Traffic Volume Detection Using Infrastructure-Based LiDAR under Different Levels of Service Conditions
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 11
Abstract
Light detection and ranging (LiDAR) technology is a key component of an autonomous vehicle’s sensing system. It also has the potential to be used at the roadside as a major infrastructure-based detection for connected and autonomous traffic infrastructure systems, as well as for the general purpose of traffic data collection and performance evaluation. Lane and movement-based traffic volume data collection is a basic function of roadside traffic sensing systems. The accuracy of volume detection is mainly impacted by occlusion for most of the advanced traffic sensing technologies, such as LiDAR, video, and radar. This paper presents research results to quantify the influence of occlusion on LiDAR systems’ traffic volume detection in different traffic demand scenarios. A method for automatic identification and classification of LiDAR specific occlusion was first developed based on the inherent characteristics of LiDAR sensors, which can report occlusion ratios of roadside LiDAR data. Then, the study was extended to accommodate all traffic demand scenarios, traffic levels of service (LOS A to E), and different truck compositions (5% to 30%) by integrating the developed method and traffic simulation. Lastly, a comprehensive case study first verified the accuracy of the simulation results using field data collected from two testbeds, and then at the third testbed, a lane and movement-based traffic volume study was demonstrated. The practical significance of this paper is to help traffic engineers making informed decisions when considering LiDAR as their choice of sensing technology in the field from two aspects: (1) the quantitative relationship between expected occlusion rate and resulted detection accuracy under various traffic conditions; (2) lessons learned from the pilot field implementation on LiDAR, installation strategy, data storage, and communication.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Nevada Department of Transportation (NDOT) (Grant No. P224-14-803/TO #13). The authors gratefully acknowledge this financial support. This research was also supported by engineers from the NDOT, the Regional Transportation Commission of Washoe County, Nevada, and the City of Reno.
References
Castillo, R. D. M., M. M. B. Tejada, M. O. Cordel, A. F. B. Laguna, and J. P. Ilao. 2017. “Vsion: Vehicle occlusion handling for traffic monitoring.” In Proc., Int. Conf. on Video and Image Processing, 167–171. Jurong West, Singapore: Nanyang Technological Univ. https://doi.org/10.1145/3177404.3177431.
Chang, J., L. Wang, G. Meng, S. Xiang, and C. Pan. 2018. “Vision-based occlusion handling and vehicle classification for traffic surveillance systems.” IEEE Intell. Transp. Syst. Mag. 10 (2): 80–92. https://doi.org/10.1109/MITS.2018.2806619.
Dollár, P., C. Wojek, B. Schiele, and P. Perona. 2009. “Pedestrian detection: A benchmark.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 304–311. New York: IEEE. https://doi.org/10.1109/CVPR.2009.5206631.
Dollár, P., C. Wojek, B. Schiele, and P. Perona. 2011. “Pedestrian detection: An evaluation of the state of the art.” IEEE Trans. Pattern Anal. Mach. Intell. 34 (4): 743–761. https://doi.org/10.1109/TPAMI.2011.155.
Fu, T., J. Stipancic, S. Zangenehpour, L. Miranda-Moreno, and N. Saunier. 2017. “Automatic traffic data collection under varying lighting and temperature conditions in multimodal environments: Thermal versus visible spectrum video-based systems.” J. Adv. Transp. 2017: 1–17. https://doi.org/10.1155/2017/5142732.
Fulari, S., L. Vanajakshi, and S. C. Subramanian. 2017. “Artificial neural network-based traffic state estimation using erroneous automated sensor data.” J. Transp. Eng. Part A: Syst. 143 (8): 05017003. https://doi.org/10.1061/JTEPBS.0000058.
Geiger, A., P. Lenz, and R. Urtasun. 2012. “Are we ready for autonomous driving? The KITTI vision benchmark suite.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 3354–3361. New York: IEEE. https://doi.org/10.1109/CVPR.2012.6248074.
Gilroy, S., E. Jones, and M. Glavin. 2019. “Overcoming occlusion in the automotive environment-a review.” IEEE Trans. Intell. Transp. Syst. 22 (1): 23–35. https://doi.org/10.1109/TITS.2019.2956813.
Guo, J., J. Xia, and B. L. Smith. 2008. Investigation of speed estimation using single loop detectors. Charlottesville, VA: Univ. of Virginia.
Hancock, M. W., and B. Wright. 2013. A policy on geometric design of highways and streets. Washington, DC: AASHTO.
He, Z., J. Hu, B. B. Park, and M. W. Levin. 2019. “Vehicle sensor data-based transportation research: Modeling, analysis, and management.” J. Intell. Transp. Syst. 23 (2): 99–102. https://doi.org/10.1080/15472450.2019.1586335.
Joshi, R. C., A. G. Singh, M. Joshi, and S. Mathur. 2019. “A low cost and computationally efficient approach for occlusion handling in video surveillance systems.” Int. J. Interact. Multi. Artif. Intell. 5 (7): 28–38. https://doi.org/10.9781/ijimai.2019.01.001.
Klein, L. A., M. K. Mills, and D. R. Gibson. 2006. Traffic detector handbook: Volume I.. McLean, VA: Turner-Fairbank Highway Research Center.
Kukkala, V. K., J. Tunnell, S. Pasricha, and T. Bradley. 2018. “Advanced driver-assistance systems: A path toward autonomous vehicles.” IEEE Consum. Electron. Mag. 7 (5): 18–25. https://doi.org/10.1109/MCE.2018.2828440.
Kwon, J., P. Varaiya, and A. Skabardonis. 2003. “Estimation of truck traffic volume from single loop detectors with lane-to-lane speed correlation.” Transp. Res. Rec. 1856 (1): 106–117. https://doi.org/10.3141/1856-11.
