Assessment of traffic sign retroreflectivity for autonomous vehicles: a comparison between handheld retroreflectometer and LiDAR data

Ziyad Nayef Aldoski; Csaba Koren

doi:10.61089/aot2024.qxy24g93

Authors

Ziyad N. Aldoski Department of Highway and Bridge, Technical College of Engineering, Duhok Polytechnic University, Mazi Qr Duhok, Kurdistan-Iraq; Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architecture, Civil Engineering and Transportation Sciences, Széchenyi István University, Győr, Hungary Author https://orcid.org/0000-0002-3362-7657
Csaba Koren Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architecture, Civil Engineering and Transportation Sciences, Széchenyi István University, Győr, Hungary Author https://orcid.org/0000-0002-1034-0557

DOI:

https://doi.org/10.61089/aot2024.qxy24g93

Keywords:

traffic signs, retroreflectivity, autonomous vehicles, LIDAR, traffic safety

Abstract

This study investigates the critical role of retroreflectivity in traffic signs, particularly in the context of autonomous vehicles (AVs), where accurate detection is paramount for road safety. Retroreflectivity, influencing visibility and legibility, is essential for ensuring safe road conditions. The study aims to assess traffic sign retroreflectivity using handheld retroreflectometers and LiDAR data, offering a comprehensive comparison of results with a specific focus on the RA1 and RA2 traffic sign classes. In a real-world setting, an AV equipped with LiDAR sensors, GPS units, and a stereo camera collects data on traffic signs, including point cloud attributes such as intensity and density. Simultaneously, a handheld retroreflectometer measures retroreflectivity coefficients from identified traffic signs. While retroreflectometers provide precision, they face limitations regarding time-consuming measurements and handling large or elevated signs. In contrast, LiDAR systems efficiently evaluate retroreflective features for numerous signs without such constraints. Despite both methods consistently yielding accurate retroreflectivity, the study reveals a limited correlation between LiDAR point cloud data and handheld retroreflectivity coefficients. The implications of these findings are significant, particularly in the selection and maintenance of retroreflective materials in traffic signs, with direct repercussions on overall road safety. The results offer valuable insights into leveraging LiDAR technology to enhance AVs' detection capabilities. Recommendations for further research include exploring factors influencing LiDAR intensity, establishing a more accurate relationship between intensity and retroreflectivity, correcting the point cloud during intensity calibration, and testing empirical prediction models with a larger sample size. These endeavors aim to generate a robust regression graph and determine correlation coefficients, providing a more nuanced understanding of the intricate relationship between LiDAR data and handheld retroreflectivity coefficients in the context of traffic sign assessment.

References

3M Safety Transportation. (2023). Driving road safety in the right direction. https://multimedia.3m.com/mws/media/1534960O/traffic-signage-brochure-2018-pdf.pdf.

Ai, C., & Tsai, Y. J. (2016). An automated sign retroreflectivity condition evaluation methodology using mobile LIDAR and computer vision. Transportation Research Part C: Emerging Technologies, 63, 96–113. https://doi.org/10.1016/j.trc.2015.12.002.

Aldoski, Z. N., & Koren, C. (2023). Impact of Traffic Sign Diversity on Autonomous Vehicles A Literature Review. Periodica Polytechnica Transportation Engineering, 51(4), 1–13. https://doi.org/10.3311/PPtr.21484.

Almutairy, F., Alshaabi, T., Nelson, J., & Wshah, S. (2019). ARTS: Automotive Repository of Traffic Signs for the United States. IEEE Transactions on Intelligent Transportation Systems, 22(1), 457–465. https://doi.org/10.1109/TITS.2019.2958486.

Babić, D., Babić, D., Fiolic, M., & Ferko, M. (2022). Road Markings and Signs in Road Safety. Encyclopedia, 2(4), 1738–1752. https://doi.org/ 10.3390/encyclopedia2040119.

Ben-Bassat, T., Shinar, D., Almqvist, R., Caird, J. K., Dewar, R. E., Lehtonen, E., Salmon, P. M., Sinclair, M., Summala, H., Zakowska, L., & Liberman, G. (2019). Expert evaluation of traffic signs: conventional vs. alternative designs. Ergonomics, 62(6), 734–747. https://doi.org/10.1080/00140139.2019.1567829.

Calvi, A., Gaca, S., Kamiński, T., Kieć, M., & Kruszewski, M. (2021). Guidelines for the Use of Non-Standard Road Signs–Polish Experiences. Archives of Civil Engineering, 67(1). https://doi.org/10.24425/ace.2020.136483.

Carlson, P. J., Brimley, B., Chrysler, S. T., Gibbons, R., & Terry, T. (2017). Recommended guidelines for nighttime overhead sign visibility. Transportation Research Record: Journal of the Transportation Research Board, 2617(1), 27–34. http://dx.doi.org/10.3141/2617-04.

Conshohocken, W. (2008). Standard Test Method for Coefficient of Retroreflection of Retroreflective Sheeting Utilizing the Coplanar Geometry 1. Annual Book of ASTM Standards, 03(Reapproved 2013), 1–8. https://doi.org/10.1520/E0810-03R13.2.

