智能汽车对无信号交叉口行人的避撞控制

潘洺铭; 孙宇波; 刘强

doi:10.3963/j.jssn.1674-4861.2021.02.003

智能汽车对无信号交叉口行人的避撞控制

doi: 10.3963/j.jssn.1674-4861.2021.02.003

潘洺铭^{1, 2,},
孙宇波^{1, 2},
刘强^{1, 2, ,}

1.
中山大学智能工程学院广州 510006
2.
广东省智能交通系统重点实验室广州 510006

基金项目:

国家自然科学基金项目 51675540

东莞市社会科技发展重点项目 20185071551596

详细信息

作者简介:
潘洺铭(1996—), 硕士研究生. 研究方向: 自动驾驶和交通安全.E-mail: panmm6@mail2.sysu.edu.cn

通讯作者:
刘强(1981—), 博士, 教授.研究方向: 自动驾驶和交通安全.E-mail: liuq32@mail.sysu.edu.cn

中图分类号: U463.6;U492.8
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- 被引次数: 0
出版历程
- 收稿日期: 2020-05-23

Steering Collision Avoidance Control of Intelligent Vehicles for Crossing Pedestrians at Unsignalized Intersections

PAN Mingming^{1, 2
,},
SUN Yubo^{1, 2},
LIU Qiang^{1, 2
, ,}

1.
School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China
2.
Guangdong Province Key Laboratory of Intelligent Transportation System, Guangzhou 510006, China

摘要

摘要: 针对智能汽车在无信号交叉口对横穿行人的避撞问题，研究了主动转向避撞控制策略。基于多层模型预测控制方法，采用分层控制策略设计局部规划层控制器与全局跟踪层控制器，在此基础上根据交叉口处汽车与行人的轨迹特征计算人车碰撞剩余时间，改进传统人工势场法构造避撞函数，规划出既能规避交叉口内存在碰撞风险的行人又能使偏差最小的局部避撞路径，并使智能汽车在满足多项动力学约束时准确跟踪参考路径，通过搭建CarSim/Simulink联合仿真平台，结合广东省2006—2018年交通事故数据库选取对交叉口人车碰撞有显著影响的因素，设计仿真场景进行仿真分析。结果表明：智能汽车能在多个初始点完成对参考路径的跟踪，控制器对不同速度和附着条件有较高的鲁棒性，高速低附着场景中，智能汽车横向加速度小于0.4 g、质心侧偏角小于2°、前轮侧偏角小于2.5°，各约束量满足舒适性和平稳性条件；4个典型交叉口场景中，智能汽车以不同速度直行或转弯通过交叉口，均能识别横穿行人中存在碰撞风险的行人实现主动转向避撞。
- 交通安全 /
- 智能汽车 /
- 主动转向避撞 /
- 多层模型预测控制 /
- 无信号交叉口 /
- 横穿行人
Abstract: An active steering collision avoidance control strategy is proposed to cope with pedestrian-vehicle collision avoidance of intelligent vehicles at unsignalized intersections. Based on the multi-layer model predictive control method, the local planning layer controller and the global tracking layer controller are designed by the hierarchical control strategy. On this basis, the remaining time of pedestrian-vehicle collision is calculated according to the trajectory characteristics of vehicles and pedestrians at intersections, and the modified traditional artificial potential field method is employed to construct the collision avoidance function. The local collision avoidance path is planned to minimize the deviation of tracking the global reference path and avoid any pedestrian-vehicle collision at intersections. By CarSim/Simulink co-simulation platform, various scenarios are designed for simulation analysis. The factors that have significant effects on pedestrian-vehicle collision at intersections are selected from the traffic accident database of Guangdong Province from 2006 to 2018. The results show that intelligent vehicles can track the reference path at different initial points, and the controllers have good robustness subjected to different speeds and adhesions. In a high-speed and low-adhesion scenario, the lateral acceleration of intelligent vehicles is less than 0.4 g; the side-slip angle is less than 2°; the front wheel yaw angle is less than 2.5°. At the four typical unsignalized intersections, intelligent vehicles at different speeds can steer to avoid collision for crossing pedestrians when going straight or turning through the intersection.
- transportation safety /
- intelligent vehicles /
- active steering collision avoidance /
- multi-layer model predictive control /
- unsignalized intersections /
- crossing pedestrians

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图 1 局部规划层汽车模型

Figure 1. Vehicle dynamics model of the local planning layer

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图 2 行人运动状态分布

Figure 2. Distribution of pedestrian movement states

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图 3 横穿行人与汽车位置关系

Figure 3. Position relationship between pedestrians and vehicles

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图 4 放大后的行人活动区域与汽车位置关系

Figure 4. Position relationship between the enlarged pedestrians' activity area and vehicles

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图 5 转向避撞控制逻辑

Figure 5. Control logic of steering collision avoidance

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图 6 全局跟踪层汽车模型

Figure 6. Vehicle dynamics model of the global tracking layer

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图 7 干燥路面路径跟踪结果

Figure 7. Path tracking results of dry pavements

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图 8 干燥路面控制量仿真结果

Figure 8. Simulation results of dry pavement control quantity

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图 9 干燥路面约束量仿真结果

Figure 9. Simulation results of dry pavement constraints

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图 10 湿滑路面路径跟踪结果

Figure 10. Results of path tracking on wet pavements

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图 11 湿滑路面约束量仿真结果

Figure 11. Simulation results of wet pavement constraints

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图 12 无信号交叉口仿真场景示意图

Figure 12. Simulation scenario of unsignalized intersections

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图 13 场景1：匀速直行

Figure 13. Scenario 1:Going straight at a constant speed

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图 14 场景2：匀速直行

Figure 14. Scenario 2: Going straight at a constant speed

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图 15 场景3：匀速右转

Figure 15. Scenario 3:Turning right at a constant speed

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图 16 场景4：匀速左转

Figure 16. Scenario 4:Turning left at a constant speed

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表 1 显著性影响因素

Table 1. Significant influencing factors

影响因素	变量分组	β	SE	x²	95% CI	P
车速/（km/h）	30~45	-0.509	0.210	1	-0.920~-0.098	0.015
车速/（km/h）	45~60	-0.157	0.072	1	-0.297~-0.016	0.028
行驶状态	左转	0.138	0.068	1	0.005~0.271	0.042
行驶状态	右转	0.162	0.073	1	0.019~0.304	0.026
路口区域	湿滑	-0.138	0.055	1	-0.245~-0.030	0.012
路面状况	进口	-0.143	0.069	1	-0.279~-0.007	0.039

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表 2 仿真场景参数

Table 2. Parameters of simulation scenario

项目	场景1	场景2	场景3	场景4
车速/（km/h）	30/45/60	30/45/60	30/45/60	30/45/60
汽车行驶路径	匀速直行	匀速直行	匀速右转	匀速左转
进出口	西-东	西-东	南-东	西-北
行人数量	3	3	2	2
横穿汽车路径行人编号	行人1，2	行人1，2	行人1	行人2
路面附着系数μ	0.85	0.4	0.4	0.4

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