Advancing Cooperative Autonomous Driving with Hybrid PID-MPC Control
Our latest paper has been accepted for publication in IEEE Transactions on Intelligent Transportation Systems (IEEE T-ITS), one of the world’s highest-ranked journals in the field of automotive engineering and intelligent transport technologies.
The research addresses one of the key challenges of future connected and autonomous vehicles (CAVs): enabling multiple vehicles to safely travel together in coordinated platoons while seamlessly handling the entire driving lifecycle—from joining a platoon, through cooperative cruising, to safely leaving it. Rather than treating these maneuvers as separate problems, the proposed framework integrates them into a single end-to-end control solution deployed on an edge computing platform close to the vehicles.
Our solution combines the simplicity of a lightweight PID controller for vehicle joining with the predictive capabilities of Model Predictive Control (MPC) for cooperative driving and platoon reconfiguration. By leveraging Multi-access Edge Computing (MEC), computationally demanding optimization is performed close to the vehicles, enabling real-time decision making while maintaining strict safety and performance requirements.
The proposed approach was validated in a comprehensive simulation environment integrating SUMO, OMNeT++, Simu5G, and the PlaaS framework. The results demonstrate safe and stable platooning under various traffic scenarios, including emergency braking, vehicle joining and leaving maneuvers, as well as scalability to platoons of ten autonomous vehicles, all while meeting real-time computational constraints.
This publication represents another important milestone in AIL’s long-term research on connected and autonomous mobility, edge computing, and next-generation intelligent transportation systems.
Read the full paper on IEEE Xplore: https://ieeexplore.ieee.org/document/11574779