A Technical White Paper

Abstract

LiDAR has become increasingly prevalent in roadway sensing as automated-vehicle technologies have proliferated, and laser-scanning systems have become more capable and accessible. Because LiDAR can produce three-dimensional representations of the roadway environment and can report return-signal intensity, it is natural to ask whether LiDAR can also be used to determine the retroreflective performance of pavement markings.

The central issue is measurement geometry. Pavement-marking retroreflectivity is not simply an intrinsic brightness value; standardized measurements are made under defined illumination and observation geometries intended to represent a standard nighttime driving condition. ASTM E1710, ASTM E3320, and CEN EN1436 define measurement approaches based on this standardized geometry. LiDAR systems, by contrast, do not have a single standardized geometry for pavement-marking retroreflectivity assessment.

A laboratory study of 16 pavement-marking materials at nine geometries demonstrates the consequence of this difference. Depending on geometry, the difference between the maximum and minimum correlation (K) factors across the tested materials ranged from 27.8% to 122.7%. The results therefore do not support a universal conversion factor between arbitrary LiDAR intensity measurements and standardized 30-meter retroreflectivity.

LiDAR remains highly useful for roadway mapping, marking detection, and environmental characterization. If LiDAR intensity is used to estimate standardized retroreflectivity, it should be calibrated against actual retroreflectometer measurements for the relevant marking and roadway conditions. For standardized retroreflectivity assessment, a retroreflectometer using the applicable measurement geometry remains the appropriate reference.

1. Introduction

LiDAR—Light Detection and Ranging—uses laser light pulses and time-of-flight measurements to determine distance. Scanning in horizontal and vertical directions allows a system to create a three-dimensional representation of the environment surrounding a vehicle. Many systems also provide return-signal intensity, which can assist in identifying and differentiating objects.

Retroreflective pavement markings can generate relatively strong LiDAR returns. This creates an opportunity for automated roadway systems to use LiDAR not only to locate markings but potentially to infer aspects of their optical performance. However, a LiDAR return-intensity measurement is not intrinsically the same quantity as standardized pavement-marking retroreflectivity.

This paper examines how different pavement markings perform when retroreflectivity is measured at different geometries. The analysis focuses on the distinction between LiDAR sensing and standardized photometric retroreflectivity measurement, with particular emphasis on the effects of measurement geometry.

2. Fundamentals of Pavement-Marking Retroreflectivity

Retroreflection is the preferential return of incident light toward its source. It differs from specular reflection, in which light behaves approximately like a mirror, and diffuse reflection, in which incident light is scattered in many directions.

Pavement markings obtain retroreflective behavior through optical elements incorporated into or applied to the marking. The principal examples are glass beads and prismatic cube-corner elements.

Figure 1. Retroreflective behavior of a glass-bead pavement marking.

3. Standardized Retroreflectivity Measurement

Field measurements are performed using either handheld or mobile retroreflectometers. The standards identified for pavement-marking retroreflectivity include ASTM E1710, ASTM E3320, and CEN EN1436.

All three standards identified in the source material define a 30-meter geometry based on a defined standard vehicle and human driver.

Figure 2. Standard 30-meter geometry and the associated illumination and observation angles.

4. LiDAR and Measurement Geometry

LiDAR systems typically use near-infrared light, although other wavelengths can be used. In addition to range, many systems provide return-signal intensity. This intensity can assist with object classification, and retroreflective objects can produce artificially higher intensities.

Unlike standardized retroreflectometry, there is no single standard LiDAR geometry for pavement-marking measurement. Sensors may be mounted at different heights and may observe the pavement at different distances. In many systems, the emitter and receiver are very close to one another, producing a relatively small observation angle.

Consequently, two LiDAR systems may observe the same pavement marking under materially different optical conditions. A LiDAR measurement therefore cannot be assumed to represent the standardized 30-meter retroreflectivity geometry simply because the target is the same pavement marking.

Figure 3. Vehicle outfitted with multiple LiDAR systems for short-range and long-range observation.
Note different illumination entrance angles

5. Laboratory Investigation of Geometry Effects

The laboratory investigation evaluated whether measurements made at different geometries could be correlated sufficiently to permit a non-standard measurement to be related to 30-meter retroreflectivity.

