Advanced Self-Driving Vehicle Model for Complex Road Navigation Using Integrated Image Processing and Sensor Fusion

This paper presents a sophisticated self-driving vehicle (SDV) model that addresses the challenges of navigating complex road networks characterized by high traffic and unpredictable environments. The model integrates state-of-the-art image processing techniques with advanced sensor fusion, utilizing EfficientDet D0 and Haar Cascade object detection models to identify obstacles, road signs, and traffic signals accurately. The integration of data from cameras and ultrasonic sensors enables the creation of a precise 2D map of the vehicle's surroundings, which, combined with a robust decision-making algorithm, allows for optimal performance in challenging traffic scenarios. The SDV prototype was tested extensively in a custom-built artificial environment, where it demonstrated its ability to handle various real-world scenarios, including lane detection, obstacle avoidance, and decision-making in the presence of stationary obstacles and heavy traffic. The experimental results confirm the model's effectiveness in enhancing SDV capabilities, paving the way for safer and more efficient autonomous transportation systems. It is found from our experiments that the average precision for obstacle detection models is 0.729, the average recall is 0.758, and the prototype's ability to process at 24 frames per second highlights the efficiency and accuracy of our proposed model.