• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.251-261
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• 2019

The importance of autonomous driving systems that utilize V2X services such as V2V(Vehicle to Vehicle) and V2I(Vehicle to Infrastructure) for safer and more comfortable driving is increasing with the recent development of autonomous vehicles. Partly autonomous vehicles based on environmental sensors have limitations for predicting and determining areas beyond the recognition distance of the mounted sensors and in response to atypical objects that are difficult to detect. Therefore, it is important to utilize the V2X service to improve the limit of sensor detection performance and to make driving safer and more comfortable. However, there may be an accident risk of autonomous vehicles due to incorrect information provided by V2X. Thus, the application of technology to prevent this needs to be considered. In this pater, we used the ISO-26262 Part3 Process and performed HARA (Hazard Analysis and Risk Assessment) to derive the risk sources of autonomous vehicles due to V2I malfunctions by using the communication between vehicles and infrastructure among V2X. We also developed ASIL ratings based on the simulations and real vehicle tests of the malfunctions of major cases of usnig V2I.

• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.51-65
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• 2018

Intentional aggressive driving (IAD) is defined as a hazardous driving event that the aggressive driver intentionally threatens neighbor drivers with abrupt longitudinal and lateral maneuvering. This study developed a methodology for detecting IAD events based on the analysis of interactions between aggressive driver and normal driver. Three major aggressive events including rear-close following, side-close driving, and sudden deceleration were analyzed to develop the algorithm. Then, driving simulation experiments were conducted using a multi-agent driving simulator to obtain data to be used for the development of the detection algorithm. In order to detect the driver's intention to attack, a relative evaluation index (Erratic Driving Index, EDI) reflecting the driving pattern was derived. The derived IAD event detection algorithm utilizes both the existing absolute detection method and the relative detection method. It is expected that the proposed methodology can be effectively used for detecting IAD events in support of in-vehicle data recorder technology in practice.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.1
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• pp.17-26
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• 2010

This study presents a decision making process for installation of wind barrier which is used to reduce the wind acting on running vehicle on expressway. At the first stage of this study, the lateral deviations of running vehicles under side winds were computed from the commercial softwares, CarSim and TruckSim, and then the critical wind speeds for car accident were evaluated from predefined risk index. To determine whether it is needed to install wind barrier or not, cost and benefit from wind barrier are calculated during lifetime. In obtaining car accidental risk, probabilistic distribution of wind speed, daily traffic volume, mixture ratio in the volume, and duration time for wind speed range are considered. It is recommended to install wind barrier if benefit from the barrier installation exceed construction cost. In the numerical examples, case studies were shown for risk and benefit calculation and main risky regions on Korean highway were all evaluated to identify the number of installation sites.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.394-397
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• 2018

최근 인공지능 연구가 활발하게 진행이 되면서 여러 기업에서 자율 주행연구도 활발하게 진행되고 있다. 하지만 실제 상황에서 자동차 주행 데이터를 얻기에는 여러 위험사항들과 경제적인 낭비가 있다. 그렇기 때문에 게임 상에서 데이터를 수집하고 딥러닝을 이용해 학습을 하기로 했다. 본 논문에서는 실제 세계와 유사한 환경을 가지고 있는 자동차 게임을 이용하여 자율 주행을 시도 했다. 자율 주행 시 많이 쓰이는 End to End 방법으로 데이터를 수집하면 두 가지 데이터가 저장된다. 하나는 이미지 데이터고 두 번째는 방향키 데이터다. 이러한 데이터들을 numpy 타입으로 40분간 데이터를 수집한 후 딥러닝에 많이 쓰이는 tensorflow를 사용하여 구현한 CNN을 이용하여 학습이 되는 것을 확인을 하고 91.9%의 정확도를 얻었다. 이를 기반으로 실세계에서의 사용 가능성을 확인했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.116-118
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• 2023

자율주행차가 제공하는 새로운 시장과 경쟁력, 인력 및 시간 절약, 교통 체증 문제 해결 등의 장점을 다루고, UN 사이버 보안 법률에 따른 자율주행차의 기술적인 요구사항을 준수해야 한다. 하지만 자율주행차에 대한 기술적인 요구사항을 준수하는 것으로는 모든 사이버 공격에 대해서 막을 수 없다. 자율주행차의 법적 요구사항과 사이버 보안 위협에 대처하는 방법을 다룬다. 특히 RTOS(Real Time OS)와 같은 실시간 시스템에 매우 위험할 수 있는 DRAM(Dynamic Random Access Memory)에 대한 로우해머링 공격 기법에 대해 분석하고 로우해머링에 대한 보안 방법을 제시한다. 그리고 자율 주행 시스템의 안전과 신뢰성을 보장하기 위해 하드웨어 기반 또는 소프트웨어 기반 방어 기술을 소개하고 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• Journal of Platform Technology
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• v.11 no.3
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• pp.24-31
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• 2023

Recently, interest in UAM (Urban Air Mobility, UAM), which can take off and land vertically in the operation of urban air transportation systems, has been increasing. Therefore, various start-up companies are developing related technologies as eco-friendly future transportation with advanced technology. However, studies on ways to increase safety in the operation of UAM are still insignificant. In particular, efforts are more urgent to improve the safety of risks generated in the process of attempting to land in the city center by UAM equipped with autonomous driving. Accordingly, this study proposes a plan to safely land by avoiding dangerous region that interfere when autonomous UAM attempts to land in the city center. To this end, first, the latitude and longitude coordinate values of dangerous objects observed by the sense of the UAM are calculated. Based on this, we proposed to convert the coordinates of the distorted planar image from the 3D image to latitude and longitude and then use the calculated latitude and longitude to compare the pre-learned feature descriptor with the HOG (Histogram of Oriented Gradients, HOG) feature descriptor to extract the dangerous Region. Although the dangerous region could not be completely extracted, generally satisfactory results were obtained. Accordingly, the proposed research method reduces the enormous cost of selecting a take-off and landing site for UAM equipped with autonomous driving technology and contribute to basic measures to reduce risk increase safety when attempting to land in complex environments such as urban areas.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.287-300
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• 2021

Collaborative Cyber-Physical Systems (CCPS) are those systems that contain tightly coupled physical and cyber components, massively interconnected subsystems, and collaborate to achieve a common goal. The safety of a single Cyber-Physical System (CPS) can be achieved by following the safety standards such as ISO 26262 and IEC 61508 or by applying hazard analysis techniques. However, due to the complex, highly interconnected, heterogeneous, and collaborative nature of CCPS, a fault in one CPS's components can trigger many other faults in other collaborating CPSs. Therefore, a safety assurance technique based on fault criticality analysis would require to ensure safety in CCPS. This paper presents a Fault Criticality Matrix (FCM) implemented in our tool called CPSTracer, which contains several data such as identified fault, fault criticality, safety guard, etc. The proposed FCM is based on composite hazard analysis and content-based relationships among the hazard analysis artifacts, and ensures that the safety guard controls the identified faults at design time; thus, we can effectively manage and control the fault at the design phase to ensure the safe development of CPSs. To justify our approach, we introduce a case study on the Platooning system (a collaborative CPS). We perform the criticality analysis of the Platooning system using FCM in our developed tool. After the detailed fault criticality analysis, we investigate the results to check the appropriateness and effectiveness with two research questions. Also, by performing simulation for the Platooning, we showed that the rate of collision of the Platooning system without using FCM was quite high as compared to the rate of collisions of the system after analyzing the fault criticality using FCM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.246-248
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• 2017

Self-driving cars have three main functions. The first recognizes the surrounding environment, judge the risk, and lastly plans the drive path. Therefore, the driving operation is minimized. And it refers to a human friendly car capable of safe driving on its own. The reason for the need for self-driving car was to reduce traffic jams on limited roads and to reduce carbon dioxide emissions. Driving ahead of these self-driving car businesses can be expected to attract and expand the existing business and expand the new business and create new business opportunities for ICT firms. It is urgent for the concerned agencies to establish legal and institutional basis for self-driving cars. By doing so, new services could be provided to consumers. Therefore, this paper introduces the technological development trends for self-driving cars.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.69-83
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• 2009

According to the accident statistics of 2006, it can be recognized that drivers' characteristics and driving behaviors are the most causational factors on the traffic accidents. At present, 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. However, it is also true that the system preventing any possible vehicle accidents in advance has not been developed. In this study, we developed critical value for deciding dangerous driving behaviors. The developed critical value could be used to contribute to safety driving management systematization and safety driving behaviors.