• Title/Summary/Keyword: Autonomous vehicles

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Design of Wideband High Gain Trapezoidal Monopole Antenna using Backside Frequency Selective Surface (후면 주파수 선택 표면을 이용한 광대역 고이득 평면 사다리꼴 모노폴 안테나 설계)

  • Hong, Seungmo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.14 no.6
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    • pp.473-478
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    • 2021
  • This paper designed a wideband, high gain planar trapezoidal monopole antenna using backside frequency selective surface (FSS) according to the need for wideband and high gain antenna required in various fields such as rapidly increasing wireless communication, autonomous vehicles, 5G wireless communication and wideband applications. The proposed antenna uses a dual metallic to have a structural difference from the existing FSS. By solving the complexity of the design antenna using genetic algorithms (GA) and high frequency structural simulators (HFSS) simulations, the proposed antenna is not only produce a high efficiency but also presents a wide bandwidth of 3.52 to 5.92 GHz and a gain of 10.5 dBi over the entire bandwidth, with the highest gain of 11.8 dBi at 5.1 GHz. It has been confirmed that the gain increased 8.6 dBi as the 36% impedance bandwidth of 1.8 GHz compared to the existing antenna improved to the 50% impedance bandwidth of 2.4 GHz.

Road Image Recognition Technology based on Deep Learning Using TIDL NPU in SoC Enviroment (SoC 환경에서 TIDL NPU를 활용한 딥러닝 기반 도로 영상 인식 기술)

  • Yunseon Shin;Juhyun Seo;Minyoung Lee;Injung Kim
    • Smart Media Journal
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    • v.11 no.11
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    • pp.25-31
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    • 2022
  • Deep learning-based image processing is essential for autonomous vehicles. To process road images in real-time in a System-on-Chip (SoC) environment, we need to execute deep learning models on a NPU (Neural Procesing Units) specialized for deep learning operations. In this study, we imported seven open-source image processing deep learning models, that were developed on GPU servers, to Texas Instrument Deep Learning (TIDL) NPU environment. We confirmed that the models imported in this study operate normally in the SoC virtual environment through performance evaluation and visualization. This paper introduces the problems that occurred during the migration process due to the limitations of NPU environment and how to solve them, and thereby, presents a reference case worth referring to for developers and researchers who want to port deep learning models to SoC environments.

Long Short-Term Memory Neural Network assisted Peak to Average Power Ratio Reduction for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communication

  • Waleed, Raza;Xuefei, Ma;Houbing, Song;Amir, Ali;Habib, Zubairi;Kamal, Acharya
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.1
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    • pp.239-260
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    • 2023
  • The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other symmetrical modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR) which results in the consumption of more power, creating non-linear distortion and increasing the bit error rate (BER). To overcome this situation, we have contributed our symmetry research into three dimensions. Firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, which turns into a better performance of BER. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system.

Analysis of the Effects of the Truck Platooning Using a Meta-analysis (메타분석을 이용한 화물차 군집주행의 효과 분석)

  • Kim, Yejin;Jeong, Harim;Ko, Woori;Park, Joong-gyu;Yun, Ilsoo
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.1
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    • pp.76-90
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    • 2022
  • The platooning refers to a form in which one or more following vehicles along the path of the leading vehicle(directly driven by the driver) drive in one platoon using V2V, V2I communication and vehicle-mounted sensor. Platooning has emerged in line with the increasing demand for cargo volume and advanced transportation logistics systems, and is expected to have effects such as increasing capacity, reducing labor costs, and reducing fuel consumption. However, compared to general passenger cars, research on autonomous driving of trucks and verification of their effects are insufficient. Therefore, in this study, meta-analysis was conducted on the theme of the effect of truck platooning, and the results of existing studies related to platooning effects were integrated into one reliable, generalized, and objective summary estimate. In conclusion, it was analyzed that the introduction of truck platooning would have an effect of 13.93% increase in capacity, 38.76% decrease in conflict, and 8.13% decrease in fuel consumption.

Design of AHRS using Low-Cost MEMS IMU Sensor and Multiple Filters (저가형 MEMS IMU센서와 다중필터를 활용한 AHRS 설계)

  • Jang, Woojin;Park, Chansik
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.7 no.1
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    • pp.177-186
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    • 2017
  • Recently, Autonomous vehicles are getting hot attention. Amazon, the biggest online shopping service provider is developing a delivery system that uses drones. This kinds of platforms are need accurate attitude information for navigation. In this paper, a structure design of AHRS using low-cost inertia sensor is proposed. To estimate attitudes a Kalman filter which uses a quaternion based dynamic model, bias-removed measurements from MEMS Gyro, raw measurements from MEMS accelerometer and magnetometer, is designed. To remove bias from MEMS Gyro, an additional Kalman filter which uses raw Gyro measurements and attitude estimates, is designed. The performance of implemented AHRS is compared with high price off-the-shelf 3DM-GX3-25 AHRS from Microstrain. The Gyro bias was estimated within 0.0001[deg/s]. And from the estimated attitude, roll and pitch angle error is smaller than 0.2 and 0.3 degree. Yaw angle error is smaller than 6 degree.

