RF Assisted Autonomous Vehicle Navigation and Collision Avoidance System

This paper presented the design, implementation, and experimental validation of an RF-Assisted Autonomous Vehicle Navigation and Collision Avoidance System. By combining 433 MHz RF-based V2V communication with IR-based short-range obstacle detection on an Arduino Uno platform, the system achieved over 90% obstacle detection accuracy, sub-0.5-second response times, and a 94% navigation success rate across varied environmental conditions. The RF communication layer extended the system's collision-awareness beyond the line-of-sight range of onboard sensors, demonstrating a clear advantage over conventional sensor-only architectures. Future work will explore LiDAR or ultrasonic sensor integration to extend detection range, mesh-network RF protocols for dense multi-vehicle fleets, AI-based predictive path planning using machine learning models, GPS integration for long-range navigation, and intelligent power management for extended battery life. Large-scale outdoor trials across diverse weather and road conditions will further validate system reliability for real-world deployment in smart transportation infrastructure.

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