№2, 2024
INTEGRATION OF MACHINE LEARNING-BASED DETECTION SYSTEMS INTO AUTONOMOUS VEHICLES
The incorporation of machine learning (ML) driven detecting systems into autonomous vehicles (AVs) signifies a revolutionary advancement in contemporary transportation. The present study investigates the use of ML systems, namely in the domains of traffic sign identification, pedestrian detection, and obstacle resolution. The work deals with a review of the technical advances in neural networks concerning their capability for real-time data processing for accurate and reliable navigation. Further, this paper points out that the challenges to be faced during the implementation of these systems include requirements for data management, high-performance computing, and exploitation of cloud technologies for scalable solutions. The results indicate that machine learning-based detection strategies greatly enhance autonomous vehicle performance and safety, while emphasising persistent issues concerning security and legal frameworks. This paper highlights the significance of ongoing technological advancements in machine learning and its influence on the development of autonomous driving (pp.24-31).
Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L., Berriel, R., Paixão, T. M., Mutz, F., De Paula Veronese, L., Oliveira-Santos, T., & De Souza, A. F. (2021). Self-driving cars: A survey. Expert Systems with Applications, 165, 113816. https://doi.org/10.1016/j.eswa.2020.113816
Bansal, M., Krizhevsky, A., & Ogale, A. (2019). ChauffeurNet: Learning to drive by imitating the best and synthesizing the worst. arXiv preprint arXiv:1812.03079
Bojarski, M., Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., & Zhang, X. (2016). End to End Learning for Self-Driving Cars. arXiv preprint arXiv:1604.07316
Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015). DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving. In IEEE International Conference on Computer Vision (pp. 2722-2730). https://doi.org/10.1109/ICCV.2015.312
Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017). Multi-view 3D object detection network for autonomous driving. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1907-1915). https://doi.org/10.1109/CVPR.2017.691
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 37(3), 362-386. https://doi.org/10.1002/rob.21918
Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3354-3361). https://doi.org/10.1109/CVPR.2012.6248074
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
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020). Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art. Foundations and Trends® in Computer Graphics and Vision, 12(1–3), 308 p.
Kiran, B. R., & Sathyamoorthy, D. (2021). Deep reinforcement learning for autonomous driving: A survey. arXiv preprint arXiv:2002.00444
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Neuhold, G., Ollmann, T., Rota Bulo, S., & Kontschieder, P. (2017). The Mapillary Vistas dataset for semantic understanding of street scenes. In IEEE International Conference on Computer Vision (ICCV) (pp. 5000-5009). https://doi.org/10.1109/ICCV.2017.534
Shalev-Shwartz, S., Shammah, S., & Amnon, S. (2016). Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020). A survey of autonomous driving: Common practices and emerging technologies. IEEE Access, 8, 58443-58469. https://doi.org/10.1109/ACCESS.2020.2983149
