## 1 Introduction  
Vehicular communication has become an emerging technology in intelligent transportation system (ITS) due to the offered road safety, comfort ride, traffic efficiency and infotainment [1]. This promising technology encourages most of the leading car companies to jointly work with national government agencies and wireless network research groups to develop communication-based products related to the transportation sector [2]. ITS is a system that exchanges information among vehicles, infrastructure and people; it can also be called vehicle-to-everything (V2X) communication. V2X includes various communications such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-device (V2D) and vehicle-to-network (V2N). In the near future, all autonomous vehicles on the road will be able to connect in a self-organized way either through V2V or V2I. Thus, it is important to ensure that the communication between V2V and V2I is efficient and reliable to effectively share safety-related and infotainment services. Due to the high complexity and the wide range of functionalities of the modern vehicular infotainment systems, a practical application based on developing a system capable of estimating the driving complexity in real time is presented [3]. In order to fulfil the vehicular networks environment, an international American standard for V2V and V2I communications has been proposed by the IEEE 802.11 working group for short-range inter-vehicle communication in the 5.9 GHz frequency band called IEEE 802.11p [4,5]. IEEE 802.11 p allows vehicles to directly communicate with each other without the need to go through the infrastructure. Additionally, different methods can be implemented at IEEE 802.11 p to mitigate the impact of the imperfect channel state information (CSI) and increase the required safety related data transmissions for vehicular ad-hoc networks [6]. Moreover, to increase the road safety, several frameworks was developed to link the characteristics of V2V communications; such as packet loss probability and packet transmission delay, with the physical mobility characteristics of the vehicular system such as safe inter-vehicular distance [7]. Furthermore, a V2V-based method for the detection of road traffic congestion using fuzzy logic is suggested to solve the problem of road congestion and increase the system accuracy for detecting road traffic congestion [8]. V2V is becoming an important part in modern ITS as there is no mandate for roadside units or infrastructure to facilitate the communication among vehicles, hence vehicles can be used to transfer and exchange messages. To increase the fault tolerance and the stability, a peer-to-peer approach can be applied for the autonomous vehicles network to increase the real-time capability, efficiency and cost effectiveness of the network [9]. Moreover, cooperative communication is one of the most well-known strategies which show how the combination between V2I communications and V2V communications can improve the capacity of vehicular networks [10]. However, performance enhancement in the vehicular network is always affected by the autonomous vehicle's position, mobility, speeds, transmission range and density. These issues can have an adverse effect on the performance of the vehicular networks [11]. The density and the transmission range of the vehicles are two of the main factors on which the connectivity in vehicular communications depends. Therefore, in order to improve vehicular connectivity, combination between V2I and V2V is a strong candidate. In the previous work proposed in [12], the combination between V2I and V2V using either multi-hop or cooperative communication was the authors' main focus to improve quality-of-service (QoS) across the vehicular network.