scholarly journals Design And Implementation Of Intelligent Fog Guidance System

2021 ◽  
Vol 257 ◽  
pp. 03072
Author(s):  
Hongmei Cui ◽  
Yahong Tan ◽  
Wenzhang Lin
Keyword(s):  
Traditional Method ◽  
Guidance System ◽  
The Road ◽  
Design And Implementation ◽  
Front End ◽  
Geographical Environment ◽  
Road Condition ◽  
Information Board

The fog-prone areas of expressway are mainly affected by special geographical environment, most of which occur in mountainous and hilly areas, lake depressions, remote suburbs and other places. The intelligent fog zone guidance system can automatically change the working mode according to the visibility of the environment. In addition, it can also communicate with the Information Board on the spot and announce the road condition ahead of time, so that vehicles can be reminded and guided at the front end of the road where the fog occurs and at the road where the fog occurs, which makes up for the deficiency of the traditional method.

Algorithm Design and Implementation of Dynamic Star Simulator

2013 ◽  
Vol 722 ◽  
pp. 187-193
Author(s):  
Xiao Fang Zhao ◽  
Guang Bin Liu ◽  
Chao Shan Liu
Keyword(s):  
Traditional Method ◽  
Algorithm Design ◽  
Star Tracker ◽  
Simulation Algorithm ◽  
Arc Length ◽  
Visual Axis ◽  
Star Image ◽  
Design And Implementation ◽  
Simulation Testing ◽  
Celestial Sphere

In order to realize the simulation testing and calibration of star tracker in the lab, a simulation algorithm for dynamic and celestial sphere star image was proposed. This algorithm could complete simulation of star image at any time and any visual axis point, also could realize in-orbit simulation according to a given track. A new method of space district dividing was also presented in order to improve the traditional method of space district dividing by ascension and declination, fully considered declination arc length gradually shortened with latitude increasing. It can remarkably enhance the updating frequency simulation star image.

Microwave Front End for True Ground Speed Measurements

1995 ◽  
Vol 48 (1) ◽  
pp. 88-96 ◽  
Author(s):  
Jürgen Kehrbeck ◽  
Eberhardt Heidrich ◽  
Werner Wiesbeck
Keyword(s):  
Doppler Spectrum ◽  
Automotive Applications ◽  
Distance Measurements ◽  
Theoretical Simulation ◽  
Ground Speed ◽  
The Road ◽  
Dual Channel ◽  
Front End ◽  
Sensor Module ◽  
Microwave Components

A dual channel microwave Doppler-Sensor-Module for 24 GHz is presented. This front end is well suited for true ground speed and distance measurements in all kinds of automotive applications. The microwave components such as oscillator, mixer, antenna and their characteristics in the MIC are discussed. The influence of the antenna pattern and the road surface on the Doppler spectrum is treated in a 3D-field theoretical simulation. This simulation takes the antenna nearfield and the distributed scattering of the road into account.

scholarly journals Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Johannes Masino ◽  
Jakob Thumm ◽  
Guillaume Levasseur ◽  
Michael Frey ◽  
Frank Gauterin ◽  
...  
Keyword(s):  
Data Mining ◽  
Support Vector ◽  
Matlab Toolbox ◽  
Data Set ◽  
The Road ◽  
Acceleration Sensors ◽  
Road Surfaces ◽  
Road Condition ◽  
Sensor Signals

This work aims at classifying the road condition with data mining methods using simple acceleration sensors and gyroscopes installed in vehicles. Two classifiers are developed with a support vector machine (SVM) to distinguish between different types of road surfaces, such as asphalt and concrete, and obstacles, such as potholes or railway crossings. From the sensor signals, frequency-based features are extracted, evaluated automatically with MANOVA. The selected features and their meaning to predict the classes are discussed. The best features are used for designing the classifiers. Finally, the methods, which are developed and applied in this work, are implemented in a Matlab toolbox with a graphical user interface. The toolbox visualizes the classification results on maps, thus enabling manual verification of the results. The accuracy of the cross-validation of classifying obstacles yields 81.0% on average and of classifying road material 96.1% on average. The results are discussed on a comprehensive exemplary data set.

scholarly journals Applying the METRo model for road-condition forecasting in Norway 

2021 ◽  
Author(s):  
Stephanie Mayer ◽  
Fabio Andrade ◽  
Torge Lorenz ◽  
Luciano de Lima ◽  
Anthony Hovenburg ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Heat Dissipation ◽  
Road Traffic ◽  
Weather Forecast ◽  
Road Surface ◽  
Balance Model ◽  
Weather Station ◽  
The Road ◽  
Road Surface Temperature ◽  
Road Condition