Liu, J., P. Jayakumar, J. L. Stein, and T. Ersal. 2014. “A multi-stage optimization formulation for MPC-based obstacle avoidance in autonomous vehicles using a LIDAR sensor.” In Vol. 46193 of Proc., Dynamic Systems and Control Conf. New York: ASME. https://doi.org/10.1115/DSCC2014-6269.
Mallikarjuna, C., A. Phanindra, and K. R. Rao. 2009. “Traffic data collection under mixed traffic conditions using video image processing.” J. Transp. Eng. 135 (4): 174–182. https://doi.org/10.1061/(ASCE)0733-947X(2009)135:4(174.
Manual, Highway Capacity. 2010. HCM2010. Washington, DC: Transportation Research Board.
Moutakki, Z., I. M. Ouloul, K. Afdel, and A. Amghar. 2017. “Real-time video surveillance system for traffic management with background subtraction using codebook model and occlusion handling.” Trans. Telecommun. 18 (4): 297–306. https://doi.org/10.1515/ttj-2017-0027.
Moutakki, Z., I. M. Ouloul, K. Afdel, and A. Amghar. 2018. “Real-time system based on feature extraction for vehicle detection and classification” Trans. Telecommun. 19 (2): 93–102. https://doi.org/10.2478/ttj-2018-0008.
Mukhtar, A., L. Xia, and T. B. Tang. 2015. “Vehicle detection techniques for collision avoidance systems: A review.” IEEE Trans. Intell. Transp. Syst. 16 (5): 2318–2338. https://doi.org/10.1109/TITS.2015.2409109.
Oh, S., S. G. Ritchie, and C. Oh. 2002. “Real-time traffic measurement from single loop inductive signatures.” Transp. Res. Rec 1804 (1): 98–106. https://doi.org/10.3141/1804-14.
Phan, H. N., L. H. Pham, D. N. N. Tran, and S. V. U. Ha. 2017. “Occlusion vehicle detection algorithm in crowded scene for traffic surveillance system.” In Proc., Int. Conf. on System Science and Engineering (ICSSE), 215–220. New York: IEEE. https://doi.org/10.1109/ICSSE.2017.8030868.
Singh, N. K., L. Vanajakashi, and A. K. Tangirala. 2018. “Segmentation of vehicle signatures from inductive loop detector (ILD) data for real-time traffic monitoring.” In Proc., 10th Int. Conf. on Communication Systems & Networks, 601–606. New York: IEEE. https://doi.org/10.1109/COMSNETS.2018.8328281.
Veeraraghavan, H., O. Masoud, and N. P. Papanikolopoulos. 2003. “Computer vision algorithms for intersection monitoring” IEEE Trans. Intell. Transp. Syst. 4 (2): 78–89. https://doi.org/10.1109/TITS.2003.821212.
Velazquez-Pupo, R., A. Sierra-Romero, D. Torres-Roman, Y. V. Shkvarko, J. Santiago-Paz, D. Gómez-Gutiérrez, and M. Romero-Delgado. 2018. “Vehicle detection with occlusion handling, tracking, and OC-SVM classification: A high performance vision-based system.” Sensors 18 (2): 374. https://doi.org/10.3390/s18020374.
Velodyne. 2016. VLP-16 manual: User’s manual and programming guide. San Jose, CA: Velodyne LiDAR.
Velodyne. 2018. VLP-32C user manual. San Jose, CA: Velodyne LiDAR.
Wang, H., Y. Cai, X. Chen, and L. Chen. 2016. “Occluded vehicle detection with local connected deep model.” Multimedia Tools Appl. 75 (15): 9277–9293. https://doi.org/10.1007/s11042-015-3141-0.
Wang, H., B. Wang, B. Liu, X. Meng, and G. Yang. 2017. “Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle.” Rob. Auton. Syst. 88 (Feb): 71–78. https://doi.org/10.1016/j.robot.2016.11.014.
Wu, J., H. Xu, Y. Zheng, and Z. Tian. 2018. “A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data.” Accid. Anal. Prev. 121 (Dec): 238–249. https://doi.org/10.1016/j.aap.2018.09.001.
Zeng, Y., Y. Hu, S. Liu, J. Ye, Y. Han, X. Li, and N. Sun. 2018. “Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving.” IEEE Robot. Autom. Lett. 3 (4): 3434–3440. https://doi.org/10.1109/LRA.2018.2852843.
Zhang, G., R. P. Avery, and Y. Wang. 2007. “Video-based vehicle detection and classification system for real-time traffic data collection using uncalibrated video cameras.” Transp. Res. Rec. 1993 (1): 138–147. https://doi.org/10.3141/1993-19.
Zhao, J., Y. Li, H. Xu, and H. Liu. 2019a. “Probabilistic prediction of pedestrian crossing intention using roadside LiDAR data.” IEEE Access. 7 (Jul): 93781–93790. https://doi.org/10.1109/ACCESS.2019.2927889.
Zhao, J., H. Xu, H. Liu, J. Wu, Y. Zheng, and D. Wu. 2019b. “Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors.” Transp. Res. Part C: Emerging. Technol. 100 (Mar): 68–87. https://doi.org/10.1016/j.trc.2019.01.007.
Zhao, J., H. Xu, Y. Tian, and H. Liu. 2020. “Towards application of light detection and ranging sensor to traffic detection: An investigation of its built-in features and installation techniques.” J. Intell. Transp. Syst. 1–22. https://doi.org/10.1080/15472450.2020.1807346.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 11 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Dec 5, 2020
Accepted: Jul 8, 2021
Published online: Sep 11, 2021
Published in print: Nov 1, 2021
Discussion open until: Feb 11, 2022
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