• 한국도로학회:학술대회논문집
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• 2010.09a
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• pp.261-264
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.305-306
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• 2012

• Transportation Technology and Policy
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• v.9 no.4
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• pp.47-52
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• 2012

• 한국도로학회:학술대회논문집
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• 1999.11a
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• pp.35-40
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• 1999

• Transportation Technology and Policy
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• v.9 no.6
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• pp.19-25
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• 2012

• Journal of Drive and Control
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• v.16 no.3
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• pp.94-102
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• 2019

• Journal of Korean Port Research
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• v.14 no.3
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• pp.303-312
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• 2000

In this paper, we describe an image processing algorithm which is able to recognize the road lane. This algorithm performs to recognize the interrelation between AGV and the other vehicle. We experimented on AGV driving test with color CCD camera which is setup on the top of vehicle and acquires the digital signal. This paper is composed of two parts. One is image preprocessing part to measure the condition of the condition of the lane and vehicle. This finds the information of lines using RGB ratio cutting algorithm, the edge detection and Hough transform. The other obtains the situation of other vehicles using the image processing and viewport. At first, 2 dimension image information derived from vision sensor is interpreted to the 3 dimension information by the angle and position of the CCD camera. Through these processes, if vehicle knows the driving conditions which are lane angle, distance error and real position of other vehicles, we should calculate the reference steering angle.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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• pp.113-120
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• 2017

In this paper, we present a night-time vehicle detection method using CNN (Convolutional Neural Network) classification. The camera based night-time vehicle detection plays an important role on various advanced driver assistance systems (ADAS) such as automatic head-lamp control system. The method consists mainly of thresholding, labeling and classification steps. The classification step is implemented by existing CIFAR-10 model CNN. Through the simulations tested on real road video, we show that CNN classification is a good alternative for night-time vehicle detection.

• Journal of Drive and Control
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• v.17 no.1
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• pp.13-20
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• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.450-451
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• 2008

The Bimodal tram is developed in KRRI (Korea Railroad Research Institute). This vehicle will be used in the public transportation system. The Bimodal tram has the advantages of both bus and train. Bus system has the advantages of flexibility of the routes delivering passengers to the destination and easy accessibility. Train is to meet the scheduled arrival and massive public transportations. The vehicle is the rubber tired tram and is all wheel steered single articulation. The vehicle can be automatically operated by navigation control system (NCS). For the automatic driving, the vehicle lanes will be marked with permanent magnets that are buried in the road. The control algorithm developed for navigation control has to be verified before being applied in the vehicle. In this purpose, we design the simulator for controller test of the bimodal tram.