Machine Learning based Vehicle-to-Infrastructure Communication in VANETs

Vehicular network plays a major role in understanding the detail study of vehicle communications. Multiple vehicles in local communication range need to exchange the safety and infotainment information via common roadside infrastructure in Vehicular Ad hoc Networks (VANETs). Vehicle-to-Infrastructure (V2I) communication model help to improve the efficiency of intelligent transport system by providing safety warnings and reducing vehicle collisions. Machine learning is an artificial intelligence component that gives the machine an ability to automatically learn without being expressly trained to improve from experience. Since VANET is imprecise and uncertain in nature, Machine Learning (ML) and Software Agents (SAs) combining approaches resolve the issues of V2I communication challenges in VANETs. This paper proposes ML based V2I Communication in VANETs using software agent approach. The proposed agent-based model is made up of both static and mobile agents. Proposed model executes decision tree algorithm and Q-Learning algorithm to identify the event as non critical or critical and the destination vehicle respectively to improve bandwidth utilization, packet delivery ratio and end-to-end delay.