Paper Title
Object Detection and Prevention Control for Autonomous Car

Abstract
The rapid advancements in autonomous vehicle technology have ushered in a new era of transportation, promising increased safety and efficiency. This project aims to develop a robust system for detecting and preventing obstacles in the path of autonomous vehicles. It integrates state-of-the-art computer vision techniques, deep learning algorithms, and sensor fusion to enhance perception. Convolutional Neural Networks (CNNs) analyze data from LiDAR, radar, and cameras for real-time object detection. Advanced decision-making algorithms adjust speed, trajectory, or trigger emergency braking upon detection of obstacles. Vehicle-to-Everything (V2X) communication enhances situational awareness and collaborative obstacle avoidance. The system adapts to diverse environmental conditions and continuously improves through machine learning. Successful implementation will advance autonomous vehicle safety, enabling them to navigate complex environments alongside human-driven vehicles, reducing accidents, and ensuring safer transportation