## 1 Introduction  
The growing need to improve road safety has led the automotive industry to develop advanced driver assistance systems (ADAS), which significantly contribute to the reduction and mitigation of accidents [1]. On the other hand, the efficient use of energy for vehicle propulsion has become the main driver for the research and development of novel powertrains; it is worth mentioning that the automotive sector greatly contributes to the global production of polluting emissions [2]. In the context of automated driving, vehicle platooning represents an enabling technology for both increased occupant safety and energy savings. Vehicle platooning has been extensively studied in the last decades as it represents a suitable method for reducing the energy consumption and greenhouse gas emissions of heavy-duty vehicles. Different platooning test projects (e.g., Chauffeur, Sattre, Energy-ITS, SCANIA) were carried out in different countries to investigate different platooning systems [3; 4], vehicle mixes, types of infrastructure, and sensors. The focus of these projects was to investigate the potential for energy consumption reduction and an increase in road capacity, the latter representing a key task of the transportation sector. The advantages linked to drag reduction represent a key aspect in the scientific community [5; 6; 7]. The research efforts highlighted that heavy-duty vehicles (HDV) are the most indicated for energy-oriented platooning, given that they can exploit the benefits of the slipstream effect for viable inter-vehicle distances. On the contrary, this effect cannot be exploited by light-duty vehicles unless the inter-vehicle distance is less than 3 m. Still, driving close to the previous vehicle leads to safety concerns; thus, trade-off conditions or dedicated systems need to be carefully investigated. More specifically, in [8], multi-vehicle collision avoidance strategies using active emergency braking systems were developed to improve the safety of a string of vehicles in complex scenarios by also gathering information from the infrastructure. The safety issue can also be managed through the selection of a proper spacing policy that links the vehicle speed to the inter-vehicular distance. The author of [9] performed a comparative study of five different spacing policies by analysing the performances in terms of stability, comfort, and safety for adaptive cruise control systems. To accomplish the cooperative task of platooning control, cooperative adaptive cruise control (CACC) has been developed; unlike classic ACC, it gathers information from all vehicles in the string, i.e., distance, velocity, and accelerations. The exchange of data relies on vehicle-to-everything (V2X) and/or vehicle-to-vehicle (V2V) technology. The main disadvantage of the classic ACC is that string stability is not ensured, as demonstrated by the on-field test in [10], where the adaptive cruise control of several vehicles was tested. On the contrary, CACC systems guarantee string stability, as demonstrated by the authors of [11], where the focus was to design and validate a CACC system. Moreover, string stability can also be guaranteed by a proper design of the spacing policy. In [12], string stability was investigated by developing a delay-based spacing policy that guarantees the same speed profile in a spatial domain. In the literature, the cooperative strategy has been implemented using different algorithms. The authors of [13] developed a proportional string stable feedback control strategy by using information on distance and speed errors; the study proved to be feasible through an experimental validation conducted over a platoon of trucks. Other works have used more advanced control techniques, as done by the authors of [14], where a model predictive control (MPC) strategy, combined with the topography information responsible for generating efficient speed profiles, was deployed to drive vehicle platoons.