Cooperative Global Path Planning for Multiple Platforms

With the development of AI, big data, and mobile communication, intelligent transportation has become popular in recent years. Path planning is a typical topic of intelligent transportation, attracting significant attention from researchers. However, existing studies only focus on the path planning of a single platform, which may lead to unexpected traffic congestion. This is because multiple platforms can provide route planning services, the optimal planning calculated by one single platform may be not good in practice, since multiple platforms may lead the users to the same roads, which causes unexpected traffic congestion. Although in the view of each platform, the planning is optimal. Fortunately, with the rise of data sharing and cross-platform cooperation, the data silos between different platforms are gradually being broken. Based on this, we propose Cooperative Global Path Planning (CG PP) framework to over-come the above shortcoming. CGPP allows the path planning request target platform to send some queries to cooperative platforms to optimize its path planning results. Such queries should be 'easy' enough to answer, and the query frequency should be small. Based on the above principle, we design a query decision model based on multi-agent reinforcement learning in CGPP framework to decide the query range and query frequency. We design action and reward specifically for the CGPP problem. Furthermore, we propose the Self-adjusting Query Area algorithm to enhance the precision of query results and the Query Reuse Optimization algorithm to further minimize the number of queries. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.