• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.197-205
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• 2024

Development of AI and big data-based algorithms to advance and optimize the recognition and detection performance of various static/dynamic vehicles in front and around the vehicle at a time when securing driving safety is the most important point in the development and commercialization of autonomous vehicles. etc. are being studied. However, there are many research cases for recognizing the same vehicle by using the unique advantages of radar and camera, but deep learning image processing technology is not used, or only a short distance is detected as the same target due to radar performance problems. Therefore, there is a need for a convergence-based vehicle recognition method that configures a dataset that can be collected from radar equipment and camera equipment, calculates the error of the dataset, and recognizes it as the same target. In this paper, we aim to develop a technology that can link location information according to the installation location because data errors occur because it is judged as the same object depending on the installation location of the radar and CCTV (video).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• The Journal of Bigdata
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• v.9 no.1
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• pp.127-140
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• 2024

This study investigated the impact of vehicle grade operation restrictions in green transportation areas on the concentration of fine particulate matter (PM2.5) year by year. The results indicate that these restrictions positively affected the reduction of PM2.5 levels. The green transportation area policy reduced vehicle emissions and encouraged the use of public and eco-friendly transportation, thereby improving air quality. A notable outcome was the decrease in PM2.5 concentrations, which is expected to positively impact the health of residents in urban areas. The study considered various factors and variables related to the effectiveness of the vehicle grade operation restrictions policy. It was determined that there is a need to discuss the implementation methods of the policy, regional characteristics, and other environmental factors. These findings provide important implications for managing fine particulate matter and urban planning, suggesting that reference materials and ongoing research will be necessary considering future urban sustainability.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.107-108
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• 2023

본 논문에서는 교량의 변위를 IoT 장치를 이용하여 실시간 측정하고 추출된 데이터를 이용하여 교량의 이상징후를 AI 기반으로 진단 및 모니터링 하는 방법을 제안한다. AI 모델 학습 학습을 위해서 비정상 상태의 교량이 필요하지만, 실제 교량에 인위적으로 비정상 상태를 만들 수 없으므로, 탄성 받침을 이용하여 모의 교량을 제작하였다. 탄성 받침을 이용하여 제작에 반영 및 모의교량에 적합한 모의 차량도 제작하여 정상적 데이터와 비정상적 데이터를 수집하였다. 수집된 데이터를 전처리 과정을 통해 AI 분석을 통해 교량의 이상 징후를 진단 및 모니터링하였으며, 제안 모델을 실험한 결과 96.7%의 정확도가 도출되었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.75-82
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• 2021

The number of traffic accidents is about 230,000, and due to non-recurring congestion and high driving speed, the number of deaths per traffic accident on freeways is more than twice compared to other roads. Currently, traffic information is provided based on nodes and links using the centerline of the road, but it does not provide detailed speed information. Recently, installing sensors for vehicles to monitor obstacles and measure location is becoming common not only for autonomous vehicles but also for ordinary vehicles as well. The analysis using large-capacity location-based data from such sensors enables real time service according to processing speed. This study presents an mapping method for individual vehicle data analysis based on space. The processing speed of large-capacity data was increased by using method which applied a quaternary notation basis partition method that splits into two directions of longitude and latitude respectively. As the space partition was processed, the average speed was similar, but the speed standard deviation gradually decreased, and decrease range became smaller after 9th partition.

