  Autonomous driving perceives surroundings with line-of-sight sensors that are
compromised under environmental uncertainties. To achieve real time global
information in high definition map, we investigate to share perception
information among connected and automated vehicles. However, it is challenging
to achieve real time perception sharing under varying network dynamics in
automotive edge computing. In this paper, we propose a novel real time dynamic
map, named LiveMap to detect, match, and track objects on the road. We design
the data plane of LiveMap to efficiently process individual vehicle data with
multiple sequential computation components, including detection, projection,
extraction, matching and combination. We design the control plane of LiveMap to
achieve adaptive vehicular offloading with two new algorithms (central and
distributed) to balance the latency and coverage performance based on deep
reinforcement learning techniques. We conduct extensive evaluation through both
realistic experiments on a small-scale physical testbed and network simulations
on an edge network simulator. The results suggest that LiveMap significantly
outperforms existing solutions in terms of latency, coverage, and accuracy.
