Predictive Wildlife Collision Prevention using Deep Learning

Abstract: Animal vehicle collisions are a big problem on highways and forest roads people get hurt animals die and cars end up damaged. Most current systems just spot animals and throw out basic alerts but they do not actually think ahead about where the animal might go or how likely a crash is. That is where this paper steps in. We Introduce Preventing and Predictive Wildlife Collision using Deep Learning a smarter framework focused on stopping accidents before they even happen. Here is how it works means we use YOLOv8 to spot animals and DeepSORT to keep track of where they are moving and LSTM to predict what they will do next especially if they might try to cross the road. The system looks at movement patterns and figures out how risky a potential collision is and quickly sends warning alerts to nearby cars using MQTT based communication by mixing animal detection future prediction and smart alerts and our approach brings better safety to the road and actually tackles the problem of animal vehicle collisions heads on.