• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.487-489
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• 1998

To evaluate vehicle performances and driving behavior of a vehicle, it is necessary to acquisit and analyze vehicle data during the vehicle driving, which affect fuel economy and emissions. An in-vehicle data acquisition system, which is called Mode Survey System(MOSS), is designed and developed to analyze the traffic and driving patterns of the vehicle. MOSS is a stand-alone system based on the 68HC11 MCU. MOSS logs various data relating to powertrain and vehicle driving such as vehicle speed, engine RPM, gear position, brake, clutch, fuel consumption, and others. The driving patterns are dependent on the driver's habit and the road and traffic conditions, these driving patterns would be able to make a official driving mode to be used in emission, fuel efficiency, shift survey, catalyst durability, and other tests using the analyzed driving patterns.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.101-106
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• 2024

For the commercialization of automated vehicles, it is necessary to create various scenarios that can evaluate driving safety and establish a data system that can verify them. Depending on the vehicle's ODD (Operational Design Domain), there are numerous scenarios with various parameters indicating vehicle driving conditions, but no systematic methodology has been proposed to create and combine scenarios to test them. Therefore, projects are actively underway abroad to establish a scenario library for real-world testing or simulation of autonomous vehicles. However, since it is difficult to obtain data, research is being conducted based on simulations that simulate real road. Therefore, in this study, parameters calculated through individual vehicle trajectory data extracted based on roadside CCTV image-based driving environment DB was proposed through the extracted data. This study can be used as basic data for safety standards for scenarios representing various driving behaviors.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.45-50
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the drowsy driving study, 10 drivers drove approximately 37 km of a monotonous highway (about 22 min) twice. The results suggested that the appropriate duration of eyes continuously closed was 4 seconds. The results from real-world driving data were presented in the other paper - part 1.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.37-44
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• 2015

In this paper, we propose the assistance method to enable safe driving through analysis of dangerous driving behavior using real-time alarm by vehicle speed, azimuth data and smartphone. For this method, smartphone is receiving driving data from digital tachograph using communication. Safe driving habit is a very important issue to commercial vehicle because that driver's long time driving than other vehicle type driver. Existing methods are very inefficient to improve immediately dangerous driving habits during driving because proceed driving behavior analysis after the vehicle operation. We propose the new safe driving assistance method that can prevent traffic accidents by real-time and improve the driver's wrong driving habits through real-time dangerous driving behavior analysis and notification the result to the driver. We have confirmed that the method in this paper will help to improve driving habits and can be applied through the proposed method implementation and simulation experiment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.15-28
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• 2008

According to the accident statistics published by the National Police Agency in 2006, it can be recognized that drivers' characteristics and driving behaviors are the most causational factors on the traffic accidents. At present, although many recording tools such as digital speedometer or black box are distributed in the market to meet social requests of decreasing traffic accidents and increasing safe driving behaviors, it is also true that it still lacks in obvious categories for dangerous driving types and then, the efficiency of the categories to be studied has been low. In this study, dangerous driving types are redefined. They are grouped into 7 classifications in the first level, and the seven classifications are regrouped into 16 in more detail. To verify the redefined dangerous driving types, a Data-logger is developed to receive and analyze the data that occur from the driving behaviors of the test vehicle. The developed Data-logger can be used to construct a real time warning system and safe driving management system with dangerous driving patterns based on acceleration, deceleration, Yaw rate, image data, etc.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.499-510
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• 2015

The purpose of this research is to identify new service opportunities for enhancing driving safety of intra-city buses based on driving behavior analysis. Service opportunity identification involves finding target customers of service (to whom), motivations for service (why), service contents (what), and service delivery process (when, where). This paper presents an analysis of driving behaviors using the operational data of intra-city buses in conjunction with traffic accident data and drivers' driving history data. This paper also presents four identified service opportunities based on the data analysis results. This research would contribute to enhancing driving safety of intra-city buses in Korea and serve as a basis for developing new services for driving safety enhancement.

• ETRI Journal
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• v.45 no.2
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• pp.240-253
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• 2023

Spatial information is geometrical information combined with the properties of an object. In city areas where unmanned aerial vehicle (UAV) usage demand is high, it is necessary to determine the appropriate driving altitude considering the height of buildings for safe driving. In this study, we propose a data-provision method that generates the driving altitude of UAVs with a pseudo-3D building model. The pseudo-3D building model is developed using high-precision spatial information provided by the National Geographic Information Institute. The proposed method generates the driving altitude of the UAV in terms of tile information, including the UAV's starting and arrival points and a straight line between the two points, and provides the data to users. To evaluate the efficacy of the proposed method, UAV driving altitude information was generated using data of 763 551 pseudo-3D buildings in Seoul. Subsequently, the generated driving altitude data of the UAV was verified in AirSim. In addition, the execution time of the proposed method and the calculated driving altitude were analyzed.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.75-82
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• 2014

The purpose of this study is to identify the effects of sensation seeking and driving confidence level on reckless driving behavior in motor sports club's members. Data was collected from 265participants of the Trackday, driving school. Subjects answered the questionnaire using convenient sampling method. Data which is obtained through self-administration was analyzed by using the frequency analysis and multiple regression. Results are as follows. First sensation seeking has influenced on reckless driving behavior such as, drunken driving and errors. Second, driving confidence level has influenced on reckless driving behavior such as, drunken driving, overspeed driving and errors.