Cost Effective loT-Based Smart System for Avoidance of Obstacle and Intruder Detection

This study introduces a cost-effective integrated system that significantly enhances the safety and security of autonomous vehicles by merging obstacle avoidance and intru-sion detection functionalities through Internet of Things (loT) technologies. The system employs ultrasonic sensors for accurate obstacle detection, with an Arduino Uno and Raspberry Pi 3 handling data processing and control tasks. Facial recognition technology is incorporated to monitor and identify unauthorized individuals in real time. This integration allows the vehicle to autonomously navigate while also securing itself against potential intruders, all achieved with readily available and affordable components. The system demonstrates strong performance in detecting and avoiding obstacles in straightforward scenarios, although accuracy may diminish in more complex environments. The facial recognition component achieves consistent and re-liable results under controlled conditions. By combining these technologies in a budget-friendly solution, this research offers practical advancements in intelligent and secure autonomous vehicle systems, suitable for applications in various domains including robotics and smart vehicle technology.