Abstract
The future in which autonomous vehicles will become commonplace is very near. The biggest companies around the world are testing autonomous vehicles. But despite the rapid progress in this area, there are still unresolved technical problems that prevent the spreading of self-driving vehicles. As a result, we are still quite far from “self-driving” cars, even though marketing is the opposite of us. Of course, driver assistance technologies capable of keeping the lane, braking, and following the road rules (under human supervision) are entering the market thanks to Tesla.
Nowadays, there is a problem; an autonomous car may suddenly stop. Some colors cause panic in self-driving vehicles, becoming a safety threat. This research paper approached a model using a deep neural network that identifies color combinations to prevent panic in autonomous cars by combining outputs from event-based cameras. Finally, we show the advantages of using event-based vision, and this approach outperforms algorithms based on standard cameras.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmad, W., Sahil, S., Mughal, A.: Predicting solar intensity using cluster analysis. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawiński, B. (eds.) ICCCI 2018, Part I. LNCS (LNAI), vol. 11055, pp. 549–560. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98443-8_50
Antonio, J., Gutiérrez, T., Villalobos, P.Q.: Design and implementation of an automatic control system using motors and control sensors to stabilize a horizontal platform with a LiDAR sensor. Computación y Sistemas 26(1), 389–397 (2022). https://doi.org/10.13053/CyS-26-1-3906
Bekka, R., Kherbouche, S., Bouhissi, H.E.: Distraction detection to predict vehicle crashes: a deep learning approach. Computación y Sistemas 26(1), 373–387 (2022). https://doi.org/10.13053/CyS-26-1-3871
Campos, R.L., Perez, L.O.R., Carranza, J.M.: Following and overtaking: a policy for autonomous car driving. Computacion y Sistemas 24(3), 1149–1157 (2020). https://doi.org/10.13053/CYS-24-3-3475
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc. (2015)
Lugo Sánchez, O.E., Sossa, H., Zamora, E.: Robust place recognition using convolutional neural networks. Computacion y Sistemas 24(4), 1589–1605 (2020). https://doi.org/10.13053/CYS-24-4-3340
Mandal, G., Bhattacharya, D., De, P.: Real time vision based overtaking assistance system for drivers at night on two-lane single carriageway. Computacion y Sistemas 25(2), 403–416 (2021). https://doi.org/10.13053/CyS-25-2-3783
Maqueda, A.I., Loquercio, A., Gallego, G., Garcia, N., Scaramuzza, D.: Event-based vision meets deep learning on steering prediction for self-driving cars. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition June 2018, pp. 5419–5427 (2018). https://doi.org/10.1109/CVPR.2018.00568
Qassim, H., Verma, A., Feinzimer, D.: Compressed residual-VGG16 CNN model for big data places image recognition. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018 January 2018, pp. 169–175 (2018). https://doi.org/10.1109/CCWC.2018.8301729
Ramos, S., Gehrig, S., Pinggera, P., Franke, U., Rother, C.: Detecting unexpected obstacles for self-driving cars: fusing deep learning and geometric modeling. In: IEEE Intelligent Vehicles Symposium, Proceedings 2017-July–December, pp. 1025–1032 (2017). https://doi.org/10.1109/IVS.2017.7995849
Shim, I., et al.: Self-driving-car Boss. IEEE Trans. Intell. Transp. Syst. [11] 16(4), 1–15 (2015)
Yang, Z., Zhang, Y., Yu, J., Cai, J., Luo, J.: End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions. In: Proceedings - International Conference on Pattern Recognition 2018 August–January 2018), pp. 2289–2294 (2018). https://doi.org/10.1109/ICPR.2018.8546189
Ziyaden, A., Yelenov, A., Pak, A.: Long-context transformers: a survey. In: 2021 5th Scientific School Dynamics of Complex Networks and their Applications (DCNA), pp. 215–218 (2021). https://doi.org/10.1109/DCNA53427.2021.9587279
Acknowledgments
This research is conducted within the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan under the grant number AP09260670 “Development of methods and algorithms for augmentation of input data for modifying vector embeddings of words”.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Okimbek, S., Razak, N., Akhmetov, I., Pak, A. (2022). Avoiding Hazardous Color Combinations in Traffic Signs on STN Based Model for Autonomous Vehicles. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_64
Download citation
DOI: https://doi.org/10.1007/978-3-031-16014-1_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16013-4
Online ISBN: 978-3-031-16014-1
eBook Packages: Computer ScienceComputer Science (R0)