scholarly journals Characteristics of Laser Backscattering Intensity to Detect Frozen and Wet Surfaces on Roads

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jungil Shin ◽  
Hyunsuk Park ◽  
Taejung Kim
Keyword(s):  
Surface Condition ◽  
Laser Scanner ◽  
Time Of Day ◽  
Road Surface ◽  
Driving Safety ◽  
Surface Conditions ◽  
Active Sensor ◽  
Roadside Vegetation ◽  
Signal Characteristics ◽  
Wet Surface

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.

A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

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.

scholarly journals Road Surface Condition Forecasting in France

2009 ◽  
Vol 48 (12) ◽  
pp. 2513-2527 ◽  
Author(s):  
L. Bouilloud ◽  
E. Martin ◽  
F. Habets ◽  
A. Boone ◽  
P. Le Moigne ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Surface Condition ◽  
Road Surface ◽  
Snow Layer ◽  
Surface Conditions ◽  
The Road ◽  
Road Surface Temperature ◽  
On The Road

Abstract A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.

scholarly journals Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
QiMing Wang ◽  
JinMing Xu ◽  
Tao Sun ◽  
ZhiChao Lv ◽  
GaoQiang Zong
Keyword(s):  
Machine Learning ◽  
Surface Condition ◽  
Image Features ◽  
Road Surface ◽  
Driving Safety ◽  
Error Performance ◽  
Training Time ◽  
The Road ◽  
Network Training ◽  
Proposed Model

Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition.

scholarly journals Weakly Supervised Learning for Evaluating Road Surface Condition from Wheelchair Driving Data

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 2
Author(s):  
Takumi Watanabe ◽  
Hiroki Takahashi ◽  
Yusuke Iwasawa ◽  
Yutaka Matsuo ◽  
Ikuko Eguchi Yairi
Keyword(s):  
Supervised Learning ◽  
Surface Condition ◽  
Road Surface ◽  
Supervised Machine Learning ◽  
Accelerometer Data ◽  
Weakly Supervised Learning ◽  
Weak Supervision ◽  
Surface Conditions ◽  
Feature Representations ◽  
Weakly Supervised

Providing accessibility information about sidewalks for people with difficulties with moving is an important social issue. We previously proposed a fully supervised machine learning approach for providing accessibility information by estimating road surface conditions using wheelchair accelerometer data with manually annotated road surface condition labels. However, manually annotating road surface condition labels is expensive and impractical for extensive data. This paper proposes and evaluates a novel method for estimating road surface conditions without human annotation by applying weakly supervised learning. The proposed method only relies on positional information while driving for weak supervision to learn road surface conditions. Our results demonstrate that the proposed method learns detailed and subtle features of road surface conditions, such as the difference in ascending and descending of a slope, the angle of slopes, the exact locations of curbs, and the slight differences of similar pavements. The results demonstrate that the proposed method learns feature representations that are discriminative for a road surface classification task. When the amount of labeled data is 10% or less in a semi-supervised setting, the proposed method outperforms a fully supervised method that uses manually annotated labels to learn feature representations of road surface conditions.

scholarly journals Influence of Winter Road Conditions and Signal Delay on Pedestrian Route Choice in Japan's Snowiest Metropolis

2005 ◽  
Vol 1939 (1) ◽  
pp. 145-153
Author(s):  
Thambiah Muraleetharan ◽  
Kunio Meguro ◽  
Takeo Adachi ◽  
Toru Hagiwara ◽  
Sei'ichi Kagaya
Keyword(s):  
Questionnaire Survey ◽  
Choice Behavior ◽  
Field Survey ◽  
Surface Condition ◽  
Route Choice ◽  
Road Surface ◽  
Field Surveys ◽  
Surface Conditions ◽  
The Road ◽  
Route Choice Behavior

