• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.63-70
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• 2018

In this work, we analyze the relationship between the accumulated mileage of hybrid electric vehicle(HEV) and the data provided from vehicle parts. Data were collected while traveling over 70,000 Km in various paths. The data collected in seconds are aggregated for 10 minutes and characterized in terms of centrality, variability, normality, and so on. We examined whether the statistical properties of vehicle parts are different for each cumulative mileage interval of a hybrid car. When the cumulative mileage interval is categorized into =< 30,000, <= 50,000, and >50,000, the statistical properties are classified by the mileage interval as 82.3% accuracy. This indicates that if the data of the vehicle parts is collected by operating the hybrid vehicle for 10 minutes, the cumulative mileage interval of the vehicle can be estimated. This makes it possible to detect the abnormality of the vehicle part relative to the accumulated mileage. It can be used to detect abnormal aging of vehicle parts and to inform maintenance necessity.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.39-47
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• 2016

In this paper, we propose vehicle SoC fault diagnosis platform using FlexRay protocol in order to detect the faults of semiconductor control chip even after vehicle production. Before FlexRay protocol by sending NFI (Null Frame Indicator) bit among the header segment and a specific identifier in the payload segment of FlexRay frame, this technique can be distinguishable from normal mode and test mode. By using this technique, it is possible to detect the faults such as performance degradation of vehicle network system caused by the aging or several problems of vehicle semiconductor chip. Also high reliability and safety of vehicle can be maintained by using structural test for vehicle SoC fault detection.

• Journal of information and communication convergence engineering
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• v.4 no.3
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• pp.123-129
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• 2006

The limited number of roads and the increasing number of vehicles demand the automatic regulation of overspeed vehicles, illegal vehicles, and overloaded vehicles and the automatic charge calculation depending on the type of the vehicle. To meet such requirements, it is important to remove the shadow of the vehicle as processing and recognizing an image captured by a camera. The shadow of the vehicle is likely to cause misclassification of the vehicle type due to diverse errors and mistakes occurring when detecting geometrical properties of the vehicle. In case that shadows of two different vehicles are overlapped, not only the type of the vehicles may be misclassified but also it is difficult to accurately identify the type of the vehicles. In this paper, we propose a robust algorithm to remove the shadow of a vehicle by calculating the luminance, the chrominance, the gradient density of the cast shadow from information acquired using the image subtraction of the background, and to recognize the substantial vehicle figure. Even when it is hard to detect and split a target vehicle from its shadow as shadows of vehicles are attached to each other, our robust algorithm can detect the vehicle figure only. We implemented our system with a general camera and conducted experiments on various vehicles on general roads to find out our vehicle shade removal algorithm is efficient when detecting and recognizing vehicles.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.609-620
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• 2016

Recently, there are active studies on a forward collision warning system to prevent the accidents and improve convenience of drivers. For collision evasion, the vehicle detection system is required. In general, existing learning-based vehicle detection methods use the entire appearance of the vehicles from rear-view images, so that each vehicle types should be learned separately since they have distinct rear-view appearance regarding the types. To overcome such shortcoming, we learn Haar-like features from the lower part of the vehicles which contain tail lights to detect vehicles leveraging the fact that the lower part is consistent regardless of vehicle types. As a verification procedure, we detect tail lights to distinguish actual vehicles and non-vehicles. If candidates are too small to detect the tail lights, we use HOG(Histogram Of Gradient) feature and SVM(Support Vector Machine) classifier to reduce false alarms. The proposed forward vehicle detection method shows accuracy of 95% even in the complicated images with many buildings by the road, regardless of vehicle types.

• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.996-1003
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• 2012

We present a nearby vehicle detection method in the adjacent lane using in-vehicle front view camera. Nearby vehicles in adjacent lanes show various appearances according to their relative positions to the host vehicle. Therefore, most conventional methods use motion information for detecting nearby vehicles in adjacent lanes. However, these methods can only detect overtaking vehicles which have faster speed than the host vehicle. To solve this problem, we use the feature of regions where nearby vehicle can appear. Consequently, our method cannot only detect nearby overtaking vehicles but also stationary and same speed vehicles in adjacent lanes. In our experiment, we validated our method through various whether, road conditions and real-time implementation.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.16-24
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• 2012

This paper proposes a method utilizing Differential Global Position System (DGPS) with Real-Time Kinematic (RTK) and pre-built Geo-graphic Information System (GIS) to detect lane departure of a vehicle. The position of a vehicle measured by DGPS with RTK has 18 cm-level accuracy. The preconditioned GIS data giving accurate position information of the traffic lanes is used to set up coordinate system and to enable fast calculation of the relative position of the vehicle within the traffic lanes. This relative position can be used for safe driving by preventing the vehicle from departing lane carelessly. The proposed system can be a key component in functions such as vehicle guidance, driver alert and assistance, and the smart highway that eventually enables autonomous driving supporting system. Experimental results show the ability of the system to meet the accuracy and robustness to detect lane departure of a vehicle at high speed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.9
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• pp.72-79
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• 2004

A method for thruster fault diagnosis for launch vehicle upper stage was developed. In order to protect the launch vehicle against the occurrence of faults, it is necessary to detect and identify the fault, as well as to reconfigure the controller of the vehicle. Considering the upper stage launch vehicle using reaction control system, an analytical method was adopted in order to detect the fault occurred in thruster. The fault detection scheme can be applied to the system regardless of the form of thruster fault occurred - leakage or lock-out. Results from processor-in-the-loop simulation are provided to demonstrate the validity of this fault detection and isolation scheme for the upper stage launch vehicle.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.269-275
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• 2017

Automatic vehicle detection is a very important function for autonomous vehicles. Conventional vehicle detection approaches are based on visible-light images obtained from cameras mounted on a vehicle in the daytime. However, unlike daytime, a visible-light image is generally dark at night, and the contrast is low, which makes it difficult to recognize a vehicle. As a feature point that can be used even in the low light conditions of nighttime, the rear lamp is virtually unique. However, conventional rear lamp-based detection methods seldom cope with atypical lamps, such as LED lamps, or flashing turn signals. In this paper, we detect atypical lamps by blurring the lamp area with a low pass filter (LPF) to make out the lamp shape. We also propose to detect flickering of the turn signal lamp in a manner such that the lamp area is vertically projected, and the maximum difference of two paired lamps is examined. Experimental results show that the proposed algorithm has a higher F-measure value of 0.24 than the conventional lamp pairing-based detection methods, on average. In addition, the proposed algorithm shows a fast processing time of 6.4 ms per frame, which verifies real-time performance of the proposed algorithm.

• Journal of IKEEE
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• v.23 no.1
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• pp.235-241
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• 2019

In this paper, we propose a new algorithm to detect rear-approaching vehicle using the frame similarity of ROI(Region of Interest) based on deep learning algorithm for use in agricultural machinery systems. Since the vehicle detection system for agricultural machinery needs to detect only a vehicle approaching from the rear. we use Faster R-CNN model that shows excellent accuracy rate in deep learning for vehicle detection. And we proposed an algorithm that uses the frame similarity for ROI using constrained conditions. Experimental results show that the proposed method has a detection rate of 99.9% and reduced the false positive values.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.411-417
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• 2022

In this paper, we propose a sensor with a C-shaped load cell to detect force change when a person sitting on the chair in an electrical transport-convenience vehicle is pushing ground by both heels. The load cell built in the vehicle is mechanically deformed by the vertical force owing to the human weight and the horizontal force by ground-pushing feet. The deformation rate of the load cell and its distribution are simulated using finite element analysis. In the simulation, the applied loads are preset in the range of 10 kg - 100 kg with a step size of 10 kg, and the ground-pushing force by feet is increased to 40 N with a step size of 5 N with respect to each applied load level. The resistance change of the load cell was observed to be linear in simulation as well as in measurement. the maximum difference between simulation and measurement was 0.89 % when the strain gauge constant was 2.243. The constant has a large influence on the difference. The proposed sensor was fabricated by connecting an instrument amplifier and a microcontroller to a load cell and used to detect the force by ground-pushing feet. To detect foot driving, the reference signal was set to 130% of the load, and the duration of the sensor output signal exceeding the reference signal was set to 0.6 s. In a test of a vehicle built with the proposed sensor, the footpushing force by the worker could be successfully detected even when the worker was working.