| 引用本文: | 吴文静,陈润超,马芳武,贾洪飞.车辆对行人速度障碍自主避碰的驾驶方法[J].哈尔滨工业大学学报,2019,51(9):74.DOI:10.11918/j.issn.0367-6234.201804180 |
| WU Wenjing,CHEN Runchao,MA Fangwu,JIA Hongfei.A driving method of autonomous collision avoidance for the velocity obstacle of pedestrians[J].Journal of Harbin Institute of Technology,2019,51(9):74.DOI:10.11918/j.issn.0367-6234.201804180 |
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| 摘要: |
| 针对过街行人与智能车辆之间运动协调中的安全避碰问题,设计一种CVIS环境下的基于行人避碰的车辆驾驶控制器. 结合速度障碍法的基本原理提出车辆对过街行人的避碰规则,在此基础上搭建模型预测控制框架,提出车辆对行人的自主避碰算法. 综合考虑车辆驾驶的操作约束,以最小化车速变化及满足驾驶员操作舒服性要求为控制目标,在车辆对行人避碰的前提下优化车辆的驾驶策略. 分别设置车辆直行避碰与允许换道避碰两种控制场景,在MATLAB环境下对车辆驾驶控制效果进行仿真实验. 结果表明:车辆对不同情况的过街行人,能够通过加速或减速进行避碰；通过与七次多项式换道轨迹进行对比,自主避碰驾驶的安全性更高. |
| 关键词: 智能交通 主动避碰 速度障碍 模型预测控制 智能车辆 |
| DOI：10.11918/j.issn.0367-6234.201804180 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2016YFB0101601) |
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| A driving method of autonomous collision avoidance for the velocity obstacle of pedestrians |
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WU Wenjing1,CHEN Runchao1,MA Fangwu2,JIA Hongfei1
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(1.School of Transportation, Jilin University, Changchun 130022, China; 2. College of Automotive Engineering, Jilin University, Changchun 130022, China)
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| Abstract: |
| In this paper, a driving controller for vehicles to avoid collision pedestrians in CVIS environment is designed. Based on the basic principle of the velocity obstacle method, a vehicle collision condition for pedestrian crossing was proposed, and an autonomous vehicle collision avoidance algorithm for pedestrians was established under the framework of model predictive control. Considering the operation constraints of driving, the vehicle driving strategy was optimized to minimize the change of vehicle speed and meet the driving comfort requirement under the premise of achieving pedestrian collision avoidance. Two control scenarios (i.e., vehicles going straight and changing lanes to avoid collision) were set up, and simulation experiments were carried out to verify the control effect in MATLAB environment. Results show that vehicles could avoid collisions by accelerating or decelerating when pedestrians cross the street under different conditions. By comparing with seven polynomials lane change trajectories, it showed that the safety of the autonomous collision avoidance driving was higher. |
| Key words: intelligent transportation active collision avoidance velocity obstacle model predictive control intelligent vehicle |