Investigation of pedestrian route choice behavior on icy surfaces is important for the effective improvement of walkways in winter. The objective of this research was to investigate pedestrian route choice behavior in winter. Field surveys and questionnaire surveys were conducted to fulfill this objective. Video cameras were used in the field surveys to clarify the movements of pedestrians. How pedestrians chose their routes was investigated by observing their movements. According to the field survey, when the signal was green, the probability that the pedestrian would cross became extremely high, regardless of the road surface conditions. However, when the walkway surface was icy, the probability that the pedestrian would wait for a green signal decreased by a considerable value. This indicates that when the wait becomes long, the probability that the pedestrian will cross becomes low during the snowy season. A questionnaire survey was also conducted to clarify the factors affecting pedestrian route choice behavior. The questionnaire asked about different road surface conditions. The results from the survey indicate that even if part of a road section has a good surface condition, it has a strong influence on route choice behavior. It indicates that pedestrians feel uncomfortable in walking on slippery walkways and they prefer to choose bare walkways. On the basis of the data from the field survey and questionnaire survey, logit models were developed to express quantitatively the route choice behaviors of pedestrians. These models can be used to predict the probability that a pedestrian will select a route as a function of pedestrian delay at signalized intersections and the road surface conditions in winter.

scholarly journals Introducing Road Surface Conditions into a Microscopic Traffic Simulation

10.29007/cqps ◽  
2019 ◽  
Author(s):  
Thomas Weber ◽  
Patrick Driesch ◽  
Dieter Schramm
Keyword(s):  
Traffic Simulation ◽  
Surface Condition ◽  
Road Surface ◽  
Automated Driving ◽  
Microscopic Traffic Simulation ◽  
Surface Conditions ◽  
Main Research ◽  
Car Following ◽  
The Road ◽  
Car Following Model

The introduction of highly automated driving functions is one of the main research and development efforts in the automotive industry worldwide. In the early stages of the development process, suppliers and manufacturers often wonder whether and to what extend the potential of the systems under development can be estimated in a cheap and timely manner. In the context of a current research project, a sensor system for the detection of the road surface condition is to be developed and it is to be investigated how such a system can be used to improve higher level driving functions. This paper presents how road surface conditions are introduced in various elements of the microscopic traffic simulation such as the actual network, the network editor, a device for detection, and an adaptation of the standard Krauß car following model. It is also shown how the adaptations can subsequently affect traffic scenarios. Furthermore, a summary is given how this preliminary work integrates into the larger scope of using SUMO as a tool in the process of analyzing the effectiveness of a road surface condition sensor.

Distinction of Wet Road Surface Condition at Night-time using Texture Features

2012 ◽  
Vol 132 (9) ◽  
pp. 1488-1493 ◽  
Author(s):  
Keiji Shibata ◽  
Tatsuya Furukane ◽  
Shohei Kawai ◽  
Yuukou Horita
Keyword(s):  
Surface Condition ◽  
Texture Features ◽  
Road Surface ◽  
Night Time

scholarly journals The Effects of Roadside Woody Vegetation on the Surface Temperature of Cycle Paths

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 483
Author(s):  
Nikola Žižlavská ◽  
Tomáš Mikita ◽  
Zdeněk Patočka
Keyword(s):  
Solar Radiation ◽  
Surface Temperature ◽  
Well Being ◽  
Road Surface ◽  
Woody Vegetation ◽  
Surface Model ◽  
Main Hypothesis ◽  
Roadside Vegetation ◽  
Different Types ◽  
Aerial Vehicle

The article is on the effects of woody vegetation growing on the roadside on the temperature of the surface of cycle paths. The main hypothesis of the study is that vegetation has the effect of lowering the temperature of the surroundings in its shadow and thus improves the comfort of users of cycle paths in the summer months. The second hypothesis is to find out which type of road surface is most suitable for the thermal well-being of users. This goal was achieved by measuring the temperature of selected locations on cycle paths with different types of construction surfaces with nearby woody vegetation using a contactless thermometer over several days at regular intervals. The positions of the selected locations were measured using GNSS and the whole locality of interest was photographed using an unmanned aerial vehicle (UAV), or drone, and subsequently a digital surface model (DSM) of the area was created using a Structure from Motion (SfM) algorithm. This model served for the calculation of incident solar radiation during the selected days using the Solar Area Graphics tool with ArcGIS software. Subsequently, the effect of the shade of the surrounding vegetation on the temperature during the day was analysed and statistically evaluated. The results are presented in many graphs and their interpretation used to evaluate the effects of nearby woody vegetation and the type of road surface on the surrounding air temperature and the comfort of users of these routes. The results demonstrate the benefits of using UAVs for the purpose of modelling the course of solar radiation during the day, showing the effect of roadside vegetation on reducing the surface temperature of the earth’s surface and thus confirming the need for planting and maintaining such vegetation.

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