• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.1
• /
• pp.50-58
• /
• 2013

Estimating a driver's head pose is an important task in driver-assistance systems because it can provide information about where a driver is looking, thereby giving useful cues about the status of the driver (i.e., paying proper attention, fatigued, etc.). This study proposes a system for estimating the head pose using monocular images, which includes a novel use of backprojection. The system can use a single image to estimate a driver's head pose at a particular time stamp, or an image sequence to support the analysis of a driver's status. Using our proposed system, we compared two previous pose estimation approaches. We introduced an approach for providing ground-truth reference data using a mannequin model. Our experimental results demonstrate that the proposed system provides relatively accurate estimations of the yaw, tilt, and roll angle. The results also show that one of the pose estimation approaches (perspective-n-point, PnP) provided a consistently better estimate compared to the other (pose from orthography and scaling with iterations, POSIT) using our proposed system.

• Journal of Korean Elementary Science Education
• /
• v.25 no.3
• /
• pp.231-243
• /
• 2006

The purpose of this study was to investigate misconceptions regarding the structure of flowers and the function of the course based on the 'Flower' section of 5th grade elementary school science courses. It also sought to investigate how misconceptions are changed before and after the application of a Driver learning model, and finally analysing any differences in the correction of misconceptions. A questionnaire was created for 199 5th grade elementary school pupils. The major results before and after using an applied Driver learning mode teaching plan are as follows: In the response for questions, 13.6% and 14.5% of misconceptions were corrected for male and female pupils, respectively. For rural and urban pupils, 14.8% and 11.2% of misconceptions were corrected, respectively. In the comparison of male and female pupils according to the reasons for selection of responses before and after using an applied Driver learning model teaching plan, 27.8% of male and 30.0% of female pupils scientific conceptions showed improvement. For rural and urban schools, 26.6% and 32.2% of scientific conceptions were improved, respectively. Data from this study may help teachers to reconsider their own conceptions regarding the study of the flower as it is presently conducted in elementary school.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.1
• /
• pp.30-35
• /
• 2005

An automatic transmission pattern with the fixed standard shift map can provide comfortable shift to driver. However it may be a complain to provide shift by the same shift pattern for driver because the inclination of a driver may be various. Therefore, in this paper, we design the decision module, which can decide the driving style using input to decide the inclination of the driver and driving manner. The goal of this paper is to calibrate the shift map according to the inclination of the driver using the decided driving manner from the proposed module. As a result, the proposed intelligent automatic transmission system can provide a suitable shift point and time to the driver. To verify the performance of the proposed system, the real data that is obtained from the road test will be used.

• Journal of the Ergonomics Society of Korea
• /
• v.30 no.1
• /
• pp.203-211
• /
• 2011

Objective: The aim of this study is to introduce a methodology to assess driver's emotion stability by using bio-signals. Background: Psychophysiological analysis of driver's behavior has been conducted to improve the driving safety and comfort. However, the variability of bio-signal and individual difference made it difficult to assess the psychophysiological status of drivers that can be expressed as emotional stability of drivers. Method: Two experimental studies were reviewed and summarized. New techniques assessing emotional stability of drivers were explained. Statistical concept and multidimensional space were used to identify the emotionally stable conditions. Conclusion: Psychophysiological approach can provide information of driver's emotional status. The experimental methodology and algorithm used in this study showed the possibility of parameterization of psychophysiological response. Application: Currently measured statistical and geometrical data can be further applied to develop an interactive device monitoring and reacting driver's emotion when driver experiences emotionally unstable or uncomfortable situation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.7
• /
• pp.609-614
• /
• 2015

Implanted telemetry systems provide the ability to monitor different species of animals while they move within their cages. Species monitored include mice, rats, rabbits, dogs, pigs, primates, sheep, horses, cattle, and others. A miniature transmitter implanted in each animal measures one or more parameters. Parameters measured include arterial pressure, intra-pleural pressure, left ventricular pressure, intra-ocular pressure, bladder pressure, ECG, EMG, EEG, EOG, temperature, activity, and other parameters and transmits the data via radio frequency signals to a nearby receiver. Every conventional dedicated transmitter contains one or more sensors, cpu and battery. Due to the expected life of the battery, the measuring time is limited. To overcome these problems, electromagnetic inductive coupling based wireless power transmission technology using multiple transmit coils were proposed, with each coil having a different active area driven by the coil driver. In this research, a parallel resonance based coil driver and serial resonance based coil driver are proposed. From the experiments we see that the parallel coil driver shows better performance under a low impedance and multiple coils configuration. However, the serial coil driver is more efficient for high impedance transmit coils.

• Journal of information and communication convergence engineering
• /
• v.11 no.2
• /
• pp.95-102
• /
• 2013

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.

• Journal of the Korea Safety Management & Science
• /
• v.14 no.4
• /
• pp.85-91
• /
• 2012

To verify the effect of driver's personal characteristics of driver on the accident frequency through railway accidents caused by human errors and the relationship with aptitude test. To prove the relevance between the driver's personal characteristics and human error accidents. Accident data from 2010 to 2011 was analyzed which collected from a train crew department in K national corporation, and 31 drivers gave an personal interview from Sep. 2011 to Nov. 2011 who had controlled a train alone and caused an accident. Compared between driver's personal characteristics and accident rate, and accident induction possibility surveyed from normal person and disqualified in aptitude tests. Accidents was occurred with the age 40s (27%) and 50s (25%), and with the experience between 15 years and 20 years (38%) and over 20 years (30%). Because more aged, more experienced, it can be seen in the correlation between driver's age and accidents induction caused by human errors like illusion. First of all it must be checked whether working conditions and environmental factors are human error-prone. Most accidents occur when received civil complaints or manager at the riding. Therefore accidents can be prevented when investigated through subsequent surveys how often human error happens, even though no accident, and safety device installed based on the error frequency.

• 한국HCI학회:학술대회논문집
• /
• 2008.02c
• /
• pp.150-151
• /
• 2008

With the proliferation of vehicles and advancement of Information Technology, the technology of Telematics, which provides valuable services to people by collecting and analyzing the information from drivers, vehicles and Telematics environments (e.g. traffic information, road condition, weather information, etc.), has been a hot research area in IT and automotive recently. As the information technology revolution brings more and more assistance for driver information processing becomes increasing important. Therefore, drivers' workload is very essential factor for safety driving in Telematics environment. For managing drivers' workload effectively, ETRI haven been developing vehicle and driver management system which can collect data from drivers and vehicle in realtime and analyze the data to manage drivers' and vehcles' status since 2007. This technology will apply to commercial vehicle telematics such as texi or truck management system in the future for increasing driving safety. In this presentation, I would like to explain what we had developed so far and discuss future direction.

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

• The Journal of the Korea Contents Association
• /
• v.7 no.1
• /
• pp.116-123
• /
• 2007

This paper proposed a new amplitude width modulation for OLED data driver IC. The data driver controls brightness of OLED by adjusting amplitude and width of the data drive current pulse. There were two conventional methods; pulse amplitude modulation(PAM) and pulse width modulation(PWM). The PWM method suffered from lower light emitting time efficiency at low luminance signal. The PAM method suffered from large chip area using DACs for each column. The proposed method was aiming at accurately controlling of the current level by MSB data and light emitting efficiency by LSB data to improve the inefficiencies of the PAM and a PWM. The proposed AWM driver circuit implemented using $0.35-{\mu}m$ 3-poly 4-metal CMOS high voltage process. The simulation result shows the improvement in the accuracy of the gray level control even though the driver circuit is smaller than the PAM.