Eyes on the Road: A Comprehensive Review of Object Detection and Tracking in Autonomous Vehicles

This review explores recent advancements in object detection and tracking techniques designed for autonomous vehicles (AVs). It focuses on cutting-edge algorithms like YOLOv4, YOLOv5, and YOLOv7, which tackle key challenges such as navigating adverse weather, meeting real-time processing demands, and handling multi-object tracking. The study highlights innovations like lightweight architectures, attention mechanisms, and domain adaptation techniques that improve detection accuracy, even in complex scenarios. Diverse datasets and standardized evaluation metrics are emphasized as vital for consistent performance measurement. The review also explores deep learning approaches, including model compression, to enhance efficiency. Key areas of focus include integrating object detection with tracking, optimizing algorithms for edge devices, and ensuring real-time capabilities. These insights contribute to building safer, smarter, and more reliable autonomous driving technologies.