• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.357-363
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• 2009

This paper deals with the Filtered-x Least Mean Square algorithm for a active vibration control in vehicle vibration reduction. Before applying the proposed FxLMS algorithm to automobile, the performance of the FxLMS algorithm is simulated using sensor data of a vehicle. The FxLMS algorithm requires that reference signal be a representation of disturbance signal and the plant model be incorporated into the computation path. To this end, The system identification is carried out to obtain the plant model based on the measurement results. A tachometer signal is used as reference signal. The FxLMS control algorithm is first tested using simulation and applied to a vehicle. Experimental results show that the proposed control algorithm can reduce vibration level in a short period of time.

• Interaction and multiscale mechanics
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• v.3 no.4
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• pp.365-372
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• 2010

Station is an important building in high-speed railway, and its vibration and noise may significantly affect the comfort of waiting passengers. A coupling vibration model for train-structure system is established to analyze and evaluate the vibration level of a typical waiting hall under dynamic train load. The motion of a four-axle vehicle with two suspension system is modeled in multi-body dynamics with linear springs and dampers employed. The station is modeled as a whole finite element structure which is 113 m in longitudinal and 163.5 m in lateral, and the stiffness of the station foundation is considered. According to the assumptions that both wheel and rail are rigid bodies and keep contact to each other in vertical direction, and the wheel/rail interaction and displacement coordination in horizontal direction is defined by the simplified Kalker creep theory, the vehicle spatial vibration model has 27 degrees-of-freedom. An overall analysis procedure is made of the train moving through the station, by which the dynamic responses of the train and the station are calculated. According to the comparison between analysis and test results, the actual connection status between different parts of the station is estimated and the vibration level of the waiting hall is evaluated.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.2
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• pp.139-147
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• 2004

The purpose of engine control TCS is to regulate engine torque to keep driven wheel slip in a desired range. In this paper, engine control TCS using sliding mode control law based on engine model and estimated load torque is proposed. This system includes a two-level controller. Slip controller calculates desired wheel torque, and engine torque controller determines throttle angle for engine torque corresponding to desired wheel torque. Another issue is to measure load torque for model based controller design. Luenberger observer with state variables of load torque and engine speed solves this problem as estimating load torque. The performance of controller and observer is certificated by simulation using 8-degree vehicle model, Pacejka tire model, and 2-state engine model. The simulation results in various maneuvers during slippery and split road conditions showed that acceleration performance and ability of the vehicle with TCS is improved. Also, the load torque observer could estimate real load torque very well, so its performance was proved.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.101-107
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• 2010

The aim of this paper is to come up with a methodology of constructing an efficient model for multiple representations which can manage and reconcile real-time data about large-scale roads in Vector Domain. In other words, we suggested framework based on a bottom-up approach, which is allowed to integrate data from the network of the lowest level sequentially and perform automated matching in order to produce variable-scale map. Finally, we applied designed multi-LoD model to in-vehicle application.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.134-139
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• 2021

Currently, the autonomous vehicle market is commercializing a level 3 autonomous vehicle, but it still requires the attention of the driver. After the level 3 autonomous driving, the most notable aspect of level 4 autonomous vehicles is vehicle stability. This is because, unlike Level 3, autonomous vehicles after level 4 must perform autonomous driving, including the driver's carelessness. Therefore, in this paper, we propose the Emergency-notification and Driver-response Confirmation System(EDCS) for an autonomousvehicle safety that notifies the driver of an emergency situation and recognizes the driver's reaction in a situation where the driver is careless. The EDCS uses the emergency situation delivery module to make the emergency situation to text and transmits it to the driver by voice, and the driver response confirmation module recognizes the driver's reaction to the emergency situation and gives the driver permission Decide whether to pass. As a result of the experiment, the HMM of the emergency delivery module learned speech at 25% faster than RNN and 42.86% faster than LSTM. The Tacotron2 of the driver's response confirmation module converted text to speech about 20ms faster than deep voice and 50ms faster than deep mind. Therefore, the emergency notification and driver response confirmation system can efficiently learn the neural network model and check the driver's response in real time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.93-101
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• 2021

The purpose of this study is to analyze the adequacy of production capacity of the assembly process system of mobile bath vehicle's top box panel and process design through a simulation analysis. Towards this end, the layout of the facility designed with pre-verification job using a simulation modeling and an experiment, and facility, logistics process, and personnel input method were made into a simulation model, and the design system's adequacy was evaluated through an experiment. To produce 120 mobile bath vehicles annually, it was analyzed that 14 general workers and seven skilled workers were adequate through the experiment. It was also identified that three painting process lines carried out through outsourcing were adequate. Production lead time was 201.7 hours on average and it was 230 hours maximum. To meet customer delivery service level of 95% within the deadline when establishing a customer order and vehicle delivery plan, it was analyzed that more than 215 hours of lead time is needed minimum. If the process cycle time is reduced to 85% upon system stabilization and skillfulness improvement, it was analyzed that annual output of 147 vehicles can be achieved without additional production line expansion.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.340-352
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• 2022

This article presents a modeling method for the uncorrelated measurement error of the ultra-short baseline (USBL) acoustic positioning system for aiding navigation of underwater vehicles. The Mahalanobis distance (MD) and principal component analysis are applied to decorrelate the errors of USBL measurements, which are correlated in the x- and y-directions and vary according to the relative direction and distance between a reference station and the underwater vehicles. The proposed method can decouple the radial-direction error and angular direction error from each USBL measurement, where the former and latter are independent and dependent, respectively, of the distance between the reference station and the vehicle. With the decorrelation of the USBL errors along the trajectory of the vehicles in every time step, the proposed method can reduce the threshold of the outlier decision level. To demonstrate the effectiveness of the proposed method, simulation studies were performed with motion data obtained from a field experiment involving an autonomous underwater vehicle and USBL signals generated numerically by matching the specifications of a specific USBL with the data of a global positioning system. The simulations indicated that the navigation system is more robust in rejecting outliers of the USBL measurements than conventional ones. In addition, it was shown that the erroneous estimation of the navigation system after a long USBL blackout can converge to the true states using the MD of the USBL measurements. The navigation systems using the uncorrelated error model of the USBL, therefore, can effectively eliminate USBL outliers without loss of uncontaminated signals.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.347-350
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• 1999

자율무인잠수정은 자율운항을 위해서 자동화된 제어시스템이 필요하다. 제어시스템은 기능적 측면에서 임무계획단계(mission planning level), 임무제어단계(mission control level), 선체제어단계(vehicle control level)로 구분한다. 자율무인잠수정의 효과적인 임무 수행을 위해서는 임무제어단계의 운행 경로 설정과 제어가 중요하다. 자율무인잠수정은 잠수정의 주변환경을 추상화한 후 탐색기법을 이용하여 경로를 설정한다. 이때 검색기법의 효율적 적용을 위해서는 효과적으로 추상화된 탐색모형이 필요하다. 대표적인 탐색모형으로는 3차원 격자절대좌표 모형(3-dimensional grid unit coordinate model)(1)을 들 수 있다. 그러나 이 모형은 불필요한 동작의 반복, 이동 격자에 따른 비일관성과 같은 취약점이 존재한다. 본 연구에서는 이 모형의 취약점을 개선하기 위해서 자율무인잠수정의 위치 기반 상대적 격자좌표 모형(relative grid unit coordinate model based on AUV state)을 제안한다.