• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.125-135
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• 2012

This paper proposes the new object detection system with two laser-scanners and a camera for classifying the objects and predicting the location of objects on road street. This detection system could be applied the new C-ITS service such as ADAS(Advanced Driver Assist System) or (semi-)automatic vehicle guidance services using object's types and precise position. This study describes the some examples in other countries and feasibility of object detection system based on a camera and two laser-scanners. This study has developed the heterogenous sensor's fusion method and shows the results of implementation at road environments. As a results, object detection system at roadside infrastructure is a useful method that aims at reliable classification and positioning of road objects, such as a vehicle, a pedestrian, and obstacles in a street. The algorithm of this paper is performed at ideal condition, so it need to implement at various condition such as light brightness and weather condition. This paper should help better object detection and development of new methods at improved C-ITS environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8A
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• pp.639-647
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• 2012

Vision-based on-road vehicle detection is one of the key techniques in automotive driver assistance systems. However, due to the huge within-class variability in vehicle appearance and environmental changes, it remains a challenging task to develop an accurate and reliable detection system. In general, a vehicle detection system consists of two steps. The candidate locations of vehicles are found in the Hypothesis Generation (HG) step, and the detected locations in the HG step are verified in the Hypothesis Verification (HV) step. Since the final decision is made in the HV step, the HV step is crucial for accurate detection. In this paper, we propose using a reduced multivariate polynomial pattern classifier (RM) for the HV step. Our experimental results show that the RM classifier outperforms the well-known Support Vector Machine (SVM) classifier, particularly in terms of the fast decision speed, which is suitable for real-time implementation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1416-1423
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• 2017

Lane detection technology is one of the most important issues on car safety and self-driving capability of autonomous vehicle. This paper introduces an accurate lane detection scheme based on OCC(Optical Camera Communication) for moving vehicles. For lane detection of moving vehicles, the streetlights and the front camera of the vehicle were used for a transmitter and a receiver, respectively. Based on the angle information of multiple streetlights in a captured image, the distance from sidewalk can be calculated using non-linear regression analysis. Simulation results show that the proposed scheme shows robust performance of accurate lane detection.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.112-118
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• 2018

This paper describes the design and implementation of a vehicle internal alarm system using raspberry-pie and gas sensor. It provides a notification system for sleepiness during driving, a driving time notification system and a smoking detection system. We coded using 'Python'. And we use 'MySQL' and 'PHP' to build the necessary servers and web pages for gathering sensing data and monitoring. The developed system was tested by several methods. All experiments showed satisfactory response signals and detected with immediate responses.

• Smart Structures and Systems
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• v.2 no.3
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• pp.275-293
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• 2006

For health monitoring purpose usually the structure is instrumented with a large scale and multichannel measurement system. In case of highway bridges, operating vehicle could be utilized to reduce the number of measuring devices. First this paper presents a static damage detection algorithm of using operating vehicle load. The technique has been validated by finite element simulation and simple laboratory test. Next the paper presents an approach of using this technique to field application. Here operating vehicle load data has been used by instrumenting the bridge at single location. This approach gives an upper hand to other sophisticated global damage detection methods since it has the potential of reducing the measuring points and devices. It also avoids the application of artificial loading and interruption of any traffic flow.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.45.3-45
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• 2001

This paper deal with a fault detection algorithm for front wheel passenger car systems by using robust $H{\infty}$ control theory. Firstly, we present a unified formulation of vehicle dynamics for front wheel car systems and transform this formulation into state space form. Also, by considering the cornering stiffness which depends on the tyre-road contact conditions, a multiplicative uncertainty for vehicle model is described. Next, the failures of sensor and actuator for vehicle system are defined in which the fault .lter is considered. From the nominal vehicle model, an augmented system includes the multiplicative uncertainty and the model of fault filter is proposed. Lastly by using $H{\infty}$ norm property the fault detect conditions are deefi.ned, and the actuator and sensor failures are detected and isolated by designing the robust $H{\infty}$ controller, respectively.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.8-15
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• 2011

This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, in order to effectively extract spotlight of interest, a pre-signal-processing process based on camera lens filter and labeling method is applied on road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process use light tracking method and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with visible light mono-camera and tested it in urban and rural roads. Through the test, classification performances are above 89% of precision rate and 94% of recall rate evaluated on real-time environment.

• 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.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.3073-3078
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• 2011

Driverless Train Control System has been recently introduced and commercialized in Korea. It is expected that the vehicle with driverless operation system will be used in new lines such as Sinbundang line soon. Therefore it is necessary to change the system operation and conception of the existing train operation system and the necessity of driverless vehicle monitoring system meeting a new paradigm is rising. In order to dispel concerning about safety issues caused by driverless train operation, the importance to establish vehicle error detection, useful fault diagnosis and rapid action plans is higher than ever. For this, efficient and higher level of vehicle supervision & control system should be essentially supported. In this study, remote driverless vehicle monitoring system using by radio communication is suggested to be used for monitoring and controlling important parts of the vehicle and diagnose and take quick actions when vehicles are in trouble at control tower at real time.