A study of the road surface condition detection technique for deployment on a vehicle

JSAE Review ◽  
2003 ◽  
Vol 24 (2) ◽  
pp. 183-188 ◽  
Author(s):  
M Yamada
Keyword(s):  
Surface Condition ◽  
Road Surface ◽  
Detection Technique ◽  
The Road

A Study of the Road Surface Condition Detection Technique based on the Image Information for Deployment on a Vehicle

2004 ◽  
Vol 124 (3) ◽  
pp. 753-760 ◽  
Author(s):  
Muneo Yamada ◽  
Koji Ueda ◽  
Isao Horiba ◽  
Sadayuki Tsugawa ◽  
Shin Yamamoto
Keyword(s):  
Surface Condition ◽  
Road Surface ◽  
Detection Technique ◽  
Image Information ◽  
The Road

scholarly journals Pavement State Recognition Method Considering Slope

2021 ◽  
Vol 2113 (1) ◽  
pp. 012080
Author(s):  
Xiuhao Xi ◽  
Jun Xiao ◽  
Qiang Zhang ◽  
Yanchao Wang
Keyword(s):  
Surface Condition ◽  
Lateral Force ◽  
Longitudinal Force ◽  
Operating Conditions ◽  
Road Surface ◽  
Tire Model ◽  
State Recognition ◽  
Adhesion Coefficient ◽  
The Road ◽  
Real Time Tracking

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.

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 Wheel slip ratio regulation for investigating the vehicle's dynamic behavior during braking and steering input

2021 ◽  
Vol 22 ◽  
pp. 17
Author(s):  
Ali Shahabi ◽  
Amir Hossein Kazemian ◽  
Said Farahat ◽  
Faramarz Sarhaddi
Keyword(s):  
Dynamic Behavior ◽  
Fuzzy Controller ◽  
Surface Condition ◽  
Road Surface ◽  
Wheel Slip ◽  
Slip Ratio ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
The Road ◽  
Optimal Value

In this study, the vehicle's dynamic behavior during braking and steering input is investigated by considering the quarter-car model. The case study for this research is a Sport-Utility Vehicle (SUV) with the anti-lock braking system (ABS) and nonlinear dynamic equations are considered for it along with Pacejka tire model. Regulating the wheel slip ratio in the optimal value for different conditions of the road surface (dry, wet and icy) during braking is considered as the ABS control strategy. In order to regulating the wheel slip ratio in the optimal value, an intelligent adaptive fuzzy controller that can perform online parameter estimation is considered. In this regard, the proposed controller tracks the optimal wheel slip ratio with changing the condition of the road surface from dry to wet and icy. The adaptive fuzzy controller consists of linguistic base, inference engine and defuzzifier section. The wheel slip ratio and vehicle longitudinal acceleration are selected as inputs of the controller, controller adapter and detector of the road surface condition. During braking and steering input, effective parameters of the wheel that are affected on the vehicle's dynamic behavior and its stability are investigated.

scholarly journals A Crowdsourcing Technique For Road Surface Monitoring Using Smarthphone Sensors

2021 ◽  
Author(s):  
Shahram Sattar
Keyword(s):  
Information System ◽  
Surface Condition ◽  
Road Surface ◽  
Detection Accuracy ◽  
Active Monitoring ◽  
The Road ◽  
Detection Approach ◽  
Smartphone Sensors ◽  
Road Surface Monitoring ◽  
Surface Monitoring

Road surface monitoring is a key factor in providing safe road infrastructure for road users. As a result, road surface condition monitoring aims to detect road surface anomalies such potholes, cracks and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road rehabilitation and maintenance. Several studies have been developed to utilize smartphone sensors (e.g., Global Positioning system (GPS) and accelerometers) mounted on a moving vehicle to collect and process the data to monitor and tag roadway surface defects. Geotagged images or videos from the roadways have also been used to detect the road surface anomalies. However, existing studies are limited to identifying roadway anomalies mainly from a single source or lack the utility of combined and integrated multi-sensors in terms of accuracy and functionality. Therefore, low-cost, more efficient pavement evaluation technologies and a centralized information system are necessary to provide the most up-to-date information about the road status due to the dynamic changes on the road surface This information will assist transportation authorities to monitor and enhance the road surface condition. In this research, a probabilistic-based crowdsourcing technique is developed to detect road surface anomalies from smartphone sensors such as linear accelerometers, gyroscopes and GPS to integrate multiple detections accurately. All case studies from the proposed detection approach showed an approximate 80% detection accuracy (from a single survey) which supports the inclusiveness of the detection approach. In addition, the results of the proposed probabilistic-based integration approach indicated that the detection accuracy can be further improved by 5 to 20% with multiple detections conducted by the same vehicle along the same road segments. Finally, the development of the web-based Geographic Information System (GIS) platform would facilitate the real-time and active monitoring of road surface anomalies and offer further improvement of road surface quality control in large cities like Toronto.

10405 Analysis of the Road Surface Condition in a mine

2015 ◽  
Vol 2015.21 (0) ◽  
pp. _10405-1_-_10405-2_
Author(s):  
Yosuke YAMADA ◽  
Hajime TAKADA ◽  
Yoshifusa MATUURA
Keyword(s):  
Surface Condition ◽  
Road Surface ◽  
The Road

A risk-based approach to winter road surface condition classification

2017 ◽  
Vol 44 (3) ◽  
pp. 182-191
Author(s):  
Liping Fu ◽  
Lalita Thakali ◽  
Tae J. Kwon ◽  
Taimur Usman
Keyword(s):  
Surface Condition ◽  
Risk Index ◽  
Weather Conditions ◽  
Road Surface ◽  
Winter Weather ◽  
Single Class ◽  
The Road ◽  
Consistent Condition ◽  
Adverse Weather ◽  
Highway Network

This paper presents a risk-based approach for classifying the road surface conditions of a highway network under winter weather events. A relative risk index (RRI) is developed to capture the effect of adverse weather conditions on the collision risk of a highway in reference to the normal driving conditions. Based on this index, multiple risk factors related to adverse winter weather conditions can be considered either jointly or separately. The index can also be used to aggregate different types of road conditions observed on any given route into a single class for risk-consistent condition classification and reporting. Two example applications are shown to illustrate the advantages of the proposed approach.

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