Applications and Challenges of Computer Vision in Autonomous Driving

Abstract

Abstract The rapid evolution of autonomous driving technology has spurred deeper exploration into its multifaceted domains. This paper focuses on enhancing the intelligence and safety of autonomous driving systems through the application of computer vision. Specifically, the integration of EIoU loss and Varifocal Loss functions within the YOLOv5 algorithm facilitates the training of higher-quality samples at reduced costs. This enhanced YOLOv5 algorithm is adept at detecting traffic entities and incorporates the Matrix NMS algorithm to further refine the loss function adjustments. Moreover, this study employs the proximal policy optimization algorithm to construct a vehicular behavioral decision model for autonomous driving. This model utilizes cubic polynomial equations to depict lane-changing trajectories and introduces a safety distance rule to mitigate collision risks during autonomous maneuvers. Comparative analyses reveal that the modified YOLOv5 algorithm surpasses its predecessors—YOLOv3 and YOLOv4—in terms of detection accuracy and processing speed. Notably, the improved YOLOv5 algorithm exhibits lower network loss values than its original version, achieving faster and more stable convergence. In practical applications, the algorithm successfully identified and labeled 20,025 intelligent vehicular targets, with initial testing accuracy reaching 96.12%. This accuracy improved to 96.36% following the application of EIoU adaptive tuning to reduce risk-free class loss, and further fine-tuning elevated the accuracy to 96.54%. The integration of computer vision into autonomous driving technology thus holds significant promise for advancing the field.