Characterization of In-Vehicle Network Sensor Data Traffic in Autonomous Vehicles

The softwarization of vehicles and the evolution towards autonomous driving is imposing increasing flexibility and reliability demands to future in-vehicle networks (IVN). Research on 6G advocates for a seamless integration of vehicles with cellular networks for a deep edge-edge-cloud continuum that facilitates the opportunistic offloading of in-vehicle processing to the edge/cloud. Realizing this vision requires a seamless connection of IVNs with the cellular networks, which can be facilitated through the gradual adoption of in-vehicle wireless subnetworks. These subnetworks can support increasing dependable and deterministic service levels using predictive schedulers that can anticipate in-vehicle traffic flows and patterns to schedule communication resources and computing workloads. This requires an accurate characterization of IVN traffic, and this study progresses the state-of-the-art with a first characterization of IVN traffic in autonomous vehicles. The study characterizes the data captured by a full suite of sensors as well as the processed data for supporting automated driving. We also derive spatial and time correlations between the IVN data that can serve to anticipate network demands and predict traffic flows for the support of deterministic IVN services.