Technical Papers
Dec 22, 2014

Critical Assessment of an Enhanced Traffic Sign Detection Method Using Mobile LiDAR and INS Technologies

Publication: Journal of Transportation Engineering
Volume 141, Issue 5

Abstract

Traffic signs are important roadway assets that provide critical guidance, including regulations and safety-related information, to road users. Traffic signs need to be inventoried by transportation agencies. However, the traditional manual methods carried out in the field are dangerous, labor-intensive, and time-consuming. There is a need to develop alternative methods to cost-effectively inventory traffic signs. The research reported in this paper, sponsored by the U.S. DOT Research and Innovative Technology Administration Program, is to critically assess an alternative traffic sign inventory method using mobile light detection and ranging (LiDAR), and inertial navigation system (INS), technologies. The contribution of this paper is three-fold, as follows: (1) an alternative traffic sign inventory method is proposed using mobile LiDAR and INS technologies, (2) a key LiDAR parameter calibration procedure (including a sensitivity study of the key parameters) is proposed to achieve a desirable traffic sign detection rate, and (3) the reliability and productivity of the proposed method is critically assessed (by quantitatively measuring the detection rate and processing time of the proposed method). Actual data, collected on an interstate highway (I-95) and a local urban road (37th Street in Savannah, Georgia), were used to critically assess the performance. Results show that the proposed method can correctly detect 94.0 and 91.4% of the traffic signs on interstate highways and local urban roads with less than seven false-positive cases. Results also show that when compared to the in-field manual survey test conducted by Georgia DOT, the proposed method can potentially reduce the processing time for sign inventory by approximately 76%. The results demonstrate that the proposed method is promising for establishing a cost-effective traffic sign inventory method for transportation agencies. Future research directions are also recommended.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

The research reported in this paper was sponsored by the U.S. DOT Research and Innovative Technology Administration (RITA) Program (DTOS59-10-H-0003). The writers would like to thank the U.S. DOT RITA Program for its support. This paper represents the opinion of the writers, who are responsible for its research, content, and conclusions.

