• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.62-69
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• 2017

Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.88-93
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• 2012

Lane detection methods using camera, which are part of the driver assistance system, have been developed due to the growth of the vehicle technologies. However, lane detection methods are often failed by varying road conditions such as rainy weather and degraded lanes. This paper proposes a method for lane detection which is robust in varying road condition. Lane candidates are extracted by intensity comparison and lane detection filter. Hough transform is applied to compute the lane pair using lane candidates which is straight line in image. Then, a curved lane is calculated by using B-Snake algorithm. Also, weighting value is computed using previous lane detection result to detect the lanes even in varying road conditions such as degraded/missed lanes. Experimental results proved that the proposed method can detect the lane even in challenging road conditions because of weighting process.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1327-1335
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• 2012

TPMS (Tire Pressure Monitoring System) is a safety assistant system to prevent the serious accident due to the damaged tire by the abnormal tire pressure. It is designed to transmit the measured data for pressure and temperature of tires from the sensor unit installed in each tire to signal processing unit installed in a vehicle. Based on the received information, a driver monitors the condition of tires using a display device, to maintain the optimum travelling condition. Since TPMS should employ the wireless communication technique, it may suffer from various interferences from external electrical or electronics devices. In order to suppress them, the beamforming techniques such as switching, minimum-variance distortionless-response (MVDR), and generalized sidelobe canceler (GSC) have been considered for TPMS. In this paper, we calculate computational complexities of three beamformers and suggest mathematical basis to compare their performance of the complexity.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.18-25
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• 2016

As more people have cars, the threat of traffic accidents is posed on men and women of all ages. The main culprit of traffic accidents is driving while intoxicated or drowsy. The method to recognize and prevent the cause of traffic accidents is to use lane detection. In this study, a total of 4,000 frames (day image: 2,900 frames, night image: 1,100 frames) were used to test lane detection. According to the test, in the case of day image, when the threshold of Sobel edge detection technique was detected with second-order differential equation, there was the highest candidate lane detection rate which was 86.1%. In the threshold of Canny edge detection technique, the highest detection rate of 88.0% was found at Low=50, and High=300. In the case of night image, the threshold of Sobel edge detection technique, when horizontal calculation and vertical calculation had second-order differential equation, and when horizontal-vertical calculation had 1.5th-order differential equation, there was the highest detection rate which was 83.1%. In the threshold of Canny edge detection technique, the highest detection rate of 89.9% was found at Low=50, and High=300.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.287-310
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• 2022

Companies' interest in developing AI-based intelligent new products is increasing. Recently, the main concern of companies is to innovate customer experience and create new values by developing new products through the effective use of Artificial intelligence technology. However, due to the nature of products based on radical technologies such as artificial intelligence, intelligent products differ from existing products and development methods, so it is clear that there is a limitation to applying the existing development methodology as it is. This study proposes a new research method based on KANO-TOPSIS for the successful development of AI-based intelligent new products by using car voice assistants as an example. Using the KANO model, select and evaluate functions that customers think are necessary for new products, and use the TOPSIS method to derives priorities by finding the importance of functions that customers need. For the analysis, major categories such as vehicle condition check and function control elements, driving-related elements, characteristics of voice assistant itself, infotainment elements, and daily life support elements were selected and customer demand attributes were subdivided. As a result of the analysis, high recognition accuracy should be considered as a top priority in the development of car voice assistants. Infotainment elements that provide customized content based on driver's biometric information and usage habits showed lower priorities than expected, while functions related to driver safety such as vehicle condition notification, driving assistance, and security, also showed as the functions that should be developed preferentially. This study is meaningful in that it presented a new product development methodology suitable for the characteristics of AI-based intelligent new products with innovative characteristics through an excellent model combining KANO and TOPSIS.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.907-915
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• 2013

Tire Pressure Monitoring System (TPMS) is an auxiliary safety system for recognizing the condition of tires based on the pressure and temperature data transmitted from the sensor unit installed on a tire of the vehicle. Using TPMS, a driver can frequently check the state of tires and it aids to maintain the optimum running condition of the vehicle. Since TPMS must utilize the wireless communication technique to transmit data from a sensor unit to a signal processing unit installed in the vehicle, it suffers from interference signals caused by various external electrical or electronic devices. In order to suppress high-power interference signals, we employ beamforming techniques based on the uniform linear antenna array. As the number of the antennas is increased, the performance of the interference suppression is improved. However, there is the limit of the number of antennas, installed in the center of a vehicle, because of its size. In this paper, we compare and analyze the performance of the beamformer, when reducing the interelement spacing of antennas, to increase the number of the receiving antennas. For the performance analysis of the beamformers, we consider the switching beamformer and minimum-variance distortionless-response (MVDR) beamformer for TPMS, recently proposed.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• International Journal of Highway Engineering
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• v.12 no.3
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• pp.37-47
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• 2010

This research is aimed at comprehensively evaluating the condition of a road surface of a highway in satisfaction of its users. This research conducted an overall evaluation of a road surface condition by adding qualitative data, or a driver's satisfaction to the existing quantitative elements, whereas the existing research put its focus on a correlation analysis with quantitative factors and qualitative factors through a statistical method. As for an evaluation method, this research conducted an overall evaluation by using Grey System Theory which makes possible an integrated evaluation. The analyzed results make it possible to diagnose the current conditions of each section of object roads and to predict the potentially changeable conditions for the time to come. In addition, these analyzed results could hopefully be applied to the maintenance of freeways through diverse methods. It is hoped that the evaluation of a road surface condition of a highway in satisfaction of its user could be helpful to keeping up the satisfaction of a driver and passenger on the highway by more than a certain level. In addition, the analyzed data on the influence of data value observed by comprehensively evaluating a variety of elements could be used as a secondary means of the decision-making process in relation to road maintenance. On top of that, it could be used as a means of improving road maintenance system and offering the improved driving environment of the highway.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.155-166
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• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.9
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• pp.495-504
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• 2017

An amphibious scrubbing/suction dredging machine with cylindrical rotating brush, housing, and turbidity barrier was newly-developed to remove both sediments with about 10 cm thickness and periphyton attached on various structures in urban water-circulating systems through the scrubbing, suction, and dredging processes. Based on the field application and long-term monitoring, the increase in both suspended solids (SS) and turbidity of water during the scrubbing, suction, and dredging processes was negligible (p>0.05). In some cases, the turbidity of water initially increased, however, the turbidity was stabilized within 20 minutes from the start of dredging processes. The concentration changes in TN and TP of water were not statistically different (p>0.05) before and after the scrubbing, suction, and dredging processes, indicating that benthic nutrients released from sediments were not significantly diffused, and were not supposed to cause significant water pollution. Also, water treatment facilities along with an amphibious scrubbing/suction dredging machine could be more effective since the removal of contaminant loadings through the scrubbing, suction, and dredging processes was much greater than that through simple coagulation/precipitation processes. Finally, GPS-based realtime tracking and operation program have been developed and applied in various urban water-circulating systems, and development of driver cooperative autonomous driving system is in progress to eliminate the need for manual driving of an amphibious scrubbing/suction dredging machine.