Abstract
The quality of lane markings is pivotal for safe operations and efficient trajectory generations of connected and autonomous vehicles (AVs). However, most studies are devoted to enhancing in-vehicle detection systems and ignore the impact of faulty lane markings. An instrumented vehicle was employed to mimic the data input of an AV and real-world trials were conducted on (1) live motorways; and (2) a controlled motorway facility. From the live motorway data, causal factors affecting computer vision lane detection and classification algorithms were examined, and an enhanced lane classification algorithm was developed to overcome the limitations posed by poor lane markings. In the controlled motorway facility, experiments to modify the physical appearance of the lane markings were conducted to further test the performance of the developed algorithm. The detection rates of the developed lane classification algorithm were compared with the lane departure warning (LDW) system already implemented in the vehicle. Findings revealed that the LDW system is accurate over 95% and 54% of the time when lanes are faded by 50% and 75% respectively. Further testing on the quality of the lane markings was carried out virtually in such a way that the experiments were replicated in a simulation environment to (1) identify lane marking conditions that can be reliably adopted for safe operations of AVs, (2) estimate the effect of adverse weather and lighting conditions on road markings detection, and (3) address localization issues for AVs. Simulation results show that poor lane markings have a significant negative impact on AV safety, especially in inclement weather and poor light conditions inducing an increase in conflicts and delays. This can be compensated for if more sophisticated sensors are employed in AVs, and the operators of road networks develop lane-based digital road maps.
Get full access to this article
View all available purchase options and get full access to this article.
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 paper is based on a project (CAVIAR) commissioned by National Highways (UK). The authors would like to express their special thanks to John Matthewson (from National Highways) and Jon de Souza and Eugenie Blyth (both from Galliford Try, the project partner) for their assistance in this research during the project. The opinions in this paper are those of the authors and do not necessarily reflect those of the National Highways or Galliford Try. The authors remain solely responsible for any errors or omissions.
References
Austroads. 2019. Guide to traffic management Part 10: Transport control—Types of devices. Sydney, Australia: Austroads.
Bajpai, J. N. 2016. “Emerging vehicle technologies & the search for urban mobility solutions.” Urban Plann. Transport Res. 4 (1): 83–100. https://doi.org/10.1080/21650020.2016.1185964.
Burghardt, T. E., R. Popp, B. Helmreich, T. Reiter, G. Böhm, G. Pitterle, and M. Artmann. 2021. “Visibility of various road markings for machine vision.” Case Stud. Constr. Mater. 15 (May): e00579. https://doi.org/10.1016/j.cscm.2021.e00579.
Carlson, P. J., E. S. Park, and D. H. Kang. 2013. “Investigation of longitudinal pavement marking retroreflectivity and safety.” Transp. Res. Rec. 2337 (1): 59–66. https://doi.org/10.3141/2337-08.
Carreras, A., X. Daura, J. Erhart, and S. Ruehrup. 2018. “Road infrastructure support levels for automated driving. Session EU-TP1488.” In Proc., 25th ITS World Congress, 12–20. Copenhagen, Denmark: ERTICO.
Catapult Transport Systems UK. 2017. “Future proofing infrastructure for connected and automated vehicles.” Accessed October 9, 2023. https://s3-eu-west-1.amazonaws.com/media.ts.catapult/wp-content/uploads/2017/04/25115313/ATS40-Future-Proofing-Infrastructure-for-CAVs.pdf.
Choubane, B., J. Sevearance, C. Holzschuher, J. Fletcher, and C. Wang. 2018. “Development and implementation of a pavement marking management system in Florida.” Transp. Res. Rec. 2672 (12): 209–219. https://doi.org/10.1177/0361198118787081.
Formosa, N., M. Quddus, S. Ison, M. Abdel-Aty, and J. Yuan. 2020. “Predicting real-time traffic conflicts using deep learning.” Accid. Anal. Prev. 136 (Mar): 105429. https://doi.org/10.1016/j.aap.2019.105429.
