行人过街场景下车辆避障路径规划与控制方法

doi:10.3969/j.issn.1674-8484.2022.03.010

摘要/Abstract

摘要：

为解决行人横穿过街场景下自动驾驶车辆安全避障问题,设计了一种基于行车风险场的车辆避障控制模型。考虑了行人加速运动中的潜在风险,采用了基于行人斥力场重心的新型避障函数,以优化横向避障距离。采用行车风险场理论,来构建全局路径规划层。基于模型预测控制(MPC),构建局部路径规划和跟踪控制器。在PreScan-Carsim平台上进行了仿真试验。结果表明：与跟踪传统静态全局路径相比,动态行车风险场下的避障行驶稳定性提高了7.21%,横向安全性提高了4.63%。因此,设计的控制器能够达到安全避障的目标。

关键词: 智能汽车, 行车风险场, 模型预测控制(MPC), 车辆避障, 路径规划与跟踪控制

Abstract:

A vehicle obstacle avoidance control model was designed based on the driving risk field to avoid autonomous vehicles in the scenes that pedestrians pass cross streets. An obstacle avoidance function was used based on pedestrian repulsion-field gravity-center considering the potential risks of pedestrians in accelerated motion to optimize the lateral obstacle avoidance distance. The driving risk field theory was used to construct a global path planning layer. A local path planning and tracking controller was constructed based on Model Predictive Control (MPC). Some simulation tests were carried out on the PreScan-Carsim platform. The results show that the obstacle avoidance driving stability and lateral safety in the dynamic driving risk field are respectively increases by 7.21% and 4.63% compared with tracking the traditional static global path. Therefore, the designed controller achieves the goal of safe obstacle avoidance.

Key words: intelligent vehicles, driving risk field, model predictive control (MPC), vehicle obstacle avoidance, path planning and tracking control

中图分类号:

U491.2

李文礼, 肖凯文, 任勇鹏, 李超, 易帆. 行人过街场景下车辆避障路径规划与控制方法[J]. 汽车安全与节能学报, 2022, 13(3): 489-501.

LI Wenli, XIAO Kaiwen, REN Yongpeng, LI Chao, Yi Fan. Path planning and control method for vehicle obstacle avoidance in pedestrian crossing scenes[J]. Journal of Automotive Safety and Energy, 2022, 13(3): 489-501.

图/表 20

参考文献 21

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质量	1.650 t
长×宽	4.91.8 m
前轴至质心距离, l_a	1.40 m
后轴至质心距离, l_b	1.65 m
前轮侧偏刚度, C_f	66.9 kN/rad
后轮侧偏刚度, C_r	62.7 kN/rad
转动惯量, I_z	3 234 kg·m^-2

质量	1.650 t
长×宽	4.91.8 m
前轴至质心距离, l_a	1.40 m
后轴至质心距离, l_b	1.65 m
前轮侧偏刚度, C_f	66.9 kN/rad
后轮侧偏刚度, C_r	62.7 kN/rad
转动惯量, I_z	3 234 kg·m^-2

车辆起始位置	(0,5) m
车辆目标位置	(100,5) m
行人起始位置	(23,2) m
车道总宽, L	7.60 m
目的地引力因数, k_a	0.01
道路边界斥力场因数, k_r	0.01
行人斥力场因数, k_P	4.00
行人初始速度, v	0.75 m/s
行人加速度, α	0.25 m·s^-2
行人初始航向角, θ	0°
采样周期	100 ms

车辆起始位置	(0,5) m
车辆目标位置	(100,5) m
行人起始位置	(23,2) m
车道总宽, L	7.60 m
目的地引力因数, k_a	0.01
道路边界斥力场因数, k_r	0.01
行人斥力场因数, k_P	4.00
行人初始速度, v	0.75 m/s
行人加速度, α	0.25 m·s^-2
行人初始航向角, θ	0°
采样周期	100 ms

	局部规划层	轨迹跟踪层
采样周期	10 ms	1 ms
预测时域, N_P	20	25
控制时域, N_c	2	10
权重矩阵, Q	100	^{[2 000,0; 0,10 000]}
权重矩阵, R	20	5×10⁴
避障权重因数, Sobs	20 000	—
控制量约束	—	[-10°, 10°]
控制增量约束	[-7.84, 7.84]m·s^-2	[-0.85°, 0.85°]
松弛因子, ε	—	10
松弛因子权重, ρ	—	1 000