Robustness Analysis of a Distributed Adaptive Model Predictive Control for Connected and Automated Vehicles Against Delays

Vehicle platooning control is a key-point for smart roads and cooperative-intelligent transport systems (C-ITS), being able to reduce the risk of rear-end collisions and improve road safety and traffic fluidity. To deal with constraints deriving from the surrounding environment, such as safety distance and speed limits, platooning technologies can benefit from advanced and distributed control strategies based on the model predictive control (MPC), suitable to provide adaptive behavior against variations of environmental and meteorological parameters provided by the Smart Road. Since platooning technologies are based on vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communications, the control strategy needs to be robust with respect to communication delays. This article performs a robustness analysis against such delays using a real-time and low-cost hardware-in-the-loop (HIL) testbed based on Raspberry Pi boards. The testbed emulates a platooning architecture featured by three vehicles, managed by a distributed MPC strategy, and a road infrastructure manager (IM). Results show good robustness of the proposed control strategy with respect to communication delays, considering a variable transmission delay in the range [100, 400] ms and two scenarios: urban and suburban. The obtained results pave the way to real-road tests, considering connected vehicles equipped with Internet of Things (IoT) Gateways based on Raspberry Pi hardware.