Inter-stakeholders Relationship in the Envisioned Autonomous Driving Era

Autonomous vehicles are expected to arrive sooner than expected. Autonomous vehicles of higher automation rely on both on-board and on-road deployed sensory data. Advanced approaches to improve the situational awareness of autonomous vehicle suggest to implement federated learning, where the raw data need to be transferred from vehicles to edges or clouds and vice-versa. This consequently generates dynamically varying communication link demands. The envisioned new era of autonomous driving demands the strong interplay of key stakeholders like: city authorities and communication network providers. In this paper, we study this relationship, where the traffic efficiency on different road segments may be achieved by incentivizing the autonomous vehicles through better communication resources on alternate routes. We model profit functions of the involved stakeholder. To carryout experiments, we use real traffic data of 8 months, which were collected through sensors deployed at Ernst-Reuter-Platz, Berlin, Germany. We developed an extensive validation framework to validate the approach, which comprises of SUMO, network simulator, and contributed modules. Results show that proposed approach achieves the traffic efficiency and help network operators to use the under-utilized network resources on the alternate paths.