Real-Time Adaptive Cruise Control Architecture for Autonomous Vehicles Using Raspberry Pi

The advancement of autonomous vehicle technologies has intensified the demand for intelligent driver-assistance systems capable of enhancing road safety and traffic efficiency. Adaptive Cruise Control (ACC) plays a critical role in autonomous mobility by automatically regulating vehicle speed while maintaining a safe distance from preceding vehicles. This research presents the design and implementation of a Real-Time Adaptive Cruise Control Architecture for Autonomous Vehicles Using Raspberry Pi, focusing on a low-cost, embedded, and sensor-integrated solution. The proposed system employs Raspberry Pi as the central processing unit due to its computational capability, GPIO interfacing flexibility, and suitability for edge-based real-time applications. Distance measurement sensors such as ultrasonic or LiDAR modules are integrated to continuously monitor the proximity of leading vehicles. A camera module may also be incorporated for object detection and lane awareness. Sensor data are processed in real time using control algorithms that dynamically adjust throttle and braking mechanisms to maintain predefined safe distance thresholds. The architecture follows a closed-loop feedback control strategy, ensuring continuous monitoring, decision-making, and actuation. Experimental validation demonstrates that the system effectively adapts vehicle speed under varying traffic conditions with minimal latency. The proposed framework highlights the feasibility of implementing intelligent cruise control using cost-effective embedded hardware while ensuring scalability for future AI-driven enhancements. The developed architecture contributes toward safer, efficient, and intelligent autonomous transportation systems.