Abstract
The automotive industry is rapidly moving towards the highest level of autonomy. However, one of the major challenges for highly autonomous vehicles is the differentiation between driving modes according to different driving situations. Different driving zones have different driving safety regulations. For example, German traffic regulations require a higher degree of safety measurements for highway driving. Therefore, a classification of the different driving scenarios on a highway is necessary to regulate these safety assessments. This paper presents a novel vision-based approach to the classification of German highway driving scenarios. We develop three different and precise algorithms utilizing image processing and machine learning approaches to recognize speed signs, traffic lights, and highway traffic signs. Based on the results of these algorithms, a weight-based classification process is performed, which determines the current driving situation either as a highway driving mode or not. The main goal of this research work is to maintain and to ensure the high safety specifications required for the German highway. Finally, the result of this classification process is provided as an extracted driving scenario-based feature on the perceptual level of a system known as situation awareness to provide a high level of driving safety. This study was realized on a custom-made hardware unit called “CE-Box”, which was developed at the Department of Computer Engineering at TU Chemnitz as an automotive test solution for testing automotive software applications on an embedded hardware unit.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors J. Hum. Factors Ergon. Soc. 37(1), 32–64 (1995)
German Road Traffic Rules and Regulations. https://www.stvo.de/strassenverkehrsordnung. Accessed 01 Apr 2020
German Statistics Portal. https://www.destatis.de/EN/Themes/Society-Environment/Traffic-Accidents/_node.html. Accessed 01 Apr 2020
German Highway. https://www.thelocal.de/20190201/are-germanys-autobahns-really-the-safest-highways-in-the-world. Accessed 01 May 2020
Endsley, M.R.: Situation awareness in future autonomous vehicles: beware of the unexpected. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 824, pp. 303–309. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96071-5_32
Nine, J., Manoharan, S., Hardt, W.: Concept of the comprehension level of situation awareness using an expert system. In: 14th International Forum on Strategic Technology (IFOST), Tomsk, Russian Federation (2019)
Philipsen, M.P., Jensen, M.B., Mogelmose, A., Moeslund, T.B., Trivedi, M.M.: Traffic light detection: a learning algorithm and evaluations on challenging dataset. In: IEEE 18th International Conference on Intelligent Transportation System (ITSC), pp. 2341–2345 (2015)
Chiang, C.C., Ho, M.C., Liao, H.S., Pratama, A., Syu, W.C.: Detecting and recognizing traffic lights by genetic approximate ellipse detection and spatial texture layouts. Int. J. Innov. Comput. Inf. Control 7(12), 6919–6934 (2011)
Charette, R.D., Nashashibi, F.: Real time visual traffic lights recognition based on spot light detection and adaptive traffic lights templates. In: IEEE Intelligent Vehicles Symposium, pp. 358–363 (2009)
Nine, J., Saleh, S., Khan, O., Hardt, W.: Traffic light sign recognition for situation awareness using monocular camera. In: Symposium International Symposium on Computer Science, Computer Engineering and Educational Technology (ISCSET), Laubusch, Germany (2019)
Li, W., Li, H., Dong, T., Yao, J., Wei, L.: Improved traffic signs detection based on significant color extraction and geometric features. In: 8th International Congress on Image and Signal Processing (CISP), pp. 616–620 (2015)
Torresen, J., Bakke, J.W., Yang, Y.: A camera based speed limit sign recognition system. In: Proceedings of 13th ITS World Congress and Exhibition, pp. 115–129 (2006)
Ardianto, S., Chen, C.J., Hang, H.M.: Real-time traffic sign recognition using color segmentation and SVM. In: International Conference on Systems, Signals and Image Processing, pp. 1–5 (2017)
Miyata, S.: Automatic recognition of speed limits on speed-limit signs by using machine learning. J. Imaging 3, 25 (2017)
Pon, A.D., Andrienko, O., Harakeh, A., Waslander, S.L.: A hierarchical deep architecture and mini-batch selection method for joint traffic sign and light detection. arXiv:1806.07987 (2018)
BelgiumTS - Belgian Traffic Sign Dataset for training of Speed Limit Sign. https://btsd.ethz.ch/shareddata/. Accessed 01 May 2020
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, July 2011
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Nine, J., Manoharan, S., Sapkota, M., Saleh, S., Hardt, W. (2020). Highway Traffic Classification for the Perception Level of Situation Awareness. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-64559-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64558-8
Online ISBN: 978-3-030-64559-5
eBook Packages: Computer ScienceComputer Science (R0)