• 한국자동차공학회논문집
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• 제16권6호
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• pp.1-9
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• 2008

The current test methods are insufficient to evaluate and ensure the safety and reliability of vehicle system for all possible dynamic situation including the worst case such as rollover, spin-out and so on. Although the known NHTSA J-turn and Fish-hook steering maneuvers are applied for the vehicle performance assessment, they aren't enough to estimate other possible worst case scenarios. Therefore, it is crucial for us to verify the various worst cases including the existing severe steering maneuvers. This paper includes the procedure to search for other useful worst case based upon the existing worst case scenarios mentioned above and its application in simulation basis. The only human steering angle is selected as a design parameter here and optimized to maximize the index function to be expressed in terms of either roll angle or yaw rate. The obtained scenarios were enough to generate the worst case to meet NHTSA worst case definition (ex.2-inch wheel lift). Additionally, as an application, the worst case steering maneuver is acquired for the vehicle to operate with a simple ESP system. It has been concluded that the new procedure in this paper is adequate to create other feasible worst case scenarios for a vehicle system both with an intelligent safety control system and without it.

• 한국지능시스템학회논문지
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• 제22권2호
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• pp.225-231
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• 2012

본 논문에서는 접근 각도 개념을 이용하여 과소작동기 형태의 무인 수중 잠수정의 경로 추적 제어기 설계 방법을 제안한 다. 과소 작동기 형태를 가지는 무인 수중 잠수정은 종 방향 추진력과 회전력에 의해 속도와 방향을 조절하나, 횡 방향 추 진기가 없기 때문에 횡 방향에 대한 움직임을 제어 할 수 없다. 이러한 무인 수중 잠수정의 과소 작동기 문제를 해결하기 위하여 본 논문에서는 기준 경로에 대한 접근 각도 개념을 제안하고, 제안한 접근 각도를 이용하여 경로 추적 제어기를 설 계한다. 이를 위해 동체 고정 좌표계에서 새로운 오차 방정식을 구하고, 리아푸노프 방법을 기반으로 경로 추적 제어기를 설계한다. 본 논문에서는 컴퓨터 시뮬레이션 통해 제안한 방법에 의해 설계된 제어기의 성능을 검증한다.

• 자동차안전학회지
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• 제13권4호
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.