基于行人视野注意力场的人车微观交互模型

doi:10.19562/j.chinasae.qcgc.2022.06.002

Abstract

Abstract:

From the perspective of pedestrian visual cognition， a pedestrian-vehicle micro-interaction model based on the atten-tion field of pedestrian vision is proposed. The attention field of pedestrian vision is constructed to drive the pedes-trian field of vision， and the artificial potential field is used to drive the pedestrian movement，. The target capture algorithm is used to control the target capture in the pedestrian visual field. In order to verify the effectiveness of the model， drones are used to collect the pedestrian-vehicle interaction data from the bird's-eye perspective and analyze them. Pedestrian crossing styles are divided into three types： conservative， cautious and adventurous. Simulation scenarios and interactive models are built on the Pygame platform， then， different types of interactive data are used as the model input， the similarity between the pedestrian spatiotemporal trajectory output by the model and the collected real spatiotemporal trajectory is experimentally compared. The results show that the pedestrian-vehicle mi-cro-interaction model based on pedestrian visual attention field is 25.48% more accurate than the conventional artifi-cial potential field model， and it can effectively reproduce the pedestrian-vehicle interaction process in the actual traf-fic scene.

Key words: pedestrian-vehicle interaction, pedestrian crossing style, pedestrian attention field of vision, artificial potential field

Wenli Li,Kaiwen Xiao,Xiaohui Shi,Fenghua Liang,Ping Li. Pedestrian-Vehicle Micro-Interaction Model Based on Attention Field of Pedestrian Vision[J].Automotive Engineering, 2022, 44(6): 808-820.

Figures/Tables 22

Frame	id	（x，y）/m	（x^tar，y^tar）/m	（x^p，y^p）/m	$θ$ /（°）
10	3	12.5，2.7	19.7，24.5	25.0，4.3	0
20	3	13.6，3.4	19.7，24.5	23.4，9.2	13
$? ?$
150	3	19.7，24.5	19.7，24.5	7.9，23.7	0

id	$A m / (m ? s - 2)$	$v a / (m ? s - 1)$	$D i n i t / m$
1	2.05	0.78	11.46
2	1.68	0.73	13.67
$? ?$
231	0.98	1.25	15.64
$P o b s$	together $? (0 : 无 ?, 1 : 有)$	$t w a i t / s$	$v c / (m ? s - 1)$
0.87	1	0	10.45
0.68	0	4	15.56
$? ?$
0.59	0	10	5.68

变量类型	变量	变量简述
被解释变量	行人过街风格Style	0：让出交互主导权，让来车先行
		1：谨慎交互，平稳前行，不奔跑
		2：积极争夺交互主导权，侵略性强，奔跑
解释变量	最大加速度 $A m / (m · s - 2)$	行人在整个交互区域的最大加速度
	平均速度 $v a / (m · s - 1)$	行人在整个交互区域的平均速度
	来车初始横向距离 $D i n i t / m$	初始标定时刻，来车与行人的横向距离
	车辆平均速度 $v c / (m · s - 1)$	车辆在交互区域的平均速度
	行人等待时间 $t w a i t / s$	行人与车辆交互前的停止时间
	是否同行together	同一交互区域内，是否有其他行人（0：否，1：是）
	关注度 $P o b s / %$	整个标定时间内，行人对来车的关注度

势场类别	参数	最优取值
势场类别	参数	保守型	谨慎型	冒险型
视野注意力势场	视野转动惯量 J	0.01	0.10	1.00
	过街风格待定系数 $μ$	0.11	0.44	0.91
	视野最远距离 l $/ m$	12.50
	车速待定系数 $? λ$	0.03
人工势场	目的地引力常量 $K a$	$2.5 × 10 - 3$	$5.0 × 10 - 3$	$7.5 × 10 - 3$
	车辆斥力常量 $K r$	10	25	40
	待定系数 K	0.10	0.30	0.60
	目的地引力场距离阈值 d $/ m$	20
	驾驶员反应时间 ^［13］ $t r / s$	3
	舒适减速度 ^［13］b $/ (m ? s - 2)$	2.5
	参考车速 $? v * / (k m ? h - 1$ ）	20

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势场类别	常规人工势场			视野注意力场 + 常规人工势场（本文方法）
势场类别	MAPE/%	MAE /m	RMSE /m	MAPE/%	MAE /m	RMSE /m
保守型	8.44	4.85	7.78	2.98	1.57	2.05
谨慎型	7.01	4.50	5.71	1.98	1.29	1.92
冒险型	6.23	4.00	4.47	3.37	2.09	2.39
平均值	7.23	4.45	5.99	2.78	1.65	2.12

Pedestrian-Vehicle Micro-Interaction Model Based on Attention Field of Pedestrian Vision

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