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 Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 114 ◽  
Author(s):  
Ikuko Yairi ◽  
Hiroki Takahashi ◽  
Takumi Watanabe ◽  
Kouya Nagamine ◽  
Yusuke Fukushima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Surface Condition ◽  
Learning Technologies ◽  
Road Surface ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Prototype System ◽  
Wheelchair Users ◽  
The Road

Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis acceleration data obtained by a smart phone attached on a wheelchair seat, such as environmental factors, e.g., curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g., wheelchair users’ feelings of tiredness and strain. Our goal is to realize a system that provides the road accessibility visualization services to users by online/offline pattern matching using impersonal models, while gradually learning to improve service accuracy using new data provided by users. As the first step, this paper evaluates features acquired by the DCNN (deep convolutional neural network), which learns the state of the road surface from the data in supervised machine learning techniques. The evaluated results show that the features can capture the difference of the road surface condition in more detail than the label attached by us and are effective as the means for quantitatively expressing the road surface condition. This paper developed and evaluated a prototype system that estimated types of ground surfaces focusing on knowledge extraction and visualization.

scholarly journals Laboratory experiment on the nano-TiO2 photocatalytic degradation effect of road surface oil pollution

2020 ◽  
Vol 9 (1) ◽  
pp. 922-933
Author(s):  
Qing’e Wang ◽  
Kai Zheng ◽  
Huanan Yu ◽  
Luwei Zhao ◽  
Xuan Zhu ◽  
...  
Keyword(s):  
Photocatalytic Degradation ◽  
Oil Pollution ◽  
Road Surface ◽  
Test Method ◽  
Driving Safety ◽  
The Road ◽  
Road Surfaces ◽  
The Many ◽  
The Impact ◽  
Degradation Effect

AbstractOil leak from vehicles is one of the most common pollution types of the road. The spilled oil could be retained on the surface and spread in the air voids of the road, which results in a decrease in the friction coefficient of the road, affects driving safety, and causes damage to pavement materials over time. Photocatalytic degradation through nano-TiO2 is a safe, long-lasting, and sustainable technology among the many methods for treating oil contamination on road surfaces. In this study, the nano-TiO2 photocatalytic degradation effect of road surface oil pollution was evaluated through the lab experiment. First, a glass dish was used as a substrate to determine the basic working condition of the test; then, a test method considering the impact of different oil erosion degrees was proposed to eliminate the effect of oil erosion on asphalt pavement and leakage on cement pavement, which led to the development of a lab test method for the nano-TiO2 photocatalytic degradation effect of oil pollution on different road surfaces.

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 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.

MagTrack: Detecting Road Surface Condition using Smartphone Sensors and Machine Learning

2019 ◽  
Author(s):  
Meenu Rani Dey ◽  
Utkalika Satapathy ◽  
Pranali Bhanse ◽  
Bhabendu Kr. Mohanta ◽  
Debasish Jena
Keyword(s):  
Machine Learning ◽  
Surface Condition ◽  
Road Surface ◽  
Smartphone Sensors

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.

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