• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.277-283
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• 2021

Traffic data by vehicle classification are important data used as basic data in various fields such as road and traffic design. Traffic data is collected through permanent and temporary surveys and is provided as an annual average daily traffic (AATD) in the statistical yearbook of road traffic. permanent surveys are collected through traffic collection equipment (AVC), and the AVC consists of a loop sensor that detects traffic volume and a piezo sensor that detects the number of axes. Due to the nature of the buried type of traffic collection equipment, missing data is generated due to failure of detection equipment. In the existing method, it is corrected through historical data and the trend of traffic around the point. However, this method has a disadvantage in that it does not reflect temporal and spatial characteristics and that the existing data used for correction may also be a correction value. In this study, we proposed a method to correct the missing traffic volume by calculating the axis correction coefficient through the accumulated number of axes acquired by using a piezo sensor that can detect the axis of the vehicle. This has the advantage of being able to reflect temporal and spatial characteristics, which are the limitations of the existing methods, and as a result of comparative evaluation, the error rate was derived lower than that of the existing methods. The traffic volume correction system using axis count is judged as a correction method applicable to the field system with a simple algorithm.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.696-701
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• 2000

In this study, a mathematical model fur analyzing the shift characteristics is proposed. The proposed model comprises power transmission system and vehicle system, which are coupled. And On-road car test is carried out in order to extract model parameters. Tile model is composed of a detailed powertrain, an engine/AT housing, a simplified suspension system. tires and a vehicle body model. On the test, the vehicle accelerations and pitch ratio are measured by using accelerometers and gyro sensor. The other data, for example speeds, a throttle position and a brake signal, are taken from sensors which already exist in the vehicle. Using natural frequency and characteristic equation, vehicle model parameters are extracted from experimental data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.237-244
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• 2016

There are two destinations to aware the risk of common life. Recognition of the condition of pedestrian's own and the environmental factor awareness both are beneficial for risk awareness. It is good way of advancing the crime prevention effectivity that including IoT technology at the crime prevention research. The purpose of this research is that advanced way of crime prevention with multi-sensor data fusion of the condition of pedestrian and environmental factors. The 3-axis acceleration sensor is available to recognize the gait and the illumination sensor also useful to infer the road state. This research suggest a novel way of assess these factors and the result is the degree of danger.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.153-164
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• 2008

According to the economic growth, many highways are constructed for increasing need of better life style. Especially roads and roadside facilities are used for accident prevention and offering mobility for drivers. For these purpose, roads and roadside facilities should be well maintained and managed. Now, many roads and roadside facilities are constructed in many areas. Because of traditional surveying method requires much time and surveying efforts, we designed and developed mobile mapping system for highway maintenance and management purpose using multi sensors. We tested our mobile mapping system and data management process. Using developed database, road managers can easily check the information of facility conditions, positions, and attributes. We are expecting low cost and efficient road maintenance process by using our system.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.53-61
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• 2020

Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.524-526
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• 2021

As the invention of automobiles and construction of roads for vehicles began, the occurrence of traffic accidents began to increase. Accordingly, efforts were made to prevent traffic accidents by changing the road construction method and using signal systems such as traffic lights, but until now, numerous human and property damages have occurred every year due to traffic accidents caused by freezing of the road due to bad weather. In this paper, we propose a method of transmitting ice detection data detected using vehicle sensor data to vehicle navigation to reduce traffic accidents caused by road freezing.

• Journal of Korea Spatial Information System Society
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• v.3 no.1 s.5
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• pp.117-126
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• 2001

We have developed the Mobile Multi Sensor System(MMSS) for the data construction of 4S application and for basic technology acquisition of mobile mapping system in Korea. Using this MMSS, we will collect the information of road and road facilities for DB creation and also construct the Digital Elevation Model(DEM) as ancillary data in urban area. The MMSS consist of the integrated navigation sensor, DGPS & IMU, and digital CCD camera set. In the S/W aspect, we developed the post-processing components for extracting the 3D coordinate information (Spatial Information) and the client program for the MMSS user group. In this paper, we will overview the MMSS constitution and post-processing program, and introduce the utilization plan of MMSS.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.140-146
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• 2007

This paper demonstrates the development of a mobile robot for patrol. We fuse differential GPS, angle sensor and odometry data using the framework of extended Kalman filter to localize a mobile robot in outdoor environments. An important feature of road environment is the existence of curbs. So, we also propose an algorithm to find out the position of curbs from laser range finder data using Hough transform. The mobile robot builds the map of the curbs of roads and the map is used fur tracking and localization. The patrol robot system consists of a mobile robot and a control station. The mobile robot sends the image data from a camera to the control station. The remote control station receives and displays the image data. Also, the patrol robot system can be used in two modes, teleoperated or autonomous. In teleoperated mode, the teleoperator commands the mobile robot based on the image data. On the other hand, in autonomous mode, the mobile robot has to autonomously track the predefined waypoints. So, we have designed a path tracking controller to track the path. We have been able to confirm that the proposed algorithms show proper performances in outdoor environment through experiments in the road.