Road Surface Condition Inspection Using a Laser Scanner Mounted on an Autonomous Driving Car

Author(s):  
Kenta Urano ◽  
Kei Hiroi ◽  
Shinpei Kato ◽  
Nozomi Komagata ◽  
Nobuo Kawaguchi
2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jungil Shin ◽  
Hyunsuk Park ◽  
Taejung Kim

A frozen or wet road surface is a cause of skidding and accidents, so road surface condition is important information for driving safety. Some instruments and methods have been developed to investigate road surface conditions based on optical imagery, although an active sensor is needed, regardless of the time of day. Recently, the laser scanner, which acquires backscattering intensity data related to reflectivity, has become popular in various fields. There is a need to investigate road surface conditions (frozen, wet, or dry) using laser backscattering intensity. This study tries to analyze signal characteristics of laser backscattering intensity to detect frozen and wet surfaces on roads. An ice target with a 7 cm thickness was placed on a road surface, and a wet surface was made due to the melting ice. The ice target, wet surface, dry surface, and roadside vegetation were scanned using a laser scanner. As a result, backscattering signals from the top surface of the ice target were missing due to its smoothness. Dry and wet asphalt surfaces showed distinguishable intensity ranges in their signals. The thick sidewall of the ice target and vegetation at the roadside showed overlapping intensity ranges. An ice sheet is only a few millimeters thick on a real road surface, and the roadside vegetation might be easily distinguished by using texture or auxiliary data. Therefore, laser backscattering intensity can be used to detect frozen, wet, and dry road surfaces, regardless of the time of day. The laser scanner can be installed to acquire information about road surface conditions from observation stations and vehicles in an application for transportation.


2012 ◽  
Vol 132 (9) ◽  
pp. 1488-1493 ◽  
Author(s):  
Keiji Shibata ◽  
Tatsuya Furukane ◽  
Shohei Kawai ◽  
Yuukou Horita

2021 ◽  
Author(s):  
Masahiro Yagi ◽  
Tomoyuki Takase ◽  
Sho Takahashi ◽  
Toru Hagiwara ◽  
Tomonori Ohiro ◽  
...  

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.


Author(s):  
J. Choi ◽  
L. Zhu ◽  
H. Kurosu

In the current study, we developed a methodology for detecting cracks in the surface of paved road using 3D digital surface model of road created by measuring with three-dimensional laser scanner which works on the basis of the light-section method automatically. For the detection of cracks from the imagery data of the model, the background subtraction method (Rolling Ball Background Subtraction Algorithm) was applied to the data for filtering out the background noise originating from the undulation and gradual slope and also for filtering the ruts that were caused by wearing, aging and excessive use of road and other reasons. We confirmed the influence from the difference in height (depth) caused by forgoing reasons included in a data can be reduced significantly at this stage. Various parameters of ball radius were applied for checking how the result of data obtained with this process vary according to the change of parameter and it becomes clear that there are not important differences by the change of parameters if they are in a certain range radius. And then, image segmentation was performed by multi-resolution segmentation based on the object-based image analysis technique. The parameters for the image segmentation, scale, pixel value (height/depth) and the compactness of objects were used. For the classification of cracks in the database, the height, length and other geometric property are used and we confirmed the method is useful for the detection of cracks in a paved road surface.


2021 ◽  
Vol 2113 (1) ◽  
pp. 012080
Author(s):  
Xiuhao Xi ◽  
Jun Xiao ◽  
Qiang Zhang ◽  
Yanchao Wang

Abstract For the problem of road surface condition recognition, this paper proposes a real-time tracking method to estimate road surface slope and adhesion coefficient. Based on the fusion of dynamics and kinematics, the current road slope of the vehicle which correct vertical load is estimated. The effect of the noise from dynamic and kinematic methods on the estimation results is removed by designing a filter. The normalized longitudinal force and lateral force are calculated by Dugoff tire model, and the Jacobian matrix of the vector function of the process equation is obtained by combining the relevant theory of EKF algorithm. The road adhesion coefficient is estimated finally. The effectiveness of the algorithm is demonstrated by analyzing the results under different operating conditions, such as docking road and bisectional road, using a joint simulation of Matlab/Simulink and Carsim.


Author(s):  
Mikko Perttunen ◽  
Oleksiy Mazhelis ◽  
Fengyu Cong ◽  
Mikko Kauppila ◽  
Teemu Leppänen ◽  
...  

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