• International journal of advanced smart convergence
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• 제13권2호
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• pp.25-34
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• 2024

The paper investigates how the semiconductor and electric vehicle industries are addressing safety and security concerns in the era of autonomous driving, emphasizing the prioritization of safety over security for market competitiveness. Collaboration between these sectors is deemed essential for maintaining competitiveness and value. The research suggests solutions such as advanced autonomous driving technologies and enhanced battery safety measures, with the integration of AI chips playing a pivotal role. However, challenges persist, including the limitations of big data and potential errors in semiconductor-related issues. Legacy automotive manufacturers are transitioning towards software-driven cars, leveraging artificial intelligence to mitigate risks associated with safety and security. Conflicting safety expectations and security concerns can lead to accidents, underscoring the continuous need for safety improvements. We analyzed the expansion of electric vehicles as a means to enhance safety within a framework of converging security concerns, with AI chips being instrumental in this process. Ultimately, the paper advocates for informed safety and security decisions to drive technological advancements in electric vehicles, ensuring significant strides in safety innovation.

• 대한임베디드공학회논문지
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• 제19권2호
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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.

• 대한안전경영과학회지
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• 제10권2호
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• pp.43-52
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• 2008

It's declining the number of deaths in total traffic accidents, but the death of elder drivers has increasing than younger drivers. So this paper wish to prevent the traffic accident of the elder drivers using driving simulator. It can help to make better policies and planning for elder drivers.

• 한국심리학회지 : 문화 및 사회문제
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• 제17권3호
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• pp.305-320
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• 2011

본 연구는 기존 연구들이 일반 성격특성과 위험운전행동의 관계를 살펴본 것과 달리, 운전상황에서 특정적으로 나타나는 개인의 특성(운전분노, A형 운전행동)이 교통안전에 어떠한 영향을 미치는지를 살펴보았다. 또한 생활스트레스가 운전자의 위험운전행동을 예측하는 중요한 변수임을 고려하여 일반성격(자극추구성향, A형 성격유형, 법규위반성향)과 생활스트레스, 교통안전의 관계를 검증하였다. 이를 위해, 전국의 사업용 운전자 1158명을 대상으로 설문조사를 실시하고 사고관련 자료(교통안전도지수)를 수집하였다. 연구결과, 운전분노와 A형 운전행동 등 운전 상황에서의 개인특성은 일반성격을 통제하고도 유의하게 사고위험(교통안전도지수)을 예측하였으며, 생활스트레스는 일반 성격특성과 교통안전도지수 사이를 매개하는 것으로 나타났다. 이러한 결과는 운전자의 위험운전행동을 예측할 때, 일반성격 뿐만 아니라 운전 상황에서의 특성 및 생활스트레스 수준을 함께 고려해야 함을 시사한다. 이 밖에 연구의 의의와 제한점, 추후연구 방향 등을 논의하였다.

• 한국도로학회논문집
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• 제14권4호
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• pp.149-162
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• 2012

PURPOSES : The causes of traffic accidents can be classified into the factors of highway users, vehicles, and driving environments. Traffic accidents result from the deficiency in single or combination of these three factors. The objective of this study is to define the "potentially hazardous sections of highway" in terms of traffic safety considering these three factors. METHODS : The test drivers performed repeated driving on these highway sections. The drivers and passengers recorded the sections on which the driving was uncomfortable, and the speeds on the sections excluding the uncomfortable sections were used for the development of the model. RESULTS : The model is composed of three sub-models for each of the horizontal curve, tangent, and the section where the curve starts/ends. The safe driving behavior coefficients by the horizontal curvature were derived by comparing the maximum operating speeds at which the vehicle may slide or deviate and the speeds at which the drivers feel comfort. The safety speeds on tangent were derived by the length of tangent section considering the driver's desired speeds under the traffic condition on which the drivers hardly influenced by the other vehicles. For the sections where the curve starts/ends, the driving behaviors were classified by the distances between the curves, and the safe acceleration/deceleration speeds were derived on which the drivers enter/exit the curve sections safely. CONCLUSIONS : Safety speed could then be regarded that the model suggested in this study may be useful to define the potentially hazardous highway section and contribute the improvement of highway safety.

• 인터넷정보학회논문지
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• 제24권1호
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• 자동차안전학회지
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• 제10권2호
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• pp.50-55
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• 2018

Choosing automobile safety options is price-performance matter. The best fit options to buyer who has a certain driving habit are problem of MCDM (Multi Criteria Decision Making) because price of safety option, statistics of relating accident, consequence of accident, and driving habit are the multi criteria to be evaluated. In this paper, PROMETHEE-GAIA methodology is applied for solving this MCDM problem. The result shows that a different driving habit makes different choosing priority of safety options.

• 자동차안전학회지
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• 제13권1호
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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.

• International Journal of Railway
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• 제3권3호
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• pp.90-94
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• 2010

In this paper, driving functions of the Korea High-speed Trains were decomposed based on systems engineering architecture. In order to analyze the driving function, various systems engineering tools and methods were used. Moreover, interfaces of decomposed driving functions were analyzed to figure out purposes of the driving functions. Through activity modeling of driving function of the Korea High-Speed Trains, main functions were derived when starting, speeding and stopping. When the high speed train is speeding, pre-departure checks and wheel slide prevention are essential driving activities for the safety and when the high speed train runs high speed, maintaining driving stability by monitoring bogie hunting and monitoring drivers' safe operation by vigilance systems is important. Furthermore, when the train is braking, the driver should checks brake and suspensions as safety actions.

• 소음진동
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• 제8권5호
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• pp.929-935
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• 1998

Safety diagnosis of electric train driving system is performed using vibration signals of running electric train. Safety diagnosis is tried on the viewpoints of the appreciation of superannuation and the fault diagnosis of motor, reduction gear and bogie. The appreciation of superannuation is checked by the vibration levels of driving parts and the fault diagnosis is done by analyzing the frequencies of the vibration signals which are measured directly from a running electric train. The results shows that the vibration levels of each parts increase as the train gets older and each parts have their own frequency patterns of the vibration. Vibration propagation path is also investigated using calculated the coherence value between bogie and driving system. As the results, it is known that vibration signal can be utilized successfully for the safety diagnosis of the driving part of electric train.