• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.271-278
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• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.153-163
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• 2017

This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.119-137
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• 2020

Over the last two decades, the attitudes to cars have changed from buying a car to sharing a car, especially among young people. Shared transport services and autonomous vehicles together can resolve the accessibility issue of shared transport services. Furthermore, they will make it possible to develop a new model of apartments without car parking. Therefore, the study estimated the demand for car sharing by young people and the running efficiency of car-sharing dealing with their car-based trip demand. The study chose nine apartment complexes for study sites where a majority of the residents were young people. The questionnaire survey was conducted to collect data on the trip demands of young people. The results showed that there are significant differences in the car-sharing use patterns and demand between the apartment houses located in the Capital region and non-capital region. Young people living in apartments in the Capital region used car sharing once per day per person for approximately 80 minutes per trip and tended to hire that between 8 AM and 10 AM. On the other hand, the young people living in apartments in the non-capital region used car sharing twice per day per person for approximately 200 minutes per trip. They tended to hire that frequently in the afternoon and evening as well as in the morning. The results also showed that a single car-sharing vehicle could deal with 3~4 trips per day in the Capital region and around 2 trips per day in the non-capital region.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.126-132
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• 2015

Technology forecasting is about understanding a status of a specific technology in the future, based on the current data of the technology. It is useful when planning technology management strategies. These days, it is common for countries, companies, and researchers to establish R&D directions and strategies by utilizing experts' opinions. However, this qualitative method of technology forecasting is costly and time consuming since it requires to collect a variety of opinions and analysis from many experts. In order to deal with these limitations, quantitative method of technology forecasting is being studied to secure objective forecast result and help R&D decision making process. This paper suggests a methodology of technology forecasting based on quantitative analysis. The methodology consists of data collection, principal component analysis, and technology forecasting by logistic regression, which is one of the data mining techniques. In this research, patent documents related to autonomous vehicle are collected. Then, the texts from patent documents are extracted by text mining technique to construct an appropriate form for analysis. After principal component analysis, logistic regression is performed by using principal component score. On the basis of this result, it is possible to analyze R&D development situation and technology forecasting.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.186-201
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• 2021

As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m~0.21m (7~30%) for each section when compared with the existing lane-level map matching method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.95-111
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• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.173-184
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• 2018

As the portable traffic information collection system using probe vehicles spreads, it is becoming possible to collect road hazard information such as portholes, falling objects, and road surface freezing using in-vehicle sensors in addition to existing traffic information. In this study, we developed a integration and decision algorithm that integrates time and space in real time when multiple probe vehicles detect events such as road hazard information based on GPS coordinates. The core function of the algorithm is to determine whether the road hazard information generated at a specific point is the same point from the result of detecting multiple GPS probes with different GPS coordinates, Generating the data, (3) continuously determining whether the generated event data is valid, and (4) ending the event when the road hazard situation ends. For this purpose, the road risk information collected by the probe vehicle was processed in real time to achieve the conditional probability, and the validity of the event was verified by continuously updating the road risk information collected by the probe vehicle. It is considered that the developed hybrid processing algorithm can be applied to probe-based traffic information collection and event information processing such as C-ITS and autonomous driving car in the future.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.149-157
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• 2019

Recent research has been actively carried out on systems that want to optimize resource utilization by analyzing the intended behavior and pattern of behavior of objects (users, consumers). A service system that applies information about an object's location or behavior must include a location trigger processing system for tracking an object's real-time location. In this paper, we analyze design problems for the implementation of a context-awareness based location trigger system, and present system models based on analysis details. For this purpose, this paper introduces the concept of location trigger for intelligent location tracking techniques about moving situations of objects, and suggests a mobile agent system with active rules that can perform monitoring and appropriate actions based on sensing information and location context information, and uses them to design and implement the location trigger system for context-awareness based location services. The proposed system is verified by implementing location trigger processing scenarios and trigger service and action service protocols. In addition, through experiments on mobile agents with active rules, it is suggested that the proposed system can optimize the role and function of the application system by using rules appropriate to the service characteristics and that it is scalable and effective for location-based service systems. This paper is a preliminary study for the establishment of an optimization system for utilizing resources (equipment, power, manpower, etc.) through the active characteristics of systems such as real-time remote autonomous control and exception handling over consumption patterns and behavior changes of power users. The proposed system can be used in system configurations that induce optimization of resource utilization through intelligent warning and action based on location of objects, and can be effectively applied to the development of various location service systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.62-73
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• 2022

As the competition among the autonomous vehicle (AV, here after) developers are getting fierce, Korean government has been supporting developers by deregulating safety standards and providing financial subsidies. Recently, some OEMs announced their plans to market Lv3 and Lv4 automated driving systems. However, these market changes raised concern among public road management sectors for monitoring road conditions and alleviating hazardous conditions for AVs and human drivers. In this regards, the authors proposed a methodology for monitoring road infrastructure to identify hazardous factors for AVs and categorizing the hazards based on their level of impact. To evaluate the degrees of the harm on AVs, the authors suggested a methodology for managing road hazard factors based on vehicle performance features including vehicle body, sensors, and algorithms. Furthermore, they proposed a method providing AVs and road management authorities with potential risk information on road by delivering them on the monitoring map with node and link structure.