Vehicle Trajectory Tracking Using Adaptive Kalman Filter from Roadside Lidar
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 6
Abstract
Recently, roadside lidar sensors have been adopted as a reliable measure to extract high-resolution vehicle trajectory data from the field. The trajectory-level data can be extracted from roadside lidar detection using a series of data processing algorithms such as background filtering, object clustering, object classification, and object tracking. However, the results from current methods are associated with trajectory fluctuations, indicating that vehicle trajectory optimization remains a challenge. Previous studies used traditional Kalman filters to optimize vehicle trajectories, but there remains room for improvement in the smoothing effect. This paper addresses the issue by presenting an effective method for trajectory smoothing using the adaptive Kalman filter. The proposed method demonstrates two significant highlights: a multipoint matching algorithm for velocity estimation, and a robust adaptive strategy for Kalman filter. The performance of the proposed method was found to be satisfactory after evaluation using field data collected at a site in Reno, Nevada. Additionally, the method can be implemented under simple prior assumptions, making it user-friendly for real-world applications.
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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.
References
Cheng, Y., X. Qin, J. Jin, B. Ran, and J. Anderson. 2011. “Cycle-by-cycle queue length estimation for signalized intersections using sampled trajectory data.” Transp. Res. Rec. 2257 (1): 87–94. https://doi.org/10.3141/2257-10.
Cui, Y., H. Xu, J. Wu, Y. Sun, and J. Zhao. 2019. “Automatic vehicle tracking with roadside LiDAR data for the connected-vehicles system.” IEEE Intell. Syst. 34 (3): 44–51. https://doi.org/10.1109/MIS.2019.2918115.
During, M., and K. Lemmer. 2016. “Cooperative maneuver planning for cooperative driving.” IEEE Intell. Transp. Syst. Mag. 8 (3): 8–22. https://doi.org/10.1109/MITS.2016.2549997.
Kalman, R. E. 1960. “A new approach to linear filtering and prediction problems.” Trans. ASME J. Basic Eng. 82 (Jan): 35–45. https://doi.org/10.1115/1.3662552.
Kesting, A., and M. Treiber. 2008. “Calibrating car-following models by using trajectory data: Methodological study.” Transp. Res. Rec. 2088 (1): 148–156. https://doi.org/10.3141/2088-16.
Khan, M. A., W. Ectors, T. Bellemans, Y. Ruichek, A.-H. Yasar, D. Janssens, and G. Wets. 2018. “Unmanned aerial vehicle-based traffic analysis: A case study to analyze traffic streams at urban roundabouts.” Procedia Comput. Sci. 130 (Apr): 636–643. https://doi.org/10.1016/j.procs.2018.04.114.
Klein, L. 2020. Traffic flow sensors: Technologies, operating principles, and archetypes, spotlight. Bellingham, WA: SPIE Press.
Kong, X., Z. Xu, G. Shen, J. Wang, Q. Yang, and B. Zhang. 2016. “Urban traffic congestion estimation and prediction based on floating car trajectory data.” Future Gener. Comput. Syst. 61 (11): 97–107. https://doi.org/10.1016/j.future.2015.11.013.
Krajewski, R., J. Bock, L. Kloeker, and L. Eckstein. 2018. “The highD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems.” In Proc., 21st Int. Conf. on Intelligent Transportation Systems (ITSC). 2118–2125. New York: IEEE. http://doi: 10.1109/ITSC.2018.8569552.
Li, X., A. Ghiasi, Z. Xu, and X. Qu. 2018. “A piecewise trajectory optimization model for connected automated vehicles: Exact optimization algorithm and queue propagation analysis.” Transp. Res. Part B Methodol. 118 (Nov): 429–456. https://doi.org/10.1016/j.trb.2018.11.002.
Lio, W. H., G. C. Larsen, and G. R. Thorsen. 2021. “Dynamic wake tracking using a cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale validation.” Renewable Energy 172 (Jan): 1073–1086. https://doi.org/10.1016/j.renene.2021.03.081.
Lv, B., R. Sun, H. Zhang, H. Xu, and R. Yue. 2019a. “Automatic vehicle-pedestrian conflict identification with trajectories of road users extracted from roadside LiDAR sensors using a rule-based method.” IEEE Access 7 (Jan): 161594–161606. https://doi.org/10.1109/ACCESS.2019.2951763.
Lv, B., H. Xu, J. Wu, Y. Tian, S. Tian, and S. Feng. 2019b. “Revolution and rotation-based method for roadside LiDAR data integration.” Opt. Laser Technol. 119 (Mar): 105571. https://doi.org/10.1016/j.optlastec.2019.105571.
Ma, Y., and J. Zhu. 2021. “Left-turn conflict identification at signal intersections based on vehicle trajectory reconstruction under real-time communication conditions.” Accid. Anal. Prev. 150 (Feb): 105933. https://doi.org/10.1016/j.aap.2020.105933.
Sazara, C., R. V. Nezafat, and M. Cetin. 2017. “Offline reconstruction of missing vehicle trajectory data from 3D LIDAR.” In Proc., 2017 IEEE Conf. Computer Visualization Pattern Recognition, 792–797. New York: IEEE. https://doi.org/10.1109/IVS.2017.7995813.
