一种基于模糊运算的车辆防撞预警系统设计
A Vehicle Collision Warning System Based on Fuzzy Arithmetic
DOI: 10.12677/CSA.2017.79092, PDF, HTML, XML, 下载: 1,635  浏览: 2,106  科研立项经费支持
作者: 张福洋, 李全彬*：江苏师范大学物理与电子工程学院，江苏 徐州
关键词: 模糊逻辑；鱼群算法；预警；主动避障；Fuzzy Logic； Fish Swarm Algorithm； Warning System； Active Obstacle Avoidance
摘要: 本文提出一种基于模糊运算的智能车辆防撞预警系统。针对动态环境不易侦测的特点建立动态避障规则库，利用鱼群算法刻画和解答智能车辆在动态环境下的避障问题。进而，利用智能车辆和障碍物之间的距离和操作者的反应时间建立模糊函数模型。操作者如果没有在预警条件下及时做出反应，该系统将会采用最优路线。实验结果证明，该系统可以根据环境条件做出相对合理的选择，准确地做出预警或者主动避障。
Abstract: This article puts forward a kind of intelligent vehicle collision warning system based on fuzzy logic. Firstly, according to the characteristics of dynamic environment, which are not easy to detect, the dynamic obstacle avoidance rule base is established. The problem of obstacle avoidance of intelligent vehicle in dynamic environment is described and answered by using Fish Swarm Algorithm. Secondly, the fuzzy function model is established on basis of two factors: the distance between the intelligent vehicle and the obstacle and the time of the driver’s reflection. If the driver does not respond to warning timely, the system will adopt the best optimal route to stop the vehicle. The experimental result shows that the system can make a reasonable choice in accord with changes of the environment in making a warning or triggering an active obstacle avoidance.
文章引用：张福洋, 李全彬. 一种基于模糊运算的车辆防撞预警系统设计[J]. 计算机科学与应用, 2017, 7(9): 805-813. https://doi.org/10.12677/CSA.2017.79092

参考文献

[1] Li, L., Lu, G., Wang, Y., et al. (2014) A Rear-End Collision Avoidance System of Connected Vehicles. IEEE International Conference on Intelligent Transportation Systems, Qingdao, 8-11 October 2014, 63-68.
[2] 刘祖兵, 袁亮, 蒋伟. 基于模糊逻辑的移动机器人避障研究[J]. 机械设计与制造, 2017(3): 101-104.
[3] 黄宜庆, 彭凯, 袁梦茹. 基于多策略混合人工鱼群算法的移动机器人路径规划[J]. 信息与控制, 2017, 46(3): 283- 288.
[4] Liu, C.G., Chang, J.G., et al. (2009) Path Planning for Mobile Robot Based on an Improved Probabilistic Roadmap Method. Chinese Journal of Electronics, 18, 395-399.
[5] 柳长安, 鄢小虎, 刘春阳, 等. 基于改进蚁群算法的移动机器人动态路径规划方法[J]. 电子学报, 2011, 39(5): 1220-1224.
[6] Yin, L., Yin, Y. and Lin, C.J. (2010) A New Potential Field Method for Mobile Robot Path Planning in the Dynamic Environments. Asian Journal of Control, 11, 214-225.
https://doi.org/10.1002/asjc.98
[7] 颜明重, 黄冰逸, 朱大奇. 基于神经动力学的水下目标观测路径规划[J]. 船海工程, 2017, 46(2): 103-107.
[8] 刘二辉, 姚锡凡. 基于改进遗传算法的自动导引小车路径规划及其实现平台[J]. 计算机集成制造系统, 2017, 23(3): 465-472.
[9] 卢月品, 赵阳, 孟跃强, 等. 基于改进遗传算法的狭窄空间路径规划[J]. 计算机应用研究, 2015(2): 413-418.
[10] 徐晓晴, 朱庆保. 动态环境下基于多人工鱼群算法和避碰规则库的机器人路径规划[J]. 电子学报, 2012, 40(8): 1694-1700.
[11] 吴经贤, 李颖, 胡妮娜, 等. 一种基于AT89S52的车辆防追尾预警系统设计[J]. 集美大学学报(自然版), 2017, 22(1): 53-60.
[12] 唐阳山, 夏道华. 驾驶员对汽车防撞安全距离检测仿真研究[J]. 计算机仿真, 2016, 33(7): 449-453.
[13] 王振华. 自适应模糊逻辑交通灯管理系统的设计[J]. 电子设计工程, 2017, 5(2): 175-177.
[14] 张立朝, 赵鹏, 张合朝, 等. 用于空间分析的栅格中间层构建流程与方法研究[J]. 测绘科学技术学报, 2016, 33(2): 196-200.