Formosa, N., M. Quddus, A. Papadoulis, and A. Timmis. 2022. “Validating a traffic conflict prediction technique for motorways using a simulation approach.” Sensors 22 (2): 566. https://doi.org/10.3390/s22020566.
Furda, A., and L. Vlacic. 2011. “Driving in real-world city decision making.” IEEE Intell. Transp. Syst. Mag. 3 (1): 4–17. https://doi.org/10.1109/MITS.2011.940472.
Gupta, A., and A. Choudhary. 2018. “A framework for camera-based real-time lane and road surface marking detection and recognition.” IEEE Trans. Intell. Veh. 3 (4): 476–485. https://doi.org/10.1109/TIV.2018.2873902.
Hadj-bachir, M., and P. De Souza. 2019. “LIDAR sensor simulation in adverse weather condition for driving assistance development.” Accessed October 9, 2023. https://hal.archives-ouvertes.fr/hal-01998668/.
Hallmark, S., D. Veneziano, and T. Litteral. 2019. Preparing local agencies for the future of connected and autonomous vehicles. Minnesota Department of Transportation Research Services and Library.
Hatfield, J., S. Murphy, R. F. S. Job, and W. Du. 2009. “The effectiveness of audio-tactile lane-marking in reducing various types of crash: A review of evidence, template for evaluation, and preliminary findings from Australia.” Accid. Anal. Prev. 41 (May): 365–379. https://doi.org/10.1016/j.aap.2008.12.003.
Highways England. 2020. Inspection and assessment of road markings and road studs. Guildford, UK: Highways England.
Hu, J., S. Sun, J. Lai, S. Wang, Z. Chen, and T. Liu. 2023. “CACC simulation platform designed for urban scenes.” IEEE Trans. Intell. Veh. 8 (4): 2857–2874. https://doi.org/10.1109/TIV.2023.3234890.
Kang, J. M., T. S. Yoon, E. Kim, and J. B. Park. 2020. “Lane-level map-matching method for vehicle localization using GPS and camera on a high-definition map.” Sensors 20 (8): 1–22. https://doi.org/10.3390/s20082166.
Katrakazas, C., M. Quddus, and W. H. Chen. 2019. “A new integrated collision risk assessment methodology for autonomous vehicles.” Accid. Anal. Prev. 127 (Jun): 61–79. https://doi.org/10.1016/j.aap.2019.01.029.
Katrakazas, C., M. Quddus, W.-H. Chen, and L. Deka. 2015. “Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions.” Transp. Res. Part C Emerging Technol. 60 (May): 416–442. https://doi.org/10.1016/j.trc.2015.09.011.
Khan, J. A., L. Wang, E. Jacobs, A. Talebian, S. Mishra, C. A. Santo, M. Golias, and C. Astorne-Figari. 2019. “Smart cities connected and autonomous vehicles readiness index.” In Proc., 2nd ACM/EIGSCC Symp. on Smart Cities and Communities, SCC 2019, 1–8. New York: Association for Computing Machinery. https://doi.org/10.1145/3357492.3358631.
Khattak, Z. H., M. D. Fontaine, and B. L. Smith. 2021. “Exploratory investigation of disengagements and crashes in autonomous vehicles under mixed traffic: An endogenous switching regime framework.” IEEE Trans. Intell. Transp. Syst. 22 (12): 7485–7495. https://doi.org/10.1109/TITS.2020.3003527.
Kim, J. G., J. H. Yoo, and J. C. Koo. 2018. “Road and lane detection using stereo camera.” In Proc., 2018 IEEE Int. Conf. on Big Data and Smart Computing, BigComp 2018, 649–652. New York: IEEE.
Kmiotek, P., and Y. Ruichek. 2008. “Multisensor fusion based tracking of coalescing objects in urban environment for an autonomous vehicle navigation.” In Proc., 2008 IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, 52–57. New York: IEEE.
Kuutti, S., S. Fallah, K. Katsaros, M. Dianati, F. Mccullough, and A. Mouzakitis. 2018. “A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications.” IEEE Internet Things J. 5 (2): 829–846. https://doi.org/10.1109/JIOT.2018.2812300.
