Automated and coordinated vehicles' driving (platooning) is very challenging due to the multibody control complexity and the presence of unreliable time-varying wireless intervehicular communication (IVC). We propose a novel controller for vehicle platooning based on consensus and analytically demonstrate its stability and dynamic properties. Traditional approaches assume the logical control topology as a constraint fixed a priori, and the control law is designed consequently; our approach makes the control topology a design parameter that can be exploited to reconfigure the controller, depending on the needs and characteristics of the scenario. Furthermore, the controller automatically compensates outdated information caused by network losses and delays. The controller is implemented in Plexe, which is a state-of-the-art IVC and mobility simulator that includes basic building blocks for platooning. Analysis and simulations show the controller robustness and performance in several scenarios, including realistic propagation conditions with interference caused by other vehicles. We compare our approach against a controller taken from the literature, which is generally considered among the most performing ones. Finally, we test the proposed controller by implementing the real dynamics (engine, transmission, braking systems, etc.) of heterogeneous vehicles in Plexe and verifying that platoons remain stable and safe, regardless of real-life impairments that cannot be modeled in the analytic solution. The results show the ability of the proposed approach to maintain a stable string of realistic vehicles with different control-communication topologies, even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.

Cooperative driving is an essential component of future intelligent road systems. It promises greater safety, reducing accidents due to drivers distraction, improved infrastructure utilization, and fuel consumption reduction with platooning applications. Proper platoon management requires Inter-Vehicular Communication (IVC), longitudinal control and lateral control for stability and safety, and proper application protocols and algorithms to manage platoons and perform coordinated maneuvers. This work shows how a longitudinal controller based on distributed consensus can, at the same time, guarantee stability and performance in regime platoon operations, and be at the hearth of maneuvering protocols and algorithms, as it remains stable in face of changes of platoon topology and control gains. The adoption of a single control algorithm for two fundamental tasks greatly simplify the overall design of the system and improves stability and safety as it is not required to switch between different controllers during platoon operation. The theoretical properties are proven in the first part of the paper. The second part of the paper is devoted to its implementation in a state-ofthe- art mobility and IVC simulator, which is used for an extensive experimental campaign showing the dynamic properties of the system and its performance in a set of typical platoon maneuvers as join, leave and inclusion of a vehicle in the middle of the platoon. All simulations include realistic details of the vehicle dynamics (mass, dimensions, power train dynamics) as well as extremely detailed modeling of the communication network, from IEEE 802.11p protocol details, to collisions, packet errors, path loss and fading on the channel, and source-destination based delays.

Automated and coordinated vehicles' driving (platooning) is gaining more and more attention today and it represents a challenging scenario heavily relying on wireless Inter-Vehicular Communication (IVC). In this paper, we propose a novel controller for vehicle platooning based on consensus. Opposed to current approaches where the logical control topology is fixed a priori and the control law designed consequently, we design a system whose control topology can be reconfigured depending on the actual network status. Moreover, the controller does not require the vehicles to be radar equipped and automatically compensates outdated information caused by network delays. We define the control law and analyze it in both analytical and simulative way, showing its robustness in different network scenarios. We consider three different wireless network settings: uncorrelated Bernoullian losses, correlated losses using a Gilbert-Elliott channel, and a realistic traffic scenario with interferences caused by other vehicles. Finally, we compare our strategy with another state of the art controller. The results show the ability of the proposed approach to maintain a stable string of vehicles even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.

Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision.