• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.91-101
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• 2003

This study modeled 4-lane highway in three-dimensional virtual reality in order to overcome difficulties of field experiment. and the research subject was placed in a driving simulator. We survey the driver's cognitive characteristics to the alignment changes in the three-dimensional virtual reality highway. Especially, maximizing the identity of driving movements and virtual scenery on the basis of the data obtained by dynamic analysis module. we minimized simulator sickness for the graphic module of driving simulator. And we carried out cognitive evaluation on the basis of adjective words extracted by dictionary and the opinion of specialist. In this study LISREL model was used to detect the causal relation between geometry and safety in cognitive side, and found that geometric change affects the safety of drivers by static and dynamic road safety model in three-dimensional combined alignments. As the result, for constructing safety road. we consider drivers' cognitive characteristics as human factors in road design, and we think that they are very important factors to improve road safety.

• Korean Journal of Culture and Social Issue
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• v.22 no.2
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• pp.285-311
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• 2016

This study analysed the published papers dealing with traffic behaviors since 2004 in south Korea. The following information was coded for each papers; year of publication, source, authors, main topic, and subtopic. The annual numbers of publication in 2004 and 2005 showed 6 articles and 7 articles. Since 2006, The annual numbers were increasing more than 10 papers. It means that the researches on traffic behavior were rich. The driver was main topic of 73.2% of articles. Cognition & Perception, Fatigue and Stress, and Alcohol were the main interest sub-topics dealing with main topic driver. Elderly driver was 10.4%, the interest in elderly drivers grew with population aging. And the dominant publications were Journal of traffic safety research, Journal of Korean Psychology Association, and Journal of the Koean Data Analysis Society with 60% of all articles for last 10 years.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1037-1045
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• 1996

In this paper, the parallel model for stereo camera is employed to find the heralding angle and the distance between a leading vehicle and the following vehicle, BART(Binocular Autonomous Research Team vehicle). Two TDNNs (Time Delay Neural Network) such as S-TDNN and A-TDNN are introduced to control BART. S-TDNN controls the speed of the following vehicle while A-TDNN controls the steering angle of BATR. A human drives BART to collect data which are used for training the said neural networks. The trained networks performed the vehicle tracking function satisfactorily under the same driving conditions performed by the human driver. The neural network approach has good portability which decreases costs and saves development time for the different types of vehicles.

• International Journal of Automotive Technology
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• v.3 no.2
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• pp.71-78
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• 2002

A new control method far vision-based autonomous vehicle is proposed to determine navigation direction by analyzing lane information from a camera and to navigate a vehicle. In this paper, characteristic featured data points are extracted from lane images using a lane recognition algorithm. Then the vehicle is controlled using new Levenberg-Marquardt neural network algorithm. To verify the usefulness of the algorithm, another algorithm, which utilizes the geometric relation of a camera and vehicle, is introduced. The second one involves transformation from an image coordinate to a vehicle coordinate, then steering is determined from Ackermann angle. The steering scheme using Ackermann angle is heavily depends on the correct geometric data of a vehicle and a camera. Meanwhile, the proposed neural network algorithm does not need geometric relations and it depends on the driving style of human driver. The proposed method is superior than other referenced neural network algorithms such as conjugate gradient method or gradient decent one in autonomous lateral control .

• Journal of Auto-vehicle Safety Association
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• v.11 no.4
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• pp.39-49
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• 2019

Autonomous vehicle is a car which drives itself without any human interaction. SAE provides technical definitions for autonomous and international standards for test evaluation. Accordingly, automobile industry is actively researching development and evaluation of various ADAS (Advanced Driver Assistance Systems), : representative technology of autonomous technology. Recently, ADAS is in the commercialization level such as ACC, LKAS, AEB, and HDA etc. And it also has issues about safety evaluation. The purpose of HDA in ADAS is reduced the driving load on highway. It has a function which can maintain lane keeping and control distance from forward vehicle. This function is evaluated to be useful for accident prevention. Therefore, this paper proposes the safety evaluation scenario of HDA, considering the domestic highway design criteria and the situation that may arise on the actual highway. We compared and analyzed the data acquired through simulation and actual vehicle test. And verified the reliability of the proposed safety evaluation scenario. The verified result is expected safety evaluation of HDA is possible even under the bad condition, which cannot be tested.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.253-260
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• 2008

