A Thorough Review of Vehicle Detection and Distance Estimation Using Deep Learning in Autonomous Cars

Abstract

Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle detection and distance calculation. Deep Learning Networks (DLNs) have emerged as powerful tools to address this challenge, offering remarkable capabilities in accurately detecting and estimating vehicle positions. This study comprehensively reviews DLN applications for vehicle detection and distance estimation. It examines prominent DLN models such as YOLO, R-CNN, and SSD, evaluating their performance on widely used datasets such as KITTI, PASCAL VOC, and COCO. Analysis results indicate that YOLOv5, developed by Farid et al. achieves the highest accuracy level with a mAP (mean Average Precision) of 99.92%. Yang et al. showcased that YOLOv5 performs exceptionally in detection and distance estimation tasks, with a mAP of 96.4% and a low mean relative error (MRE) of 10.81% for distance estimation. These achievements highlight the potential of DLNs to enhance the accuracy and reliability of vehicle detection systems in autonomous vehicles. The study also emphasizes the importance of backbone architectures like DarkNet 53 and ResNet in determining model efficiency. The choice of the appropriate model depends on the specific task requirements, with some models prioritizing real-time detection and others prioritizing accuracy. In conclusion, developing DLN-based methods is crucial in advancing autonomous vehicle technology. Research and development remain crucial in ensuring road safety and efficiency as autonomous vehicles become more common in transportation systems.

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