EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing
This addresses the challenge of maintaining up-to-date HD maps for autonomous driving systems, which is incremental as it builds on existing crowdsourcing methods with a focus on resource efficiency.
The paper tackles the problem of updating high-definition maps for autonomous vehicles by crowdsourcing data from connected vehicles, proposing EdgeMap to minimize network resource usage while meeting latency requirements, and shows it reduces resource usage by over 30% compared to state-of-the-art solutions.
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.