Lane Offset Survey for One-Lane Horizontal Curvatures Using Binocular Stereo Vision Measurement System
Publication: Journal of Surveying Engineering
Volume 147, Issue 4
Abstract
A disproportionate number of serious traffic accidents caused by lane offset occur at horizontal curvatures. Widely used methods for vehicle trajectory measurement, such as the vehicle position system and unmanned aerial vehicle (UAV), are unsuitable for wheel lane offset detection due to the resolution of data and shooting scope of cameras, respectively. To evaluate the lane offset risk of horizontal curvatures, automatic methods are proposed for horizontal alignment and vehicle trajectory measurement using a roadway inspection system (RIS), integrating a binocular stereo vision measurement system, initial measurement unit, and global positioning system (GPS). A mask region-based convolutional neural network (R-CNN) model is applied to detect lane markings. Based on binocular stereo vision technology, the lane offset of a vehicle on horizontal curvatures is measured continuously. To investigate the impacts of horizontal alignments on lane offset, inertial measurement unit (IMU) data and road scene images are applied for horizontal alignment measurement, including point of curve (PC) and point of tangent (PT) stations, curve length, curve radius, and turning direction. Four one-lane horizontal curvatures on highway ramps are selected as a test bed. Based on field data, the impact of horizontal alignments on lane offset is analyzed, and hazardous locations with lane offset risks are detected. This study can facilitate traffic safety analysis and the horizontal alignment design of roadway curvatures.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code that support the findings of this study are available from the author upon reasonable request.
References
AASHTO. 2011. A policy on geometric design of highways and streets. 6th ed. Washington, DC: AASHTO.
Ai, C., and Y. Tsai. 2004. “Automatic horizontal curve identification and measurement method using GPS data.” J. Transp. Eng. 141 (2): 04014078. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000740.
Bancroft, J. B., and G. Lachapelle. 2011. “Data fusion algorithms for multiple inertial measurement units.” Sensors 11 (7): 6771–6798. https://doi.org/10.3390/s110706771.
Bassani, M., G. Marinelli, and M. Piras. 2016. “Identification of horizontal circular arc from spatial data sources.” J. Surv. Eng. 142 (4): 04016013. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000186.
Casal, G., D. Santamarina, and M. E. Vázquez-Méndez. 2017. “Optimization of horizontal alignment geometry in road design and reconstruction.” Transp. Res. Part C: Emerging Technol. 74 (Jan): 261–274. https://doi.org/10.1016/j.trc.2016.11.019.
Chen, Y., M. Quddus, and X. Wang. 2018. “Impact of combined alignments on lane departure: A simulator study for mountainous freeways.” Transp. Res. Part C: Emerging Technol. 86 (Jan): 346–359. https://doi.org/10.1016/j.trc.2017.11.010.
Cheng, J., and Y. Lee. 2006. “Model for three-dimensional highway alignment.” J. Surv. Eng. 132 (12): 913–920. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:12(913.
Easa, A. M., H. Dong, and J. Li. 2007a. “Use of satellite imagery for establishing road horizontal alignments.” J. Surv. Eng. 133 (1): 29–35. https://doi.org/10.1061/(ASCE)0733-9453(2007)133:1(29).
Easa, S. M., H. Dong, and J. Li. 2007b. “Approximate extraction of spiralled horizontal curves from satellite imagery.” J. Surv. Eng. 133 (1): 36–40. https://doi.org/10.1061/(ASCE)0733-9453(2007)133:1(36.
Fathi, H., F. Dai, and M. Lourakis. 2015. “Automated as-built 3D reconstruction of civil infrastructure using computer vision: Achievements, opportunities, and challenges.” Adv. Eng. Inf. 29 (2): 149–161. https://doi.org/10.1016/j.aei.2015.01.012.
Fu, K., Y. Xie, H. Jing, and J. Zhu. 2019. “Fast spatial–temporal stereo matching for 3D face reconstruction under speckle pattern projection.” Image Vision Comput. 85 (May): 36–45. https://doi.org/10.1016/j.imavis.2019.02.007.
Hutton, J. M., K. M. Bauer, C. A. Fees, and A. Smiley. 2015. “Evaluation of left-turn wheel wandering using the naturalistic driving study data.” J. Saf. Res. 54 (Sep): 5–15. https://doi.org/10.1016/j.jsr.2015.06.016.
Imran, M., Y. M. Hssan, and D. Patterson. 2006. “GPS-GIS-based procedure for tracking vehicle path on horizontal alignments.” Comput.-Aided Civ. Infrastruct. Eng. 21 (5): 383–394. https://doi.org/10.1111/j.1467-8667.2006.00444.x.
Ishikawa, K., Y. Amano, T. Hashizume, J. Takiguchi, and N. Kajiwara. 2007. “A mobile mapping system for precise road line localization using a single camera and 3D road model.” J. Rob. Mechatron. 19 (2): 174–180. https://doi.org/10.20965/jrm.2007.p0174.
Li, L., W. Luo, and K. C. P. Wang. 2018. “Lane marking detection and reconstruction with line-scan imaging data.” Sensors 18 (5): 1635. https://doi.org/10.3390/s18051635.
Li, Z., M. V. Chitturi, A. R. Bill, and D. A. Noyce. 2012. “Automated identification and extraction of horizontal curve information from geographic information system roadway maps.” Transp. Res. Rec. 2291 (1): 80–92. https://doi.org/10.3141/2291-10.
Lin, Z., P. He, C. Zhang, and Z. Zhang. 2020. “A robust approach to reading recognition of pointer meters based on improved mask-RCNN.” Neurocomputing 388 (May): 90–101. https://doi.org/10.1016/j.neucom.2020.01.032.
Luo, W., and L. Li. 2018. “Automatic geometry measurement for curved ramps using inertial measurement unit and 3D LiDAR system.” Autom. Constr. 94 (Oct): 214–232. https://doi.org/10.1016/j.autcon.2018.07.004.
Luo, W., L. Li, and K. C. P. Wang. 2018. “Automatic horizontal curve identification and measurement using mobile mapping systems (MMS)” J. Surv. Eng. 144 (4): 04018007. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000257.
Malekabadi, A. J., M. Khojastehpour, and B. Emadi. 2019. “Comparison of block-based stereo and semi-global algorithm and effects of pre-processing and imaging parameters on tree disparity map.” Sci. Hortic. 247 (Mar): 264–274. https://doi.org/10.1016/j.scienta.2018.12.033.
Masuda, H., and J. He. 2015. “TIN generation and point-cloud compression for vehicle-based mobile mapping system.” Adv. Eng. Inf. 29 (4): 841–850. https://doi.org/10.1016/j.aei.2015.05.007.
Mozas-Calvache, A. T., and J. L. Pérez-García. 2017. “Analysis and comparison of lines obtained from GNSS and UAV for large-scale maps.” J. Surv. Eng. 143 (3): 04016028. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000215.
Nolan, J., R. Eckels, M. J. Olsen, and K. S. Yen. 2007. “Analysis of the multipass approach for collection and processing of mobile laser scan data.” J. Surv. Eng. 143 (3): 04017004. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000224.
Rasdorf, W., D. J. Findley, C. V. Zegeer, C. A. Sundstrom, and J. E. Hummer. 2012. “Evaluation of GIS applications for horizontal curve data collection.” J. Comput. Civ. Eng. 26 (2): 191–203. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000127.
Shahi, K., H. Z. M. Shafri, E. Taherzadeh, S. Mansor, and R. Muniandy. 2015. “A novel spectral index to automatically extract road networks from worldview-2 satellite imagery.” Egypt. J. Remote Sens. Space. Sci. 18 (1): 27–33. https://doi.org/10.1016/j.ejrs.2014.12.003.
Song, Z., H. Sing, J. Li, and H. Pu. 2018. “Circular curve-fitting method for field surveying data with correlated noise.” J. Surv. Eng. 144 (4): 04018010. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000262.
Tsai, Y., J. Wu, and Z. Wang. 2010. “Horizontal roadway curvature computation algorithm using vision technology.” Comput.-Aided Civ. Infrastruct. Eng. 25 (2): 78–88. https://doi.org/10.1111/j.1467-8667.2009.00622.x.
Wang, Y., Y. Gao, A. Achim, and N. Dahnoun. 2014. “Robust obstacle detection based on a novel disparity calculation method and G-disparity.” Comput. Vision Image Understanding 123 (Jun): 23–40. https://doi.org/10.1016/j.cviu.2014.02.014.
Xie, K., K. Ozbay, H. Yang, and C. Li. 2019. “Mining automatically extracted vehicle trajectory data for proactive safety analytics.” Transp. Res. Part C: Emerging Technol. 106 (Sep): 61–72. https://doi.org/10.1016/j.trc.2019.07.004.
Xu, B., L. Wang, and T. Duan. 2019. “A novel hybrid calibration method for FOG-based IMU.” Measurement 147 (Dec): 106900. https://doi.org/10.1016/j.measurement.2019.106900.
Yan, R., Z. Ma, G. Kokogiannakis, and Y. Zhao. 2016. “A sensor fault detection strategy for air handing units using cluster analysis.” Autom. Constr. 70 (Oct): 77–88. https://doi.org/10.1016/j.autcon.2016.06.005.
Yoo, J. H., S. W. Lee, S. K. Park, and D. H. Kim. 2017. “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. https://doi.org/10.1109/TITS.2017.2679222.
Information & Authors
Information
Published In
Journal of Surveying Engineering
Volume 147 • Issue 4 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 20, 2020
Accepted: Apr 27, 2021
Published online: Jun 30, 2021
Published in print: Nov 1, 2021
Discussion open until: Nov 30, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Jiajin Lang, Jiafa Mao, Ronghua Liang, Non-horizontal target measurement method based on monocular vision, Systems Science & Control Engineering, 10.1080/21642583.2022.2068167, 10, 1, (443-458), (2022).