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Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, isexpected to significantly contribute to the transportation system's safety andefficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperativedriving application, has been the subject of many studies in recent years. Theprimary motivation behind using CACC is to reduce traffic congestion andimprove traffic flow, traffic throughput, and highway capacity. Since theinformation flow between cooperative vehicles can significantly affect thedynamics of a platoon, the design and performance of control components aretightly dependent on the communication component performance. In addition, thechoice of Information Flow Topology (IFT) can affect certain platoonsproperties such as stability and scalability. Although cooperative vehiclesperception can be expanded to multiple predecessors information by using V2Xcommunication, the communication technologies still suffer from scalabilityissues. Therefore, cooperative vehicles are required to predict each other'sbehavior to compensate for the effects of non-ideal communication. The notionof Model-Based Communication (MBC) was proposed to enhance cooperative vehiclesperception under non-ideal communication by introducing a new flexible contentstructure for broadcasting joint vehicles dynamic/drivers behavior models. Byutilizing a non-parametric (Bayesian) modeling scheme, i.e., Gaussian ProcessRegression (GPR), and the MBC concept, this paper develops a discrete hybridstochastic model predictive control approach and examines the impact ofcommunication losses and different information flow topologies on theperformance and safety of the platoon. The results demonstrate an improvementin response time and safety using more vehicles information, validating thepotential of cooperation to attenuate disturbances and improve traffic flow andsafety.

Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control