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HD Map (EdgeMap) Crowdsources Data From Connected Vehicles in Auto Edge Computing

Researchers present a “crowdsourcing HD map to minimize the usage of network resources while maintaining the latency requirements,” utilizing a novel DATE algorithm.

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New research paper from University of Nebraska-Lincoln, Xidian University and University of North Carolina at Charlotte.

Abstract

“High definition (HD) map needs to be updated frequently to capture road changes, which is constrained by limited specialized collection vehicles. To maintain an up-to-date map, we explore crowdsourcing data from connected vehicles. Updating the map collaboratively is, however, challenging under constrained transmission and computation resources in dynamic networks. In this paper, we propose EdgeMap, a crowdsourcing HD map to minimize the usage of network resources while maintaining the latency requirements. We design a DATE algorithm to adaptively offload vehicular data on a small time scale and reserve network resources on a large time scale, by leveraging the multi-agent deep reinforcement learning and Gaussian process regression. We evaluate the performance of EdgeMap with extensive network simulations in a time-driven end-to-end simulator. The results show that EdgeMap reduces more than 30% resource usage as compared to state-of-the-art solutions.”

Find the “EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing” technical paper here. Published 2022.

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