  Autonomous driving needs various line-of-sight sensors to perceive
surroundings that could be impaired under diverse environment uncertainties
such as visual occlusion and extreme weather. To improve driving safety, we
explore to wirelessly share perception information among connected vehicles
within automotive edge computing networks. Sharing massive perception data in
real time, however, is challenging under dynamic networking conditions and
varying computation workloads. In this paper, we propose LiveMap, a real-time
dynamic map, that detects, matches, and tracks objects on the road with
crowdsourcing data from connected vehicles in sub-second. We develop the data
plane of LiveMap that efficiently processes individual vehicle data with object
detection, projection, feature extraction, object matching, and effectively
integrates objects from multiple vehicles with object combination. We design
the control plane of LiveMap that allows adaptive offloading of vehicle
computations, and develop an intelligent vehicle scheduling and offloading
algorithm to reduce the offloading latency of vehicles based on deep
reinforcement learning (DRL) techniques. We implement LiveMap on a small-scale
testbed and develop a large-scale network simulator. We evaluate the
performance of LiveMap with both experiments and simulations, and the results
show LiveMap reduces 34.1% average latency than the baseline solution.
