Computer Vision Applications in Autonomous Vehicles: A Study of Techniques and Challenges
Keywords:
Autonomous vehicles, Computer vision, Object detection, Lane detection, Deep learning, SLAM (Simultaneous Localization and Mapping), Real-time processing, Environmental variability, Autonomous navigationAbstract
The rapid advancement of autonomous vehicles has underscored the critical role of computer vision in enabling safe and efficient self-driving systems. Computer vision applications in autonomous vehicles primarily involve real-time detection, recognition, and tracking of objects in dynamic environments. This paper provides a comprehensive overview of key techniques used in computer vision for autonomous vehicles, including image processing, object detection, lane detection, and simultaneous localization and mapping (SLAM). We explore the integration of machine learning and deep learning models, such as convolutional neural networks (CNNs), to enhance feature extraction and pattern recognition capabilities. Furthermore, the paper delves into the challenges faced in computer vision for autonomous vehicles, such as handling environmental variability, ensuring accuracy in adverse weather conditions, and managing computational efficiency. By analyzing existing methodologies and identifying persistent challenges, this study aims to inform future developments in autonomous vehicle vision systems and highlight areas for further research.
References
Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015). DeepDriving: Learning affordance for direct perception in autonomous driving. Proceedings of the IEEE International Conference on Computer Vision, 2722-2730.
Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3354-3361.
Hadsell, R., Erkan, A., Sermanet, P., Scoffier, M., Grimes, M., Ben, J., ... & LeCun, Y. (2009). Learning long-range vision for autonomous off-road driving. Journal of Field Robotics, 26(2), 120-144.
Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., ... & Ng, A. Y. (2015). An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Levinson, J., Montemerlo, M., & Thrun, S. (2007). Map-based precision vehicle localization in urban environments. Robotics: Science and Systems III, 121-128.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. European Conference on Computer Vision, 21-37.
Meyer, G., & Beiker, S. (2016). Road Vehicle Automation 3. Springer.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779-788.
Shladover, S. E. (2018). Connected and automated vehicle systems: Introduction and overview. Journal of Intelligent Transportation Systems, 22(3), 190-200.
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Copyright (c) 2020 KUNAL KUMAR (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
