Visibility of various road markings for machine vision

Abstract The same features of road markings – retroreflectivity and daytime visibility – are the key parameters for their recognition by both human drivers and for machine vision (MV) utilised by the emerging technology of automated vehicles. For the purpose of a side-by-side performance assessment of various road markings, 8 materials, differing in colour and retroreflectivity, were tested under laboratory conditions for visibility by LiDAR and by cameras under different weather conditions. Visibility was evaluated under various intensities of rain and fog; simulated effects of glare from oncoming vehicle were also tested. The response of MV equipment depended on (1) the equipment itself, (2) retroreflectivity of road marking, (3) their structure, (4) their colour, and (5) the utilised glass beads. Overall, the highest MV intensities were measured with structured cold plastic reflectorised with ‘premium’ glass beads (refractive index 1.6–1.7) and with white road marking tape. Unexpectedly, orange paint under dry conditions furnished LiDAR recognition disproportionally high to its retroreflectivity. Poorest outcome gave greyish paint imitating severely worn markings. During rain and during fog, use of the ‘premium’ glass beads resulted in significantly improved camera contrast ratio, but there was no such correlation with LiDAR intensity. On average, introduction of moisture lowered the measured contrast ratio by 80 % (range 69 %–86 %) and LiDAR response intensity by 84 % (range 72 %–96 %). Results from this case study can be used for development of road marking materials with improved recognition by MV.

[1]  Tomasz E. Burghardt,et al.  Horizontal road markings for human and machine vision , 2020 .

[2]  Chris Davies Effects of Pavement Marking Characteristics on Machine Vision Technology , 2017 .

[3]  Alessandro Calvi,et al.  A Study on Driving Performance Along Horizontal Curves of Rural Roads , 2015 .

[4]  Long Chen,et al.  Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision , 2018, IEEE/CAA Journal of Automatica Sinica.

[5]  Stefan Muckenhuber,et al.  Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar , 2020, Sensors.

[6]  Tomasz E. Burghardt,et al.  Materials selection for structured horizontal road markings: financial and environmental case studies , 2020, European Transport Research Review.

[7]  Matti Kutila,et al.  Automotive LiDAR performance verification in fog and rain , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[8]  Christoph Stiller,et al.  The Role of Machine Vision for Intelligent Vehicles , 2016, IEEE Transactions on Intelligent Vehicles.

[9]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[10]  D. Watzenig,et al.  Sensors for Automated Driving , 2020, Autonomous Vehicles.

[11]  Ted R Miller BENEFIT-COST ANALYSIS OF LANE MARKING , 1992 .

[12]  Tomasz E. Burghardt,et al.  Yellow thermoplastic road markings with high retroreflectivity: Demonstration study in Texas , 2021 .

[13]  Michael A. Regan,et al.  The Possible Safety Benefits of Enhanced Road Markings: A Driving Simulator Evaluation , 2006 .

[14]  J. Claybrook,et al.  Autonomous vehicles: No driver…no regulation? , 2018, Science.

[15]  Roland Bremond,et al.  Review of the Mechanisms of Visibility Reduction by Rain and Wet Road , 2009 .

[16]  Bart van Arem,et al.  Gaps in the Control of Automated Vehicles on Roads , 2020, IEEE Intelligent Transportation Systems Magazine.

[17]  Mohammed Hadi,et al.  Effect of Environmental Conditions on Performance of Image Recognition-Based Lane Departure Warning System , 2007 .

[18]  C C Rhodes,et al.  PRINCIPLES OF GLASS-BEAD REFLECTORIZATION , 1952 .

[19]  Michael Gatscha,et al.  Rainvision: The Impact of Road Markings on Driver Behaviour – Wet Night Visibility , 2016 .

[20]  Werner Ritter,et al.  A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down? , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[21]  Anton Pashkevich,et al.  Yellow pedestrian crossings: from innovative technology for glass beads to a new retroreflectivity regulation , 2019 .

[22]  Darko Babić,et al.  Application and Characteristics of Waterborne Road Marking Paint , 2015 .

[23]  Mehrdad Dianati,et al.  A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications , 2018, IEEE Internet of Things Journal.

[24]  Ernst D. Dickmanns,et al.  An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles , 1990, IEEE Trans. Syst. Man Cybern..

