Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity.

Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data

There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities.

Human Factors Assessment of Pavement Marking Retroreflectivity in Simulated Rain and Dry Conditions

Pavement marking retroreflectivity standards are typically developed with dry conditions in mind, however, driving at night during rainfall is seemingly one of the most challenging and stressful situations for a driver. Furthermore, existing research indicates continuous wet retroreflectivity is relatively weakly correlated with dry retroreflectivity and deteriorates differently over time, leading to the obvious conclusion that dry retroreflectivity standards alone are not enough to ensure that pavement markings meet the needs of drivers across the breadth of roadway conditions that may occur. Consequently, developing standards for minimum continuous wet retroreflectivity for new installations and for maintenance purposes represents an important area for research. This study aims to develop new installation and maintenance values for continuous wet retroreflectivity based on a multifaceted, closed-course study of detectability of pavement markings in simulated rain and dry conditions. A series of 20 pavement marking samples was evaluated in relation to detection distance and subjective rating. The results of the study indicated that pavement markings need to be maintained at a continuous wet retroreflectivity value of 50 mcd/m2/lux based on a participant pool that skewed older in age, but that likely represents something close to the 85th-percentile driver. Additional salient findings included observed wet retroreflectivity loss in the existing literature of approximately 7% per month, as well as the maximum preview time in simulated rain conditions being substantially lower than in dry conditions.