• 자동차안전학회지
• /
• 제12권4호
• /
• pp.13-22
• /
• 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.

• 융합신호처리학회논문지
• /
• 제4권4호
• /
• pp.58-65
• /
• 2003

기존의 횡 방향제어 알고리즘은 도로에서 발생할 수 있는 변수를 고려하여 알고리즘을 작성해야 했다. 이러한 제어 알고리즘을 작성하기 위해서는 주행해야 하는 도로에 따라 파라미터를 재조정해야 하는 문제와 대량의 계산이 요구되는 모델링 문제가 있었다. 본 논문에서는 지능적 횡 방향제어가 가능한 학습알고리즘에 관해 연구하였다. 학습알고리즘은 인공지능 알고리즘 중 자기구성 알고리즘을 사용하였으며 학습데이터는 도로의 특징점을 이용하였다. 컴퓨터를 이용한 시뮬레이션 결과 본 논문의 학습알고리즘에 의한 조향제어가 가능한 것을 알 수 있었고 실제로 주행이 가능한 자율이동로봇에 적용하여 학습에 의한 횡 방향제어가 가능한 것을 확인하였다.

• 자동차안전학회지
• /
• 제12권1호
• /
• pp.15-25
• /
• 2020

This paper presents a longitudinal control algorithm for ensuring takeover time of autonomous vehicle using V2V communication. In the autonomous driving of more than level 3, autonomous systems should control the vehicles by itself partially. However if the driver's intervention is required for functional safety, the driver should take over the control reasonably. Autonomous driving system has to be designed so that drivers can take over the control from autonomous vehicle reasonably for driving safety. In this study, control algorithm considering takeover time has been developed based on computation method of takeover time. Takeover time is analysed by conditions of longitudinal velocity of preceding vehicle in time-velocity plane. In addition, desired clearance is derived based on takeover time. The performance evaluation of the proposed algorithm in this study was conducted using 3D vehicle model with actual driving data in Matlab/Simulink environment. The results of the performance evaluation show that the longitudinal control algorithm can control while securing takeover time reasonably.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2007년도 춘계학술대회A
• /
• pp.861-866
• /
• 2007

This paper presents development of a vehicle lateral and longitudinal control for autonomous driving control and test results obtained using an electric vehicle. Sliding control theory has been used to develop a vehicle speed and distance control algorithm. The longitudinal control algorithm that maintains safety and comfort of the vehicle consists of a cruise and STOP&GO control depending on traffic conditions. Desired steering angle is determined through the lateral position error and the yaw angle error based on preview optimal control. Motor control inputs have been directly derived from the sliding control law. The performance of the autonomous driving control which is integrated with a lateral and longitudinal control is investigated by computer simulations and driving test using an electric vehicle. Electric vehicle system consists of DC driving motor, an electric power steering system, main controller (Autobox)

• 한국융합학회논문지
• /
• 제9권10호
• /
• pp.249-255
• /
• 2018

본 논문은 자율주행 개인화를 위한 역 충돌시간 및 차두시간 융합 기반 인간중심 제어 알고리즘 개발에 관한 것이다. 운전자 및 탑승자의 자율주행에 대한 이질감 최소화를 위해 인간중심적 주행제어 기술이 필요하다. 운전자가 선행차량과 함께 주행하는 조건에서 운전자의 주행특성을 분석하고, 분석된 결과를 종방향 자율주행 제어에 반영하였다. 주행특성으로 가속도, 역 충돌시간, 차두시간 분포가 분석되었고, 운전자의 주행특성이 반영된 제어기 구성을 위해 역 충돌시간 및 차두시간을 이용한 종방향 제어기를 구성하였다. 본 연구에서 제안된 제어 알고리즘은 Matlab/Simulink 환경에서 구성되었으며 실 주행데이터 기반 성능평가가 수행되었다.

