Automated Mobility and Cooperation Compliance in Mixed Vehicle Traffic Environments

Automated vehicles have the potential to transform transportation systems through enhanced safety, efficiency, and sustainability. This chapter first reviews cooperative control approaches for Connected Automated Vehicles (CAVs) at conflict areas such as merging roadways, intersections, and lane-changing maneuvers. Formulating and solving optimal control problems with hard safety constraints enables the derivation of cooperative trajectories. When solutions to such problems become computationally intractable, online control methods based on Control Barrier Functions (CBFs) can be used to still guarantee all constraint satisfaction at the expense of some possible performance loss. The chapter examines extending these approaches to mixed traffic environments, introducing new techniques to guarantee safety despite the presence of unpredictable Human-Driven Vehicles (HDVs). Finally, a cooperative compliance framework is proposed to incentivize HDVs to align their behavior with CAV objectives using virtual, refundable tokens, without any monetary transactions. The goal is to provide foundations and specific new techniques aimed at optimizing automated mobility in mixed traffic.