Lai, J., J. Hu, L. Cui, Z. Chen, and X. Yang. 2020. “A generic simulation platform for cooperative adaptive cruise control under partially connected and automated environment.” Transp. Res. Part C Emerging Technol. 121 (Dec): 102874. https://doi.org/10.1016/j.trc.2020.102874.
Li, B., D. Song, H. Li, A. Pike, and P. Carlson. 2018. “Lane marking quality assessment for autonomous driving.” In Proc., IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 8443–8448. New York: IEEE.
Liu, Y., M. Tight, Q. Sun, and R. Kang. 2019. “A systematic review: Road infrastructure requirement for Connected and Autonomous Vehicles (CAVs).” J. Phys. Conf. Ser. 1187 (4): 042073. https://doi.org/10.1088/1742-6596/1187/4/042073.
Lv, C., D. Cao, Y. Zhao, D. J. Auger, M. Sullman, H. Wang, L. M. Dutka, L. Skrypchuk, and A. Mouzakitis. 2018. “Analysis of autopilot disengagements occurring during autonomous vehicle testing.” IEEE/CAA J. Autom. Sin. 5 (1): 58–68. https://doi.org/10.1109/JAS.2017.7510745.
MacEacheron, C., E. Hildebrand, and T. Hanson. 2019. “The deterioration of pavement marking retroreflectivity: Are we ready to adopt minimum standards?” In Proc., 12th Int. Transportation Specialty Conf. 2018, 228–237. Fredericton, Canada: Canadian Society for Civil Engineering.
Mahlberg, J. A., R. S. Sakhare, H. Li, J. K. Mathew, D. M. Bullock, and G. C. Surnilla. 2021. “Prioritizing roadway pavement marking maintenance using lane keep assist sensor data.” Sensors 21 (18): 1–16. https://doi.org/10.3390/s21186014.
Mamun, A., E. P. Al-Ping, J. Hossen, A. Tahabilder, and B. Jahan. 2022. “A comprehensive review on lane marking detection using deep neural networks.” Sensors 22 (19): 7682. https://doi.org/10.3390/s22197682.
Matowicki, M., O. Pribyl, and P. Pribyl. 2016. “Analysis of possibility to utilize road marking for the needs of autonomous vehicles.” In Proc., 2016 Smart Cities Symp. Prague (SCSP), 1–6. New York: IEEE.
Neumeister, D., and D. Pape. 2019. Automated vehicles and adverse weather. Washington, DC: Federal Highway Administration.
Niu, J., J. Lu, M. Xu, P. Lv, and X. Zhao. 2016. “Robust lane detection using two-stage feature extraction with curve fitting.” Pattern Recognit. 59 (Nov): 225–233. https://doi.org/10.1016/j.patcog.2015.12.010.
Papadoulis, A. 2019. Vol. 3 of Safety impact of connected and autonomous vehicles on motorways: A traffic microsimulation study. Loughborough: Loughborough Univ.
Park, S., D. Kim, and K. Yi. 2016. “Vehicle localization using an AVM camera for an automated urban driving.” In Proc., 2016 IEEE Intelligent Vehicles Symp., 871–876. New York: IEEE.
Pike, A. M., H. G. Hawkins, and P. J. Carlson. 2007. “Evaluating the retroreflectivity of pavement marking materials under continuous wetting conditions.” Transp. Res. Board 2015 (1): 81–90. https://doi.org/10.3141/2015-10.
Sanusi, F., J. Choi, Y. H. Kim, and R. Moses. 2022. “Development of a knowledge base for multiyear infrastructure planning for connected and automated vehicles.” J. Transp. Eng. Part A Syst. 148 (4): 03122001. https://doi.org/10.1061/jtepbs.0000656.
