EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing
- URL: http://arxiv.org/abs/2201.07973v1
- Date: Thu, 20 Jan 2022 03:18:41 GMT
- Title: EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing
- Authors: Qiang Liu, Yuru Zhang, Haoxin Wang
- Abstract summary: 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.
The results show that EdgeMap reduces more than 30% resource usage as compared to state-of-the-art solutions.
- Score: 9.642145335196641
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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