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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2021: 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) pp 91–100Cite as

Low-Cost Deep Learning-Based Prototype for Automatic Identification of Traffic Signs in Vehicles

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1401))

Abstract

The automotive industry has evolved in recent years with new driving assistance systems, which make it evolve towards autonomous driving, in which the figure of the driver becomes less relevant over time until it becomes unnecessary to have a person driving the vehicle. To continue with the development of autonomous cars, information surrounding the vehicle is necessary. As a contribution to this evolution towards autonomous driving, a low-cost prototype is presented, which can be installed in any type of car, capable of capturing images of driving employing a camera and processing information from traffic signs on the road. This information can be used as input for another system, in which, thanks to this already processed signal information, it can make other types of driving decisions. This prototype has been made using a set of 5 Deep Learning classifiers and implemented. As a result of the work, a prototype capable of detecting and classifying images with an accuracy of over 90% has been obtained. However, this prototype is not yet usable as it is not adapted to all environmental situations, for which future studies are proposed.

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References

  1. Cireşan, D., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012). https://doi.org/10.1016/j.neunet.2012.02.023

    Article  Google Scholar 

  2. Fairfield, N., Urmson, C.: Traffic light mapping and detection. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 5421–5426 (2011). https://doi.org/10.1109/ICRA.2011.5980164

  3. Geng, K., Dong, G., Yin, G., Hu, J.: Deep dual-modal traffic objects instance segmentation method using camera and LIDAR data for autonomous driving. Remote Sens. 12(20), 3274 (2020). https://doi.org/10.3390/rs12203274. https://www.mdpi.com/2072-4292/12/20/3274

  4. Heinzler, R., Schindler, P., Seekircher, J., Ritter, W., Stork, W.: Weather influence and classification with automotive Lidar sensors. In: Proceedings of the IEEE Intelligent Vehicles Symposium, June 2019, vol. 2019, pp. 1527–1534. Institute of Electrical and Electronics Engineers Inc. (2019). https://doi.org/10.1109/IVS.2019.8814205

  5. Jonsson, R., Kollmats, A.: Unintended lane departure prediction using neural networks. Master’s thesis in System, Control and Mechatronic/Complex Adaptive Systems. Ph.D. thesis, Chalmers, Gothernburg (2018)

    Google Scholar 

  6. Jung, J.W., Leu, V.Q., Do, T.D., Kim, E.K., Choi, H.H.: Adaptive PID speed control design for permanent magnet synchronous motor drives. IEEE Trans. Power Electron. 30(2), 900–908 (2015). https://doi.org/10.1109/TPEL.2014.2311462

    Article  Google Scholar 

  7. Kaempchen, N., Schiele, B., Dietmayer, K.: Situation assessment of an autonomous emergency brake for arbitrary vehicle-to-vehicle collision scenarios. IEEE Trans. Intell. Transp. Syst. 10(4), 678–687 (2009). https://doi.org/10.1109/TITS.2009.2026452

    Article  Google Scholar 

  8. Kumar, V.R., Klingner, M., Yogamani, S., Milz, S., Fingscheidt, T., Mäder, P.: SynDistNet: self-supervised monocular fisheye camera distance estimation synergized with semantic segmentation for autonomous driving. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 61–71 (2021)

    Google Scholar 

  9. Kumpakeaw, S., Dillmann, R.: Semantic road maps for autonomous vehicles. In: Berns, K., Luksch, T. (eds.) Autonome Mobile Systeme 2007. Informatik aktuell. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74764-2_32

  10. Li, D., Zhao, D., Zhang, Q., Chen, Y.: Reinforcement learning and deep learning based lateral control for autonomous driving (October 2018). http://arxiv.org/abs/1810.12778

  11. Li, P., Qin, T., Shen, S.: Stereo vision-based semantic 3D object and ego-motion tracking for autonomous driving. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds) Computer Vision, ECCV 2018. Lecture Notes in Computer Science, vol. 11206. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_40

  12. Liu, L., Li, H., Dai, Y., Pan, Q.: Robust and efficient relative pose with a multi-camera system for autonomous driving in highly dynamic environments. IEEE Trans. Intell. Transp. Syst. 19(8), 2432–2444 (2018). https://doi.org/10.1109/TITS.2017.2749409

    Article  Google Scholar 

  13. Liu, X., Jin, G., Wang, Y., Yin, C.: A deep learning-based approach to line crossing prediction for lane change maneuver of adjacent target vehicles. In: 2021 IEEE International Conference on Mechatronics (ICM), pp. 1–6. IEEE (March 2021). https://doi.org/10.1109/ICM46511.2021.9385665. https://ieeexplore.ieee.org/document/9385665/

  14. Maturana, D., Chou, P.W., Uenoyama, M., Scherer, S.: Real-time semantic mapping for autonomous off-road navigation. In: Hutter, M., Siegwart, R. (eds.) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol. 5. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67361-5_22

  15. Nie, L., Guan, J., Lu, C., Zheng, H., Yin, Z.: Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network. In: IET Intelligent Transport Systems. vol. 12, pp. 485–494. Institution of Engineering and Technology (August 2018). https://doi.org/10.1049/iet-its.2016.0293

  16. Rosén, E.: Autonomous emergency braking for vulnerable road users. In: IRCOBI Conference, pp. 618–627 (2013). http://www.ircobi.org/wordpress/downloads/irc13/pdf_files/71.pdf

  17. SAE International: Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. Technical report, On-Road Automated Driving (ORAD) committee (2014). https://doi.org/10.4271/J3016_201401. https://saemobilus.sae.org/content/j3016_201401

  18. Sharma, S., Tewolde, G., Kwon, J.: Behavioral cloning for lateral motion control of autonomous vehicles using deep learning. In: IEEE International Conference on Electro Information Technology, May 2018, vol. 2018, pp. 228–233. IEEE Computer Society (October 2018). https://doi.org/10.1109/EIT.2018.8500102

  19. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 International Joint Conference on Neural Networks, pp. 1453–1460. IEEE (July 2011). https://doi.org/10.1109/IJCNN.2011.6033395. http://ieeexplore.ieee.org/document/6033395/

  20. Yuan, Y., Zhang, J.: A novel initiative braking system with nondegraded fallback level for ADAS and autonomous driving. IEEE Trans. Ind. Electron. 67(6), 4360–4370 (2020). https://doi.org/10.1109/TIE.2019.2931279

    Article  Google Scholar 

  21. Zeng, Y., et al.: RT3D: real-time 3-D vehicle detection in LiDAR point cloud for autonomous driving. IEEE Robot. Autom. Lett. 3(4), 3434–3440 (2018). https://doi.org/10.1109/LRA.2018.2852843

    Article  Google Scholar 

  22. Zhou, L., Deng, Z.: Lidar and vision-based real-time traffic sign detection and recognition algorithm for intelligent vehicle. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 578–583 (2014). https://doi.org/10.1109/ITSC.2014.6957752

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Acknowledgments

This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R, PID2020-112726RB-I00.

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Authors and Affiliations

  1. Computer Science Department, University of Oviedo, Oviedo, Spain

    Enol García González, José R. Villar & Enrique de la Cal

Authors
  1. Enol García González
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  2. José R. Villar
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  3. Enrique de la Cal
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Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. BISITE Research Group, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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González, E.G., Villar, J.R., de la Cal, E. (2022). Low-Cost Deep Learning-Based Prototype for Automatic Identification of Traffic Signs in Vehicles. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_9

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