• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.464-470
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• 2016

In this paper, we propose an improved face detection algorithm and determination method for drowsiness status of driver from the opening and closing frequency of the detected eye. For this purpose, face, eyes, nose, and mouth are detected based on conventional Viola-Jones face detection algorithm and spatial correlation of face. Here the spatial correlation of face is performed by DFP(Detect Face Part) based on seven characteristics. The experimental results on Caltect face image database revealed that the detection rates of noise particularly showed the improved performance of 13.78% in comparison to that of the previous Viola-Jones algorithm. Furthermore, we analyze the driver's drowsiness determination cumulative value of the eye closed state as a function of time based on SVM (Support Vector Machine) and PERCLOS(Percentage Closure of Eyes). The experimental results confirmed the usefulness of the proposed method by obtaining a driver's drowsiness determination rate of 93.28%.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• Transportation Technology and Policy
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• v.13 no.1
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• pp.31-36
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• 2016

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.343-348
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• 2007

This paper presents an adaptive controller for the compensation of state-dependent disturbance with unknown amplitude in a digital servo motor system. The state-dependent disturbance is caused by friction and eccentricity between the wheel axis and the motor driver of a mobile robot servo system. The proposed control scheme guarantees an asymptotical stability for both the velocity and position regulation. An experimental result shows the effectiveness of the adaptive disturbance compensator for wheeled-mobile robot in a low velocity diffusion tracking. A comparative experimental study with a simple PI controller is presented.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.87-95
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• 2020

Purpose: In this paper, we propose an algorithm for detecting respiratory rate and heart beat of a driver of car by exploiting Doppler radar, and verifying the feasibility of the study through experiments. Method: In this paper, we propose a weighted peak detection technique using peak frequency values. The tests are performed in stop-state and driving-state, and the experiment result is analyzed by two proposed algorithms. Result: The results showed more than 95% and 96% accuracy of respiratory and heart rate, respectively. It also showed more than 72% and 84% accuracy of those even for driving experiments. Conclusion: The proposed detection scheme for vital signs can be used for the safety of the driver as well as for prevention of a large size of car accidents.

• Journal of Power Electronics
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• v.16 no.1
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• pp.362-373
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• 2016

The on-line measurement of high-power IGBT collector current is important for the hierarchical control and short-circuit and overcurrent protection of its driver and the sensorless control of the converter. The conventional on-line measurement methods for IGBT collector current are not suitable for engineering measurement due to their large-size, high-cost, low-efficiency sensors, current transformers or dividers, etc. Based on the gate driver, this paper has proposed a current measuring circuit for IGBT collector current. The circuit is used to conduct non-intervention on-line measurement of IGBT collector current by detecting the voltage drop of the IGBT power emitter and the auxiliary emitter terminals. A theoretical analysis verifies the feasibility of this circuit. The circuit adopts an operational amplifier for impedance isolation to prevent the measuring circuit from affecting the dynamic performance of the IGBT. Due to using the scheme for integration first and amplification afterwards, the difficult problem of achieving high accuracy in the transient-state and on-state measurement of the voltage between the terminals of IGBT power emitter and the auxiliary emitter (u_Ee) has been solved. This is impossible for a conventional detector. On this basis, the adoption of a two-stage operational amplifier can better meet the requirements of high bandwidth measurement under the conditions of a small signal with a large gain. Finally, various experiments have been carried out under the conditions of several typical loads (resistance-inductance load, resistance load and inductance load), different IGBT junction temperatures, soft short-circuits and hard short-circuits for the on-line measurement of IGBT collector current. This is aided by the capacitor voltage which is the integration result of the voltage uEe. The results show that the proposed method of measuring IGBT collector current is feasible and effective.

• Smart Structures and Systems
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• v.13 no.2
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• pp.281-303
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• 2014

In order to suppress the wind-induced vibrations of the Canton Tower, a pair of active mass driver (AMD) systems has been installed on the top of the main structure. The structural principal directions in which the bending modes of the structure are uncoupled are proposed and verified based on the orthogonal projection approach. For the vibration control design in the principal X direction, the simplified model of the structure is developed based on the finite element model and modified according to the field measurements under wind excitations. The AMD system driven by permanent magnet synchronous linear motors are adopted. The dynamical models of the AMD subsystems are determined according to the open-loop test results by using nonlinear least square fitting method. The continuous variable gain feedback (VGF) control strategy is adopted to make the AMD system adaptive to the variation in the intensity of wind excitations. Finally, the field tests of free vibration control are carried out. The field test results of AMD control show that the damping ratio of the first vibration mode increases up to 11 times of the original value without control.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.905-910
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• 2022

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.90-102
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• 2022

Currently, in our society with a substantial and increasing fraction of the elderly population, transport safety for elderly drivers is becoming the center of attention. However, deficient data on vehicle crashes in South Korea limits the growth of traffic accident research pertaining to the country. So, we complemented South Korean vehicle crash data by examining USA vehicle crash data, especially the data of Ohio State, and analyzing the influential factors of elderly driver-involved crashes of the State. Subsequently, we suggested a way of improving the South Korean dataset. Notably, our study showed that the influential factors were vehicle speed, posted speed, and following other vehicles too close and provided them in the South Korean dataset.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1851-1863
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• 2017

Interior permanent magnet synchronous motor (IPMSM) is most commonly used in the automotive industry as a traction motor for electric vehicle (EV). In electric vehicle, the torque output rapidly changes according to the operation of the accelerator and the braking of the driver. The transient torques are thus generated very frequently in accordance with the variable speed control of the driver. Therefore, in this paper, a method for improving the torque response in the transient states of IPMSM is proposed. In order to complement the disadvantages of the conventional PI current controller in the field oriented control (FOC), the finite-state model predictive current control and 2D-LUT is applied to improve the torque response at the torque transient period. Simulation and experiment results are given to verify the reliability of the proposed method.