Towards Learning-Based Control of Connected and Automated Vehicles: Challenges and Perspectives

The exploitation of communication technologies enables connected and automated vehicles (CAVs) to operate more collaboratively, that is, by exchanging or even negotiating future trajectories and control actions. That way, CAVs (or agents) can establish a networked control system such as to safely automate road traffic in a collaborative fashion. A rich body of literature is available, e.g., on intersection automation, automated lane change or lane merging scenarios. These control concepts, though, are most tailored to the particular application and are in general not applicable to multiple scenarios. This chapter conveys the challenges and perspectives of modeling and optimization-based control techniques for the safe coordination of multiple connected agents in road traffic scenarios. Along these lines, the perspective of generalizing controller design to serve multiple use cases simultaneously instead of designing separate controllers for every use case is discussed. Moreover, the opportunities of learning-based control in case of model uncertainties and mixed-traffic scenarios, involving connected and non-connected agents, are outlined.