CoMap: Proactive Provision for Crowdsourcing Map in Automotive Edge Computing

TL;DR

CoMap introduces a proactive, cost-efficient crowdsourcing map system for automotive edge computing, utilizing Bayesian neural networks and distributed resource allocation to meet latency requirements.

Contribution

The paper presents CoMap, a novel system with a CROP algorithm that proactively manages network resources for crowdsourcing HD maps in automotive edge environments.

Findings

Reduces resource usage by up to 80.4% compared to existing solutions.

Effectively predicts resource demands using Bayesian neural networks.

Balances resource utilization across infrastructures.

Abstract

Crowdsourcing data from connected and automated vehicles (CAVs) is a cost-efficient way to achieve high-definition maps with up-to-date transient road information. Achieving the map with deterministic latency performance is, however, challenging due to the unpredictable resource competition and distributional resource demands. In this paper, we propose CoMap, a new crowdsourcing high definition (HD) map to minimize the monetary cost of network resource usage while satisfying the percentile requirement of end-to-end latency. We design a novel CROP algorithm to learn the resource demands of CAV offloading, optimize offloading decisions, and proactively allocate temporal network resources in a fully distributed manner. In particular, we create a prediction model to estimate the uncertainty of resource demands based on Bayesian neural networks and develop a utilization balancing scheme to resolve the imbalanced resource utilization in individual infrastructures. We evaluate the performance of CoMap with extensive simulations in an automotive edge computing network simulator. The results show that CoMap reduces up to 80.4% average resource usage as compared to existing solutions.