Vehicle-to-vehicle communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative adaptive cruise control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers, such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a neuralnetwork-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific stochastic model predictive controller is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented, and its performance is evaluated using realistic driving scenarios from safety pilot model deployment. Index Terms-Cooperative adaptive cruise control (CACC), cutin maneuver, model predictive controller (MPC), neural networks, stochastic hybrid systems (SHS).
In REF , Kazemi et al. proposed a neural network-based entry detection and trajectory prediction scheme for the sudden cut-in behavior in the cooperative adaptive cruise control (CACC) system, where vehicle-to-vehicle (V2V) communication is used to improve the response accuracy of the platoon system.
A Learning-Based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles
Computer Science Mathematics