面向自动驾驶汽车测试的交通车辆交互过程建模

doi:10.19562/j.chinasae.qcgc.2022.12.004

Abstract

Abstract:

In order to meet the requirements of autonomous vehicle testing on the realness of the test scenes， a coupled feature modeling method is proposed， aiming at the interaction relationship between traffic vehicles. The dataset of traffic vehicle behaviors is established by combining the virtual data built based on mechanism model and the real scene data. Deep learning is adopted to set up the behavior decision model， following model and lane change model of traffic vehicles with local information response. The single model with relatively simple structure can enhance the expandability of scene simulation. In view of the high requirements of autonomous vehicle testing on model robustness， a framework of distributed execution and centralized adversarial training is constructed to conduct traffic vehicle model optimization for enhancing its robustness to input disturbance. The simulation environment for traffic vehicle interaction is created with a simulation on the model performed， and the effectiveness of the model is verified by comparing simulation data distribution with real data one and quantifying evaluation indicators.

Key words: autonomous vehicles, testing, interaction relationship, deep learning, traffic scenes

Yuande Jiang,Bing Zhu,Xiangmo Zhao,Jian Zhao,Bingbing Zheng. Modeling of Traffic Vehicle Interaction for Autonomous Vehicle Testing[J].Automotive Engineering, 2022, 44(12): 1825-1833.

Figures/Tables 16

References 22

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年龄	人数	驾龄	人数
18~30	42	3年以下	31
30~40	36	4~10年	45
41~60	9	10年以上	11

模型	参数名称	数值
换道决策模型	T₁	30（3 s）
	GRU层数	2
	全连接层	（64，3）
自注意力跟驰模型	T₂	20（2 s）
	自注意力层数	2
	全连接层	（80，32，1）
换道规划模型	T₃	30（3 s）
	T₄	50（5 s）
	自注意力层数	2
	全连接层	（270，100）

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Modeling of Traffic Vehicle Interaction for Autonomous Vehicle Testing

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