• Journal of Drive and Control
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• v.16 no.4
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• pp.48-55
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• 2019

The Electronic Stability Program (ESP), a system that improves vehicle safety, also known as YMC (Yaw Motion Controller) or VDC (Vehicle Dynamics Control), is a system that operates in unstable or sudden driving and braking situations. Developing conditions such as unstable or sudden driving and braking situations in a vehicle are very dangerous unless you are an experienced professional driver. Additionally, many repetitive tests are required to collect reliable data, and there are many variables to consider such as changes in the weather, road surface, and tire condition. To overcome this problem, in this paper, hardware and control software such as the ESP controller, vehicle engine, ABS, and TCS module, composed of three control zones, are modeled using MATLAB/SIMULINK, and the vehicle, climate, and road surface. Various environmental variables such as the driving course were modeled and studied for the real-time ESP real-time simulator that can be repeatedly tested under the same conditions.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1159-1160
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• 2008

For safety railway vehicle movement, analysis and recoding of railway vehicle speed and driving information has written by law. nowadays, base in europe, these system is spread of various advanced country. In this paper, We implement an event recorder which record the driving and breaking information of running railway vehicle. For the event recorder, We designed the data structures with data analysis tools and a protocol between Event recorder and data analysis tool. The verification of Analysis tool was tested by to made data by Event Recorder's emulator and information of real railway driving recording.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.1
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• pp.71-77
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• 2007

Dangerous driving is a major cause of traffic accidents in Korea. It becomes more serious for commercial vehicles due to higher fatality rates. The Safe Driving Management System (SDMS), developed in this research, is a comprehensive solution that monitors and stores driving conditions of vehicles, detects dangerous driving situations, and analyzes the results in real time. The Safe Driving Management System consists of a vehicle movement information controller, a dangerous driving detection algorithm and a vehicle movement data report and analysis program. The dangerous driving detection algorithm detects and classifies dangerous driving conditions into representative cases such as sudden acceleration, sudden braking, sudden lane change, and sudden turning. Both computer simulation and vehicle test have been conducted to develop and verify the algorithm. The Safe Driving Management System has been implemented on commercial buses to verify its reliability and objectivity. It is expected that the system can contribute to prevention of traffic accidents, systemization of safe driving management and reduction of commercial vehicle operation costs.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.57.2-57.2
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• 2014

Protostellar jets and outflows are signatures of star formation and promising mechanisms for driving supersonic turbulence in molecular clouds. We quantify outflow-driven turbulence through three-dimensional numerical simulations using an isothermal version of the total variation diminishing code. We drive turbulence in real space using a simplified spherical outflow model, analyze the data through density probability distribution functions (PDFs), and investigate density and velocity power spectra. The real-space turbulence-driving method produces a negatively skewed density PDF possessing an enhanced tail on the low-density side. It deviates from the log-normal distributions typically obtained from Fourier-space turbulence driving at low densities, but can provide a good fit at high densities, particularly in terms of mass-weighted rather than volume-weighted density PDF. We find shallow density power-spectra of -1.2. It is attributed to spherical shocks of outflows themselves or shocks formed by the interaction of outflows. The total velocity power-spectrum is found to be -2.0, representative of the shock dominated Burger's turbulence model. Our density weighted velocity power spectrum is measured as -1.6, slightly less that the Kolmogorov scaling values found in previous works.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.89-102
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• 2007

Generally, it is well known that driving in tunnel imposes large burden to driver because of spatial constraint, limited visual field and so on. And such a burden of driver result in high accident occurrence. In this reason, studies dealing with features of driving and traffic flow in tunnel have been performed. However, information about characteristics of drivers and traffic in a very long tunnel is not accumulated yet. The purpose of this study is to identify the relations between tunnel length and burden of driver, driving patterns, traffic flow characteristics using the tunnel simulator that realizing various tunnel situations. For this, the tunnel simulation program was developed along 11km-length section. And biological data of 10 subjects gained from driving condition in simulation program was analyzed and compared with the result of real driving condition.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.67-74
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• 2019

In this paper, we design and implement an analytics platform based on bulk cargo DTG data for crackdown on overloaded trucks. DTG(digital tachograph) is a device that stores the driving record in real time; that is, it is a device that records the vehicle driving related data such as GPS, speed, RPM, braking, and moving distance of the vehicle in one second unit. The fast processing of DTG data is essential for finding vehicle driving patterns and analytics. In particular, a big data analytics platform is required for preprocessing and converting large amounts of DTG data. In this paper, we implement a big data analytics framework based on cargo DTG data using Spark, which is an open source-based big data framework for crackdown on overloaded trucks. As the result of implementation, our proposed platform converts real large cargo DTG data sets into GIS data, and these are visualized by a map. It also recommends crackdown points.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.173-188
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• 2024

With the advancement of autonomous vehicle (AV) technology, autonomous driving on real roads has become feasible. However, there are challenges in achieving complete autonomy due to perceptual blind areas, which occur when the AV's sensory range or capabilities are limited or impaired by surrounding objects or environmental factors. This study aims to analyze AV accident patterns and safety issues of perceptual blind area that may occur in urban areas, with the goal of developing test scenarios for Level 4+ autonomous driving. It utilized AV accident data from the California Department of Motor Vehicles (DMV) to compare accident patterns and characteristics between AVs and conventional vehicles based on activation status of autonomous mode. It also categorized AV disengagement data to identify types and real-world cases of disengagements caused by perceptual blind areas. The analysis revealed that AVs exhibit different accident types due to their safe driving maneuvers, and three types of perceptual blind area scenarios were identified. The findings of this study serve as crucial foundational data for developing Level 4+ autonomous driving test scenarios, enabling the design of efficient strategies to mitigate perceptual blind areas in various scenarios. This, in turn, is expected to contribute to the effective evaluation and enhancement of AV driving safety on real roads.

• Journal of Korean Association for Spatial Structures
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• v.19 no.4
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• pp.103-110
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• 2019

The cable-based retractable membrane roof makes it impossible to maintain its shape and stiffness during driving process, unlike the hard-type retractable roof. Consequently, monitoring using a relatively simple wireless video transmission device is required. However, since video data has a bigger transmission rating than other monitoring data in terms of the structure velocity or acceleration, there is a need to develop transmission device that is easy to install and entails low maintenance cost. This paper studies on a real-time video transmission system for monitoring the cable-based retractable membrane roof while driving. A video transfer software, using the mobile network, is designed and the embedded system is constructed. Ultimately, the data transmission server is tested. Connecting a trolley to the system allows testing of the validity and efficiency of the developed system through the video data transmitted in the driving process. Result of the test shows that the developed system enables multi-device data transfer with monitoring via the mobile network.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.21-27
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• 2023

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1436-1439
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• 2005

A novel current-type data driver for active matrix organic light emitting diode (AMOLED) is proposed for current uniformity enhancement among its output channels. New architecture is composed of shadow DACs that precharge output stages, a single-real DAC that correct the output level to a real target current level and output stages that operate in 3 states of sampling, correcting and driving. Simulation results show that the proposed driving method and circuits improve the current uniformity among output channels of a current-type driver IC.