基于设计运行条件的实际道路测试场景复杂度计算模型

doi:10.19562/j.chinasae.qcgc.2024.11.005

Abstract

Abstract:

The safety of automated vehicle running on the real road is related to traffic factors， driver status， and vehicle status. One major challenge faced by automated driving is that the actual traffic environment is characterized by spatial-temporal randomness of road morphology， natural environment， traffic participants and events. And the difference in complexity of testing scenarios results in the irreproducibility of the automated driving testing process and the incompatibility of testing results， which means that the evaluation of automated driving lacks a unified and quantified testing environment benchmark. In this paper， a scenario complexity calculation model for real road test based on operational design condition （ODC） is proposed. Considering the impact of network connectivity， driver perception ability， and vehicle execution ability on the complexity of automated driving vehicles facing relevant scenarios on actual roads， a complexity calculation model element database for autonomous driving actual road testing scenarios based on the eight major categories of road level， traffic facilities， temporary traffic changes， traffic participants， natural environment， network information， driver status and vehicle status. A scenario complexity computational model of real road test based on operational design condition and analytic hierarchy process （AHP） is established， The effect transmission mechanism based on intelligent and connected technology is adopted to calculate the weight coefficient of scenario elements， and the feasibility and rationality of the proposed method are validated in the real road tests.

Key words: automated driving, real road test, operational design condition, scenario complexity

Hang Sun,Yuran Li,Linlin Zhang,Yang Zhai,Zhenyu Chen,Chen Chen. Scenario Complexity Calculation Model of Real Road Test Based on Operational Design Condition[J].Automotive Engineering, 2024, 46(11): 1983-1992.

Figures/Tables 14

References 27

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场景要素	要素编号	场景要素	数据类型
Category 1 道路等级	1	车道数量	数值型
	2	车道坡度	数值型
	3	车道曲率	数值型
Category 2 交通设施	4	信号灯	布尔型
	5	交通标志	布尔型
	6	交通标线	布尔型
	7	分隔带	布尔型
	8	道闸	布尔型
Category 3 临时交通变化	9	临时交通标志	布尔型
	10	临时信号灯	布尔型
	11	障碍物	布尔型
	12	特殊路面	布尔型
Category 4 交通参与者	13	目标数量	数值型
	14	目标类型	数值型
	15	目标状态	数值型
Category 5 自然环境	16	雨	布尔型
	17	雪	布尔型
	18	雾	布尔型
	19	PM 2.5	数值型
	20	光照度	数值型
	21	光干扰	布尔型
Category 6 网联信息	22	位置信号	布尔型
	23	蜂窝网络信号	数值型
	24	V2X信号	数值型
Category 7 驾驶员状态	25	嗜睡	布尔型
Category 8 本车状态	26	本车加速度	数值型
	27	本车转向角速度	数值型
	28	本车车速	数值型

影响因子	标度a_i
［0-2.5）	1
［2.5-5）	2
［5-7.5）	3
［7.5-10）	4
［10-12.5）	5
［12.5-15）	6
［15-17.5）	7
［17.5-20）	8
［20-23）	9

阶数m	R_i
1	0
2	0
3	0.58
4	0.90
5	1.12
6	1.24
7	1.32
8	1.41
9	1.45

要素分类	λ_max	C_R	是否符合一致性要求
Category 1	3.0	0.0	是
Category 2	5.0	1.98E-16	是
Category 3	4.0	6.58E-16	是
Category 4	3.0	0.0	是
Category 5	6.0	1.43E-16	是
Category 6	3.0	1.15E-15	是
Category 7
Category 8	3.0	1.91E-15	是

要素分类	要素名称	标度	归一化特征向量	权重系数
Category 1 道路等级	车道数量	4	0.267	0.035
	车道坡度	6	0.400	0.053
	车道曲率	5	0.333	0.044
Category 2 交通设施	信号灯	3	0.176	0.027
	交通标志	3	0.176	0.027
	交通标线	3	0.176	0.027
	分隔带	4	0.235	0.035
	道闸	4	0.235	0.035
Category 3 临时交通变化	临时交通标志	3	0.176	0.027
	临时信号灯	3	0.176	0.027
	障碍物	6	0.353	0.053
	特殊路面	5	0.294	0.044
Category 4 交通参与者	目标数量	4	0.267	0.035
	目标类型	4	0.267	0.035
	目标状态	7	0.467	0.062
Category 5 自然环境	雨	5	0.208	0.044
	雪	5	0.208	0.044
	雾	4	0.167	0.035
	PM 2.5	4	0.167	0.035
	光照度	4	0.167	0.035
	光干扰	2	0.083	0.018
Category 6 网联信息	位置信号	3	0.231	0.027
	蜂窝网络信号	5	0.385	0.044
	V2X信号	5	0.385	0.044
Category 7 驾驶员状态	嗜睡	5	1.000	0.044
Category 8 本车状态	本车加速度	2	0.286	0.018
	本车转向角速度	2	0.286	0.018
	本车车速	3	0.429	0.027

Scenario Complexity Calculation Model of Real Road Test Based on Operational Design Condition

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