基于轮胎特征点的并行大型车辆朝向角计算

doi:10.19562/j.chinasae.qcgc.2023.06.013

Abstract

Abstract:

For the poor stability of the prediction results of heading angle of parallel large vehicles in autonomous driving environment perception， this paper proposes a new method for the calculation of heading angle of parallel large vehicle. Firstly， a method based on camera inverse projection model to calculate the heading angle of target vehicle according to the location of feature points of tires in the image is proposed. Subsequently， a sub-branch is added and trained on the existing monocular 3D object detection model for the detection of feature point of tire of large vehicles. Finally， the algorithm in this paper is visually verified. The results show that the method can obtain accurate heading angle of parallel large-scale vehicle， and has better stability than the monocular 3D object detection model.

Key words: autonomous driving, environment perception, heading angle calculation, deep learning, object detection

Jiahao Zhao,Zhiquan Qi,Zhifeng Qi,Hao Wang,Lei He. Calculation of Heading Angle of Parallel Large Vehicle Based on Tire Feature Points[J].Automotive Engineering, 2023, 45(6): 1031-1039.

Figures/Tables 19

References 16

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模型	fps	参数量
无特征点检测分支	103.4	25.77×10⁶
增加特征点检测模型	62.8	42.30×10⁶

类别	召回率/%	准确率/%
轮胎	82.3	98.3
接地点	87.2	98.5
中心点	81.0	97.8
上顶点	83.2	98.3

类别	平均误差/pixel		特征点数量
类别	X	Y	特征点数量
接地点	5.87	3.06	704
中心点	4.65	3.34	749
上顶点	5.06	3.54	725
总计	5.18	3.31	2 178

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Calculation of Heading Angle of Parallel Large Vehicle Based on Tire Feature Points

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