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Top1. Introduction
The Connected Vehicle Technology which is also known as Vehicle Infrastructure Integration assimilates different technologies on-board like GPS navigation, automobile sensors, etc. to induce ability in a vehicle for the detection of any threat or risk on a roadway and interconnect over the wireless network for the transmission of information which includes permissions, forewarns and threats. In this evolving world, the transmission of this critical data can be attained using IoT or in precise terms by the IoV (Internet of Vehicle) which is a subset of IoT (Mahmood, 2020). There are some prominent categories of IoT wireless technology which include LPWAN, Cellular, Zigbee, BLE, WIFI, RFID (Al-Sarawi et al., 2017), and specifically, for the CAV technology cellular and WIFI is most reliable for stability. As the power consumption of cellular networks (3G/4G/5G) is too high (Arnold et al., 2010), thus not sustainable for the majority of IoT applications which are powered by battery-operated sensor networks but they are appropriate for CAV technology. The automation of the vehicle is classified into 5 categories: Level 1-Driver Assisted; Level 2-Partial Automation; Level 3- Conditional Automation; Level 4-High Automation and Level 5-Full Automation (Harner, 2020).
The transmission of data between the vehicles needs to be in real-time which can be attained via IoV (Gerla et al., 2014), which is a distributed network that upkeeps the use of data generated by connected cars and Vehicle Adhoc Networks (VANETs) (Golestan et al., 2016). VANETs are the subclass of Mobile Adhoc Networks (MANETs) with the major difference of mobile units being as vehicles and other several reasons such as network topology, mobility patterns, demographics, traffic patterns at different times of the day (Tonguz et al., 2007). For the vehicle units to exchange data in real-time, the network layers such as the Physical layer, MAC layer, and Application layer is developed suitably.
The leading motivation of this research study is the accidents or collisions caused due to unguided overtaking, lane shifting, and congestion triggered due to the maintenance of unsafe distance between vehicles. Human errors regarding decisions are among the prominent causes of frontal collision accidents. The factors affecting the driver during the overtaking maneuvers are essential. The research conducted recently by Figueira & Larocca regarding traffic psychology and behavior (2020) has concluded that the effect of the speed of an impeding vehicle is greater as compared to the speed of the vehicle that has to overtake because the passing sight distance completely depends upon the impeding vehicle. Thus, increasing the chances of accidents related to overtaking and lane changing. Not only accidents but traffic congestion have continued to upsurge over the past decade. According to traffic index ranking by TomTom, 239 of the cities are reporting increased levels of congestion between 2018 and 2019. According to study 71 percent extra travel time is experienced in Bengaluru in India and Manila in the Philippines which recorded the joint-highest congestion level. The traffic congestion does not only waste the time but also has a psychological effect on the drivers. The capital of Columbia i.e. Bogota is at the third position in traffic congestion estimating 68 percent congestion while another Indian city i.e. Mumbai comes fourth with 65 percent congestion. Word’s top 10 list of gridlocked cities comprises of four cities of India, only. In Russia, its Moscow with the densest traffic, where travel time is increased by 59 percent due to traffic congestion. Los Angeles in the USA has the nastiest congestion levels but yet the travel time is only increased by 42 percent much less than the cities described in this section.