• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.163-174
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• 2002

Speeding has been reported as one of the major causes for fatal traffic accidents in Korea. The resolution against this dangerous speeding comes to make the automated speed enforcement system an enforcement tool. The speed detection device, which measures speeds of each incoming vehicles using double loop sensors, requires high accuracy. The object of this study is to develop an accurate speed measurement algorithm using double loop detectors. Some important findings are summarized as follows: 1) It was found that speed measurement errors are caused by scanning rate, distance of two loops, irregular vehicle trajectories, multiple vehicles in detection zone. 2) A proposed algorithm using two signal set proved to reduce variance as well as mean of speed measurement. 3) A proposed filtering algorithm was effective to filter irregular driving vehicles and multiple vehicles in detection zone. A comprehensive field test of developed algorithm resulted in significant improvement of speed measurement accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.561-564
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• 2007

In driving a car, u-turn or left turn makes the speed of car decrease considerably or require more waiting time at the cross for the traffic signal to turn green. A more straight path, therefore, is probably faster to arrive at the destination than zig-zaged path with same distance. Previous works related to the path navigation do not consider the straightness of the path. In this paper, we have proposed the path navigation algorithm with turn heuristic for enhancing the straightness of a path. We have implement the proposed algorithm and compared it with a traditional $A^*$ algorithm. The experimental result shows that the degree of the straightness of a path is enhanced by 30% and the navigation distance of a path is deceased by 3.3%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.9
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• pp.848-857
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• 2011

Vision-based collision avoidance system for air traffic management requires a excellent multiple target detection algorithm under low signal-to-noise ratio (SNR) levels. The track-before-detect (TBD) approaches have significant applications such as detection of small and dim targets from an image sequence. In this paper, two detection algorithms with the TBD approaches are proposed to satisfy the multiple target detection requirements. The first algorithm, based on a dynamic programming approach, is designed to classify multiple targets by using a k-means clustering algorithm. In the second approach, a hidden Markov model (HMM) is slightly modified for detecting multiple targets sequentially. Both of the proposed approaches are used in numerical simulations with variations in target appearance properties to provide satisfactory performance as multiple target detection methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2072-2077
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• 2007

In driving a car, u-turn or left turn makes the speed of car decrease considerably or require more waiting time at the cross for the traffic signal to turn green. A more straight path, therefore, is probably fester to arrive at the destination than zig-zaged path with same distance. Previous works related to the path navigation do not consider the straightness of the path. In this paper, we have proposed the path navigation algorithm with turn heuristic for enhancing the straightness of a path. We have implement the proposed algorithm and compared it with a traditional $A^*$ algorithm. The experimental result shows that the degree of the straightness of a path is enhanced by 30% and the navigation distance of a path is deceased by 3.3%.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.8
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• pp.939-944
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• 2014

Traffic accidents caused by the attention dispersion are increasing and the behavior of the attention dispersion affects the front-observing rate, road keeping ability, and reaction time for a dangerous situation. Many drivers listen to a radio broadcast and they have to change the frequency for continuously listening a radio broadcast of the specific broadcasting station in case of crossing a boundary of the particular area. In this situation, the possibility of a car accident increases, because the attention dispersion of a driver might be occurred. Especially, the risk of a car accident caused by changing the frequency of a radio is more serious in the highway, due to the high speed of a vehicle. In order to reduce the risk of a car accident caused by handling a radio during driving car, in this paper, we propose an automatic frequency conversion algorithm for vehicle radio, which saves normal system frequencies of primary broadcasting stations in a database and determines new frequency of the changed area using the location information obtained from a navigation system in a boundary of the specific area. After determining new frequency, the proposed algorithm selects a frequency with better receiving rate comparing signal-to-noise ratios (SNRs) of two signals corresponding previous and new frequencies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1B
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• pp.162-175
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• 2010

