A Deep Review and Analysis of Data Exchange in Vehicle-to-Vehicle Communications Systems: Coherent Taxonomy, Challenges, Motivations, Recommendations, Substantial Analysis and Future Directions

Abstract

Data exchange in Vehicle-to-vehicle (V2V) communications systems is a field that requires automated solutions, tools and methods and the capability to facilitate early detection and even a prediction. Many studies have focused on V2V system and its classification to improve road safety, reduce traffic congestion and help streamline the vehicle flow on the road. This study aims to review and analyse literature related to data exchange in V2V communications systems. The factors considered to improve the understanding of the field's various contextual aspects were derived from published studies. We systematically searched all articles about the classification and detection of data exchange in vehicles, as well as their evaluation. Three main databases, namely, ScienceDirect, Web of Science and IEEE Xplore from 2008 to 2018, were used. These indices were considered sufficiently extensive to encompass our literature. On the basis of our inclusion and exclusion criteria, 140 articles were selected. Most articles (53/140) are studies conducted in a V2V communication system; a number of papers (51/140) covered the actual attempts to develop V2V communications; and few papers (18/140) comprised framework proposals and architectures. The last portion (18/140) of articles presented review and survey articles. V2V collision avoidance system, which is a field requiring automated solutions, tools and methods, entails the capability to facilitate early detection. Several studies have been performed on the automatic detection of V2V and their subtypes to promote accurate detection. The basic characteristics of this emerging field are identified from the aspects of motivations, open challenges that impede the technology's utility, authors' recommendations and substantial analysis of the previous studies are discussed based on seven aspect (devices, number of scenario, test location, types of sensors, number of vehicle, evaluation techniques used and types of software). A propose research methodology as new direction is provided to solve the gaps identified in the analysis. This methodology consists of four phases; investigation, develop a hardware system, study and analysis, and evaluation phases. However, research areas on V2V communication with the scope of data exchange are varied. This systematic review is expected to open opportunities for researchers and encourage them to work on the identified gaps.

Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information

“…First, a systematic review protocol on the V2V communication system is scrutinized. A critical analysis was then undertaken to figure out the gab and the main challenges in the V2V context presented in [29]. Second, research was conducted on articles related to driving behavior techniques in the vehicular communication network.…”

“…According to this analysis's assessment approach, two key categories were established from previous works: simulations and real time. A deep analysis of both sections' accuracy is conducted in [29] focused on the data exchange from previous articles to identify the gap in future directions that target data exchange in the V2V framework by conducting a deep review of the accuracy of both parts. Furthermore, a technology used in sharing information between moving vehicles based on driving behavior to create a collision alert system was developed, which achieved a higher accuracy rate than the other approaches by combining various types of scenarios, vehicle speed, and vehicle number, as shown in Figure 3.…”

A Reinforcement Learning Framework for Video Frame-Based Autonomous Car-Following

“…These analytical models are usually based on deterministic approaches requiring as inputs mathematical parameters reflecting the vehicle dynamics. The models usually require a perfect knowledge about the activity of the leader vehicle, which is practical for real-time decision making as vehicles need to continuously exchange data and this might be subject to connectivity issues and latency effect [19].…”

Multiagent Model of Rail-Road Intersection with Connected Vehicles

“…we can distinguish certain scenarios of vehicular communication such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), vehicle-to-cloud (V2C), or even everything-to-everything (X2X). The study [7] aims to review and analyse literature related to data exchange in V2V communications systems. The paper [8] deals with concepts, technologies and architectures in the Internet of Vehicles.…”

“…The study [7] aims to review and analyse literature related to data exchange in V2V communications systems. The paper [8] deals with concepts, technologies and architectures in the Internet of Vehicles. Transformation of cooperative communication technology to vehicular networking has brought a new paradigm known as a Cooperative Vehicular Networking (CVN) which has emerged as a result of advancements in wireless technologies, ad-hoc networking, and the automobile industry.…”