自动驾驶拟人连续交互测试场景生成方法

doi:10.19562/j.chinasae.qcgc.2024.09.007

Abstract

Abstract:

Scenario-based simulation test method is an important means of automated driving vehicle safety verification； however， current test scenarios generation methods are mostly for independent scenarios. How to simulate the human real driving process to generate continuous interactive test scenario with challenges has become a problem that needs to be solved urgently in automated driving test evaluation. In this paper， an automated driving anthropomorphic continuous interactive test scenarios generation method is proposed. Firstly， the architecture for anthropomorphic continuous interactive test scenarios generation is established， and the vehicle motion behavior analysis is conducted based on the HighD dataset. On this basis， the current behavior of tested automated driving vehicle based on the trajectory similarity feature is analyzed， and the prediction of the future trajectory through the state transfer matrix is realized. Then， the type of the future behaviors of the traffic vehicles based on the trajectory interaction rules are determined， and the specific trajectory is generated by Transform network. Finally， the key performance indicators such as danger and anthropomorphism of the generated test scenarios are evaluated in simulation test environment， which proves the effectiveness of the method proposed in this paper.

Key words: automated driving, continuous interactive test scenario, trajectory prediction, anthropomorphic interactive trajectory generation

Bing Zhu,Tianxin Fan,Jian Zhao,Peixing Zhang,Dongjian Song,Yue Xue,Wenbo Zhao. Generation Method for Anthropomorphic Continuous Interactive Test Scenarios of Automated Driving[J].Automotive Engineering, 2024, 46(9): 1600-1607.

Figures/Tables 12

References 16

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行为类别	统计量	纵向速度	横向速度	纵向加速度	横向加速度
类别1	均值	22.72	-0.000 1	-0.03	-0.010 5
	方差	21.69	0.010 9	0.02	0.004 6
	中位数	24.12	0.001 3	-0.06	0.000 0
类别2	均值	27.29	0.002 6	0.08	-0.002 9
	方差	236.19	0.011 9	0.04	-0.004 6
	中位数	31.13	0.010 2	0.08	0.000 0
类别3	均值	24.16	-0.006 1	0.03	0.000 0
	方差	1.71	0.006 6	0.01	0.002 6
	中位数	24.17	0.011 4	0.03	0.010 0
类别4	均值	31.73	0.061 2	0.15	0.002 8
	方差	3.57	0.248 7	0.09	0.003 5
	中位数	31.96	0.040 0	0.15	0.010 0
类别5	均值	25.63	-0.073 6	0.23	0.005 7
	方差	11.74	0.267 5	0.16	0.039 8
	中位数	25.36	-0.039 7	0.16	0.010 0

行为	1	2	3	4	5
侧方	行为1/5（前侧/靠近自动驾驶方向纵向前）	行为2/4（前侧/靠近自动驾驶方向纵向前）	行为1/5（前侧/靠近自动驾驶方向纵向前）	行为2/4（纵向重叠/靠近自动驾驶方向纵向重叠）	行为1/5（纵向重叠/靠近自动驾驶方向纵向重叠）
正前	行为1/3（前间距缩短/前）	行为2（前间距缩短）	行为1/3（前间距缩短/前）	行为2/4（纵向重叠/靠近自动驾驶方向纵向重叠）	行为1/5（纵向重叠/靠近自动驾驶方向纵向重叠）
正后	行为5（侧向纵向重叠）	行为4（侧向纵向重叠）	行为5（侧向纵向重叠）	行为2（侧向纵向重叠）	行为1（侧向纵向重叠）

网络参数	数值	网络参数	数值
Input_size	2	Output_size	1
Embedding_size	8	N_head	8
Dim_feedforward	2 048	Dropout	0
Batch_size	512	Initial_learning_rate	0.001
Gamma	0.995	Epoch	1 000

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Generation Method for Anthropomorphic Continuous Interactive Test Scenarios of Automated Driving

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