• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5299-5320
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• 2019

Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.

• Journal of Auto-vehicle Safety Association
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• v.7 no.4
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• pp.26-30
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• 2015

Exceeding the speed limit during vehicle driving is a key factor in the severity of lots of road accidents, and SLIF(Speed Limit Information Function) application is in the initial phase in Korea. SLIF helps the drivers to observe a speed limit when they are driving by providing alert and informing the current limit speed information based on external data using camera and/or digital map, for that reason, environmental conditions could be causes of SLIF malfunctions. In this study, design adequacy analysis of SLIF in respect of false recognition as the Korea traffic environment has been performed. As tentative results, road conditions and structure of speed limit sign as well as system performance often caused misrecognition.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.453-464
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• 2012

This is a research of an adaptive longitudinal control system for situated cognition in wide range, traffic accidents reduction and safety driving environment by integrated system which graft a road infrastructure's information based on IT onto the intelligent vehicle combined automobile and IT technology. The road infrastructure installed by laser scanner in intersection, speed limited area and sharp curve area where is many risk of traffic accident. The road infra conducts objects recognition, segmentation, and tracking for determining dangerous situation and communicates real-time information by Ethernet with vehicle. Also, the data which transmitted from infrastructure supports safety driving by integrated with laser scanner's data on vehicle bumper.

• Journal of Sensor Science and Technology
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• v.14 no.6
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• pp.409-414
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• 2005

For an intelligent vehicle driving which uses magnetic markers and magnetic sensors, we can get every kind of road information while moving the vehicle if we use the code that is encoded with N, S pole direction of markers. If we make it an only aim to move the vehicle, it becomes easy to control the vehicle the more we put markers close. By the way, to recognize the direction of a marker pole it is much better that the markers have no effect each other. To get road informations and move the vehicle autonomously we propose the methods of arranging magnetic sensors and algorithm of recognizing the position of the vehicle with those sensors. We verified the effectiveness of the methods with computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.490-498
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• 2011

In this paper, we present a two-stage vision-based approach to detect multi views of pedestrian in road scene images. The first stage is HG (Hypothesis Generation), in which potential pedestrian are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map, and different colors between road background and pedestrian's clothes to determine the leg position of pedestrian, then a novel symmetry peaks processing is performed to define how many pedestrians is covered in one potential candidate region. Finally, the real candidate region where pedestrian exists will be constructed. The second stage is HV (Hypothesis Verification). In this stage, all hypotheses are verified by Support Vector Machine for classification, which is robust for multi views of pedestrian detection and recognition problems.

• International Journal of Automotive Technology
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• v.6 no.2
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• pp.171-181
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• 2005

The purpose of this paper is two-fold: 1) A novel algorithm in order to extract lane-related information from road images is presented; 2) Design specifications of an image processing onboard unit capable of extracting lane­related information in real-time is also presented. Obtaining precise information from road images requires many features due to the effects of noise that eventually leads to long processing time. By exploiting a FPGA and DSP, we solve the problem of real-time processing. Due to the fact that image processing of road images relies largely on edge features, the FPGA is adopted in the hardware design. The schematic configuration of the FPGA is optimized in order to perform 3 $\times$ 3 Sobel edge extraction. The DSP carries out high-level image processing of recognition, decision, estimation, etc. The proposed algorithm uses edge features to define an Edge Distribution Function (EDF), which is a histogram of edge magnitude with respect to the edge orientation angle. The EDF enables the edge-related information and lane-related to be connected. The performance of the proposed system is verified through the extraction of lane-related information. The experimental results show the robustness of the proposed algorithm and a processing speed of more than 25 frames per second, which is considered quite successful.

• Journal of Arbitration Studies
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• v.27 no.4
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• pp.73-90
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• 2017

After the conclusion of the $18^{th}$ CPCNationalCongress, the Shanghai Free Trade Zone was established, and the One Belt One Road Initiative was brought up. These measures accelerate the development of international commercial activities as related disputes grow in variety and quantity. To better settle international commercial disputes and increase the influence of China in this area, this article reviews and analyzes the development of international commercial arbitration in China. In the conclusion part, it gives suggestions for international commercial arbitration in China in order to improve and accelerate the further development of international commercial arbitration in China.

• Journal of the Korean Society of Safety
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• v.24 no.5
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• pp.113-121
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• 2009

The objective of this study is to develop highway advisory radio service for road safety oriented system design of the point by regional groups or geographical distributions. To develop these highway advisory radio service, traffic information provided service areas, responds for incident and accident, and road condition in service sections based on traffic information of highway advisory radio service. This study contributes to service of traffic information for safety driving, which is transport congestion areas and recognition of traffic congestion status in advanced traffic information service. As result of this study, systematic design of the advanced highway and traffic safety guides to management systems by highway advisory radio service.

• Journal of information and communication convergence engineering
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• v.18 no.3
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• pp.183-187
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• 2020

The general Wi-Fi network connection structure is that a number of IoT (Internet of Things) sensor nodes are directly connected to one AP (Access Point) node. In this structure, the range of the network that can be established within the specified specifications such as the range of signal strength (RSSI) to which the AP node can connect and the maximum connection capacity is limited. To overcome these limitations, multiple middleware bridge technologies for dynamic scalability and load balancing were studied. However, these network expansion technologies have difficulties in terms of the rules and conditions of AP nodes installed during the initial network deployment phase In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road danger context aware and notification system through an intelligent risk situation recognition algorithm.