This paper is based on the ubiquitous network of telematics technology, equipped with a black box to the car by a unique address given to IPv6. The driver's black box at startup and operation of certification, and the car's driving record handling video signals in real-time sensor signals handling to analyze the records. Through the recorded data is encrypted transmission, and the Ubiquitous network of base stations, roadside sensors through seamless mobility and location tracking data to be generated. This is a file of Transportation Traffic Operations Center as a unique address IPv6 records stored in the database. The car is equipped with a black box used on the road go to Criminal cases, the code automotive black boxes recovered from the addresses and IPv6, traffic records stored in a database to compare the data integrity verification and authentication via secure. This material liability in the courtroom and the judge Forensic data are evidence of the recognition as a highly secure. convenient and knowledge in the information society will contribute to human life.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.133-144
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• 2004

A model and a measure which can evaluate the risk of rear end collision are developed. Most traffic accidents involve multiple causes such as the human factor, the vehicle factor, and the highway element at any given time. Thus, these factors should be considered in analyzing the risk of an accident and in developing safety models. Although most risky situations and accidents on the roads result from the poor response of a driver to various stimuli, many researchers have modeled the risk or accident by analyzing only the stimuli without considering the response of a driver. Hence, the reliabilities of those models turned out to be low. Thus in developing the model behaviors of a driver, such as reaction time and deceleration rate, are considered. In the past, most studies tried to analyze the relationships between a risk and an accident directly but they, due to the difficulty of finding out the directional relationships between these factors, developed a model by considering these factors, developed a model by considering indirect factors such as volume, speed, etc. However, if the relationships between risk and accidents are looked into in detail, it can be seen that they are linked by the behaviors of a driver, and depending on drivers the risk as it is on the road-vehicle system may be ignored or call drivers' attention. Therefore, an accident depends on how a driver handles risk, so that the more related risk to and accident occurrence is not the risk itself but the risk responded by a driver. Thus, in this study, the behaviors of a driver are considered in the model and to reflect these behaviors three concepts related to accidents are introduced. And safe stopping distance and accident occurrence probability were used for better understanding and for more reliable modeling of the risk. The index which can represent the risk is also developed based on measures used in evaluating noise level, and for the risk comparison between various situations, the equivalent risk level, considering the intensity and duration time, is developed by means of the weighted average. Validation is performed with field surveys on the expressway of Seoul, and the test vehicle was made to collect the traffic flow data, such as deceleration rate, speed and spacing. Based on this data, the risk by section, lane and traffic flow conditions are evaluated and compared with the accident data and traffic conditions. The evaluated risk level corresponds closely to the patterns of actual traffic conditions and counts of accident. The model and the method developed in this study can be applied to various fields, such as safety test of traffic flow, establishment of operation & management strategy for reliable traffic flow, and the safety test for the control algorithm in the advanced safety vehicles and many others.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Auto-vehicle Safety Association
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• v.11 no.4
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• pp.28-38
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• 2019

In the world, traffic accidents and environmental pollution caused by the increase of vehicles are becoming a serious social problem. According to the 2016 data published by the Korea Highway Traffic Authority, Korea owns 49.9 vehicles per 100 people. This is the 28th largest number among the 35 OECD member countries. In addition, the number of deaths from traffic accidents in Korea totaled 4,292, of which 1,714 were caused by traffic accidents involving vehicles and pedestrians. To reduce these human casualties, the automotive industry is constantly working on the development and commercialization of Adaptive Driver Assist System (ADAS). ADAS is the system providing convenience and safeness for drivers. In general, ADAS consists of Autonomous Emergency Braking (AEB), Highway Driving Assist (HDA), Adaptive Cruise Control (ACC), Lane Keeping Assist System (LKAS). Among them, the AEB detects the possibility of collision by the vehicle itself and plays a role of avoiding the collision or reducing the damage through active braking. For such AEB, Euro NCAP has been developing test-evaluation methods for the vulnerable since 2017. Therefore, In this paper analyzes the scenario of Euro NCAP VRU Test Protocol v3.0.1, which will be established in 2020, and proposes test conditions according to the Korean road traffic law. In addition, the reliability of the proposed scenario and test conditions was verified by comparing and analyzing the proposed theoretical evaluation formulas and actual test results.