• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.22-26
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• 2022

This paper proposes a new method for the recognition of road surface marks and numbers. The proposed method designates a region of interest on the road surface without first detecting a lane. The road surface markings are extracted by location and size using a connection component analysis. Distortion due to the perspective effect is minimized by normalizing the size of the road markings. The road surface marking of the connected component is recognized by matching it with the stored road marking templates. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. The system was fixedly installed in a moving vehicle, and it recorded a video like a vehicle black box. Each frame of the recorded video was extracted, and then the proposed method was tested. The results show that the proposed method is successful for the recognition of road surface marks and numbers.

• International Journal of Automotive Technology
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• v.7 no.4
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• pp.469-475
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• 2006

In this paper, an optimal suspension system with preview of the road input is synthesized for a half tracked vehicle. The main goal of this research is to improve the ride comfort characteristics of a fast moving tracked vehicle in order to maintain the driver's driving capability. Several different kinds of preview control algorithms are evaluated with active or semi-active suspension systems. The road information estimated from the motion of the 1st road-wheel is adequate to make the best use of the preview control algorithm for tracked vehicles. The ride-comfort characteristics of the tracked vehicle are more dependent on pitching angular acceleration than heaving acceleration. The pitching motion is reduced by the suspension system with hard outer suspensions and soft inner suspensions. Simulation results show that the performance of sky-hook algorithms for ride comfort nearly follow that of full state feedback algorithms.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.872-876
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• 2007

Road traffic noise map is effective method to save cost and time for environmental noise assessment. Generally, noise is calculated by using theoretical equation of noise prediction, and the calculated result can be influenced by various input factors. Especially, domestic vehicle classification method for traffic flow and heavy vehicle percentage is different from that of foreign countries. Thus, this can cause effect on the noise prediction results. In this study, noise prediction results by using domestic vehicle classification method are compared with those by foreign methods.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.390-395
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• 2009

In this paper, we design an on-road vehicle detection system based on the combination of a camera and a LIDAR system. In the proposed system, the candidate area is selected from the LIDAR data using a grouping algorithm. Then, the selected candidate area is scanned by an SVM to find an actual vehicle. The morphological edged images are used as features in a camera. The principal components of the edged images called eigencar are employed to train the SVM. We conducted experiments to show that the on-road vehicle detection system developed in this paper demonstrates about 80% accuracy and runs with 20 scans per second on LIDAR and 10 frames per second on camera.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.298-304
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• 2012

The development of low rolling resistance tire with weight reduction in tire and vehicle may induce high level of tire/road noise, especially the rumble road noise on rough road. In this paper, the design factor for good rumble noise is considered in view of tire and vehicle. For the 3 mid-sized sedans, the rumble noise is very sensitive to the test vehicle. And it is concluded that the tire with high tread part stiffness and low sidewall part stiffness shows best rumble noise performance, and the rumble noise is in trade-off relation with cavity resonance noise. So, it is desirable to select and change proper construction design factors to have good tire/vehicle rumble noise.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.303-303
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• 2000

It is difficult to determine the static axle weight of a vehicle with weigh-in-motion systems which in absence measure instantaneous axle impact forces. The difficulty in determining a static axle weight results from dynamic effects induced by vehicle/road interactions. One method to improve the problem is to quantify a statistical confidence level for measured axle weight. The quarter-car model is used to simulate vehicle motion, Also, the road input to vehicle model can be characterized in statistical terms by PSD (power spectral density) of appropriate amplitude and frequency contents other than an exact spatial distribution. The confidence levels for the measured axle weight can be obtained by the random process analysis using both vehicle model and road input.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.361-364
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• 2011

In this paper, This work demonstrates a experimental evaluation for vehicle road noise NVH performance from the component-level NVH measurements of Tire. The power unit noise from tire emitted by cars has been reduced. It has been found that tire noise dominates noise produced by the power train when vehicles are driven at high constant speed. Therefore tire pattern noise is affected by pattern and vehicle and transmission loss. Tire noise mechanism is generated by several mechanisms. The sound of tire can propagate either through the air or through the structure of vehicle. Pattern noise is the result of pressure variations through the air to the interior side of vehicle. Especially, smooth asphalt, the periodicity of tread design, groove depth is important factor, which have an influence on the reduction of tire pattern noise.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.2
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• pp.193-197
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• 2012

Road traffic noise map is effective method to save cost and time for environmental noise assessment. Generally, noise is calculated by using theoretical equation of noise prediction, and the calculated result can be influenced by various input factors. Especially, domestic vehicle classification method for traffic flow and heavy vehicle percentage is different from that of foreign countries. Thus, this can cause effect on the noise prediction results. In this study, noise prediction results by using domestic vehicle classification method are compared with those by foreign methods.

• Transactions of the Society of Information Storage Systems
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• v.11 no.2
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• pp.26-30
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• 2015

Recently, military vehicles are driven with a lot of electronic devices such as radar, antenna, and information storage devices. However, the military vehicles can be exposed to impact easily. Therefore, they have to be designed robustly in order to ensure the stability of the vehicle and the electronic devices. To achieve that, the dynamic behaviors of the military vehicle should be exactly identified. Therefore, in this research, dynamic behaviors of the vehicles were identified by carrying out road tests and we constructed finite element model to analyze the dynamic characteristics of the vehicle.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1505-1515
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• 2016

The age of autonomous vehicles has come according to development of high performance sensing and artificial intelligence technologies. And importance of the vehicle's surrounding environment sensing and observation is increasing accordingly because of its stability and control efficiency. In this paper we propose an integrated platform for efficient networking, analysis and monitoring of multiple sensing data on the vehicle that are equiped with various automotive sensors such as GPS, weather radar, automotive radar, temperature and humidity sensors. From simulation results, we could see that the proposed platform could perform realtime analysis and monitoring of various sensing data that were observed from the vehicle sensors. And we expect that our system can support drivers or autonomous vehicles to recognize optimally various sudden or danger driving environments on the road.