<p>According to the 14<sup>th</sup> Annual Road Safety Performance Index Report by the European Transport Safety Council, annually more than 100,000 accidents occur on European roads, of which 22,660 people lost their lives in 2019. The factors contributing to road traffic accidents are commonly grouped into three categories: environment, vehicle or driver. The European accident research and safety report 2013 by Volvo states in about 30% of accidents contributing factors could be attributed to weather and environment leading for example to unexpected changes in road friction, such as black ice. In this work, we are developing a solution to forecast road conditions in Norway by applying the <em>Model of the Environment and Temperature of Roads – METRo</em>, which is a surface energy balance model to predict the road surface temperature. In addition, METRo includes modules for water accumulation at the surface (liquid and frozen) and vertical heat dissipation (Crevier and Delage, 2001). The road condition is forecasted for a given pair of latitude, longitude and desired forecast time. Data from the closest road weather station and postprocessed weather forecast are used to initialize METRo and provide boundary conditions to the road weather forecast. The weather forecasts are obtained from the THREDDS service and the road weather station data from the FROST service, both provided by MET Norway. We develop algorithms to obtain the data from these services, process them to match the METRo model input requirements and send them to METRo’s pre-processing algorithms, which combine observations and forecast data to initialize the model. In a case study, we will compare short-term METRo forecasts with observations obtained by road weather stations and with observations retrieved by car-mounted environmental sensors (e.g., road surface temperature). This work is part of the project <em>AutonoWeather - Enabling autonomous driving in winter conditions through optimized road weather interpretation and forecast</em> financed by the Research Council of Norway in 2020. </p>

scholarly journals Development of a Road Condition Information System using Augmented Reality Technology on Smartphones

2020 ◽  
Vol 10 (2) ◽  
pp. 49-55
Keyword(s):  
Information System ◽  
Augmented Reality ◽  
High Speed ◽  
Human Error ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Data Conversion ◽  
The Road ◽  
Road Condition ◽  
Augmented Reality Technology

Recently, accidents involving ground transportations are getting worse and more serious. Indonesian State Police (Korlantas POLRI) recorded the number of accidents in 2018 as many as 109,215 accidents. The number has incresed 4.69 percent compared to 2017 as many as 104,327 events. Road traffic accidents are caused by human error, the driver in this case. The driver's mistake is influenced by several factors, one of them is they cannot expect the road condition when they drive a vehicle at high speed. To solve this problem, drivers need information that can show road conditions. So, we present a new approach for detecting damaged roads by applying augmented reality technology. This research produces a road condition information system to help drivers get information about road conditions via smartphone. This system uses augmented reality technology with a markerless GPS Based Tracking method. The development of this system requires several stages such as collecting the data, data conversion, data classification, and views road condition. The researchers gathered the road condition data from the Public Work Department Semarang. This department itself undertakes a task to control the road condition in Semarang The trial of this system includes all drivers in Semarang city. Based on the results of the questionnaire responded to by 93 respondents, this test obtained an average value of 68%. So this system gets a pretty good response from the driver. Through this system, all drivers can avoid the damaged road condition which can cause traffic-congested and accident.

scholarly journals Monitoring Vehicle Communication and Road Condition in VANET

2020 ◽  
pp. 530-537
Author(s):  
Saravanakumar S
Keyword(s):  
Ad Hoc ◽  
Cloud Service ◽  
Security And Privacy ◽  
Geologic Hazard ◽  
The Road ◽  
Computing Services ◽  
Cloud Server ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Reported Data

The connected vehicular ad-hoc network (VANET) and cloud computing technology allows entities in VANET to enjoy the advantageous storage and computing services offered by some cloud service provider. However, the advantages do not come free since their combination brings many new security and privacy requirements for VANET applications. In this article, we investigate the cloud-based road condition monitoring (RCoM) scenario, where the authority needs to monitor real-time road conditions with the help of a cloud server so that it could make sound responses to emergency cases timely. When some bad road condition is detected, e.g., some geologic hazard or accident happens, vehicles on site are able to report such information to a cloud server engaged by the authority. We focus on addressing three key issues in RCoM. First, the vehicles have to be authorized by some roadside unit before generating a road condition report in the domain and uploading it to the cloud server. Second, to guarantee the privacy against the cloud server, the road condition information should be reported in ciphertext format, which requires that the cloud server should be able to distinguish the reported data from different vehicles in ciphertext format for the same place without compromising their confidentiality. Third, the cloud server and authority should be able to validate the report source, i.e., to check whether the road conditions are reported by legitimate vehicles. To address these issues, we present an efficient RCoM scheme, analyze its efficiency theoretically, and demonstrate the practicality through experiments

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