References

Ai, C., and Tsai, Y. (2014). “Geometry preserving active polygon-incorporated sign detection algorithm.” J. Comput. Civ. Eng., 04014092.
Ai, C., and Tsai, Y. J. (2012). “Hybrid active contour-incorporated sign detection algorithm.” J. Comput. Civ. Eng., 28–36.
Benallal, M., and Meunier, J. (2003). “Real-time color segmentation of road signs.” Proc., IEEE Canadian Conf. on Electrical and Computer Engineering, Vol. 1823, New York, 1823–1826.
Brenner, C. (2009). “Extraction of features from mobile laser scanning data for future driver assistance systems.” Advances in GIScience, Springer, Amsterdam, Netherlands, 25–42.
Brich, S. C. (2002). “A determination of the appropriateness of Virginia’s retroreflective sign sheeting specification for fluorescent orange construction and maintenance signs.”, Charlottesville, VA.
Carlson, P. J., and Lupes, M. S. (2007). “Methods for maintaining traffic sign retroreflectivity.”, Texas Transportation Institute, College Station, TX.
Damavandi, Y. B., and Mohammadi, K. (2004). “Speed limit traffic sign detection and recognition.” Proc., IEEE Conf. on Cybernetics and Intelligent Systems, New York, 797–802.
de la Escalera, A., Moreno, L. E., Salichs, M. A., and Armingol, J. M. (1997). “Road traffic sign detection and classification.” IEEE Trans. Ind. Electron., 44(6), 848–859.
FHWA (Federal Highway Administration). (2009). Manual on uniform traffic control devices for streets and highways, Washington, DC.
Gomez-Moreno, H., Maldonado-Bascon, S., Gil-Jimenez, P., and Lafuente-Arroyo, S. (2010). “Goal evaluation of segmentation algorithms for traffic sign recognition.” IEEE Trans. Intell. Transp. Syst., 11(4), 917–930.
Hiremagalur, J., Yen, K., Lasky, T., and Ravani, B. (2009). “Testing and performance evaluation of fixed terrestrial three-dimensional laser scanning systems for highway applications.” Transportation Research Record 2098, Transportation Research Board, Washington, DC, 29–40.
Jiang, Z., McCord, M. R., and Goel, P. K. (2006). “Improved AADT estimation by combining information in image- and ground-based traffic data.” J. Transp. Eng., 523–530.
Kaasalainen, S., et al. (2009). “Radiometric calibration of LiDAR intensity with commercially available reference targets.” IEEE Trans. Geosci. Remote Sens., 47(2), 588–598.
Laefer, D. F., and Pradhan, A. R. (2006). “Evacuation route selection based on tree-based hazards using light detection and ranging and GIS.” J. Transp. Eng., 312–320.
Laflamme, C., Kingston, T., and McCuaig, R. (2006). “Automated mobile mapping for asset managers.” Proc., Shaping the ChangeInt. Federation of Surveyors Congress, Copenhagen, Denmark.
Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H., and Lopez-Ferreras, F. (2007). “Road-sign detection and recognition based on support vector machines.” IEEE Trans. Intell. Transp. Syst., 8(2), 264–278.
McQuat, G. J. (2011). “Feature extraction workflows for urban mobile-terrestrial LIDAR data.” 〈http://qspace.library.queensu.ca/bitstream/1974/6530/1/Mcquat_Gregory_J_201105_MSc.pdf〉 (Mar. 20, 2013).
Paoly, D., and Staud, A. B. (2000). “Use of mobile GIS for sign inventories.” Proc., GIS for Transportation, AASHTO, Washington, DC.
Pu, S., Rutzinger, M., Vosselman, G., and Elberink, S. O. (2011). “Recognizing basic structures from mobile laser scanning data for road inventory studies.” J. Photogramm. Remote Sens., 66(6), S28–S39.
Rottensteiner, F., and Briese, C. (2003). Automatic generation of building models from LiDAR data and the integration of aerial images, Vol. 34, Int. Society for Photogrammetry and Remote Sensing, Dresden, Germany.
Rutzinger, M., Elberink, S. O., Pu, S., and Vosselman, G. (2009). “Automatic extraction of vertical walls from mobile and airborne laser scanning data.” Int. Archives of Photogrammetry and Remote Sensing and Spatial Information Sciences, Vol. 38, Vienna, Austria, 7–11.
Taylor, D. R., Muthiah, S., Kulakowski, B. T., Mahoney, K. M., and Porter, R. J. (2007). “Artificial neural network speed profile model for construction work zones on high-speed highways.” J. Transp. Eng., 198–204.
Torresen, J., Bakke, J. W., and Sekanina, L. (2004). “Efficient recognition of speed limit signs.” Proc., Int. IEEE Conf. on Intelligent Transportation Systems, New York, 652–656.
Trimble. (2010). T3D analyst software calibration training manual, Brossard, QC, Canada.
Trimble Trident-3D Analyst version 5.0 [Computer software]. Sunnyvale, CA, Trimble Navigation.
Tsai, Y., Kim, P., and Wang, Z. (2009). “Generalized traffic sign detection model for developing a sign inventory.” J. Comput. Civ. Eng., 266–276.
Tsai, Y., and Wu, J. (2002). “Shape- and texture-based 1-D image processing algorithm for real-time stop sign road inventory data collection.” Intell. Transp. Syst. J., 7(3), 213–234.
Veneziano, D., Souleyrette, R., and Hallmark, S. (2003). “Integration of light detection and ranging technology with photogrammetry in highway location and design.” Transportation Research Record 1836, Transportation Research Board, Washington, DC, 1–6.
Wolshon, B. (2003). “Louisiana traffic sign inventory and management system.”, Louisiana Transportation Research Center, Baton Rouge, LA.
Wu, J., and Tsai, Y. J. (2005). “Real-time speed limit sign recognition based on locally adaptive thresholding and depth-first-search.” Photogramm. Eng. Remote Sens., 71(4), 405–414.

Information & Authors

Information

Published In

Go to Journal of Transportation Engineering
Journal of Transportation Engineering
Volume 141Issue 5May 2015

History

Received: Apr 10, 2012
Accepted: Oct 17, 2014
Published online: Dec 22, 2014
Published in print: May 1, 2015
Discussion open until: May 22, 2015

Permissions

Request permissions for this article.

Authors

Affiliations

Chengbo Ai, A.M.ASCE [email protected]
Research Engineer I, School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr., Atlanta, GA 30332. E-mail: [email protected]
Yi-Chang James Tsai [email protected]
P.E.
Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr., Atlanta, GA 30332; and Changjiang Scholar, Chang’an Univ., Xi’an, China (corresponding author). E-mail: [email protected]

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

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share