Largevolume data dissemination for cellularassisted automated driving with edge intelligence Abstract   Automated driving emerges as a potential technology for safe, convenient, and efficient transportation systems. In order to assist autonomous vehicles with largevolume data dissemination such as high definition (HD) map, cellular network becomes more inevitable to provide vehicles with the ability of accessing Internet. However, due to the bottleneck of wireless bandwidth and the high mobility of vehicles, efficient data dissemination assisted by cellular network is a challenging task. In this work, we investigate the largevolume data dissemination problem for cellularassisted automated driving within given delay limit. Firstly, a data dissemination scheme is designed that transmits data from the base stations to the target vehicle via a collection of relay vehicles. Secondly, a novel user satisfaction model is derived by considering the vehicular mobility patterns and erasure coding, and the data dissemination is formulated into an optimization problem. Then, to solve this NPhard problem, a lowcomplexity dynamic programming based data dissemination solution with edge intelligence is proposed. Finally, by carrying out extensive simulation, the effectiveness of proposed data dissemination scheme for cellularassisted automated driving is demonstrated, in comparison with the optimal solution based on CPLEX as well as other conventional schemes.

Utilizing Connectivity Maps to Accelerate V2I Communication in Cellular Network Dead Spots On many roads in rural and mountainous areas, the cellular network connectivity is intermittent and dead spots, i.e., zones without any coverage, are frequent. In previous work, we developed a data dissemination protocol to accelerate the transmission of messages in dead spots. It combines the cellular network with shortliving adhoc networks between vehicles. A car in a dead spot can forward messages directed towards the environment, to the peer in its adhoc network that will leave the dead spot first, effectively reducing the delay. An issue, however, is to reliably identify the peer that is most likely the first one regaining cellular network coverage. This problem can be solved if the borders of the dead spot, the vehicles are in, are previously known. For that, we use a novel technology named dead spot prediction. Here, vehicles conduct local connectivity measurements that are aggregated to socalled connectivity maps describing the locations of dead spots on a road system. In this article, we introduce the combination of the data dissemination protocol with dead spot prediction. Particularly, our protocol is amended such that connectivity maps are considered when deciding which vehicle leaves a dead spot first. Since currently only few publicly available works about dead spot prediction exist, we further created a prototype of such a predictor ourselves that will be discussed as well.