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

TL;DR

This paper examines the deployment of edge computing resources for autonomous vehicle navigation, analyzing how resource allocation affects latency, safety, and cost, based on real traffic data and peak versus average demand scenarios.

Contribution

It investigates the impact of city-area resource deployment strategies on autonomous vehicle navigation performance and safety, highlighting the importance of adaptive edge resource provisioning.

Findings

Deploying resources based on average demand reduces costs and improves utilization.

Peak-hour demand can affect travel times and navigation safety.

Adaptive resource deployment strategies are crucial for efficient edge-assisted AV systems.

Abstract

Future private and public transportation will be dominated by Autonomous Vehicles (AV), which are potentially safer than regular vehicles. However, ensuring good performance for the autonomous features requires fast processing of heavy computational tasks. Providing each AV with powerful enough computing resources is certainly a practical solution but may result in increased AV cost and decreased driving range. An alternative solution being explored in research 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. However, this demand shows large spatio-temporal variations. Thus, deploying the same amount of resources in different locations may lead to unnecessary resource over-provisioning. Taking these challenges into consideration, in this paper, we discuss the implications of deploying different amounts of resources in different city areas based on real traffic data to sustain peak versus average demand. Because deploying edge resources to handle the average demand leads to lower deployment costs and better system utilization, we then investigate how peak-hour demand affect the safe travel time of AVs and whether current turn-by-turn navigation apps would still provide the fastest travel route. The insights and findings of this paper will inspire new research that can considerably speed up the deployment of edge-assisted AVs in our society.