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Route planning for electric vehicle efficiency using the Bellman-Ford algorithm on an embedded GPU

机译:在嵌入式GPU上使用Bellman-Ford算法进行电动汽车效率的路线规划

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Traditional route planning algorithms, such as Dijkstra or A*, are efficient in their execution due to low computational complexity. With the rise in popularity of electric vehicles, which have the capability of recharging their batteries while driving, energy costs to traverse road segments can now be negative values. Dijkstra or A* cannot process negative edge weights. This work presents the use of the Bellman-Ford algorithm to plan driving routes based on energy efficiency. To overcome the increased computational complexity and ensure reasonable processing speeds this work uses an embedded graphical processing unit and a parallel implementation of Bellman-Ford on an embedded GPU system. This method allows routes to be plotted with considerable reductions in energy requirements, while maintaining the performance of traditional route planning programs.
机译:传统的路线规划算法,例如Dijkstra或A *,由于计算复杂度低,因此执行效率高。随着电动汽车的普及,电动汽车具有在行驶过程中为电池充电的能力,穿越道路的能源成本现在可以为负值。 Dijkstra或A *无法处理负边权重。这项工作介绍了使用Bellman-Ford算法来基于能效计划驾驶路线。为了克服增加的计算复杂性并确保合理的处理速度,这项工作使用了嵌入式图形处理单元和嵌入式GPU系统上Bellman-Ford的并行实现。这种方法可以绘制路线,同时大大降低了能源需求,同时保持了传统路线规划程序的性能。

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