• 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.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.255-265
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• 2007

In this paper, we propose a new illumination-robust drowsy driver monitoring system with single CCD(Charge Coupled Device) camera for intelligent vehicle in the day and night. For this system that is monitoring driver's eyes during a driving, the eye detection and the measure of eyelid movement are the important preprocesses. Therefore, we propose efficient illumination compensation algorithm to improve the performance of eye detection and also eyelid movement measuring method for efficient drowsy detection in various illumination. For real-time application, Cascaded SVM (Cascaded Support Vector Machine) is applied as an efficient eye verification method in this system. Furthermore, in order to estimate the performance of the proposed algorithm, we collect video data about drivers under various illuminations in the day and night. Finally, we acquired average eye detection rate of over 98% about these own data, and PERCLOS(The percentage of eye-closed time during a period) are represented as drowsy detection results of the proposed system for the collected video data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.40-51
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• 2020

Much time has passed since Korea's expressway congestion-threshold speed was revised in 2011. In the meantime, various expressway environments have changed owing to improved performance of vehicles, expanded operations of transport competition (i.e., the KTX), and increased speed limits along some expressway sections. In addition, the speed that expressway users expect to travel at is also increasing. Therefore, through a survey, this study investigates expressway users' perceptions of congestion, and reviews the adjustment of the expressway speed congestion threshold by analyzing expressway traffic flow. One result of the survey confirms that the threshold speed expressway users consider to be congestion has slightly increased. Analyzing traffic and speed data through a K-means algorithm found that the threshold speed for congestion is 60 km/h. In addition, assuming the congestion threshold speed increase from 40 km/h to 50 km/h and 60 km/h, frequently congested expressway sections are identified, determining that 50 km/h is appropriate as a congestion threshold for proper expressway mobility management.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.99-107
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• 2013

Controller area network (CAN) was designed for multiplexing communication between electronic control units (ECUs) in vehicles and thus for decreasing the overall wire harness. The increasing number of ECUs causes the CAN bus overloaded and consequently the error probability of data transmission increases. Since the time duration for the data transmission is proportional to CAN frame length, it is desirable to reduce the frame length. In this paper, a CAN message compression method is proposed using Data Length Code (DLC) and compression area selection algorithm to reduce the CAN frame length and the error probability during the transmission of CAN messages. By the proposed method, it is not needed to predict the maximum value of the difference in successive CAN messages as opposed to other compression methods. Also, by the use of DLC, we can determine whether the received CAN message has been compressed or not without using two ID's as in conventional methods. By simulations using actual CAN data, it is shown that the CAN transmission data is reduced up to 52 % by the proposed method, compared with conventional methods. By using an embedded test board, it is shown that 64bit EMS CAN data compression can be performed within 0.16ms and consequently the proposed algorithm can be used in automobile applications without any problem.

• Journal of IKEEE
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• v.24 no.1
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• pp.33-39
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• 2020

In this paper, we propose the design technique of the vehicle's load weight measuring system using tire pressure, which is one of the physical elements of tires. The proposed technique consists of four processes: noise correction by load and vibration, gas flow correction, data mixer and weight conversion. Noise correction by load and vibration eliminates noise that increases the tire's internal pressure due to external shocks and vibrations produced by the vehicle while it is in motion. In the gas flow correction process, the noise of the internal pressure of the tire is increased due to the temperature rise of the ground with respect to the data obtained through the noise correction process due to the load and vibration. In the data mixer process, the load and pressure on the tolerances the empty, median and the full load are classified according to the change in pressure of the tire that is delivered perpendicular to the tire in the event of cargo. In the weight conversion process, weight is expressed by weight through weight conversion algorithms using noise correction results by load and vibration and gas flow correction. The weight conversion algorithm calculates the weight conversion factor, which is the slope of the linear function with respect to the load and pressure change, and converts the weight. In order to evaluate the accuracy of the loading weight measurement system of the vehicle using the tire pneumatic system technique proposed in this paper, we propose the design technique of the vehicle's load weight measuring system using tire pressure, which is one of the physical elements of tires.. Noise correction results by load and vibration and gas flow data correction results showed reliable results. In addition, repeated weight precision test showed better weight accuracy than the standard value of 90% of domestic companies.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.20-33
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• 2021

This study is to suggest improvements of personal information protection in South Korea, according to requiring the safety of process and protection of personal information. Accordingly, based on data collection and analysis through literature research, this study derived the issues and suitable standards of personal information for major artificial intelligence services. In addition, this cases studies were reviewed, focusing on the legal compliance and porcessing compliance for personal information proection in major countries. And it suggested the improvement plan applied in South Korea. As the results, in legal compliance, it is required reorganization of related laws, responsibility and compliance to develop and provide AI, and operation of risk management for personal information protection laws in AI services. In terms of processing compliance, first, in pre-processing and refining, it is necessary to standardize data set reference models, control data set quality, and voluntarily label AI applications. Second, in development and utilization of algorithm, it is need to establish and apply a clear regulation of the algorithm. As such, South Korea should apply suitable improvement tasks for personal information protection of safe AI service.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.

• Smart Media Journal
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• v.12 no.11
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• pp.57-66
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• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• Journal of KIISE
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• v.44 no.9
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• pp.932-945
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• 2017

In this paper, a new hardware and software architecture for a stereo vision processing system including rectification, disparity estimation, and visualization was developed. The developed method, named LArge scale stereo matching method using Support Point Interpolation (LASPI), shows excellence in real-time processing for obtaining dense disparity maps from high quality image regions that contain high density support points. In the real-time processing of high definition (HD) images, LASPI does not degrade the quality level of disparity maps compared to existing stereo-matching methods such as Efficient LArge-scale Stereo matching (ELAS). LASPI has been designed to meet a high frame-rate, accurate distance resolution performance, and a low resource usage even in a limited resource environment. These characteristics enable LASPI to be deployed to safety-critical applications such as an obstacle recognition system and distance detection system for autonomous vehicles. A Field Programmable Gate Array (FPGA) for the LASPI algorithm has been implemented in order to support parallel processing and 4-stage pipelining. From various experiments, it was verified that the developed FPGA system (Xilinx Virtex-7 FPGA, 148.5MHz Clock) is capable of processing 30 HD ($1280{\times}720pixels$) frames per second in real-time while it generates disparity maps that are applicable to real vehicles.