Shi, X., Y. D. Wong, M. Z. F. Li, and C. Chai. 2018. “Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory.” Accid. Anal. Prev. 117 (5): 346–356. https://doi.org/10.1016/j.aap.2018.05.007.
Thiemann, C., M. Treiber, and A. Kesting. 2008. “Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data.” Transp. Res. Rec. 2088 (1): 90–101. https://doi.org/10.3141/2088-10.
Toth, C., D. A. Grejner-Brzezinska, and Y.-J. Lee. 2008. “Terrain-based navigation: Trajectory recovery from LiDAR data.” In Proc., 2008 IEEE/ION Position, Location Navigation Symp, 760–765. New York: IEEE. https://doi.org/10.1109/PLANS.2008.4570067.
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, C., C. Xu, and Y. Dai. 2019. “A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data.” Accid. Anal. Prev. 123 (Dec): 365–373. https://doi.org/10.1016/j.aap.2018.12.013.
Wang, Y., and M. Papageorgiou. 2005. “Real-time freeway traffic state estimation based on extended Kalman filter: A general approach.” Transp. Res. Part B Methodol. 39 (2): 141–167. https://doi.org/10.1016/j.trb.2004.03.003.
Wei, H., C. Feng, E. Meyer, and J. Lee. 2005. “Video-capture-based approach to extract multiple vehicular trajectory data for traffic modeling.” J. Transp. Eng. 131 (7): 496–505. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:7(496).
Wu, J. 2018. “An automatic procedure for vehicle tracking with a roadside LiDAR sensor.” ITE J. 88 (11): 32–37.
Wu, J., H. Xu, Y. Sun, J. Zheng, and R. Yue. 2018a. “Automatic background filtering method for roadside LiDAR data.” Transp. Res. Rec. 2672 (45): 106–114. https://doi.org/10.1177/0361198118775841.
Wu, J., H. Xu, and J. Zhao. 2018b. “Automatic lane identification using the roadside LiDAR sensors.” IEEE Intell. Transp. Syst. Mag. 12 (1): 25–34. https://doi.org/10.1109/MITS.2018.2876559.
Wu, J., H. Xu, J. Zheng, and J. Zhao. 2021. “Automatic vehicle detection with roadside LiDAR data under rainy and snowy conditions.” IEEE. Intell. Transp. Syst. Mag. 13 (1): 197–209. https://doi.org/10.1109/MITS.2019.2926362.
Wu, J., H. Xu, Y. Zheng, and Z. Tian. 2018c. “A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data.” Accid. Anal. Prev. 121 (9): 238–249. https://doi.org/10.1016/j.aap.2018.09.001.
Wu, J., H. Xu, Y. Zheng, Y. Zhang, B. Lv, and Z. Tian. 2019. “Automatic vehicle classification using roadside LiDAR data.” Transp. Res. Rec. 2673 (6): 153–164. https://doi.org/10.1177/0361198119843857.
Wu, J., Y. Zhang, and H. Xu. 2020. “A novel skateboarder-related near-crash identification method with roadside LiDAR data.” Accid. Anal. Prev. 137 (6): 105438. https://doi.org/10.1016/j.aap.2020.105438.
Xiao, W., B. Vallet, K. Schindler, and N. Paparoditis. 2016. “Simultaneous detection and tracking of pedestrian from laser scanning data.” ISPRS J. Photogramm. Remote Sens. 3 (9): 295–302. https://doi.org/10.5194/isprs-annals-III-3-295-2016.
Yue, R., H. Xu, J. Wu, R. Sun, and C. Yuan. 2019. “Data registration with ground points for roadside LiDAR sensors.” Remote Sens. 11 (11): 1354. https://doi.org/10.3390/rs11111354.
Zarchan, P., and H. Musoff. 2000. Fundamentals of Kalman filtering—A practical approach. Reston, VA: ASCE.
Zhang, J., W. Xiao, B. Coifman, and J. P. Mills. 2020. “Vehicle tracking and speed estimation from roadside lidar.” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13 (2): 5597–5608. https://doi.org/10.1109/JSTARS.2020.3024921.
Zhang, P. E., and V. G. Kovvali. 2007. “Freeway gap acceptance behaviors based on vehicle trajectory analysis.” In Proc., Transportation Research Board 86th Annual Meeting. Washington, DC: Transportation Research Board.
Zhao, J., H. Xu, H. Liu, J. Wu, Y. Zheng, and D. Wu. 2019a. “Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors.” Transp. Res. Part C Emerging Technol. 100 (9): 68–87. https://doi.org/10.1016/j.trc.2019.01.007.
Zhao, J., H. Xu, X. Xia, and H. Liu. 2019b. “Azimuth-Height background filtering method for roadside LiDAR data.” In Proc., 2019 IEEE Intelligent Transport System Conf. (ITSC), 2421–2426. New York: IEEE. http://doi: 10.1109/ITSC.2019.8917369.
Zhao, Y., J. Zheng, W. Wong, X. Wang, Y. Meng, and H. X. Liu. 2019c. “Various methods for queue length and traffic volume estimation using probe vehicle trajectories.” Transp. Res. Part C Emerging Technol. 107 (Oct): 70–91. https://doi.org/10.1016/j.trc.2019.07.008.
Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 19, 2022
Accepted: Jan 30, 2023
Published online: Apr 4, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 4, 2023
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