• The Journal of Bigdata
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• v.7 no.2
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• pp.129-137
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• 2022

With the development of the 4th industry, the logistics industry is evolving into a smart logistics system. However, ship work that transports vehicles is progressing slowly due to various problems. In this paper, we propose an stowage automation algorithm that can be used for cargo loading of vehicle carriers that shortens loading and unloading work time. The stowage automation algorithm returns the shortest distance by searching for a loading space and a movable path in the ship in consideration of the structure of the ship. The algorithm identifies walls, ramps and vehicles that have already been shipped, and can work even with randomly placed. In particular, it is expected to contribute to developing a smart logistics system for vehicle carriers by referring to the ship's master plan to search for vehicle loading and unloading space in each port and predict the shortest movable path.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.119-131
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• 2024

Understanding accurate traffic performance is crucial for ensuring efficient highway operation and providing a sustainable mobility environment. On the other hand, an immediate and precise estimation of highway traffic performance faces challenges because of infrastructure and technological constraints, data processing complexities, and limitations in using integrated big data. This paper introduces a framework for estimating traffic performance by analyzing real-time data sourced from toll collection systems and dedicated short-range communications used on highways. In particular, this study addresses the data errors arising from segmented information in data, influencing the individual travel trajectories of vehicles and establishing a more reliable Origin-Destination (OD) framework. The study revealed the necessity of trip linkage for accurate estimations when consecutive segments of individual vehicle travel within the OD occur within a 20-minute window. By linking these trip ODs, the daily average highway traffic performance for South Korea was estimated to be248,624 thousand vehicle kilometers per day. This value shows an increase of approximately 458 thousand vehicle kilometers per day compared to the 248,166 thousand vehicle kilometers per day reported in the highway operations manual. This outcome highlights the potential for supplementing previously omitted traffic performance data through the methodology proposed in this study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.669-678
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• 2018

This study developed information technology infrastructures for building a driving environment analysis platform using various big data, such as vehicle sensing data, public data, etc. First, a small platform server with a parallel structure for big data distribution processing was developed with H/W technology. Next, programs for big data collection/storage, processing/analysis, and information visualization were developed with S/W technology. The collection S/W was developed as a collection interface using Kafka, Flume, and Sqoop. The storage S/W was developed to be divided into a Hadoop distributed file system and Cassandra DB according to the utilization of data. Processing S/W was developed for spatial unit matching and time interval interpolation/aggregation of the collected data by applying the grid index method. An analysis S/W was developed as an analytical tool based on the Zeppelin notebook for the application and evaluation of a development algorithm. Finally, Information Visualization S/W was developed as a Web GIS engine program for providing various driving environment information and visualization. As a result of the performance evaluation, the number of executors, the optimal memory capacity, and number of cores for the development server were derived, and the computation performance was superior to that of the other cloud computing.

• ICROS
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• v.21 no.4
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• pp.21-26
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• 2015

지능형 시스템의 수요가 증가하면서 영상인식의 중요성이 부각되고 있다. 사람이 직접 물체 인식 과정을 모델링하는 방식을 넘어 최근에는 기계학습을 이용하여 이를 자동화하는 방법이 주를 이루고 있다. 그 중 딥러닝은 빅데이터를 활용하는 각종 분야에서 놀라운 성능을 보이며 기계학습 수준을 한 단계 진화시킨 기술로 평가 받고 있으며 영상 인식의 다양한 분야에서 응용되고 있다. 본 글에서는 딥러닝을 이용한 물체 검출 기법의 동향을 살펴보고 이를 차량 전면부 인식에 적용한 사례를 소개한다.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.26-36
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• 2022

Recently, studies have been conducted to cluster vehicles and disseminate safety messages in a VANET environment for driver safety and smoothy traffic. This paper proposes cluster management scheme for safety message dissemination through V2V communication and V2I communication in a VANET environment with high vehicle density and mobility. The proposed scheme reduces packet loss by selecting CH considering reception quality, total data owned by vehicles, moving speed, and connected vehicles, and maintaining cluster head candidates, which are the main agents of message dissemination, considering frequent cluster departures and subscriptions. In addition, the proposed scheme reduces duplicate messages by utilizing clusters by collaborating with a Road side unit(RSU). To prove the excellence of the proposed scheme, various performance evaluations are performed in terms of message packet loss and the number of RSU processing requests. As a result of performance evaluation, the cluster management scheme proposed in this paper shows better performance than the existing scheme.