Development and Execution of an AI-Driven Adaptive Cruise Control with Collision Detection and Speed Locking Mechanism

By reducing the need for constant driver intervention, adaptive cruise control (ACC) systems increase driving comfort and safety. This paper introduces an AI-based ACC system that combines dynamic speed adjustment and real-time obstacle detection. The system, which is based on YOLOv3 object detection and voice recognition technology, allows the car to respond to its surroundings and autonomously maintain a predetermined speed. To prevent collisions, the system applies the brakes or lowers speed when it detects obstacles. For BLDC motor control, the architecture combines mathematical modelling, feature extraction, and image processing. Reinforcement learning with real-time feedback optimizes speed regulation. High detection accuracy 91.4%, low response latency 0.82s, and effective speed locking under various road conditions are demonstrated by the experimental results. The system presented provides an intelligent, safe, and intuitive cruise control system, advancing semi-autonomous vehicle technologies. Future improvements and limitations are discussed to enhance real-world applicability.