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Vehicle to Vehicle (V2V) communication has a great potential to improvereaction accuracy of different driver assistance systems in critical drivingsituations. Cooperative Adaptive Cruise Control (CACC), which is an automatedapplication, provides drivers with extra benefits such as traffic throughputmaximization and collision avoidance. CACC systems must be designed in a waythat are sufficiently robust against all special maneuvers such as cutting-intothe CACC platoons by interfering vehicles or hard braking by leading cars. Toaddress this problem, a Neural- Network (NN)-based cut-in detection andtrajectory prediction scheme is proposed in the first part of this paper. Next,a probabilistic framework is developed in which the cut-in probability iscalculated based on the output of the mentioned cut-in prediction block.Finally, a specific Stochastic Model Predictive Controller (SMPC) is designedwhich incorporates this cut-in probability to enhance its reaction against thedetected dangerous cut-in maneuver. The overall system is implemented and itsperformance is evaluated using realistic driving scenarios from Safety PilotModel Deployment (SPMD).

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles