• 자동차안전학회지
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• 제15권3호
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• pp.34-42
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• 2023

Simulation-based virtual testing is known to be a major requirement for verifying the safety of autonomous driving functions. However, in the simulation environment, there is a difficulty in that all driving environments related to the autonomous driving system must be realistically modeled. In particular, C-ITS (Cooperative-Intelligent Transport Systems) is essential for urban autonomous driving of Lv.4, but the approach to modeling for C-ITS service in the MILS (Model in the Loop Simulation) environment is not yet clear. Therefore, this paper aims to deal with V2X (Vehicle to Everything) modeling methods to effectively apply C-ITS-based autonomous cooperative driving services in the MILS environment. First, major C-ITS services and use cases for autonomous cooperative driving are analyzed, and a modeling method of V2X signals for C-ITS service simulation is proposed. Based on the modeled V2X messages, the validity of the proposed approach is reviewed through simulations on the C-ITS service use case.

• 자동차안전학회지
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• 제14권2호
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• 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.

• 자동차안전학회지
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• 제11권3호
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• pp.19-29
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• 2019

Recently, autonomous vehicles have been studied actively. Autonomous vehicles can detect objects around them using their on board sensors, estimate collision probability and maneuver to avoid colliding with objects. Many algorithms are suggested to prevent collision avoidance. However there are limitations of complex and diverse environments because algorithm uses only the information of attached environmental sensors and mainly depends on TTC (time-to-Collision) parameter. In this paper, autonomous driving algorithm using I2V communication-based cooperative sensing information is developed to cope with complex and diverse environments through sensor fusion of objects information from infrastructure camera and object information from equipped sensors. The cooperative sensing based autonomous driving algorithm is implemented in autonomous shuttle bus and the proposed algorithm proved to be able to improve the autonomous navigation technology effectively.

• 센서학회지
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• 제22권2호
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• pp.111-117
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• 2013

The autonomous driving method using magnetic sensors recognizes the position by measuring magnetic fields in autonomous robots or vehicles after installing magnetic markers in a moving path. The Position estimate method using magnetic sensors has an advantage of being affected less by variation of driving environment such as oil, water and dust due to the use of magnetic field. It also has the advantages that we can use the magnet as an indicator and there is no consideration for power and communication environment. In this paper, we propose an efficient sensor system for an autonomous driving vehicle supplemented for existing disadvantage. In order to efficiently eliminate geomagnetism, we analyze the components of the horizontal and vertical magnetic field. We propose an algorithm for position estimation and geomagnetic elimination to ease analysis, and also propose an initialization method for sensor applied in the vehicle. We measured and analyzed the developed system in various environments, and we verify the advantages of proposed methods.

• 자동차안전학회지
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• 제12권4호
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• pp.13-22
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• 2020

This paper presents an adaptive feedback based actuator fault detection and tolerant control algorithms for longitudinal functional safety of autonomous driving. In order to ensure the functional safety of autonomous vehicles, fault detection and tolerant control algorithms are needed for sensors and actuators used for autonomous driving. In this study, adaptive feedback control algorithm to compute the longitudinal acceleration for autonomous driving has been developed based on relationship function using states. The relationship function has been designed using feedback gains and error states for adaptation rule design. The coefficients in the relationship function have been estimated using recursive least square with multiple forgetting factors. The MIT rule has been adopted to design the adaptation rule for feedback gains online. The stability analysis has been conducted based on Lyapunov direct method. The longitudinal acceleration computed by adaptive control algorithm has been compared to the actual acceleration for fault detection of actuators used for longitudinal autonomous driving.

• 자동차안전학회지
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• 제15권2호
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• pp.21-27
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• 2023

This paper presents a simulation tool for developing and evaluating automated driving systems' lane change algorithm in urban congested traffic. The behavior of surrounding vehicles was modeled based on driver driving data measured in urban congested traffic. Surrounding vehicles are divided into aggressive vehicles and non-aggressive vehicles. The degree of aggressiveness is determined according to the lateral position to initiate interaction with the vehicle in the next lane. In addition, the desired velocity and desired time gap of each vehicle are all randomly assigned. The simulation was conducted by reflecting the cognitive limitations and control performance of the autonomous vehicle. It was possible to confirm the change in the lane change performance according to the variation of the lane change decision algorithm.

• International Journal of Automotive Technology
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• 제5권2호
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• pp.135-144
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• 2004

In this paper, the model-matching control in the longitudinal autonomous driving system is investigated by vehicle dynamics simulation, which contains nonlinear subcomponents and simplified subcomponents. The design of the robust model-matching controller is performed by the characteristics of the 2 degrees of freedom controller, which is composed of the feedforward compensator and the feedback compensator. It makes the characteristics of tractive and brake force to be equivalent to the specific transfer function, which is suggested as the reference model. Mathematical models of vehicle dynamic analysis including the model-matching control are constructed for computer simulation. Then, simple examples on open-loop simulation without any controller and closed loop simulation with the model-matching controller are applied to check the validity of the robust controller. As the practical example, the autonomous driving system in the longitudinal direction is adopted. It is proved that the model-matching control is effective and adequate to the disturbances and the perturbations, which are shown in the responses of the change of a vehicle mass and a road gradient.

• 한국융합학회논문지
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• 제8권9호
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• pp.285-292
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• 2017

본 논문은 자율주행 개인화를 위한 순환 최소자승 기반 융합형 종방향 주행특성 구분 알고리즘에 관한 연구이다. 최근 자율주행 기술은 Level 4 완전 자율주행 단계를 위해 다양한 연구가 수행되고 있다. 자율주행 자동차의 상용화를 위해서는 탑승자의 자율주행에 대한 이질감을 최소화할 수 있어야 하며 이를 위해 자율주행 개인화 기술이 필요하다. 이 문제를 해결하기 위해 본 연구에서는 운전자의 종방향 주행특성을 수학적으로 표현하고 순환 최소자승 기법 기반 실 주행 데이터를 이용하여 주행특성을 도출하는 알고리즘을 제안하였다. 두 명의 실제 운전자 데이터를 이용하여 종방향 주행특성을 도출하였으며 두 명의 운전자를 구분하기 위해 가설검정 기반 확률적 구분 알고리즘을 적용하였다. 제안된 종방향 주행특성 도출 및 구분 알고리즘은 개별 운전자의 주행특성을 합리적으로 나타낼 수 있었으며 가설검정 기반 확률적 구분기법에 의해 주행특성이 구분될 수 있음을 확인하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.