数据驱动的智能车个性化场景风险图构建

doi:10.19562/j.chinasae.qcgc.2023.02.008

Abstract

Abstract:

In order to realize the auxiliary function of danger warning of intelligent vehicle and accurately establish the personalized assistance system for individual drivers， a data-driven personalized scenario risk map construction method for intelligent vehicles is proposed. The graph representation of the attributes and implied interaction of both dynamic and static elements in complex traffic scenes is constructed. The graph kernel method is used to measure the similarity of the graph representation data， and the driver's operation data is processed and analyzed to obtain the driver's personalized scene risk evaluation label. The recognition model is trained based on support vector machine and the mapping relationship between the driver's personalized risk evaluation mechanism and scene features is established. The risk assessment label output by the model and the real value are compared experimentally. The results show that the recognition accuracy of the driver risky driving scene recognition model based on the personalized scenario risk map can reach 95.8%， which is 38.2% higher than that of the method based on feature vector representation， and it can effectively evaluate the risk degree of the personalized scene based on the driver's driving style.

Key words: risky driving scenes recognition, scene understanding, graph representation learning, machine learning, driver-personalized learning

Gege Cui,Lü Chao,Jinghang Li,Zheyu Zhang,Guangming Xiong,Jianwei Gong. Data-Driven Personalized Scenario Risk Map Construction for Intelligent Vehicles[J].Automotive Engineering, 2023, 45(2): 231-242.

Figures/Tables 18

驾驶员1 操作特征	$S 1$		$n o r m (S 2)$
驾驶员1 操作特征	RSS	SC	RSS	SC
K=2	2.31	0.82	1 500	0.54
K=3	1.06	0.75	1 000	0.62
K=4	0.54	0.76	982	0.53
K=5	0.21	0.77	667	0.44
K=6	0.08	0.76	653	0.49
K=7	0.06	0.75	641	0.51
K=8	0.04	0.74	506	0.52
K=9	0.02	0.76	479	0.54
K=10	0.02	0.74	433	0.56

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驾驶员编号	性别	年龄/岁	驾龄/年
1	男	33	10
2	男	29	6
3	男	22	2

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Data-Driven Personalized Scenario Risk Map Construction for Intelligent Vehicles

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Figures/Tables 18

References 26

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驾驶员	驾驶员1		驾驶员2		驾驶员3
驾驶员	RSS	SC	RSS	SC	RSS	SC
K=2	2.31	0.82	9.67	0.86	11.17	0.76
K=3	1.06	0.75	0.67	0.84	2.14	0.75
K=4	0.54	0.76	1.13	0.81	0.61	0.72
K=5	0.21	0.77	0.08	0.80	0.57	0.78
K=6	0.08	0.76	0.19	0.78	0.15	0.75
K=7	0.06	0.75	0.11	0.77	0.09	0.74
K=8	0.04	0.74	0.07	0.78	0.08	0.72
K=9	0.02	0.76	0.01	0.79	0.02	0.73
K=10	0.02	0.74	0.07	0.78	0.06	0.73