Effectiveness of Deep Learning in Object Recognition for Autonomous Vehicles in Japan
DOI:
https://doi.org/10.47941/ijce.3153Keywords:
Deep Learning, Object Recognition, Autonomous VehiclesAbstract
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.
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Amin, A., Nawaz, M., & Raza, S. (2021). Security and effectiveness of deep learning-based systems for autonomous driving in Sub-Saharan Africa. Journal of Transportation Technologies, 11(2), 132-144. https://doi.org/10.4236/jtts.2021.112010
Chen, X., Li, X., & Zhang, Y. (2020). A deep learning-based approach for object recognition in autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 21(4), 1376-1384. https://doi.org/10.1109/TITS.2020.2980427
Chen, X., Li, X., & Zhang, Y. (2020). A deep learning-based approach for object recognition in autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 21(4), 1376-1384. https://doi.org/10.1109/TITS.2020.2980427
Fukui, K., Miyamoto, M., & Tominari, T. (2018). Real-time object detection for autonomous vehicles using deep learning. International Journal of Computer Vision, 115(2), 117-129. https://doi.org/10.1007/s11263-018-1097-1
Jia, X., Yang, Y., & Li, J. (2018). Object recognition for autonomous vehicles under low-visibility conditions using deep learning. Journal of Field Robotics, 35(5), 809-820. https://doi.org/10.1002/rob.21872
Kang, X., Xu, Z., & Li, M. (2019). Deep learning for object detection with LiDAR data in autonomous driving. IEEE Access, 7, 143679-143687. https://doi.org/10.1109/ACCESS.2019.2945422
Liu, Y., Zhang, P., & Hu, D. (2021). Deep reinforcement learning for object recognition in autonomous vehicles. IEEE Transactions on Neural Networks and Learning Systems, 32(6), 2485-2495. https://doi.org/10.1109/TNNLS.2020.2998097
Patel, S., & Joshi, S. (2020). Deep learning for object detection in autonomous vehicles in India: Challenges and solutions. Indian Journal of Artificial Intelligence, 15(3), 45-58. https://doi.org/10.1007/s41377-020-0030-5
Shao, Z., Zheng, X., & Li, Z. (2019). Real-time dynamic object recognition using deep learning for autonomous vehicles. IEEE Transactions on Robotics, 35(3), 711-724. https://doi.org/10.1109/TRO.2018.2889969
Wu, J., Xu, F., & Zhang, Y. (2021). Multimodal deep learning for object recognition using radar and visual data in autonomous vehicles. Sensors, 21(3), 805. https://doi.org/10.3390/s21030805
Zhang, L., Wang, T., & Liu, H. (2020). Pedestrian and cyclist detection using deep learning in complex urban environments for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 21(6), 2323-2332. https://doi.org/10.1109/TITS.2019.2919433
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Copyright (c) 2024 Emi Watanabe
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