Abstract
In the context of platooning, a high degree of cooperation between platooning members is required to perform major maneuvers. One of the most challenging issues is to perform join-maneuver due to the strong interference caused by unintended vehicles entering in the middle during this maneuver. In order to detect and identify vehicles (unintended or not) during a join maneuver, we propose a vision based solution allowing to observe the front area of the platooning member, track and identify newly joined vehicles. In this paper, we focus on delineating this region of interest (ROI) by extracting road lanes in video sequences from on-board cameras in the platooning vehicles. The performance of our method is tested and validated using videos of highway scenes in different weather conditions (sunny, rainy, cloudy).
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References
Bergenhem, C., Shladover, S., Coelingh, E., Englund, C., Tsugawa, S.: Overview of platooning systems. In: Proceedings of the 19th ITS World Congress, Vienna, Austria, 22–26 October (2012)
Segata, M., Bloessl, B., Joerer, S., Dressler, F., Cigno, R.L.: Supporting platooning maneuvers through IVC: an initial protocol analysis for the JOIN maneuver. In: 2014 11th Annual Conference on Wireless on-Demand Network Systems and Services (WONS), pp. 130–137. IEEE, April 2014
Paranjothi, A., Atiquzzaman, M., Khan, M.S.: PMCD: platoon-merging approach for cooperative driving. Internet Technol. Lett. 3(1), e139 (2020)
Hoang, G.M., Denis, B., Härri, J., Slock, D.T.M.: Distributed link selection and data fusion for cooperative positioning in GPS-aided IEEE 802.11 p VANETs. Proc. WPNC 15, 1–6 (2015)
Kasai, T., Onoguchi, K.: Lane detection system for vehicle platooning using multi-information map. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 1350–1356. IEEE, September 2010
Tran, L.A., Le, M.H.: Robust U-Net-based road lane markings detection for autonomous driving. In: 2019 International Conference on System Science and Engineering (ICSSE), pp. 62–66. IEEE, July 2019
Kuk, J.G., An, J.H., Ki, H., Cho, N.I.: Fast lane detection & tracking based on Hough transform with reduced memory requirement. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 1344–1349. IEEE, September 2010
Yan, X., Li, Y.: A method of lane edge detection based on Canny algorithm. In: 2017 Chinese Automation Congress (CAC), pp. 2120–2124. IEEE, October 2017
Dong, Y., Xiong, J., Li, L., Yang, J.: Robust lane detection and tracking for lane departure warning. In: 2012 International Conference on Computational Problem-Solving (ICCP), pp. 461–464. IEEE, October 2012
Yoo, J.H., Lee, S.W., Park, S.K., Kim, D.H.: A robust lane detection method based on vanishing point estimation using the relevance of line segments. IEEE Trans. Intell. Transp. Syst. 18(12), 3254–3266 (2017)
Wang, Z., Ren, W., Qiu, Q.: Lanenet: real-time lane detection networks for autonomous driving. arXiv preprint arXiv:1807.01726 (2018)
Daigavane, P.M., Bajaj, P.R.: Road lane detection with improved canny edges using ant colony optimization. In: 2010 3rd International Conference on Emerging Trends in Engineering and Technology, pp. 76–80. IEEE, November 2010
Low, C.Y., Zamzuri, H., Mazlan, S.A.: Simple robust road lane detection algorithm. In: 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS), pp. 1–4. IEEE, June 2014
Chetouane, A., Mabrouk, S., Jemili, I., Mosbah, M.: Vision-based vehicle detection for road traffic congestion classification. Pract. Exp. Concurr. Comput., e5983 (2020)
Chetouane, A., Mabrouk, S., Jemili, I., Mosbah, M.: A comparative study of vehicle detection methods in a video sequence. In: Jemili, I., Mosbah, M. (eds.) DiCES-N 2019. CCIS, vol. 1130, pp. 37–53. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40131-3_3
Duan, D., Xie, M., Mo, Q., Han, Z., Wan, Y.: An improved Hough transform for line detection. In: 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), vol. 2, pp. V2–354. IEEE, October, 2010
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Gharbi, H., Mabrouk, S. (2020). ROI Extraction for Intrusion Detection in Platooning Join Maneuver. In: Jemili, I., Mosbah, M. (eds) Distributed Computing for Emerging Smart Networks. DiCES-N 2020. Communications in Computer and Information Science, vol 1348. Springer, Cham. https://doi.org/10.1007/978-3-030-65810-6_3
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DOI: https://doi.org/10.1007/978-3-030-65810-6_3
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