Advances in Deep Learning-Based Object Detection and Tracking for Autonomous Driving: A Review and Future Directions

This comprehensive review investigates recent advancements in deep learning-based tracking and object detection for autonomous driving. Tracking and object detection are fundamental components of autonomous vehicles, essential for real-time perception and decision-making. This review highlights the significance of these technologies and provides an extensive analysis of various deep learning techniques, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their hybrid variants, which have shown promise in enhancing tracking and object detection accuracy and efficiency. It examines recent research endeavors, detailing methodologies, architectural innovations, and experimental findings, along with current challenges, including real-time processing demands, dataset diversity, and computational constraints. The review also outlines future research directions, such as multi-sensor fusion, attention mechanisms, and transfer learning, offering insights for researchers, engineers, and professionals in the autonomous driving field, enabling them to navigate the evolving landscape of deep learning-based object detection and tracking, and anticipate future advancements in this vital aspect of autonomous vehicle technology.