Comparative Study on Design Of AI-Based Communication Protocol For VANET
Prerna Patankar, Sanjay Dorle, Nikhil Wyawahare, Laxman Thakre
Abstract
Vehicular Ad Hoc Network (VANET) is an arising area of research and has been encouraging compelling study over recent years due to its action in crafting an Intelligent Transportation System (ITS). Which involves vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X), etc. This supports message flow and is also supported by wireless access technology. VANET is a member of MANET which is known as Mobile Ad Hoc Network which is a self-contained system with a group of automobiles or vehicles which are capable of short-range communication (SRC) using AI. Artificial intelligence has been comprehensively used to foster conventional measurements pushed. This paper, represent a brief review of most significant position based unicast routing protocols designed for vehicle-to-vehicle communications in the urban environment. We provide them with their working features for exchanging information between vehicular nodes. We describe their pros and cons. This study also provides a comparison of the vehicle-to-vehicle communication-based routing protocols. The comparative study is based on some significant factors such as mobility, traffic density, forwarding techniques and method of junction selection mechanism, and strategy used to handle local optimum situation. It also provides the simulation-based study of existing dynamic junction selection routing protocols and a static junction selection routing protocol. It provides a profound insight into the routing techniques suggested in this area and the most valuable solutions to advance VANETs. Qualities of VANET make directing a hard endeavour for analysts. Likewise, the analyst presents a major difference of the vehicle-to-vehicle (V2V) verbal trade based steering conventions. Artificial Intelligence (AI) has been widely used to enhance traditional data-driven systems in particular area of analytic scanning. Vehicle-to-vehicle (V2V) communication with AI can accumulate the awareness from assorted resources, can increase the driver’s idea, and hoping to avoid the accidents on the roads.