Singh, M. K., N. Formosa, M. Quddus, C. K. Man, C. Morton, and C. B. Masera. 2023. “Lane closures versus narrow lanes: The traffic efficiency and safety impacts of connected and autonomous vehicles at roadworks.” In Proc., Transportation Research Board 102nd Annual Meeting Transportation Research Board. Washington, DC: Transportation Research Board.
Stacy, A. R. 2019. Evaluation of machine vision collected pavement marking quality data for use in transportation asset management. College Station, TX: Texas A&M Univ.
Taubel, G., R. Sharma, and J. S. Yang. 2014. “An experimental study of a lane departure warning system based on the optical flow and Hough transform methods.” WSEAS Trans. Syst. 13 (1): 105–115.
Ukkusuri, S. V., F. Sagir, N. Mahajan, B. Bowman, and S. Sharma. 2019. Strategic and tactical guidance for the connected and autonomous vehicle future, 87. Washington, DC: Federal Highway Administration.
Van der Horst, A. 1990. “A time-based analysis of road user behaviour in normal and critical encounters.” Doctoral thesis, Electrical Engineering, Mathematics and Computer Science, Delft Univ. of Technology.
Vargas, J., S. Alsweiss, O. Toker, R. Razdan, and J. Santos. 2021. “An overview of autonomous vehicles sensors and their vulnerability to weather conditions.” Sensors 21 (16): 5397. https://doi.org/10.3390/s21165397.
Vivacqua, R. P. D., M. Bertozzi, P. Cerri, F. N. Martins, and R. F. Vassallo. 2018. “Self-localization based on visual lane marking maps: An accurate low-cost approach for autonomous driving.” IEEE Trans. Intell. Transp. Syst. 19 (2): 582–597. https://doi.org/10.1109/TITS.2017.2752461.
Watanabe, Y., K. Sato, and H. Takada. 2020. “DynamicMap 2.0: A traffic data management platform leveraging clouds, edges and embedded systems.” Int. J. Intell. Transp. Syst. Res. 18 (1): 77–89. https://doi.org/10.1007/s13177-018-0173-7.
Wu, L., S. R. Geedipally, and A. M. Pike. 2018. “Safety evaluation of alternative audible lane departure warning treatments in reducing traffic crashes: An empirical Bayes observational before–after study.” Transp. Res. Board 2672 (21): 30–40. https://doi.org/10.1177/0361198118776481.
Xiong, H., D. Yu, J. Liu, H. Huang, Q. Xu, J. Wang, and K. Li. 2020. “Fast and robust approaches for lane detection using multi-camera fusion in complex scenes.” IET Intel. Transport Syst. 14 (12): 1582–1593. https://doi.org/10.1049/iet-its.2019.0399.
Yoneda, K., N. Suganuma, R. Yanase, and M. Aldibaja. 2019. “Automated driving recognition technologies for adverse weather conditions.” IATSS Res. 43 (4): 253–262. https://doi.org/10.1016/j.iatssr.2019.11.005.
Zhang, L., F. Chen, X. Ma, and X. Pan. 2020. “Fuel economy in truck platooning: A literature overview and directions for future research.” J. Adv. Transp. 2020 (2604012): 1–10. https://doi.org/10.1155/2020/2604012.
Zhang, X., Y. Gong, Z. Li, X. Liu, S. Pan, and J. Li. 2021. “Multi-modal attention guided real-time lane detection.” In Proc., 2021 6th IEEE Int. Conf. on Advanced Robotics and Mechatronics, ICARM 2021, 146–153. New York: IEEE.
Zhang, Y., and H. Ge. 2012. “Assessment of presence conditions of pavement markings with image processing.” Transp. Res. Board 2272 (1): 94–102. https://doi.org/10.3141/2272-11.
Zheng, L., B. Li, B. Yang, H. Song, and Z. Lu. 2019. “Lane-level road network generation techniques for lane-level maps of autonomous vehicles: A survey.” Sustainability 11 (16): 1–19. https://doi.org/10.3390/su11164511.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 7, 2022
Accepted: Aug 22, 2023
Published online: Oct 28, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 28, 2024
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.