Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

Mahlberg, Justin; Cheng, Yi-Ting; Bullock, Darcy M.; Habib, Ayman

doi:10.3390/futuretransp1030039

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…In this study, geometric/morphological and learning-based lane marking extraction strategies, as described in [14,15,21], are adopted. The general workflow, including generalized intensity normalization and lane marking extraction, is presented in Figure 6.…”

Section: Lane Marking Extractionmentioning

confidence: 99%

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Generalized LiDAR Intensity Normalization and Its Positive Impact on Geometric and Learning-Based Lane Marking Detection

2022

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Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS) have been utilized to detect lane markings through intensity-based approaches. As LiDAR data continue to be used for lane marking extraction, greater emphasis is being placed on enhancing the utility of the intensity values. Typically, intensity correction/normalization approaches are conducted prior to lane marking extraction. The goal of intensity correction is to adjust the intensity values of a LiDAR unit using geometric scanning parameters (i.e., range or incidence angle). Intensity normalization aims at adjusting the intensity readings of a LiDAR unit based on the assumption that intensity values across laser beams/LiDAR units/MMS should be similar for the same object. As MMS technology develops, correcting/normalizing intensity values across different LiDAR units on the same system and/or different MMS is necessary for lane marking extraction. This study proposes a generalized correction/normalization approach for handling single-beam/multi-beam LiDAR scanners onboard single or multiple MMS. The generalized approach is developed while considering the intensity values of asphalt and concrete pavement. For a performance evaluation of the proposed approach, geometric/morphological and deep/transfer-learning-based lane marking extraction with and without intensity correction/normalization is conducted. The evaluation shows that the proposed approach improves the performance of lane marking extraction (e.g., the F1-score of a U-net model can change from 0.1% to 86.2%).

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Section: Lane Marking Extractionmentioning

confidence: 99%

“…In this study, road surface point clouds before and after intensity normalization are used as input for geometric/morphological lane marking extraction [21]. The conceptual basis of the adopted approach is that lane marking points have intensity values higher than pavement ones.…”

Section: Geometric/morphological Lane Marking Extractionmentioning

confidence: 99%

Generalized LiDAR Intensity Normalization and Its Positive Impact on Geometric and Learning-Based Lane Marking Detection

2022

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“…Through a system calibration procedure, mounting parameters between camera/LiDAR units and a GNSS/Inertial Measurement Unit (IMU) navigation system are estimated, facilitating the reconstruction of georeferenced, well-registered/georeferenced point clouds from the LiDAR scanners [ 22 ]. There are many uses for the registered point clouds, including pavement marking evaluation, lane widths, pavement distress, and ditch line mapping [ 12 , 23 , 24 , 25 ]. For lane width estimation, the pavement markings are extracted from registered point clouds.…”

Section: Evaluation Protocol—validation Of Camera Detection Lane Widt...mentioning

confidence: 99%

Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data

Mahlberg

Cheng

et al. 2022

Sensors

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The United States has over three trillion vehicle miles of travel annually on over four million miles of public roadways, which require regular maintenance. To maintain and improve these facilities, agencies often temporarily close lanes, reconfigure lane geometry, or completely close the road depending on the scope of the construction project. Lane widths of less than 11 feet in construction zones can impact highway capacity and crash rates. Crash data can be used to identify locations where the road geometry could be improved. However, this is a manual process that does not scale well. This paper describes findings for using data from onboard sensors in production vehicles for measuring lane widths. Over 200 miles of roadway on US-52, US-41, and I-65 in Indiana were measured using vehicle sensor data and compared with mobile LiDAR point clouds as ground truth and had a root mean square error of approximately 0.24 feet. The novelty of these results is that vehicle sensors can identify when work zones use lane widths substantially narrower than the 11 foot standard at a network level and can be used to aid in the inspection and verification of construction specification conformity. This information would contribute to the construction inspection performed by agencies in a safer, more efficient way.

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“…Values are reported in m/km or in/mi, and low values indicate better pavement quality (Kırbaş, 2021). Recent initiatives use LiDAR for pavement inspection because it can provide additional information on roadway drainage, pavement markings, and lane widths (Mekker et al, 2018;Ravi et al, 2020a;Ravi et al, 2020b;Cheng et al, 2020;Lin et al, 2020;Mahlberg et al, 2021b;Lin et al, 2021;Ravi et al, 2021;Mahlberg et al, 2022a;Feng et al, 2022). The data collected from these evaluations can be used to prioritize maintenance and rehabilitation efforts, optimize pavement design and material selection, and improve the overall performance of transportation infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Applications of using connected vehicle data for pavement quality analysis

Mahlberg,

Li,

Zachrisson

et al. 2024

Front. Future Transp.

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Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of <0.001, but with a modest fixed offset bias. The scalability of these techniques is illustrated with graphics characterizing IRI values obtained from 730,000 crowdsourced data segments over 5,800 miles of I-80 in April of 2022 and October 2022. This paper looks at the use of standard original equipment manufacturer (OEM) on-board sensor data from production vehicles to assess approximately 100 miles of roadway pavements before, during, and after construction. The completed construction projects observed IRI improvements of 10 in/mi to 100 in/mi. These results suggest that it is now possible to monitor pavement ride quality at a system level, even with a small proportion of connected vehicles (CV) providing roughness data.

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Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

Cited by 7 publications

References 26 publications

Generalized LiDAR Intensity Normalization and Its Positive Impact on Geometric and Learning-Based Lane Marking Detection

Generalized LiDAR Intensity Normalization and Its Positive Impact on Geometric and Learning-Based Lane Marking Detection

Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data

Applications of using connected vehicle data for pavement quality analysis

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