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Identifying and Counting Vehicles in Multiple Lanes by Using a Low-Cost Vehicle-Mounted Sensor for Intelligent Traffic Management Systems

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12344)

Abstract

There is evidence that accessing online traffic data is a key factor to facilitate intelligent traffic management, especially at intersections. With the advent of autonomous vehicles (AVs), new options for collecting such data appear. To date, much research has been performed on machine learning to provide safe motion planning and to control modern vehicles such as AVs. However, few studies have considered using the sensing features of these types of vehicles to collect traffic information of the surrounding environment. In this study, we developed new algorithms to improve a traffic management system when the traffic is a mixture of human-driven vehicles (HDVs) and modern vehicles with different levels of autonomy. The goal is to utilize the sensing ability of modern vehicles to collect traffic data. As many modern vehicles are equipped with vehicle-mounted sensors by default, they can use them to collect traffic data. Our algorithms can detect vehicles, identify their type, determine the lane they are in, and count the number of detected vehicles per lane by considering multi-lane scenarios. To evaluate our proposed approach, we used a vehicle-mounted monocular camera. The experimental work presented here provides one of the first investigations to extract real traffic data from multiple lanes using a vehicle-mounted camera. The results indicate that the algorithms can identify the detected vehicle’s type in the studied scenarios with an accuracy of 95.21%. The accuracy of identifying the lane the detected vehicle is in is determined by two proposed approaches, which have accuracies of 91.01% and 91.73%.

Keywords

  • Lane detection
  • Multiple lanes
  • Vehicle detection
  • Intelligent traffic management
  • Vehicle-mounted monocular camera

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References

  1. Lamouik, I., Yahyaouy, A., Sabri, M.A.: Smart Multi-Agent Traffic Coordinator for Autonomous Vehicles at Intersections, pp. 1–6. IEEE (2017)

    Google Scholar 

  2. Namazi, E., Li, J., Lu, C.: Intelligent intersection management systems considering autonomous vehicles: a systematic literature review. IEEE Access pp. 91946–91965 (2019)

    Google Scholar 

  3. Namazi, E., Holthe-Berg, R.N., Lofsberg, C.S., Li, J.: Using vehicle-mounted camera to collect information for managing mixed traffic. In: 15th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 222–230 (2019)

    Google Scholar 

  4. Chen, Z., Khemmar, R., Decoux, B., Atahouet, A., Ertaud, J.-Y.: Real time object detection, tracking, and distance and motion estimation based on deep learning: application to smart mobility, pp. 1–6. IEEE (2019)

    Google Scholar 

  5. Tian, S., et al.: An improved target detection and traffic parameter calculation method based on YOLO with a monocular camera. In: CICTP, pp. 5696–5708 (2019)

    Google Scholar 

  6. Hillel, A.B., Lerner, R., Levi, D., Raz, G.: Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 727–745 (2014)

    Google Scholar 

  7. Keatmanee, C., Jakborvornphan, S., Potiwanna, C., San-Uml, W., Dailey, M.N.: Vision-based lane keeping—a survey, pp. 1–6. IEEE (2018)

    Google Scholar 

  8. Andrade, D.C., et al.: A novel strategy for road lane detection and tracking based on a vehicle’s forward monocular camera. IEEE Trans. Intell. Transp. Syst. 1497–1507 (2018)

    Google Scholar 

  9. von Reyher, A., Joos, A., Winner, H.: A lidar-based approach for near range lane detection, pp. 147–152. IEEE (2005)

    Google Scholar 

  10. Goldbeck, J., Hürtgen, B., Ernst, S., Kelch, L.: Lane following combining vision and DGPS. Image Vis. Comput. 425–433 (2000)

    Google Scholar 

  11. Narita, Y., Katahara, S., Aoki, M.: Lateral position detection using side looking line sensor cameras, pp. 271–275. IEEE (2003)

    Google Scholar 

  12. Jo, Y., Han, S.-J., Lee, D., Min, K., Choi, J.: An autonomous lane-level road map building using low-cost sensors. In Eleventh International Conference on Machine Vision. (ICMV 2018), vol. 11041 (2019)

    Google Scholar 

  13. Jia, B., Chen, J., Zhang, K., Wang, Q.: Sequential monocular road detection by fusing appearance and geometric information. IEEE/ASME Trans. Mechatronics 24(2), 633–643 (2019)

    CrossRef  Google Scholar 

  14. Chao, F., Yu-Pei, S., Ya-Jie, J.: Multi-lane detection based on deep convolutional neural network. IEEE Access 150833–150841 (2019)

    Google Scholar 

  15. Cao, J., Song, C., Song, S., Xiao, F., Peng, S.: Lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments. Sensors 3166 (2019)

    Google Scholar 

  16. Zhang, W., Mahale, T.: End to end video segmentation for driving: Lane detection for autonomous car. arXiv preprint (2018)

    Google Scholar 

  17. Yuan, C., Chen, H., Liu, J., Zhu, D., Xu, Y.: Robust lane detection for complicated road environment based on normal map. IEEE Access 49679–49689 (2018)

    Google Scholar 

  18. GoPro hero7 black. https://gopro.com/en/us/shop/cameras/hero7-black/CHDHX-701-master.html. Accessed 2020

  19. Paszke, A., et al.: Automatic differentiation in PyTorch. In: 31st Conference on Neural Information Processing Systems (NIPS 2017) (2017)

    Google Scholar 

  20. Bradski, G.: The opencv library. Software Tools 120–125 (2000)

    Google Scholar 

  21. Ding, L., Goshtasby, A.: On the Canny edge detector. Pattern Recogn. 721–725 (2001)

    Google Scholar 

  22. Galamhos, C., Matas, J., Kittler, J.: Progressive probabilistic Hough transform for line detection. IEEE 554–560 (1999)

    Google Scholar 

  23. Matas, J., Galambos, C., Kittler, J.: Robust detection of lines using the progressive probabilistic Hough transform. Comput. Vis. Image Understand. 119–137 (2000)

    Google Scholar 

  24. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

    Google Scholar 

  25. Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement. arXiv preprint (2018)

    Google Scholar 

  26. Geometry. https://en.wikipedia.org/wiki/Parallel. Accessed 2019

  27. YOLO. https://pjreddie.com/darknet/yolo/. Accessed 2019

  28. Use YOLOv3 PyTorch to train KITTI. https://github.com/packyan/PyTorch-YOLOv3-kitti. Accessed 2019

  29. Deng, G., Wu, Y.: Double lane line edge detection method based on constraint conditions Hough transform, pp. 107–110. IEEE (2018)

    Google Scholar 

  30. Point-Line Distance–2-Dimensional. https://mathworld.wolfram.com/Point-LineDistance2-Dimensional.html. Accessed 2020

  31. Slope. https://en.wikipedia.org/wiki/Slope. Accessed 2019

  32. Sum of angles of a triangle. https://en.wikipedia.org/wiki/Sum_of_angles_of_a_triangle. Accessed 2019

  33. Trigonometry. https://en.wikipedia.org/wiki/Trigonometry. Accessed 2019

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Correspondence to Elnaz Namazi or Jingyue Li .

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Namazi, E., Li, J., Mester, R., Lu, C. (2020). Identifying and Counting Vehicles in Multiple Lanes by Using a Low-Cost Vehicle-Mounted Sensor for Intelligent Traffic Management Systems. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_49

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_49

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