Road rut detection system with embedded multi-channel laser sensor

Han, Jin Wook; Cui, Likun; Shi, Sheng‐Cai

doi:10.1007/s00170-021-08027-w

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Since the attitude sensor can measure the value of θ x (t), the value of v(0) can be calculated according to Figure 5, so that the formula ∆h(t) of the profile of the road surface can be deduced. See Equation (3).…”

Section: Pavement Cross-section Data Acquisitionmentioning

confidence: 99%

“…Therefore, the quality of the road surface is particularly important for the smooth operation of the road and the safe operation of vehicles [ 2 ]. Pavement section testing is the main basis for pavement quality testing [ 3 ]. Regular inspection of the road section can identify problems such as pavement level, rutting, lateral slope and structural depth in time for timely repair and maintenance to ensure the levelness and durability of the road [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Detection and Analysis of Pavement-Section Based on Laser Displacement Sensor

Han,

Xiong,

Liu

2023

Sensors

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The section detection of the pavement is the data basis for measuring the road smoothness, rutting, lateral slope, and structural depth. The detection of the Pavement-Section includes longitudinal-section inspection and cross-section inspection. In this paper, based on multiple laser displacement sensors, fused accelerometers and attitude sensors, and using vehicle-mounted high-speed detection, we design a sensor-fused pavement section data acquisition method, establish the relevant mathematical model, and realize the automatic acquisition of pavement longitudinal and transverse sections. The acceleration sensor is filtered to improve the accuracy of data acquisition, and the error of the detection system is calculated and analyzed. Through the actual measurement, the vehicle-mounted high-speed pavement profile detection method adopted in this paper can not only accurately detect the profile of the pavement profile, but also improve the detection efficiency, providing a cost-effective detection mode for road surface detection.

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Section: Pavement Cross-section Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection and Analysis of Pavement-Section Based on Laser Displacement Sensor

Han,

Xiong,

Liu

2023

Sensors

Self Cite

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“…Automatic rutting detection technology mainly includes laser detection technology, digital image detection technology, automated close-range photogrammetry, and ultrasonic detection technology [7][8][9][10]. Tomasz Garbowskia and Tomasz Gajewski used a semiautomatic inspection tool for pavement conditions to conduct three-dimensional profile scanning [11].…”

Section: Introductionmentioning

confidence: 99%

“…Scholars proposed the method of combining a vehicle-mounted stereo camera and laser for pavement rutting detection, which eliminates the influence of external light [12]. Ultrasonic rutting detection technology was based on the principle of acoustic reflection to transmit acoustic waves to the road surface, achieving the rut depth according to the time difference in echo signal, which was easily affected by environmental noise [9].…”

Section: Introductionmentioning

confidence: 99%

Measuring Surface Deformation of Asphalt Pavement via Airborne LiDAR: A Pilot Study

Zhu,

Gao,

Huang

et al. 2023

Drones

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Measuring the surface deformation of asphalt pavement and acquiring the rutting condition is of great importance to transportation agencies. This paper proposes a rutting measuring method based on an unmanned aerial vehicle (UAV) mounted with Light Detection and Ranging (LiDAR). Firstly, an airborne LiDAR system is assembled and the data acquisition method is presented. Then, the method for point cloud processing and rut depth computation is presented and the results of field testing are discussed. Thirdly, to investigate error factors, the laser footprint positioning model is established and sensitivity analysis is conducted. Factors including flight height, LiDAR instantaneous angel, and ground inclination angle are discussed. The model was then implemented to obtain the virtual rut depth and to verify the accuracy of the field test results. The main conclusions include that the measurement error increases with the flight height, instantaneous angle, and angular resolution of the LiDAR. The inclination angle of the pavement surface has adverse impact on the measuring accuracy. The field test results show that the assembled airborne LiDAR system is more accurate when the rut depth is significant. The findings of this study pave the way for future exploration of rutting measurement with airborne LiDAR.

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