改进的禁忌搜索算法在无人驾驶汽车路径规划中的研究

Improved Tabu Search Algorithm in Driverless Cars Research on Path Planning

针对复杂的无人驾驶交通环境,这里在无人驾驶汽车系统架构的基础上,提出了一种无人驾驶汽车路径规划方法,该方法结合了改进的禁忌搜索算法和改进的人工势场法。全局路径规划应用改进的禁忌搜索算法进行,局部路径规划应用改进的人工势场法进行。通过仿真对路径规划方法进行分析,验证该方法的优越性。结果表明,提出的全局路径规划方法实现了最优的时间效率和路径选择,在加入局部路径规划改善后,该方法的搜索范围变小,路径规划将更安全且更具适应性。该研究为无人驾驶技术的发展提供了一定的参考。

Aiming at the complex driverless traffic environment,it proposes a path planning method for driverless vehicles based on the driverless vehicle system architecture,this method combines an improved tabu search algorithm with an improved artificial po-tential field.The improved tabu search algorithm is used for global path planning,and the improved artificial potential field meth-od is used for local path planning.The advantages of the method are verified by analyzing the path planning method through simu-lation.The results show that the proposed global path planning method achieves the optimal time efficiency and path selection.Af-ter adding local path planning,the search range of the method becomes smaller,and the path planning will be safer and more adaptive.This study provides a reference for the development of unmanned driving technology.