• 전자통신동향분석
• /
• 제38권6호
• /
• pp.31-40
• /
• 2023

Given recent accidents involving autonomous vehicles, driver monitoring technology related to the transition of control in autonomous vehicles is gaining prominence. Driver status monitoring systems recognize the driver's level of alertness and identify possible impairments in the driving ability owing to conditions including drowsiness and distraction. In autonomous vehicles, predictive factors for the transition to manual driving should also be included. During traditional human driving, monitoring the driver's status is relatively straightforward owing to the consistency of crucial cues, such as the driver's location, head orientation, gaze direction, and hand placement. However, monitoring becomes more challenging during autonomous driving because of the absence of direct manual control and the driver's engagement in other activities, which may obscure the accurate assessment of the driver's readiness to intervene. Hence, safety-ensuring technology must be balanced with user experience in autonomous driving. We explore relevant global and domestic regulations, the new car assessment program, and related standards to extract requirements for driver status monitoring. This kind of monitoring can both enhance the autonomous driving performance and contribute to the overall safety of autonomous vehicles on the road.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.1113-1114
• /
• 1996

We describe an algorithm to control an autonomous guided vehicle with 2-diagonal driving wheels, which navigates in the clean room.

• 자동차안전학회지
• /
• 제14권1호
• /
• pp.20-25
• /
• 2022

This paper presents a development of simulation environment for validation of autonomous driving (AD) algorithm based on Robot Operating System (ROS). ROS is one of the commonly-used frameworks utilized to control autonomous vehicles. For the evaluation of AD algorithm, a 3D autonomous driving simulator has been developed based on LGSVL. Two additional sensors are implemented in the simulation vehicle. First, Lidar sensor is mounted on the ego vehicle for real-time driving environment perception. Second, GPS sensor is equipped to estimate ego vehicle's position. With the vehicle sensor configuration in the simulation, the AD algorithm can predict the local environment and determine control commands with motion planning. The simulation environment has been evaluated with lane changing and keeping scenarios. The simulation results show that the proposed 3D simulator can successfully imitate the operation of a real-world vehicle.

• 자동차안전학회지
• /
• 제7권2호
• /
• pp.44-49
• /
• 2015

This paper presents an evaluation scenario of safety performance for extraordinary service permission of autonomous vehicle driving on a motorway. Based on advanced driver assistance system (ADAS) which is already mass-production, an autonomous vehicle driving on motorway is tested on the public roads and also getting close to mass-production. Before the autonomous vehicle tested, the safety of autonomous driving system should be evaluated based on a proper test scenario. Prior to develop the test scenario, this paper reviews the licensing standards for an autonomous vehicle in California and Nevada, and the international regulations of each ADAS. To develop the scenario, the driving conditions of motorway are categorized into five modes and fundamental evaluation requirements of elements of autonomous driving system are derived. An evaluation scenario, which represents the real driving conditions, has been developed to assess the safety of autonomous vehicle. This scenario has validated by computer simulation using model predictive control (MPC) based autonomous driving algorithm.

• 드라이브 ㆍ 컨트롤
• /
• 제17권4호
• /
• pp.80-86
• /
• 2020

Autonomous driving is one of the most important new technologies of our time; it has benefits in terms of safety, the environment, and economic issues. Path following algorithms, such as automated lane keeping systems (ALKSs), are key level 3 or higher functions of autonomous driving. Pure-Pursuit and Stanley controllers are widely used because of their good path tracking performance and simplicity. However, with the Pure-Pursuit controller, corner cutting behavior occurs on curved roads, and the Stanley controller has a risk of divergence depending on the response of the steering system. In this study, we use the advantages of each controller to propose a hybrid control strategy that can be stably applied to complex driving environments. The weight of each controller is determined from the global and local curvature indexes calculated from HD map information and the current driving speed. Our experimental results demonstrate the ability of the hybrid controller, which had a cross-track error of under 0.1 m in a virtual environment that simulates K-City, with complex driving environments such as urban areas, community roads, and high-speed driving roads.