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.

This paper explores the development and testing of two Internet of Things (IoT) applications designed to leverage Vehicle-to-Infrastructure (V2I) communication for managing intelligent intersections. The first scenario focuses on enabling the rapid and safe passage of emergency vehicles through intersections by notifying approaching drivers via a mobile application. The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities. Both scenarios were tested at two distinct intelligent intersections in Lioni, Avellino, Italy, and demonstrated notable effectiveness. Results show a significant reduction in emergency vehicle response times and a measurable increase in driver awareness of pedestrians at crossings. The findings underscore the potential of V2I technologies to improve traffic flow, reduce risks for vulnerable road users, and contribute to the advancement of safer and smarter urban transportation systems.

Summary: Connectivity is a key element enabling intelligent vehicles to communicate with each other and the Smart Road. In general, the connectivity is allowed by an On‐Board Unit enabling the Vehicle to Everything communication. This paper proposes an innovative unit acting not only as a device allowing connectivity but also as an intelligent electronic control unit to perform advanced and adaptive control strategies able to give reference trajectories and actuator set‐points to the low‐level control systems of power‐train and vehicle dynamics. In particular, based on a cascade model predictive control, an adaptive control strategy is proposed, considering time‐varying parameters and information related to vehicle kinematics and dynamics. Such a strategy allows the vehicle to follow a certain origin‐destination path in the inertial frame, based on an upper controller that considers a vehicle kinematic model so as to give speed references and target values for the steering angle at the wheels to the inner predictive control loop acting on both longitudinal and lateral vehicle dynamics. To check the effectiveness of the proposed approach, a comparison is made with existing cascade control strategies. The results show better safety and lane‐keeping performance. Then, using Hardware‐In‐the‐Loop testing for the proposed Intelligent On‐Board Unit, the considered approach demonstrates robustness against parametric variations, signal delay and noise both in the Global Positioning System and in the yaw rate sensor. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the cascade control strategy, paving the way to future developments necessary before tests in a real road scenario.

Summary: Connected and automated vehicles will characterize the future of the mobility, featured by Smart Roads and Internet of Things technologies. Vehicles will behave as mobile nodes of a network enabling communication between them and with respect to the road infrastructure. In order to provide advanced driver assistance and automated driving along a Smart Road, control strategies need to take into account both parametric variations due to environmental and/or weather conditions (e.g., wind speed, presence of ice, and/or rain), and constraints related to road safety and vehicular traffic. For this reason, advanced adaptive control strategies result particularly suitable to provide a complete control of the vehicle. This article proposes an extension to a previous conference paper, concerning a Processor in the Loop testing of an Adaptive Model Predictive Control for coupled longitudinal/lateral vehicle dynamics, where the ego vehicle was simulated through a bicycle model. In the present work, a low‐cost real‐time Hardware In the Loop test bench, featured by Raspberry boards, is used to verify the control strategy in a way closer to a real application. A four‐wheel vehicle dynamic model is considered to simulate the ego vehicle, then robustness and sensitivity tests with respect to parametric variations related to road friction, wind speed and vehicle mass, and a comparison with an existing adaptive MPC are performed. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the control strategy, paving the way for future developments, where the goal will be the testing in a real road scenario by using an IoT gateway purposefully designed by the NetCom company.

In the years to come, Connected and Automated Vehicles (CAVs) are expected to substantially improve the road safety and environmental impact of the road transport sector. The information from the sensors installed on the vehicle has to be properly integrated with data shared by the road infrastructure (smart road) to realize vehicle control, which preserves traffic safety and fuel/energy efficiency. In this context, the present work proposes a real-time implementation of a control strategy able to handle simultaneously motion and hybrid powertrain controls. This strategy features a cascade of two modules, which were implemented through the model-based design approach in MATLAB/Simulink. The first module is a Model Predictive Control (MPC) suitable for any Hybrid Electric Vehicle (HEV) architecture, acting as a high-level controller featuring an intermediate layer between the vehicle powertrain and the smart road. The MPC handles both the lateral and longitudinal vehicle dynamics, acting on the wheel torque and steering angle at the wheels. It is based on a simplified, but complete ego-vehicle model, embedding multiple functionalities such as an adaptive cruise control, lane keeping system, and emergency electronic brake. The second module is a low-level Energy Management Strategy (EMS) of the powertrain realized by a novel and computationally light approach, which is based on the alternative vehicle driving by either a thermal engine or electric unit, named the Efficient Thermal Electric Skipping Strategy (ETESS). The MPC provides the ETESS with a torque request handled by the EMS module, aiming at minimizing the fuel consumption. The MPC and ETESS ran on the same Microcontroller Unit (MCU), and the methodology was verified and validated by processor-in-the-loop tests on the ST Microelectronics board NUCLEO-H743ZI2, simulating on a PC-host the smart road environment and a car-following scenario. From these tests, the ETESS resulted in being 15-times faster than than the well-assessed Equivalent Consumption Minimization Strategy (ECMS). Furthermore, the execution time of both the ETESS and MPC was lower than the typical CAN cycle time for the torque request and steering angle (10 ms). Thus, the obtained result can pave the way to the implementation of additional real-time control strategies, including decision-making and motion-planning modules (such as path-planning algorithms and eco-driving strategies).