# EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing

arXiv: https://arxiv.org/abs/2201.07973 · HTML: https://mlsanity.com/paper/2201.07973/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning](https://mlsanity.com/paper/2003.12911/) (2020)
- [LiveMap: Real-Time Dynamic Map in Automotive Edge Computing](https://mlsanity.com/paper/2012.10252/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network](https://mlsanity.com/paper/2407.21460/) (2024)

## Abstract (arXiv)

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.