• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.131-140
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• 1994

Dynamics of AUV has heavy nonlinearities and many unknown parameters due to its bluff shape and low cruising speed. Intelligent algorithms, therefore, are required to overcome these nonlinearities and unknown system dynamics. Several identification techniques have been suggested for the application of control of underwater vehicles during last decade. This paper applies the neural network to identification and motion control problem of AUVs. Nonlinear dynamic systems of an AUV are identified using feedforward neural network. Simulation results show that the learned neural network can generate the motion of AUV. This paper, also, suggest an adaptive control scheme up-dates the controller weights with reference model and feedforward neural network using error back propagation.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.932-941
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• 2014

In this paper, a simulation model for 7-phase BLDC motor drives for an Autonomous Underwater Vehicles (AUV) is proposed. A 7-phase BLDC motor is designed and the electrical characteristics are analyzed using FEA program and the power electronics drives for the 7-phase BLDC motor are theoretically analyzed and the actual implementation has been accomplished using Matlab Simulink. PI controller and fuzzy controller are compared for verifying the validity of the proposed model and the informative results are described in detail. Especially A fuzzy controller is used to characterize 7-phase BLDC motor, drive systems under normal and fault operating conditions.

• Electronics and Telecommunications Trends
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• v.33 no.6
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• pp.118-128
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• 2018

With the massive increase in bandwidth for wireless communications, free space optical (FSO) communication has attracted significant interest owing to its outstanding strengths over conventional radio frequency wireless communication such as a wide bandwidth, unlicensed spectrum, low power consumption, small size, electromagnetic interference immunity, long-range propagation, and improved security. In recent years, FSO technology has been studied intensively for use in terrestrial and underwater autonomous and unmanned mobile systems, a rapidly growing application area, including robots, drones, unmanned aerial vehicles, autonomous vehicles, unmanned trains, and unmanned submarines. In this report, we review the recent trends and key technologies for the mobile FSO system, and introduce our drone-based mobile FSO system, which is currently under development.

• Electronics and Telecommunications Trends
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• v.34 no.3
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• pp.43-54
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• 2019

Currently, the use of unmanned aerial vehicles (UAVs) is spreading from recreational purposes to the public- and commercial-use product areas. Various efforts are being made worldwide to ensure the safety of UAVs and expand their service applications and convenience, because autonomous flights are becoming increasingly popular. In order for a UAV to perform autonomous flight and mission without operator assistance, environmental perception technology, path planning technology, and flight control technology are needed. In this article, we present recent trends in these technologies.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.35-42
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• 2022

This paper presents a stochastic model predictive control (SMPC) for stop maneuver of autonomous vehicles considering perception uncertainty of stopped vehicle. The vehicle longitudinal motion should achieve both driving comfortability and safety. The comfortable stop maneuver can be performed by mimicking acceleration profile of human driving pattern. In order to implement human-like stop motion, we propose a reference safe inter-distance and velocity model for the longitudinal control system. The SMPC is used to track the reference model which contains the position uncertainty of preceding vehicle as a chance constraint. We conduct simulation studies of deceleration scenarios against stopped vehicle in urban environment. The test results show that proposed SMPC can execute comfortable stop maneuver and guarantee safety simultaneously.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.55-63
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• 2019

Vehicle communication networks have some deficient network resources to support a vast multimedia service including safety driving information, video, news and some broadcast relayed from the playgrounds such as professional baseball games for autonomous vehicles. This paper deals with the framework for providing seamless multimedia service including safety driving information using multicast in cooperated-connected vehicle communication network (CVCN). It adopts smart-switch (SS) and smart intelligent multicast agent(SIMA) to support the seamless multimedia service. The SS manages and switches multimedia streams through SIMA in CVCN network. The SIMA to operate as an access point, is composed of multicast supporting part and control part of mobile devices/autonomous vehicles in CVCN network. Therefore this proposed technique using SS and SIMA within CVCN network is a new framework for multimedia service that can disperse the load of server.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.25-34
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• 2024

The paper investigates how the semiconductor and electric vehicle industries are addressing safety and security concerns in the era of autonomous driving, emphasizing the prioritization of safety over security for market competitiveness. Collaboration between these sectors is deemed essential for maintaining competitiveness and value. The research suggests solutions such as advanced autonomous driving technologies and enhanced battery safety measures, with the integration of AI chips playing a pivotal role. However, challenges persist, including the limitations of big data and potential errors in semiconductor-related issues. Legacy automotive manufacturers are transitioning towards software-driven cars, leveraging artificial intelligence to mitigate risks associated with safety and security. Conflicting safety expectations and security concerns can lead to accidents, underscoring the continuous need for safety improvements. We analyzed the expansion of electric vehicles as a means to enhance safety within a framework of converging security concerns, with AI chips being instrumental in this process. Ultimately, the paper advocates for informed safety and security decisions to drive technological advancements in electric vehicles, ensuring significant strides in safety innovation.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.101-106
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• 2024

For the commercialization of automated vehicles, it is necessary to create various scenarios that can evaluate driving safety and establish a data system that can verify them. Depending on the vehicle's ODD (Operational Design Domain), there are numerous scenarios with various parameters indicating vehicle driving conditions, but no systematic methodology has been proposed to create and combine scenarios to test them. Therefore, projects are actively underway abroad to establish a scenario library for real-world testing or simulation of autonomous vehicles. However, since it is difficult to obtain data, research is being conducted based on simulations that simulate real road. Therefore, in this study, parameters calculated through individual vehicle trajectory data extracted based on roadside CCTV image-based driving environment DB was proposed through the extracted data. This study can be used as basic data for safety standards for scenarios representing various driving behaviors.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.

• Journal of Venture Innovation
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• v.5 no.4
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• pp.91-108
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• 2022

This study is an empirical analysis regarding what kind of factors affect the intention to use autonomous vehicles. For the empirical analysis the research model was derived from value-based adoption model base and integrated some aspects that only autonomous vehicles have. At default variables of VAM are usefulness, enjoyment, technicality, perceived cost, some autonomous vehicle related variables were added, and those are convenience, safety, security, social influence. A survey was done in order to empirically analyze with this research model, and 216 valid survey answers were chosen to analyze. Empirical analysis was done by structural equation using AMOS24. The result of empirical analysis were as follows. Variables usefulness, enjoyment, safety, security had a significant positive effect on perceived value. Technicality and perceived cost had a significant negative effect of perceived value. In addition, security and social influence had no significant effect on perceived value. Furthermore, perceived value had significant positive effect on intention to use. Among the variables that came out to be significantly positive, the most influencing variable was safety, followed by convenience, perceived cost, enjoyment, usefulness and then technicality. In addition, the analysis of mediating effect of perceived value shows that usefulness, enjoyment, convenience, safety, technicality, perceived cost had mediating role towards intention to use. However, security and social influence had no siginificant mediating effect towards intention to use. Considering all these research results this study has provided theoretical and practical implications to researchers on the intention to use autonomous vehicles.