DELTA – a part of FORCE Technology. (2020). RetroSign GRX Retroreflectometer User Manual (English Ed) (Issue November). https://roadsensors.com/products/retrosign-grx/.

European Committee for Standardization. (2007). Fixed, vertical road traffic signs - Part 1: Fixed signs EN 12899-1. 1–57. https://afesp.pt/documentos/EN12899-1_-_Sinais_Fixos.pdf.

Federal Highway Administration. (2021). FHWA-HRT-21-015: Impacts of Automated Vehicles on Highway Infrastructure. March. http://www.ntis.gov.

He Huang, Andi Xu, Xiaokun Han, Huifeng Wang, Luwan Wang, W. S. (2023). LiDAR Perception and Evaluation Method for Road Traffic Marking Retroreflection. Transportation Research Record. https://doi.org/10.1177/036119812211451.

Hrabánek, L. J., & Růžička, M. (2022). Retroreflection of traffic signing for the safe operation of agricultural machinery. Research in Agricultural Engineering, 68(1), 1–8. https://doi.org/10.17221/49/2021-RAE.

Jamal, A., Reza, I., & Shafiullah, M. (2022). Modeling retroreflectivity degradation of traffic signs using artificial neural networks. IATSS Research, 46(4), 499–514. https://doi.org/10.1016/j.iatssr.2022.08.003.

Khrapova, M. (2019). Determining the influence of factors on retroreflective properties of traffic signs. Agronomy Research, 17(1), 1041–1052. https://doi.org/https://doi.org/10.15159/AR.19.082.

Khrapova, M., Růžička, M., & Trnka, V. (2020). Recognition of retroreflective traffic signs by a vehicle camera system. Agronomy Research, 18(2), 888–903. https://doi.org/10.15159/AR.20.062.

Kim, J., Park, B., & Kim, J. (2023). Empirical Analysis of Autonomous Vehicle’s LiDAR Detection Performance Degradation for Actual Road Driving in Rain and Fog. Sensors, 23(6), 2972. https://doi.org/10.3390/s23062972.

Lengyel, H., & Szalay, Z. (2018). Classification of traffic signal system anomalies for environment tests of autonomous vehicles. Production Engineering Archives, 19. https://doi.org/10.30657/pea.2018.19.09.

Lengyel, H., Valoczi, D., & Torok, A. (2021). Determining the minimal safety level of automatic road sign recognition system - Field study survey. Transportation Research Procedia, 55(2019), 307–312. https://doi.org/10.1016/j.trpro.2021.06.035.

Lloyd, J. (2008). A brief history of retroreflective sign face sheet materials. 1–4. https://www.rema.org.uk/pub/pdf/history-retroreflective-materials.pdf.

Ontario Traffic Manual. (2020). Book 4- Ontario Traffic Manual-Ground-mounted Sign and Support Inspection and Maintenance (Issue March). https://stinson.ca/media/amasty/amfile/attach/ca44dce88c1be14e3784c9284135c7f1.pdf.

Opiela, K. S., & Andersen, C. K. (2007). Maintaining traffic sign retroreflectivity: impacts on state and local agencies. Turner-Fairbank Highway Research Center. https://www.fhwa.dot.gov/publications/research/safety/07042/07042.pdf.

ORAFOL Europe GmbH. (2023). Refl ective Solutions / Traffi c Signs & Construction Zones Principles. https://issuu.com/finishwa/docs/reflective-solutions-48821-tc-en?e=5611493/2896788.

Saleh, R. (2021). Analysis of Retroreflection and other Properties of Road Signs [Licentiate dissertation, Dalarna University]. http://urn.kb.se/resolve?urn=urn:nbn:se:du-38673.

Saleh, R., Fleyeh, H., & Alam, M. (2022). An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs. Applied Sciences, 12(5), 2413. https://doi.org/10.3390/ app12052413.

Schulte-Tigges, J., Förster, M., Nikolovski, G., Reke, M., Ferrein, A., Kaszner, D., Matheis, D., & Walter, T. (2022). Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments. Sensors, 22(19), 7146. https://doi.org/10.3390/s22197146.

Scukanec, A., Babic, D., & Sokol, H. (2014). Methodology for measuring traffic signs retroreflection. European Scientific Journal, Special Edition, 3, 135–142. https://core.ac.uk/download/pdf/328024162.pdf.

Széchenyi István University. (2024). The main page, Welcome to Admissions [Satellite video record]. Retrieved January 29, 2024. 2024. https://admissions.sze.hu/welcome.

Tschürtz, H., Wagner, F., Schröter, W., Szalay, Z., & Török, Á. (2021). System of systems safety analysis and evaluation in ZalaZONE. Periodica Polytechnica Transportation Engineering, 49(4), 317–323. https://doi.org/10.3311/PPtr.16526.

Wu, S., Wen, C., Luo, H., Chen, Y., Wang, C., & Li, J. (2015). Using mobile LiDAR point clouds for traffic sign detection and sign visibility estimation. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 565–568. https://doi.org/10.1109/IGARSS.2015.7325826.

Zhang, S., Wang, C., Cheng, M., & Li, J. (2019). Automated visibility field evaluation of traffic sign based on 3D lidar point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2/W13), 1185–1190. https://doi.org/10.5194/isprs-archives-XLII-2-W13-1185-2019.