Object Detection for Driving Assistance in Autonomous Defence Vehicle

Autonomous vehicles are robotic systems capable of unmanned navigation in unknown environments. Recent advancements have enhanced applications in both civilian and military domains improving driver-assistance systems and unmanned military drones. From a Computer Science perspective autonomous vehicles drive progress in machine learning computer vision sensor fusion and intelligent decision-making. This research explores perception technologies in autonomous driving including speed selection object detection and terrain-based navigation. The system operates without manual engineering or supervised learning enabling self-training. Accurate perception is critical as rough terrain detection at high speeds demands greater precision than obstacle avoidance. Additionally the identification of stationary and moving objects enhances adaptive cruise control pedestrian detection and parking assistance improving both safety and efficiency in autonomous vehicle systems.