Platoon Control
Platoon control focuses on coordinating the movement of multiple vehicles, typically autonomous, to improve traffic flow, fuel efficiency, and safety. Current research emphasizes developing robust and safe control algorithms, often employing reinforcement learning (including multi-agent and federated approaches), model predictive control, and graph neural networks to handle complex interactions and uncertainties in mixed-autonomy traffic. These advancements aim to address challenges like communication delays, heterogeneous vehicle dynamics, and unpredictable human driver behavior, ultimately contributing to more efficient and safer transportation systems.
Papers
Finite State Markov Modeling of C-V2X Erasure Links For Performance and Stability Analysis of Platooning Applications
Mahdi Razzaghpour, Adwait Datar, Daniel Schneider, Mahdi Zaman, Herbert Werner, Hannes Frey, Javad Mohammadpour Velni, Yaser P. Fallah
Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control
Sahand Mosharafian, Mahdi Razzaghpour, Yaser P. Fallah, Javad Mohammadpour Velni