Sixteen pavement-marking samples of various types were measured in a laboratory using a 15-meter photometric range to ASTM D4061. Each sample was measured at nine different geometries, and a correlation factor (K) was calculated.

K = Test Geometry / Geometry H

Geometry H was selected as the reference geometry at random. Similar results are obtained when other geometries are used as the basis.

6. Results: Geometry-Dependent Correlation

The K-factor results show substantial variation among the 16 pavement-marking materials. The degree of variation depends on the measurement geometry.

Geometry A produced K factors ranging from 1.84 to 4.09, a 122.7% difference between the minimum and maximum. Even Geometry G, which includes the 1.24° illumination angle and 1.05° observation angle shown for ASTM E1710, exhibited a 52.2% range.

The results demonstrate that geometry-to-geometry correlation is not a universal property of pavement markings. A conversion factor established for one marking material or measurement configuration cannot automatically be applied to another.

7. Sources of Correlation Variability

The optical response of a pavement marking depends on multiple physical and material characteristics. This study identifies pigmentation; bead size, index, and distribution; penetration; binder material index; surface characteristics; and pavement type as contributors to poor correlation.

These characteristics influence how incident light interacts with the marking and how much light is returned toward a particular observation point. Changing measurement geometry can therefore change the measured response by different amounts for different marking systems.

8. Correlating LiDAR Data to Retroreflectivity

LiDAR return intensity can contain useful information about pavement markings, but intensity should not be treated as a direct measurement of standardized retroreflectivity without calibration.

A practical approach is to use a 30-meter retroreflectometer to establish measured retroreflectivity values and use those measurements to scale LiDAR data for a particular stripe or road type. Because correlation factors vary between geometries and stripe types, multiple correlation factors will likely be required, as a change to any of the characteristics listed in Section 7 above can change the retroreflective properties in arbitrary ways for any given measurement geometry.

The validity of such a calibration depends on the population represented by the calibration data. Changes in marking material, retroreflective element characteristics, surface condition, pavement type, or LiDAR configuration can alter the relationship.

9. Practical Measurement Strategy

LiDAR and retroreflectometry should be treated as complementary technologies rather than interchangeable measurement methods. LiDAR can provide broad-area spatial information and potentially efficient screening, while retroreflectometry provides the standardized photometric reference.

10. Toward Automated Road-Marking Assessment Standards

The increasing use of automated sensing raises the question of whether pavement-marking assessment may eventually require a defined standard observer or sensor geometry for machine-based systems.

A future automated-sensing standard would need to specify sensor geometry and establish a validated relationship to established retroreflectivity metrics. The results presented here indicate that such a relationship cannot be assumed to be universal across marking materials; material-dependent optical response and calibration would need to be addressed.

11. Conclusions

1.       Retroreflectivity is a directional optical property whose measured value depends on illumination and observation geometry.

2.       The standardized 30-meter geometry establishes a defined optical relationship among the vehicle headlamp, pavement marking, and driver’s eye.

3.       LiDAR is a powerful roadway-mapping and environmental-sensing technology and can provide return-signal intensity.

4.       LiDAR systems do not have one standardized pavement-marking measurement geometry.

5.       Laboratory measurements of 16 pavement-marking materials at nine geometries showed substantial material-dependent variation in correlation factors.

6.       The observed K-factor ranges do not support a universal conversion factor from LiDAR intensity to standardized 30-meter retroreflectivity.

7.       LiDAR data can potentially be correlated to standardized retroreflectivity through calibration against measured retroreflectometer data for defined marking and roadway conditions.

8.       For standardized retroreflectivity assessment and compliance, a retroreflectometer designed for the applicable measurement geometry remains the appropriate reference measurement.

References

1. ASTM International, ASTM E3320-21, Standard Test Method for Measurement of Retroreflective Pavement Marking Materials Using a Mobile Retroreflectometer Unit, ASTM International.
2. ASTM International, ASTM E1710-18(2025), Standard Test Method for Measurement of Retroreflective Pavement Marking Materials with CEN-Prescribed Geometry Using a Portable Retroreflectometer, ASTM International.
3. CEN, EN 1436:2018, Road marking materials — Road marking performance for road users — Specifications for road marking performance, European Committee for Standardization.
4. Rennilson, J.J. and Jing Yu, Effects of Geometry on the Measurement of Road Markings, Gamma Scientific.