数据机理混合驱动的交通车意图识别方法

doi:10.19562/j.chinasae.qcgc.2022.07.006

Abstract

Abstract:

Traffic vehicle intention recognition is of great significance to improve the performance of intelligent vehicle decision-making and planning. This paper analyzes each stage of the driver’s lane changing process from the perspective of the driving behavior generation mechanism， and establishes the driver’s intention prediction model based on Markov decision process （MDP）， the lane changing feasibility analysis model based on the dynamic safety field， and the vehicle behavior recognition model based on bi-directional long short-term memory（Bi-LSTM）. By combining the above-mentioned models with a clear temporal relationship， a traffic vehicle intention recognition method driven by data and mechanism hybrid is proposed， and the NGSIM data set is used for model training and verification. The results show that the recognition accuracy of the proposed method is over 90% at 1.8 s before the traffic vehicle reaching the lane changing point， and the accuracy is 97.88% at the lane changing point， which proves high recognition accuracy and long advance recognition time.

Key words: intelligent vehicles, intention recognition, Markov decision process, data and mechanism hybrid driven

Jian Zhao,Dongjian Song,Bing Zhu,Hangzhe Wu,column：Han Jiayi,Yuxiang Liu. Traffic Vehicles Intention Recognition Method Driven by Data and Mechanism Hybrid[J].Automotive Engineering, 2022, 44(7): 997-1008.

Figures/Tables 20

IDM参数	$v ˉ$ /（m·s^-1）	$s 0$ /m	$t 0 /$ s	$a ˉ$ /（m·s^-2）	$b ˉ$ /（m·s^-2）
取值	27.19	6.73	1.53	2.01	1.77

初始速度区间/（m·s^-1）	$μ$	$σ$
<6	1.971 3	0.395 8
6-14	1.779 6	0.404 7
>14	1.476 7	0.436 3

意图	指标	模型类别
意图	指标	$π H y b$	Social-LSTM	Bi-LSTM-2
LCL	精确率	99.71	99.54	91.67
	召回率	94.71	89.02	84.26
	F1分数	97.14	94.28	87.81
LK	精确率	85.37	72.47	71.74
	召回率	93.75	92.86	88.39
	F1分数	89.36	81.84	79.21
LCR	精确率	90.36	89.02	82.28
	召回率	98.68	96.05	85.53
	F1分数	94.34	92.41	83.87
准确率		95.06	91.13	85.28

References 22

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预测时间窗长度/s	RMSE/m
6.6	5.45
5.7	4.96
4.7	3.7

（速度优势，距离优势，安全性优势）	比例/%
（1，1，1）	15.57
（1，1，0）	8.98
（1，0，1）	35.63
（1，0，0）	17.07
（0，0，0）	9.58

真实意图		LCL	LK	LCR
识别意图	LCL	0.995 7	0.085 5	0.012 4
	LK	0.002 9	0.910 3	0.055 9
	LCR	0.001 4	0.004 3	0.931 7

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Traffic Vehicles Intention Recognition Method Driven by Data and Mechanism Hybrid

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