Machine Learning and Computer Vision Techniques in Self-driving Cars

Abstract

This study explores the remarkable advancements in self-driving vehicles achieved through the application of computer vision and machine learning techniques. We examine various algorithms designed for critical functions, such as object detection, image segmentation, behavior prediction, and adaptive learning, which are all integral components of autonomous driving systems. Our research highlights key performance metrics, emphasizing accuracy, efficiency, and safety. Simulated environments and real-world testing are essential for validating the effectiveness of these methodologies.
Our findings underscore the transformative potential of self-driving technology in enhancing transportation safety and its far-reaching effect on numerous industries. Notably, self-driving cars demonstrate the ability to reduce traffic accidents and improve traffic flow, which can lead to substantial economic and social benefits. Moreover, we discuss future research avenues, including the enhancement of system robustness and safety measures, the improvement of human–AI interaction, and the utilization of edge computing and edge AI. We also address the ethical and regulatory challenges associated with the widespread adoption of autonomous vehicles.
Our comprehensive analysis indicates that self-driving technology is poised to revolutionize the transportation sector, offering safer, more efficient, and more accessible mobility solutions. As technology continues to evolve, ongoing research and development will be crucial in overcoming current limitations and realizing the full potential of autonomous driving systems.