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.

翻译：从网联自动驾驶车辆（CAV）众包数据是获取包含实时道路信息的高精地图的经济高效方式。然而，由于不可预测的资源竞争和分布式的资源需求，实现具有确定性时延性能的地图面临挑战。本文提出CoMap——一种新型众包高精（HD）地图方法，旨在在满足端到端时延百分位要求的同时最小化网络资源使用的经济成本。我们设计了一种创新的CROP算法，用于学习CAV任务卸载的资源需求、优化卸载决策，并以完全分布式的方式主动分配临时网络资源。具体而言，我们基于贝叶斯神经网络构建了预测模型以评估资源需求的不确定性，并开发了负载均衡方案以解决单个基础设施中资源利用不均的问题。我们在汽车边缘计算网络模拟器中通过大量仿真评估了CoMap的性能。结果表明，与现有方案相比，CoMap的平均资源使用量降低了高达80.4%。