Design, implementation and testing on scaled robotic vehicles platforms of novel vehicle platooning strategies.

The growing adoption of Connected and Automated Vehicles (CAVs) is transforming modern mobility looking towards the spread of autonomous driving. Among their most promising applications is vehicle platooning, which enables groups of vehicles to move cooperatively in a safe, efficient, and automated manner. This approach offers notable advantages in fuel efficiency, traffic management, and road safety. To support effective platooning, the design and implementation of novel control strategies must account for both vehicle behavior and the underlying communication framework. This thesis focuses on distributed innovative control approaches that enhance scalability and robustness by leveraging local information, enabling real-time implementation across fleets. The effectiveness of these strategies is strongly influenced by how vehicles share information within the system. The methodology adopted in this thesis involved a comprehensive robustness analysis of the implemented innovative control strategy, aimed at evaluating its effect under varying operating conditions. This was followed by an extensive simulation campaign designed to assess the controller performance and effectiveness in a realistic virtual environment. Finally, experimental testing and verification has been carried out on scaled robotic vehicles platforms, thereby providing a proof of concept for real-world applicability, including robustness to communication time delay and parameter uncertainties.