融合人车博弈论的车路云一体化系统车速规划方法

doi:10.3969/j.issn.1674-8484.2026.02.012

摘要/Abstract

摘要：

该文研究了自动驾驶车辆的人车交互行为（VPI），以解决行人突发横穿而导致的连续性与安全性的冲突。基于路侧感知与云端历史信息，构建行人过街社会力模型，以表征行人行为，并向车端下发数据。提出双层协同决策控制策略：上层引入Stackelberg博弈模型；下层采用改进模型预测控制（MPC）。以上层博弈结果为轨迹优化目标。在道路不受控行驶城市路段，进行了VPI场景的仿真实验。结果表明：相较于单一避障控制（OAC）和MPC方法，用本文方法交互耗时分别减少0.59 s和0.27 s，红绿灯通过率提高6.2%和2.9%。从而，本文方法提升车辆在通行时间限内的效率性与安全性，能模拟人车协同与冲突场景。

关键词: 自动驾驶车辆, 车辆与行人交互（VPI）, 博弈论, 社会力模型, 车速规划

Abstract:

The vehicle-pedestrian interaction (VPI) for autonomous vehicles was investigated to resolve the conflicts between the driving continuity and the safety caused by sudden pedestrian crossing. A Social Force model was constructed for pedestrian crossing with roadside perception and cloud-side historical information to characterize the pedestrian behaviors and to deliver the corresponding data to on-board terminals. A two-layer collaborative decision-making and control strategy was proposed with its upper layer introduced the Stackelberg game model and with its lower layer adopted an improved model predictive control (MPC). The lower layer took upper-layer game results as the trajectory optimization objective. Simulation experiments were conducted on VPI scenarios in uncontrolled urban road segments. The results show that the proposed method reduces interaction time by 0.59 s and 0.27 s respectively, compared with the single obstacle avoidance control (OAC) method and the MPC method; improves traffic light passing rate by 6.2% and 2.9% respectively, compared with the above two control methods. Therefore, the proposed method improves the efficiency and the safety of vehicles within limited traffic time window, and can simulate the vehicle-pedestrian collaboration and conflict scenarios.

Key words: autonomous vehicles, vehicle-pedestrian interaction (VPI), game theory, social force model, speed planning

中图分类号:

U467.1

文家燕, 邹海峰, 钟薇, 高博麟, 卢彦博. 融合人车博弈论的车路云一体化系统车速规划方法[J]. 汽车安全与节能学报, 2026, 17(2): 261-269.

WEN Jiayan, ZOU Haifeng, ZHONG Wei, GAO Bolin, LU Yanbo. Vehicle speed planning method with the vehicle-road-cloud integration system and incorporating human-vehicle game theory[J]. Journal of Automotive Safety and Energy, 2026, 17(2): 261-269.

图/表 14

参考文献 29

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车辆速度，	N(8, 0.8) m/s
期望间隙接受度，t_gap	N(4.25, 0.8) s
最大加速度，v_max	4 m/s²
最小减速度，v_min	-4 m/s²
最大观测距离，d_obs	30 m
行人距离，d_p	3.5 m
期望间歇均值，μ_gap	2.5 s
期望间隙标准差，σ_gap	0.4 s
仿真时间步长，?t	0.1 s
仿真总时长，T	10 s

车辆速度，	N(8, 0.8) m/s
期望间隙接受度，t_gap	N(4.25, 0.8) s
最大加速度，v_max	4 m/s²
最小减速度，v_min	-4 m/s²
最大观测距离，d_obs	30 m
行人距离，d_p	3.5 m
期望间歇均值，μ_gap	2.5 s
期望间隙标准差，σ_gap	0.4 s
仿真时间步长，?t	0.1 s
仿真总时长，T	10 s

行人	方法	TTC均值/ s	CR / %	YR / %
	OAC	5.22	0.14	71.2
年轻人	MPC	2.73	1.57	59.2
	本文	3.98	0.07	47.7
中年人	OAC	5.45	0.11	72.3
	MPC	2.66	2.2	57.7
	本文	4.07	0.05	50.2
老年人	OAC	5.53	0.09	74.9
	MPC	2.92	1.67	56.3
	本文	4.22	0.02	49.1

行人	方法	TTC均值/ s	CR / %	YR / %
	OAC	5.22	0.14	71.2
年轻人	MPC	2.73	1.57	59.2
	本文	3.98	0.07	47.7
中年人	OAC	5.45	0.11	72.3
	MPC	2.66	2.2	57.7
	本文	4.07	0.05	50.2
老年人	OAC	5.53	0.09	74.9
	MPC	2.92	1.67	56.3
	本文	4.22	0.02	49.1

θ₁	θ₂	θ₃	TTC均值/ s	t_gap均值/ s	CR / %	YR / %
1.2	1.5	1.1	3.96	4.22	0.072	47.5
1.5	1.5	1.1	4.02	4.55	0.066	49.3
2.0	1.5	1.1	4.14	5.21	0.051	52.6
1.2	2.0	1.1	3.74	3.94	0.23	44.3
1.2	2.5	1.1	3.31	3.48	0.48	40.4
1.2	1.5	1.3	3.57	4.03	0.12	45.2
1.2	1.5	1.5	3.36	3.88	0.34	41.8