## 1 Introduction  
According to World Health Organization, around 1.3 million people die every year because of road traffic crashes [1]. The key risk factors come from human error (speeding, wrong decisions, etc. ), irresponsible behavior (drinking, distracted driving, fatigue, etc. ), unsafe road infrastructure, and bad weather conditions (e.g., inadequate visibility and slippery roads) [2; 3; 4]. Vehicular Ad hoc Networks (VANETs) have emerged as a solution to alleviate all the factors by means of different applications [5; 6; 7]. For example, implementing an accident prevention system in VANETs that considers velocity, weather condition, risk location, nearby vehicles density, and driver fatigue can reduce the number of road crashes and consequently the number of deaths [8]. Other applications, on the other hand, can improve traffic management and the driving experience [9; 10; 11]. Nevertheless, safety and traffic management are correlated to each other in many ways. For instance, traffic congestion leads to a higher risk for car crashes, as drivers are prone to drive faster in order to compensate for the delay caused by traffic [12]. There exist many vehicular navigation systems, with Google Maps being the most popular, which recommend alternative routes to vehicles to avoid traffic congestion. Such navigation systems compute different alternative routes in terms of shortest driving distance, shortest driving time and lowest driving cost (in case of toll roads) [13]. However, taking into account merely these parameters does not actually achieve the goals of traffic management systems as navigation systems only serve each vehicle individually, thus offering better services only for their drivers. VANETs, on the other hand, accomplish substantially bigger goals. They benefit all road users, without any exception, mostly by depending only on content awareness and inter-vehicle communications. Context-awareness is expected to play a key role in VANETs, not only by ameliorating traffic management, but also other important metrics such as the driving experience, the safety of road users, and the environmental impact [14]. The inter-vehicle communication in VANETs enables a broader horizon of awareness for the state of other vehicles in the network and the condition of the surrounding environment. Such data include information about traffic lights, weather conditions, public safety information, and so on. Furthermore, the response time in VANETs is dramatically reduced as vehicles would be notified in real time, almost immediately after some situation occurs [15; 16; 17]. In terms of networking, VANETs can be defined as networks of vehicles spontaneously created, able to connect vehicles with other vehicles in the network and with the infrastructure via Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication links [18]. They are a subclass of Mobile Ad hoc Networks (MANETs), and as such are based on inter-vehicle communication and do not rely on central coordination [19]. The vehicles behave as sensor nodes and relay the messages via one-hop or multi-hop communications. The infrastructure includes Road Side Units (RSUs), road signs, Electronic Toll Collection (ETC), and so on. For example, RSUs are deployed along the roads and are used to increase robustness, connectivity, and coverage by acting as static sensors and relay nodes [20]. The evolution of the 5 th Generation of Cellular Networks (5G) marks a huge leap in the advance of VANETs. 5G base stations (5G-NR gNodeB) can serve as a gateway to the Internet and therefore enable big data storage, processing, and analyzing in the cloud infrastructure [20]. Due to the integration of Artificial Intelligence (AI), cloud platforms will be able to take better decisions to enhance the driving experience. Vehicles will be able to exchange information with many more entities, such as pedestrians, infrastructure, and networks, via Vehicle-to-Everything (V2X) communications [21].