Abstract
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.
Highlights
Another study found that pavement marking retroreflectivity for white edge lines and yellow edge lines was significantly related to crash frequency on four-lane roads [12]
In the summer 2020 data set, construction zones and temporary pavement markings were causing a large proportion of pavement markings to be undetected by lane keep assist (LKA)
This study explored the use of vehicle lane keep assist (LKA) systems to detect and determine pavement marking conditions
Summary
Difficulty in detecting pavement markings can increase driver workload and cause driver confusion, during more challenging driving conditions such as nighttime and/or inclement weather. Vehicles with lane marking sensors and/or autonomous driving encounter similar challenges. Determining locations where vehicles cannot detect pavement markings is especially important in the new frontier of connected and autonomous vehicles. According to a study conducted by the National Cooperative Highway Research Program (NCHRP), approximately 30% of state agencies perform pavement marking evaluations annually, and the remaining agencies collect pavement marking conditions bi-annually or sporadically [1]. Due to the fact of these widely varying evaluation practices and maintenance schedules, this paper proposes using on-board connected vehicle sensors to provide scalable crowdsourced data that will allow agencies to systematically evaluate their road markings and routinely program their maintenance activities
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