PORP：面向无人驾驶的路径规划并行优化策略

为了实现路径规划并行优化，解决基于位置的服务（LBS）在高峰时段遭遇大量路径规划的并发查询所导致的较高响应时间的问题，提出双层网格（DLG-index）索引，并基于此提出路径规划的并行算法（PORP）. 双层索引的顶层由完整路网的边界节点组成，底层由网格组成，网格由完整路网分割而来. 对于一个给定的查询，基于骨架图计算一条全局路径，然后将规划任务划分成多个局部优化任务. 每个局部优化任务对应此查询的全局路径通过的网格，同时，每个局部优化任务由不同的处理器独立维护. 算法能够基于复杂变化的路况，及时调整导航路线，整个调整过程分段实施，可以由多处理器依次协同完成，实现对海量并发查询做出快速响应. 与CANDS算法相比，PORP的响应时间平均减少了49.6%，处理时间平均减少了28.5%.

PORP: parallel optimization strategy of route planning for self-driving vehicles

In order to achieve the parallel optimization of route planning, and solve the problem of high response time of location-based services (LBS) caused by extensive concurrent queries during peak hours, a dual-level grid index (DLG-index) was firstly introduced, and then, based on DLG-index, a parallel optimization algorithm of route planning (PORP) was introduced. The top layer of DLG-index is a skeleton graph consisting of border nodes of the entire graph, and the bottom layer is composed of all grids partitioned by the entire graph. For a given query, the first step is to compute a global path based on the skeleton graph. Then the route planning task is divided into multiple local optimizations in grids passed by the global path. At the same time, each local optimization is maintained independently by different processors. The algorithm can optimize the planned route in real time based on varying traffic conditions. The entire optimization is implemented in several segments, which can be handled by multi-processors and achieve rapid response to massive concurrent queries. Experiments results showed that compared with CANDS algorithm, the response time of PORP was reduced by an average of 49.6% and the processing time was saved by an average of 28.5%.