Vehicle Collision Detection and Alert System using Data Science

E. V. Hemanth Nag, Maulik Dev, R. Beaulah Jeyavathana

Abstract: Vehicle collision detection is an essential aspect of modern transportation systems that aims to reduce the frequency and severity of accidents on the roads. The primary goal of collision detection is to identify potential collisions between vehicles and other objects, including pedestrians, bicycles, and stationary obstacles. The detection process involves collecting and analyzing data from various sources, such as cameras to determine the speed, and trajectory of vehicles in real - time. Machine learning algorithms are often used to analyze this data and identify potential collision scenarios, alerting drivers or autonomous systems to take appropriate actions to avoid accidents. Effective collision detection systems can help reduce the number of fatalities and injuries on the roads, improving overall road safety and promoting sustainable transportation.

Keywords: Accident Detection, YOLO, Dashboard Cameras, Bounding Box method, Machine Learning models, CNN, Neural Network