Yolov7-based Brake Light Detection Model for Avoiding Rear-End Collisions

Protection against When it comes to driver assistance systems and high-tech driverless vehicles, rear-end collisions are crucial. The most common location for vehicle crashes to occur is on the road. Brake light detection is crucial in applications like autonomous driving to avoid rear-end collisions. In this paper, we present a framework for employing deep learning (DL) to detect the presence of brake lights on approaching vehicles. To successfully increase performance in predicting rear-end collisions, the system employs the Yolov7 brake light detection model for multi-class detection. The experimental result shows that our system is better. In the near future, will implement an artificial intelligence (AI)-based model that provides a more sophisticated driver support system.