CrowdSourcing Live High Definition Map via Collaborative Computation in Automotive Edge Computing

High-definition (HD) maps will serve as the key enabler to achieve autonomous driving. To maintain up-todate information in HD maps, we explore crowdsourcing from connected and automated vehicles (CAVs). It is, however, challenging to achieve live HD maps under complex dynamics in automotive edge computing networks. In this paper, we propose a new crowdsourcing HD map (EdgeMap) to enable collaborative computation between CAVs and edge servers. We formulate the optimization problem to minimize the total usage of transmission and computing resources under the constraint of end-to-end latency. To solve the problem, we design a new DATE algorithm to adaptively partition vehicular offloadings and reserve multiple domain resources, based on asynchronous multiagent reinforcement learning and expected gradient descents. We develop an end-to-end network simulator, including vehicle and edge computation and radio transmission in the 5G network, to evaluate the performance of EdgeMap. The results show that EdgeMap can achieve more than 30% reduction in total resource usage as compared to state-of-the-art solutions.