Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?
Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?
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DOI：
10.1109/tits.2023.3275367
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发表时间：
2022-09
影响因子：
8.5
通讯作者：
Syeda Tanjila Atik;Marco Brocanelli;Daniel Grosu
中科院分区：
文献类型：
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作者：
Syeda Tanjila Atik;Marco Brocanelli;Daniel Grosu
私人和公共交通将以自动驾驶汽车（AV）为主，它比普通车辆更安全。然而，确保自主功能的良好性能需要快速处理繁重的任务。为每台自动驾驶汽车提供强大的计算资源可能会导致自动驾驶汽车成本增加和行驶里程减少。另一种解决方案是在每台 AV 上安装低功耗计算硬件，并将繁重的任务卸载到附近功能强大的边缘服务器。在这种情况下，自动驾驶汽车的反应时间取决于边缘服务器中完成导航任务的速度。为了减少任务完成延迟，边缘服务器必须配备足够的网络和计算资源来处理车辆需求，这些需求表现出较大的时空变化。因此，在不同位置部署相同的资源可能会导致不必要的资源过度配置。在本文中，我们利用真实交通数据进行模拟，讨论在不同城市地区部署异构资源以维持边缘辅助自动驾驶汽车的峰值与平均需求的影响。我们的分析表明，通过针对平均需求而不是峰值需求部署边缘资源，可以分别减少高达 60% 和 50% 的网络带宽和计算核心。我们还调查了高峰时段需求如何影响自动驾驶汽车的安全行驶时间，发现如果将它们重新路由到边缘资源负载较低的区域，则可以将其减少约 20%。因此，未来的研究必须考虑到传统的路线规划导航系统可能无法为边缘辅助自动驾驶汽车提供最快的路线。
Private and public transportation will be dominated by Autonomous Vehicles (AV), which are safer than regular vehicles. However, ensuring good performance for the autonomous features requires fast processing of heavy tasks. Providing each AV with powerful computing resources may result in increased AV cost and decreased driving range. An alternative solution is to install low-power computing hardware on each AV and offload the heavy tasks to powerful nearby edge servers. In this case, the AV’s reaction time depends on how quickly the navigation tasks are completed in the edge server. To reduce task completion latency, the edge servers must be equipped with enough network and computing resources to handle the vehicle demands, which show large spatio-temporal variations. Thus, deploying the same resources in different locations may lead to unnecessary resource over-provisioning. In this paper, we leverage simulations using real traffic data to discuss the implications of deploying heterogeneous resources in different city areas to sustain peak versus average demand of edge-assisted AVs. Our analysis indicates that a reduction in network bandwidth and computing cores of up to 60% and 50%, respectively, is achieved by deploying edge resources for the average demand rather than peak demand. We also investigate how the peak-hour demand affects the safe travel time of AVs and find that it can be reduced by approximately 20% if they would be rerouted to areas with a lower edge-resource load. Thus, future research must consider that traditional turn-by-turn navigation systems may not provide the fastest routes for edge-assisted AVs.