• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1802-1803
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• 2011

The Korea Railroad Research Institute (KRRI) is developing the Bimodal-tram. The vehicle has a navigation control system (NCS) for automatic driving. The vehicle has to follow a reference path in automatic mode. NCS uses magnetic markers to calculate the vehicle position. The vehicle lane is marked with permanent magnets that are buried in the road. In this purpose, we show the characteristics and the configuration of NCS.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.616-622
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• 2001

The road traffic noise becomes aggravated due to the rapid increase of motor vehicles. It has a great effect on the dwelling environment. Therefore we investigate the characteristics and sources of the motor vehicle noise through grasping the status of the motor vehicle noise. Traffic noise results from the collective contribution of the noise produced by individual motor vehicles. The motor vehicle noise varies enormously depending upon its type and mode of operation.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.127-130
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• 2001

In this paper, we describes a method of estimating traveling direction of a autonomous vehicle. For the development of autonomous vehicle, it is important to detect road lane and to reckon traveling direction. The object of a propose algorithm is to perform lane detection in real-time for standalone vision system. And we calculate efficent traveling direction to find steering angie for lateral control system. Therefore autonomous vehicle go forward the center of lane by adjusting the current steering angle using traveling direction.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1836-1842
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• 2015

On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don’t consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.3
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• pp.294-301
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• 2016

This paper discusses about analyzing in-wheel vehicle's dynamic motion and load torque. Since in-wheel vehicle controls each left and right driving wheels, it is dangerous if vehicle's wheels are not in a cooperative control. First, this study builds the main wheel control logic using PID control theory and evaluates the stability. Using Carsim-Matlab/Simulink, vehicle dynamic motion is simulated in virtual 3D driving road. Through this, in-wheel vehicle's driving performance can be analyzed. The target vehicle is a rear-wheel drive in D-class sedan. Second, by using the first In-wheel vehicle's performance results, it derivate the drive motor's dynamic load torque for applying the dynamometer. Extracted load torque impute to dynamometer's load motor, linear experiment in dynamometer can replicated the 3-D road driving status. Also it, will be able to evaluate the more accurate performance analysis and stability, as a previous step of actual vehicle experiment.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.148-156
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• 2013

Since the demand for new military vehicle to fulfill the necessary conditions such as multi-purpose, high-mobility, and survivability has raised continuously from the army, the prototype of a Korean light tactical vehicle was developed to meet these requirements using our own technology. In particular, the new tactical vehicle was equipped with a double wishbone independent suspension to improve ride and handling and maximize off-road driving performance. In this paper, a comprehensive virtual durability process to evaluate the service life of the prototype is presented. A reliability of the trimmed body model based on CATIA data was verified by comparison result between mode analysis and modal test. The dynamic model was constructed using ADAMS/Car, and then the weight distribution and lateral slope driving performance of it were compared with the results of static weight and lateral slope tests. The validity of the VTL(Virtual Test Lab) was checked with test results from the 3-inch spaced impact road. The durability performances of trimmed body and suspension components were evaluated through MSM(Modal Superposition Method) fatigue analysis. It is shown that the virtual durability process could be a helpful tool to find out the weak areas and improve their structures in developing new military vehicle.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.29-36
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• 2012

In this study, frequently passing vehicles with two, three, four, and five axles were chosen through traffic volume analysis in Kyung-In Expressway in order to analyze how the road roughness and vehicle speed affect on the dynamic loads for roads in various vehicle classes. Dynamic loads according to chosen vehicles are estimated by TruckSim program. Dynamic load amplification factor is ratio between dynamic and static loads, and it is also determined for each vehicle classes. From the result of dynamic loads estimated by the dynamic load amplification factor, it is shown that for three-axles vehicle, when IRI is 3.5 and vehicle speed is 100km/hr, asphalt pavements receive additional 36% of static loads in maximum. The analysis of the amplification factor according to each vehicle classes also indicates that the amplification factor increases as the distance between the axles becomes smaller and each axle receives more loads.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.68-75
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• 2011

A vehicle fuel economy is very important issue in view of fuel cost and environmental regulation. It has been improved according to the performance improvement of the vehicle engine, power train and many components. It was evaluated at given mode (LA-4, FTP-75, etc) on an engine dynamometer or computer simulation program. In this paper, the fuel economy improvement cruise control algorithms as controling a vehicle velocity by road load calculated and predicted in a real expressway with gradient was studied. Firstly, the altitude and distance data which was measured with GPS sensor was already installed in the ECU of a vehicle. Then the vehicle equipped with GPS receiver is driven the same expressway. The ECU calculates the gradient angle and the in-/decreasing velocity using the gradient angle by comparing the current received distance and altitude data from GPS with the saved data ahead of the vehicle. Therefore the ECU can calculate and predict the vehicle velocity considering tolerance velocity of next position with running. Then the ECU controls the vehicle velocity to meet this predicted velocity in all section. Three cruise control algorithms with the different velocity profiles for the improvement of fuel economy are proposed and compared with the computer simulation results that the vehicle runs on Youngdong expressway. The proposed CVELCONT2 and CVELCONT3 algorithms were improved 3.7% and 4.8% of fuel economy compared with CONSTVEL which is steady cruising algorithm. These two algorithms are recommended as the Eco-cruise drive methodologies in this paper.

• 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 Contents Association
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• v.21 no.3
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• pp.68-79
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• 2021

On the Social Internet of Things (SIoT), social activities occur through which the vehicle generates a variety of data, shares them with other vehicles, and sends and receives feedbacks. In order to share reliable information between vehicles, it is important to determine the reliability of a vehicle. In this paper, we propose a vehicle trust evaluation scheme to share reliable information among vehicles. The proposed scheme calculates vehicle trust by considering user reputation and network trust based on inter-vehicle social behaviors. The vehicle may choose to scoring, ignoring, redistributing, etc. in the social activities inter vehicles. Thereby, calculating the user's reputation. To calculate network trust, distance from other vehicles and packet transmission rate are used. Using user reputation and network trust, local trust is calculated. It also prevents redundant distribution of data delivered during social activities. Data from the Road Side Unit (RSU) can be used to overcome local data limitations and global data can be used to calculate a vehicle trust more accurately. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.