中文摘要：

停车难是各大城市亟需解决的热点问题,而目前对停车位算法的研究都在理想模型下进行,对实际场景中存在的很多重要问题没有加以考虑。对停车位发现问题中车辆位置信息不完全的模型进行研究,改进了基于引力的停车位发现算法。通过定义停车位引力因子Gg实现对停车位的动态分级,同时定义了车间斥力、引力因子Gr解决车间竞争问题,提出了一种基于引力和斥力的停车位发现算法R＆GPA。仿真结果表明该算法在车辆位置信息不完全模型中相比其他算法缩短了个体车辆停车位搜寻时间,提高了系统的整体效益。

英文摘要：

Finding parking has been a major hassle for drivers in urban environments, but current researches about parking algorithm are usually based on ideal models without considering about many important elements in real scene. This paper improves the gravity based algorithm in an incomplete information model by defining factor Gg, factor Grand repulsion formula which realizes dynamic classification of parking lots and solves the problem of parking competition. Especially, a new parking algorithm RGPA based on gravity and repulsion is proposed. Through simulation, the RGPA algorithm shortens the individual parking time and enhances the overall efficiency of system compared with the other algorithms in incomplete information model.