Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

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.