• Title/Summary/Keyword: Intelligent Vehicles

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Ambient Intelligence in Distributed Modular Systems

  • Ngo Trung Dung;Lund Henrik Hautop
    • Proceedings of the IEEK Conference
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    • summer
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    • pp.421-426
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    • 2004
  • Analyzing adaptive possibilities of agents in multi-agents system, we have discovered new aspects of ambient intelligence in distributed modular systems using intelligent building blocks (I-BLOCKS) [1]. This paper describes early scientific researches related to technical design, applicable experiments and evaluation of adaptive processing and information interaction among I-BLOCKS allowing users to easily develop ambient intelligence applications. The processing technology presented in this paper is embedded inside each DUPLO1 brick by microprocessor as well as selected sensors and actuators in addition. Behaviors of an I-BLOCKS modular structure are defined by the internal processing functionality of each I-Blocks in such structure and communication capacities between I-BLOCKS. Users of the I-BLOCKS system can do 'programming by building' and thereby create specific functionalities of a modular structure of intelligent artefacts without the need to learn and use traditional programming language. From investigating different effects of modem artificial intelligence, I-BLOCKS we have developed might possibly contain potential possibilities for developing applications in ambient intelligence (AmI) environments. To illustrate these possibilities, the paper presents a range of different experimental scenarios in which I-BLOCKS have been used to set-up reconfigurable modular systems. The paper also reports briefly about earlier experiments of I-BLOCKS in different research fields, allowing users to construct AmI applications by a just defined concept of modular artefacts [3].

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A Study on Cooperative-Intelligent Transport System Attack Scenarios and their Prevention and Response Mechanisms (C-ITS 공격 시나리오와 예방 및 대응 방안 연구)

  • Jang, Yoonsuh;Lee, Dong-Seob;Lim, Dong-Ho;Ahn, So-Hee;Shin, Jeonghoon
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.14 no.6
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    • pp.133-140
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    • 2015
  • C-ITS is a system that uses bidirectional communication between two vehicles or infrastructures to control traffic more conveniently, and safely. If C-ITS security is not properly prepared, it can cause traffic congestions and fatal traffic accidents, and therefore can affect greatly on the driver's life. This paper proposes the prevention and response mechanisms based on the cyber attack scenarios that can be used to attack C-ITS.

Fuzzy Logic Based Prediction of Link Travel Velocity Using GPS Information (퍼지논리 및 GPS정보를 이용한 링크통행속도의 예측)

  • Jhong, Woo-Jin;Lee, Jong-Soo;Ko, Jin-Woong;Park, Pyong-Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.3
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    • pp.342-347
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    • 2003
  • It is essential to develop an algorithm for the estimate of link travel velocity and for the supply and control of travel information in the context of intelligent transportation information system. The paper proposes the fuzzy logic based prediction of link travel velocity. Three factors such as time, date and velocity are considered as major components to represent the travel situation. In the fuzzy modeling, those factors were expressed by fuzzy membership functions. We acquire position/velocity data through GPS antenna with PDA embedded probe vehicles. The link travel velocity is calculated using refined GPS data and the prediction results are compared with actual data for its accuracy.

Applying the IoT platform and green wave theory to control intelligent traffic lights system for urban areas in Vietnam

  • Phan, Cao Tho;Pham, Duy Duong;Tran, Hoang Vu;Tran, Trung Viet;Huu, Phat Nguyen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.1
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    • pp.34-52
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    • 2019
  • This paper proposes an intelligent system performing an application with assistance of an Internet of Things (IoT) platform to control a traffic lights system. In our proposed systems, the traffic lights can be remotely controlled through the Internet. Based on IoT platform, the traffic conditions at different intersections of roads are collected and the traffic lights are controlled in a central manner. For the software part, the algorithm is designed based on the green wave theory to maximize the green bandwidth of arterial roads while addressing a challenging issue: the rapid changes of parameters including cycle time, splits, offset, non-fixed vehicles' velocities and traffic flow along arterial roads. The issue typically happens at some areas where the transportation system is not well organized like in Vietnam. For the hardware part, PLC S7-1200 are placed at the intersections for two purposes: to control traffic lights and to collect the parameters and transmit to a host machine at the operation center. For the communication part, the TCP/IP protocol can be done using a Profinet port embedded in the PLC. Some graphical user interface captures are also presented to illustrate the operation of our proposed system.