A Study on the Analysis of Bridge Safety by Truck Platooning (차량 군집 주행에 따른 교량 안전성 분석에 관한 연구 )

  • Sangwon Park;Minwoo Chang;Dukgeun Yun;Minhyung No
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.27 no.2
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    • pp.50-57
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    • 2023
  • Autonomous driving technologies have been gradually improved for road traffic owing to the development of artificial intelligence. Since the truck platooning is beneficial in terms of the associated transporting expenses, the Connected-Automated Vehicle technology is rapidly evolving. The structural performance is, however, rarely investigated to capture the effect of truck platooning on civil infrastructures.In this study, the dynamic behavior of bridges under truck platooning was investigated, and the amplification factor of responses was estimated considering several parameters associated with the driving conditions. Artificial intelligence techniques were used to estimate the maximum response of the mid span of a bridge as the platooning vehicles passing, and the importance of the parameters was evaluated. The most suitable algorithm was selected by evaluating the consistency of the estimated displacement.

Analysis of the Image Processing Speed by Line-Memory Type (라인메모리 유형에 따른 이미지 처리 속도의 분석)

  • Si-Yeon Han;Semin Jung;Bongsoon Kang
    • Journal of IKEEE
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    • v.27 no.4
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    • pp.494-500
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    • 2023
  • Image processing is currently used in various fields. Among them, autonomous vehicles, medical image processing, and robot control require fast image processing response speeds. To fulfill this requirement, hardware design for real-time processing is being actively researched. In addition to the size of the input image, the hardware processing speed is affected by the size of the inactive video periods that separate lines and frames in the image. In this paper, we design three different scaler structures based on the type of line memories, which is closely related to the inactive video periods. The structures are designed in hardware using the Verilog standard language, and synthesized into logic circuits in a field programmable gate array environment using Xilinx Vivado 2023.1. The synthesized results are used for frame rate analysis while comparing standard image sizes that can be processed in real time.

A Kalman filter with sensor fusion for indoor position estimation (실내 측위 추정을 위한 센서 융합과 결합된 칼만 필터)

  • Janghoon Yang
    • Journal of Advanced Navigation Technology
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    • v.25 no.6
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    • pp.441-449
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    • 2021
  • With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

Method of Multiple Scenario Transformation and Simulation Based Evaluation for Automated Vehicle Assessment (자율주행자동차 평가를 위한 다중 시나리오 변환과 시뮬레이션 기반 평가 방법)

  • Donghyo Kang;Inyoung Kim;Seong-Woo Cho;Ilsoo Yun
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.6
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    • pp.230-245
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    • 2023
  • The importance of evaluating the safety of Automated Vehicles (AV) is increasing with the advances in autonomous driving technology. Accordingly, an evaluation scenario that defines in advance the situations AV may face while driving is being used to conduct efficient stability evaluation. On the other hand, the single scenarios currently used in conventional evaluations address limited situations within short segments. As a result, there are limitations in evaluating continuous situations that occur on real roads. Therefore, this study developed a set of multiple scenarios that allow for continuous evaluation across entire sections of roads with diverse geometric structures to assess the safety of AV. In particular, the conditions for connecting individual scenarios were defined, and a methodology was proposed for developing concrete multiple scenarios based on the scenario evaluation procedure of the PEGASUS project. Furthermore, a simulation was performed to validate the practicality of these multiple scenarios.

Analysis on Lightweight Methods of On-Device AI Vision Model for Intelligent Edge Computing Devices (지능형 엣지 컴퓨팅 기기를 위한 온디바이스 AI 비전 모델의 경량화 방식 분석)

  • Hye-Hyeon Ju;Namhi Kang
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.1
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    • pp.1-8
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    • 2024
  • On-device AI technology, which can operate AI models at the edge devices to support real-time processing and privacy enhancement, is attracting attention. As intelligent IoT is applied to various industries, services utilizing the on-device AI technology are increasing significantly. However, general deep learning models require a lot of computational resources for inference and learning. Therefore, various lightweighting methods such as quantization and pruning have been suggested to operate deep learning models in embedded edge devices. Among the lightweighting methods, we analyze how to lightweight and apply deep learning models to edge computing devices, focusing on pruning technology in this paper. In particular, we utilize dynamic and static pruning techniques to evaluate the inference speed, accuracy, and memory usage of a lightweight AI vision model. The content analyzed in this paper can be used for intelligent video control systems or video security systems in autonomous vehicles, where real-time processing are highly required. In addition, it is expected that the content can be used more effectively in various IoT services and industries.