Animal Accident Prevention System using Machine Learning and IoT

This research work presents an innovative approach to Animal Accident Prevention System by combining Internet of Things (IoT) and Machine Learning (ML) technologies. The proposed system gathers real-time data on traffic flow, vehicle dynamics, and environmenta conditions by integrating sensors in vehicles. In addition to identifying high-risk situations and predicting possible collision scenarios, advanced machine learning model analyze this data to compute real-time distances between objects and vehicles. Furthermore, the proposed system is designed to expedite response and assistance in the unfortunate event of an accident by initiating emergency messages to relevant individuals, such as emergency services and designated contacts. By providing proactive accident prevention strategies, timely emergency notifications for minimizing the situation, and informed decision-making, this AAPS seeks to significantly reduce accidents through continuous learning and adaptive capabilities.