[25]  F. Bernardin,et al.  Light Transmission in Fog: The Influence of Wavelength on the Extinction Coefficient , 2019, Applied Sciences.

[26]  Tomasz E. Burghardt,et al.  Performance and environmental assessment of prefabricated retroreflective spots for road marking , 2021 .

[27]  Julius Ziegler,et al.  Making Bertha Drive—An Autonomous Journey on a Historic Route , 2014, IEEE Intelligent Transportation Systems Magazine.

[28]  Zygmunt Mierczyk,et al.  Comparison of 905 nm and 1550 nm semiconductor laser rangefinders’ performance deterioration due to adverse environmental conditions , 2014 .

[29]  Giuseppina Pappalardo,et al.  Safety effectiveness and performance of lane support systems for driving assistance and automation - Experimental test and logistic regression for rare events. , 2020, Accident; analysis and prevention.

[30]  Dhiraj Manohar Dhane,et al.  A review of recent advances in lane detection and departure warning system , 2018, Pattern Recognit..

[31]  Tomasz E. Burghardt,et al.  Emissions of Volatile Organic Compounds from road marking paints , 2018, Atmospheric Environment.

[32]  Ane Dalsnes Storsaeter,et al.  Using ADAS to Future-Proof Roads—Comparison of Fog Line Detection from an In-Vehicle Camera and Mobile Retroreflectometer , 2021, Sensors.

[33]  Tomasz E. Burghardt,et al.  Influence of Volatile Organic Compounds Emissions from Road Marking Paints on Ground-level Ozone Formation: Case Study of Kraków, Poland☆ , 2016 .

[34]  Javier Ibanez-Guzman,et al.  What Happens for a ToF LiDAR in Fog? , 2021, IEEE Transactions on Intelligent Transportation Systems.

[35]  J. H. Kim,et al.  Design of Near Infrared Reflective Effective Pigment for LiDAR Detectable Paint - ADDENDUM , 2020, MRS Advances.

[36]  Tomasz E. Burghardt,et al.  Solution for a two-year renewal cycle of structured road markings , 2021 .

[37]  Mohammed Hadi,et al.  Effect of Pavement Marking Retroreflectivity on the Performance of Vision-Based Lane Departure Warning Systems , 2011, J. Intell. Transp. Syst..

[38]  Laurent Bouillaut,et al.  Maintenance Strategy for the Road Infrastructure for the Autonomous Vehicle , 2020 .

[39]  X. Daura,et al.  Road infrastructure support levels for automated driving , 2018 .

[40]  Thomas Brandmeier,et al.  Test methodology for rain influence on automotive surround sensors , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[41]  Hermann Winner,et al.  Three Decades of Driver Assistance Systems: Review and Future Perspectives , 2014, IEEE Intelligent Transportation Systems Magazine.

[42]  Ryo Yanase,et al.  Automated driving recognition technologies for adverse weather conditions , 2019 .

[43]  D de Waard,et al.  Road-edge delineation in rural areas: effects on driving behaviour , 2000, Ergonomics.

[44]  Paul Newman,et al.  Reading between the Lanes: Road Layout Reconstruction from Partially Segmented Scenes , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[45]  Pedro J. Navarro,et al.  A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research , 2019, Sensors.

[46]  Paul J Carlson,et al.  Link between Pavement Marking Retroreflectivity and Night Crashes on Michigan Two-Lane Highways , 2014 .

[47]  Paul Newman,et al.  Reading the Road: Road Marking Classification and Interpretation , 2015, IEEE Transactions on Intelligent Transportation Systems.

[48]  Paul J Carlson,et al.  Enhancing the Roadway Physical Infrastructure for Advanced Vehicle Technologies: A Case Study in Pavement Markings for Machine Vision and a Road Map Toward a Better Understanding , 2017 .

[49]  Fernando Garcia,et al.  A Review of Sensor Technologies for Perception in Automated Driving , 2019, IEEE Intelligent Transportation Systems Magazine.

[50]  Ronen Lerner,et al.  Recent progress in road and lane detection: a survey , 2012, Machine Vision and Applications.

[51]  Wilhelm Stork,et al.  Weather Influence and Classification with Automotive Lidar Sensors , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[52]  Pavel Pribyl,et al.  Analysis of possibility to utilize road marking for the needs of autonomous vehicles , 2016, 2016 Smart Cities Symposium Prague (SCSP).