Neural Network and Cloud Computing for Predicting ECG Waves from PPG Readings

  • Kosasih, David Ishak;Lee, Byung-Gook;Lim, Hyotaek
    • Journal of Multimedia Information System
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    • v.9 no.1
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    • pp.11-20
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    • 2022
  • In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

Reinforcement Learning-Based Adaptive Traffic Signal Control considering Vehicles and Pedestrians in Intersection (차량과 보행자를 고려한 강화학습 기반 적응형 교차로 신호제어 연구)

  • Jong-Min Kim;Sun-Yong Kim
    • The Journal of the Korea institute of electronic communication sciences
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    • v.19 no.1
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    • pp.143-148
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    • 2024
  • Traffic congestion has caused issues in various forms such as the environment and economy. Recently, an intelligent transport system (ITS) using artificial intelligence (AI) has been focused so as to alleviate the traffic congestion problem. In this paper, we propose a reinforcement learning-based traffic signal control algorithm that can smooth the flow of traffic while reducing discomfort levels of drivers and pedestrians. By applying the proposed algorithm, it was confirmed that the discomfort levels of drivers and pedestrians can be significantly reduced compared to the existing fixed signal control system, and that the performance gap increases as the number of roads at the intersection increases.

A Basic Study on Vehicle Load Analyzing System for Embedded Road (임베디드 도로를 위한 차량하중 분석시스템 기초연구)

  • Jo, Byung-Wan;Yoon, Kwang-Won;Park, Jung-Hoon;Kim, Heoun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.31 no.1D
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    • pp.127-132
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    • 2011
  • Load managing method on road became necessary since overloaded vehicles occur damage on road facilities and existing systems for preventing this damage still show many problems. Accordingly, efficient managing system for preventing overloaded vehicles could be organized by using the road itself as a scale by applying genetic algorithm to analyze the load and the drive information of vehicles. First of all, accurate analysis of load using the behavior of road itself is needed for solving illegal axle manipulation problems of overloaded vehicles and for installing intelligent embedded load analyzing system. Accordingly in this study, to use the behavior of road, the transformation was measured by installing underground box type indoor model and indoor experiment was held using genetic algorithm and 10% error were checked.

MPC based Steering Control using a Probabilistic Prediction of Surrounding Vehicles for Automated Driving (전방향 주변 차량의 확률적 거동 예측을 이용한 모델 예측 제어 기법 기반 자율주행자동차 조향 제어)

  • Lee, Jun-Yung;Yi, Kyong-Su
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.3
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    • pp.199-209
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    • 2015
  • This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.

Proposed Message Transit Buffer Management Model for Nodes in Vehicular Delay-Tolerant Network

  • Gballou Yao, Theophile;Kimou Kouadio, Prosper;Tiecoura, Yves;Toure Kidjegbo, Augustin
    • International Journal of Computer Science & Network Security
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    • v.23 no.1
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    • pp.153-163
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    • 2023
  • This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

Vehicle Type Classification Model based on Deep Learning for Smart Traffic Control Systems (스마트 교통 단속 시스템을 위한 딥러닝 기반 차종 분류 모델)

  • Kim, Doyeong;Jang, Sungjin;Jang, Jongwook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.469-472
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    • 2022
  • With the recent development of intelligent transportation systems, various technologies applying deep learning technology are being used. To crackdown on illegal vehicles and criminal vehicles driving on the road, a vehicle type classification system capable of accurately determining the type of vehicle is required. This study proposes a vehicle type classification system optimized for mobile traffic control systems using YOLO(You Only Look Once). The system uses a one-stage object detection algorithm YOLOv5 to detect vehicles into six classes: passenger cars, subcompact, compact, and midsize vans, full-size vans, trucks, motorcycles, special vehicles, and construction machinery. About 5,000 pieces of domestic vehicle image data built by the Korea Institute of Science and Technology for the development of artificial intelligence technology were used as learning data. It proposes a lane designation control system that applies a vehicle type classification algorithm capable of recognizing both front and side angles with one camera.

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