Effectiveness of Deep Learning in Object Recognition for Autonomous Vehicles in Japan

Purpose: To aim of the study was to analyze the effectiveness of deep learning in object recognition for autonomous vehicles in Japan. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Deep learning has proven effective in object recognition for autonomous vehicles in Japan, particularly through advanced models like CNNs and YOLO. These technologies show high accuracy in detecting pedestrians, vehicles, and other objects, even in complex environments. Integrating sensor fusion (LiDAR, radar, cameras) enhances reliability in crowded urban areas. However, challenges remain, including data annotation, real-world conditions like narrow streets, and regulatory concerns. Despite these, ongoing advancements and collaborations suggest promising prospects for the future of autonomous vehicles in Japan. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), diffusion of innovations (DOI) theory, systems theory may be used to anchor future studies on the effectiveness of deep learning in object recognition for autonomous vehicles in Japan. Practitioners should focus on improving the quality of labeled data for training purposes and employing transfer learning techniques to make models more adaptable to various situations. From a policy perspective, governments should establish clear safety standards and guidelines for the deployment of deep learning-based object recognition systems in autonomous vehicles.