Applications and Challenges of Computer Vision in Autonomous Driving

[1] Yun, H., & Park, D. (2021). Virtualization of self-driving algorithms by interoperating embedded controllers on a game engine for a digital twining autonomous vehicle. Electronics. Search in Google Scholar

[2] Gan, Y., Wu, H., Xiao, N., Lin, L., & Li, G. (2019). Cross-modal attentional context learning for rgb-d object detection. IEEE Transactions on Image Processing. Search in Google Scholar

[3] Choi, H., Ahn, H., Joonmo, K., & Jeon, M. (2020). Adfnet: accumulated decoder features for real-time semantic segmentation. IET Computer Vision, 14(8), -. Search in Google Scholar

[4] Collin, A., Siddiqi, A., Imanishi, Y., Rebentisch, E., & Weck, O. L. (2019). Autonomous driving systems hardware and software architecture exploration: optimizing latency and cost under safety constraints. Systems Engineering. Search in Google Scholar

[5] Birnbacher, D., & Birnbacher, W. (2017). Fully autonomous driving: where technology and ethics meet. IEEE Intelligent Systems, 32(5), 3–4. Search in Google Scholar

[6] Fang, Z., & Lopez, A. M. (2020). Intention recognition of pedestrians and cyclists by 2d pose estimation. IEEE transactions on intelligent transportation systems (11), 21. Search in Google Scholar

[7] Wang, G., Ren, S., & Wang, H. (2022). Unsupervised learning of optical flow with non-occlusion from geometry. IEEE transactions on intelligent transportation systems. Search in Google Scholar

[8] Peng, Y., Zhang, G., Shi, J., Xu, B., & Zheng, L. (2022). Srai-lstm: a social relation attention-based interaction-aware lstm for human trajectory prediction. Neurocomputing (Jun.14), 490. Search in Google Scholar

[9] Huang, K., Wen, M., Wang, C., & Ling, L. (2023). Fpdt: a multi-scale feature pyramidal object detection transformer. Journal of Applied Remote Sensing (2), 17. Search in Google Scholar

[10] Jain, D. K., Zhao, X., Gonzalez-Almagro, G., Gan, C., & Kotecha, K. (2023). Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes. Information Fusion. Search in Google Scholar

[11] Li, X., Zhang, S., Chen, X., Wang, Y., Fan, Z., & Pang, X., et al. (2023). Robustness of visual perception system in progressive challenging weather scenarios. Engineering Applications of Artificial Intelligence, 119, 105740-. Search in Google Scholar

[12] Alhaija, H. A., Mustikovela, S. K., Mescheder, L. M., Geiger, A., & Rother, C. (2018). Augmented reality meets computer vision. International Journal of Computer Vision. Search in Google Scholar

[13] Wu, Y., Feng, S., Huang, X., & Wu, Z. (2021). L4net: an anchor‐free generic object detector with attention mechanism for autonomous driving. IET Computer Vision, 15(1). Search in Google Scholar

[14] Liu, L., Lu, S., Zhong, R., Wu, B., Yao, Y., & Zhang, Q., et al. (2021). Computing systems for autonomous driving: state of the art and challenges. IEEE internet of things journal (8), 8. Search in Google Scholar

[15] Wang, J., Li, Y., Zhou, Z., Wang, C., Hou, Y., & Zhang, L., et al. (2023). When, where and how does it fail? a spatial-temporal visual analytics approach for interpretable object detection in autonomous driving. IEEE transactions on visualization and computer graphics (12), 29. Search in Google Scholar

[16] Zhou, Z., Akhtar, Z., Man, K. L., & Siddique, K. (2019). A deep learning platooning-based video information-sharing internet of things framework for autonomous driving systems. International Journal of Distributed Sensor Networks, 15. Search in Google Scholar

[17] Farag, W. (2019). Real-time detection of road lane-lines for autonomous driving. Recent Patents on Computer Science, 12(2). Search in Google Scholar

[18] Palafox, P. R., Betz, J., Nobis, F., Riedl, K., & Lienkamp, M. (2019). Semanticdepth: fusing semantic segmentation and monocular depth estimation for enabling autonomous driving in roads without lane lines. Sensors, 19(14), 3224-. Search in Google Scholar

[19] Bello, S. A., Yu, S., & Wang, C. (2020). Review: deep learning on 3d point clouds. Remote Sensing. Search in Google Scholar

[20] Zhang, H., Wang, K. F., & Wang, F. Y. (2017). Advances and perspectives on applications of deep learning in visual object detection. Zidonghua Xuebao/Acta Automatica Sinica, 43(8), 1289–1305. Search in Google Scholar

[21] Machado, F., Nieto, R., Fernandez-Conde, J., Lobato, D., & Canas, J. M. (2023). Vision-based robotics using open fpgas. Microprocessors and microsystems (Nov.), 103. Search in Google Scholar

[22] Castro, R. C., Inamasu, R. Y., & Da Silva, M. M. (2022). Accuracy assessment of a gps-based auto-guidance system in an agricultural vehicle using computational vision methods. International Journal of Heavy Vehicle Systems (IJHVS)(1), 29. Search in Google Scholar