STATUS AND CONDITIONS OF USE OF ROAD SIGNS IN THREE TYPES OF SMART CARS
Keywords:
traffic sign recognition, TSR, artificial intelligence, computer vision, deep learning, CNN, ADAS, autonomous transportation, YOLO, traffic safety.Abstract
This article analyzes the theoretical foundations and practical applications of Traffic Sign Recognition (TSR) technologies in intelligent transportation systems. The operating principles of TSR systems are examined across three types of intelligent vehicles: those with in-cabin additional electronics, vehicles equipped with electronic driver assistance systems, and autonomous vehicles.
References
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint arXiv:1804.02767. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press. 3. Ciresan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012). Multi-column deep neural network for traffic sign classification. Neural Networks, 32, 333–338. 4. Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2012). Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, 32, 323–332. 5. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. 6. Szeliski, R. (2010). Computer Vision: Algorithms and Applications. London: Springer. 7. Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing (3rd ed.). Pearson Education.
