2.83（0.162）	-2.46（0.073）
	$μ ? T T C_t 1$	0.01（0.0067）	0.01（0.006 7）	0.01（0.0067）	0.01（0.006 7）	0.01（0.006 7）
	$μ ? G e n$	—	-0.09（0.076）	—	—	—
	$μ ? E x p$	—	—	-0.105（0.08）	—	—
	$μ ? E d u$	—	—	—	0.14（0.088）	—
	$μ ? S t y$	—	—	—	—	-0.016（0.009）
$σ ?$		0.44（0.033）	0.43（0.033）	0.435（0.032）	0.433（0.032）	0.43（0.03）
$ξ ?$		0.096（0.09）	0.133（0.09）	0.119（0.088）	0.116（0.086）	0.104（0.09）
AIC		271.9	270.1	269.6	268.3	254.8
BIC		283.2	282.4	281.9	280.6	280.2
负对数似然		133.9	131.05	130.8	130.1	127.4

Table 4

Fig.4

Fig.5

Fig.6

Table 5

Table 6

Parameter estimation of the non- stationary POT model for frontal collision"

参数		模型#0	模型#1	模型#2	模型#3	模型#4
参数		估计值（误差）	估计值（误差）	估计值（误差）	估计值（误差）	估计值（误差）
$σ ?$	$σ ? 0$	1.003 4（0.107）	0.868（0.109 9）	1.011（0.065 2）	1.1994（0.149 9）	1.234（0.004 2）
	$σ ? G A P$	0.003 4（0.000 02）	0.003 4（0.000 02）	0.003 4（0.000 02）	0.0034（0.000 02）	0.003 4（0.000 02）
	$σ ? G e n$	—	0.037 6（0.045）	—	—	—
	$σ ? E x p$	—	—	0.063（0.018 4）	—	—
	$σ ? E d u$	—	—	—	0.063（0.000 000 02）	—
	$σ ? S t y$	—	—	—	—	0.0506（0.004 2）
$ξ ?$		-0.812 2（0.095）	-0.7854（0.116）	-0.951 6（0.073 6）	-0.95（0.149 9）	-0.9516（0.159）
AIC		30.820 58	31.830 05	31.475 05	28.758 91	21.016 53
BIC		38.148 54	38.803 01	38.991 8	36.086 86	34.787 13
负对数似然		12.410 29	12.737 53	11.495 9	11.382 7	8.508 264

Table 6

Fig.7

Table 7

Parameter estimation of binary extreme value model for following and frontal collision"

类型	冲突指标	观测数	阈值		超出数			参数估计					AIC
类型	冲突指标	观测数	$z 1$	$z 2$	$z 1$	$z 1$	总	$σ ? z 1$	$ξ ? z 1$	$σ ? z 2$	$ξ ? z 2$	$α$
跟车	-THW&DRAC	1 188	-2.6	1.27	150	189	99	1.07	-0.360	0.66	0.009	0.781	2 227.04
	-THW&GAP	1 054	-2.48	-13.6	119	679	86	1.092	-0.336	6.43	-0.4865	0.882	11 500.2
	-THW&TTC_t₁	645	-2.62	-12.42	77	152	66	1.937	-0.1087	5.571	-0.1977	0.907	6 578.02
	-THW&TTC	656	-2.76	-1.09	78	78	14	1. 039	-0.46	0.625	-0.431	0.807	6 262.3
正向	TTC&GAP	659	-1.33	-5.66	97	79	13	0.936	-0.6424	5.573	-0.4952	0.997	6 057.2
	TTC&DRAC	571	-1.03	1.52	69	152	50	0.677	-0.307	0.692	-0.088	0.854	4 548.4
	TTC&-THW	655	-1.12	-2.78	78	79	14	0.646	-0.4978	1.927	-0.06692	0.947	6 307.2

Table 7

Fig.8

Table 8

Fig.9

Fig.10

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冲突指标	冲突数	跟车	正向
冲突指标	冲突数	515	342
TTC	平均值	-4.63	-2.500
	最小值	-0.76	-0.118
	最大值	-15.76	-5.387
TTC_t₁	平均值	-16.083	-17.252
	最小值	-0.493	-4.297
	最大值	-30.831	-33.73
GAP	平均值	-9.812	-7.544
	最小值	-2.675	-1.341
	最大值	-29.347	-15.968
THW	平均值	5.826	4.991
	最小值	0.34	0.34
	最大值	15.738	18.764
DRAC	平均值	1.902	0.939
	最小值	0.000 52	0.063
	最大值	5.565	5.757
事故计数		4	3

协变量	符号	说明
性别	Gen	1-男性；2-女性
驾驶经验	Exp	1-驾龄>10/驾驶里程>4万公里；0-其他
教育水平	Edu	1-本科以上学历；0-其他
驾驶风格	Sty	1-耐心谨慎型；2-焦虑分心型；3-冒进型；4-危险型；5-混合型

正面碰撞		跟车碰撞
二元变量	MAE	二元变量	MAE
TTC&DRAC	0.006	-THW&DRAC	0.000 28
TTC&-THW	0.014 0	-THW&GAP	0.012 5
TTC& GAP	0.024 3	-THW&TTC_t1	0.021 2
-	-	-THW&TTC	0.021

Risk assessment in overtaking scenarios using extreme value theory and intelligent and connected information

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