IoT-based V2X communication for real-time dynamic obstacle prediction and adaptive RRT path planning

This paper proposes an IoT-based vehicle–road cooperative communication system to address real-time collision avoidance and path planning challenges in dynamic traffic environments. Traditional path planning methods often struggle with dynamic obstacles, particularly when vehicles and pedestrians frequently change positions. To overcome these limitations, we introduce the GR-RRT model, which integrates V2X communication, a GRU-based dynamic obstacle prediction module, and an adaptive RRT path planning algorithm. This integration allows for enhanced obstacle trajectory prediction and real-time collision avoidance. Experimental results show that the GR-RRT model significantly outperforms traditional methods in terms of trajectory prediction accuracy, collision avoidance success rate, and path planning efficiency. The model exhibits particularly strong performance in multi-vehicle cooperation and dynamic environments, demonstrating its potential for improving safety and efficiency in future intelligent transportation systems.