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兵工学报 ›› 2009, Vol. 30 ›› Issue (9): 1236-1241.

• 论文 • 上一篇    下一篇

基于蚁群算法的弯曲道路边界的识别

马雷,武波涛,李昊   

  1. 燕山大学车辆与能源学院,河北秦皇岛066004
  • 收稿日期:2007-08-27 上线日期:2014-12-25
  • 通讯作者: 马雷 E-mail:malei97yan@163.com
  • 作者简介:马雷(1967-),男,副教授,博士后。
  • 基金资助:
    河北省教育厅科研项目计划编号(2006326)

Identifying of the Bent Lane Based on Ant Colony Algorithm

MA Lei, WU Bo tao, LI Hao   

  1. College of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, Hebei, China
  • Received:2007-08-27 Online:2014-12-25
  • Contact: MA Lei E-mail:malei97yan@163.com

摘要: 路径识别是智能车辆导航技术关键步骤之一。用抛物线模型拟合道路边界,使直线路径与弯曲路径的表述统一化。将蚁群算法应用于路径识别,利用信息素的正反馈作用,使搜索尽快地在较优的路径上收敛,从而确定抛物线参数。实验证明算法的准确性与实时性都满足实际需要。

关键词: 自动控制技术 , 智能车辆 , 蚁群算法 , 弯曲路径识别

Abstract: Lane identifying is one of the key steps of the navigation technology in intelligent vehicle. Lane edge was simulated with parabola model, so the descriptions of the linear lane and the bent lane are unified. The lane edge was identified by an ant colony algorithm in which the search is converged in the better path as soon as possible by the positive feed back of hormone and the paraooia parameters are determined. The simulated results show that the veracity and the real-time meet the actual need.

Key words: automatic control technology , intelligent vehicle , ant colony algorithm , recognizing of bend lane

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