Currently, geographical information is interpreted and adopted in a wide range of context, and used for meeting diverse demands, such as, battlefield, traffic management, or public safety. With such an explosive increase of location-based applications, a considerable amount of research on the localization technique has been carried out. Among them, RSS (Received Signal Strength)-based approach is used especially for the indoor localization due to intrinsic limitations of the indoor environment. In this paper, we perform theoretical and empirical studies on enhancing the accuracy of the RSS-based localization on the IEEE 802.15.4 network. To this end, we set up an indoor testbed and implement a localization system on it. In addition to the theoretical analysis of the localization algorithm that we used, an empirical analysis on the effect of the factors which affect the accuracy of a localization system is carried out. Finally, we suggest some critical guidelines that should be considered for building a highly accurate localization system.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.72-77
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• 2011

Spectrum sensing is a critical functionality of CR network; it allow secondary user to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to primary use. Recently, wideband service has been increase for processing abundance of data traffic. So CR network needs a realizable implementation design of spectrum sensing for wideband. To get high resolution performance of wideband sensing must precede algorithm processing for reliability signal detection. By the way, the performance of spectrum sensing can be degraded due to fading and shadowing. In order to overcome this problem, we propose system model of wideband sensing scheme on energy detected collaborative technique. we divide wideband into narrowbands and use narrowbands to detect signal excepting some narrowbands including bad channel through the CSI. And we simulate and analyze in terms of detection probability with various SNR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1704-1709
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• 2016

In this paper, we propose efficient uplink data transmission method in ultra dense wireless networks as in intra-aircraft, where large-scale APs and wireless sensors are deployed. In the ultra dense wireless networks, a performance degradation is inevitable due to the inter-AP interference. However, the performance degradation can be avoided if a scheduling algorithm can estimate the amount of interference caused by each wireless sensor and reflects it. SGIR (Signal-to-Generating Interference Ratio) based scheduling algorithms is a typical example. Unfortunately, the scheduling algorithms based on the interference caused by wireless sensors necessarily yield large scale exchange of information through backhaul which connects APs. Therefore, we, in this paper, propose a novel scheme which can dramatically reduce the amount of information which are exchanged through backhaul connection. Monte-Carlo simulation results show that the proposed scheme can reduce the amount of backhaul traffic by 27% without loss of data transmission rate.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.117-130
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• 2011

The purpose of this study is to produce a better way to detect crossroad accidents, which involves an efficient method to produce background images in consideration of object movement and preserve/demonstrate the candidate accident region. One of the prior studies proposed an employment of traffic signal interval within crossroad to detect accidents on crossroad, but it may cause a failure to detect unwanted accidents if any object is covered on an accident site. This study adopted inverse perspective mapping to control the scale of object, and proposed different ways such as producing robust background images enough to resist surrounding noise, generating candidate accident regions through information on object movement, and by using edge information to preserve and delete the candidate accident region. In order to measure the performance of proposed algorithm, a variety of traffic images were saved and used for experiment (e.g. recorded images on rush hours via DVR installed on crossroad, different accident images recorded in day and night rainy days, and recorded images including surrounding noise of lighting and shades). As a result, it was found that there were all 20 experiment cases of accident detected and actual effective rate of accident detection amounted to 76.9% on average. In addition, the image processing rate ranged from 10~14 frame/sec depending on the area of detection region. Thus, it is concluded that there will be no problem in real-time image processing.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1131-1142
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• 2018

The autonomous vehicles are being developed and operated widely because of the advantages of reducing the traffic accident and saving time and cost for driving. The vehicle localization is an essential component for autonomous vehicle operation. In this paper, localization algorithm based on sensor fusion is developed for cost-effective localization using in-vehicle sensors, GNSS, an image sensor and reference images that made in advance. Information of the reference images can overcome the limitation of the low positioning accuracy that occurs when only the sensor information is used. And it also can acquire estimated result of stable position even if the car is located in the satellite signal blockage area. The particle filter is used for sensor fusion that can reflect various probability density distributions of individual sensors. For evaluating the performance of the algorithm, a data acquisition system was built and the driving data and the reference image data were acquired. Finally, we can verify that the vehicle positioning can be performed with an accuracy of about 0.7 m when the route image and the reference image information are integrated with the route path having a relatively large error by the satellite sensor.