• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.39-52
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• 2021

This paper presents a longitudinal acceleration controller that can be applied to real vehicles (nonlinear and time-varing systems) with only a simple experiment regardless of the type of vehicle and the control interface structure. The controller consists of a feedforward term for fast response, a zero-throttle acceleration compensation term, and a feedback term (P gain) to compensate for errors in the feedforward term, and another feedback term (I gain) to respond to disturbances such as slope. In order to easily apply it to real vehicles, there are only two tuning parameters, feedforward terms of throttle and brake control. And the remaining parameters can be calculated immediately when the two parameters are decided. The tuning procedure is also unified so that it can be quickly and easily applied to various vehicles. The performance of the controller was evaluated using MATLAB/Simulink and Truksim's European Ben model. In addition, the controller was successfully implemented to 3 medium-sized vehicle (HMC Solati), which is composed of different control interface characteristic. Vehicle driving performance was evaluated on the test track and on the urban roads in Siheung and Seoul.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.11-16
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• 2019

This paper suggests an algorithm for detecting fault of longitudinal controller in autonomous vehicles. Guaranteeing safety in fault situation is essential because electronic devices in vehicle are dependent each other. Several methods like alarm to driver, ceding control to driver, and emergency stop are considered to cope with fault. This research investigates the fault monitoring process in fail-safe system, for controller which is responsible for accelerating and decelerating control in vehicle. Residual is computed using desired acceleration control command and actual acceleration, and detection of its abnormal increase leads to the decision that system has fault. Before computing residual for controller, health monitoring process of acceleration signal is performed using hardware and analytic redundancy. In fault monitoring process for controller, a process model which is fitted using driving data is considered to improve the performance. This algorithm is simulated via MATLAB tool to verify performance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.6
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• pp.295-301
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• 2001

In this paper, an impedance control using a serial chain of spring-damper system is proposed for a vehicle platoon. For safety of the vehicle platoon, it is required to regulated the distance between each vehicle at a preassigned value even in case of vehicle model error, or moise in the measurement signal. Since the spring-damper system is physically stable and widely used to represent the interaction with the uncertain environments, it is appropriate to the longitudinal control of the vehicle platoon. By considering the nonholonomic characteristics of the vehicle motion, the lateral control and the longitudinal control of the vehicle paltoon are unified in the proposed algorithm. Computer simulation is carried out to verify the robustness against the uncertainties such as the vehicle model error and the measurement noise.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.26-38
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• 2022

This paper proposes multiple RLS and actuator performance index-based adaptive actuator fault-tolerant control and detection algorithms for longitudinal autonomous driving. The proposed algorithm computes the desired acceleration using feedback law for longitudinal autonomous driving. When actuator fault or performance degradation exists, it is designed that the desired acceleration is adjusted with the calculated feedback gains based on multiple RLS and gradient descent method for fault-tolerant control. In order to define the performance index, the error between the desired and actual accelerations is used. The window-based weighted error standard deviation is computed with the design parameters. Fault level decision algorithm that can represent three fault levels such as normal, warning, emergency levels is proposed in this study. Performance evaluation under various driving scenarios with actuator fault was conducted based on co-simulation of Matlab/Simulink and commercial software (CarMaker).

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.16-22
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• 2023

This paper presents a proactive longitudinal motion planning algorithm for improving the safety of an automated bus. Since the field of view (FOV) of the autonomous vehicle was limited depending on onboard sensors' performance and surrounding environments, it was necessary to implement vehicle-to-vehicle (V2V) communication for overcoming the limitation. After a virtual V2V-equipped target was constructed considering information obtained from V2V communication, the reference motion of the ego vehicle was determined by considering the state of both the V2V-equipped target and the sensor-detected target. Model predictive control (MPC) was implemented to calculate the optimal motion considering the reference motion and the chance constraint, which was deduced from manual driving data. The improvement in driving safety was confirmed through vehicle tests along actual urban roads.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.410-418
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• 2007

For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.39-44
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• 2022

This paper presents a development of throttle and brake controller for autonomous vehicle simulation environment. Most of 3D simulator control autonomous vehicle by throttle and brake command. Therefore additional longitudinal controller is required to calculate pedal input from desired acceleration. The controller consists of two parts, feedback controller and feedforward controller. The feedback controller is designed to compensate error between the actual acceleration and desired acceleration calculated from autonomous driving algorithm. The feedforward controller is designed for fast response and the output is determined by the actual vehicle speed and desired acceleration. To verify the performance of the controller, simulations were conducted for various scenarios, and it was confirmed that the controller can successfully follow the target acceleration.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.224-232
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• 2015

In order to develop an active safety system which avoids or mitigates collisions with preceding vehicles such as autonomous emergency braking (AEB), accurate state estimation of the nearby vehicles is very important. In this paper, an algorithm is proposed using 3 dynamic models to better estimate the state of a vehicle which has various dynamic patterns in both longitudinal and lateral direction. In particular, the proposed algorithm is based on the Interacting Multiple Model (IMM) method which employs three different dynamic models, in cruise mode, lateral maneuver mode and longitudinal maneuver mode. In addition, a Probabilistic Data Association Filter (PDAF) is utilized as a data association algorithm which can improve the reliability of the measurement under a clutter environment. In order to verify the performance of the proposed method, it is simulated in comparison with a Kalman filter method which employs a single dynamic model. Finally, the proposed method is validated using radar data obtained from the field test in the proving ground.

• Safety and Health at Work
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• v.9 no.1
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• pp.109-114
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• 2018

Background: Although work absenteeism is in the focus of occupational health, longitudinal studies on organizational absenteeism records in hospital work are lacking. This longitudinal study tests time pressure and lack of time autonomy to be related to higher sickness absenteeism. Methods: Data was collected for 180 employees (45% nurses) of a Swiss hospital at baseline and at follow-up after 1 year. Absent times (hours per month) were received from the human resources department of the hospital. One-year follow-up of organizational absenteeism records were regressed on self-reported job satisfaction, time pressure, and time autonomy (i.e., control) at baseline. Results: A multivariate regression showed significant prediction of absenteeism by time pressure at baseline and time autonomy, indicating that a stress process is involved in some sickness absenteeism behavior. Job satisfaction and the interaction of time pressure and time autonomy did not predict sickness absenteeism. Conclusion: Results confirmed time pressure and time autonomy as limiting factors in healthcare and a key target in work redesign.