无人驾驶汽车跨区域的APSO联合任务卸载优化

Optimization of cross regional APSO joint task offloading for driverless vehicles

为了解决车路协同中无人驾驶汽车跨区域计算任务卸载问题,构建了一种无人驾驶汽车和路侧单元(road side unit,RSU)的联合任务卸载优化模型,旨在将计算任务的总能耗和时延的加权和最小化,即求出计算任务的总能耗和时延的加权和的最小值,为此提出自适应粒子群优化算法(adaptive particle swarm optimization,APSO)优化车辆计算任务卸载过程中的任务卸载策略和任务发射功率来达到计算任务的总能耗和时延的加权和的最小值。结果表明:基于APSO的无人驾驶汽车的跨区域联合任务卸载优化模型能显著降低无人驾驶汽车计算任务的总能耗和时延的加权和,同时对于所构建的跨区域联合任务卸载优化模型采用APSO求解优于采用模拟退火算法(simulated annealing,SA)和遗传算法(genetic algorithm,GA)求解。

In order to solve the problem of cross region computing task offloading of driverless vehicles in vehicle road collaboration,a joint task offloading optimization model of driverless vehicles and road side unit(RSU)is constructed aiming to minimize the weighted sum of the total energy consumption and delay of computing tasks,that is,to find the minimum value of the weighted sum of the total energy consumption and delay of computing tasks.Therefore,an adaptive particle swarm optimization algorithm(APSO)is proposed to optimize the task offloading strategy and task transmission power in the process of vehicle computing task offloading to achieve the minimum value of the weighted sum of the total energy consumption and delay of computing tasks.The experiment results show that the cross region joint task offloading optimization model of the driverless vehicle based on APSO can significantly reduce the total energy consumption and the weighted sum of the delay of the driverless vehicle computing tasks.At the same time,APSO is better than simulated annealing algorithm(SA)and genetic algorithm(GA)in solving the optimization model for cross regional joint task offloading.