• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.91-104
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• 2022

The operational design domain (ODD) of autonomous vehicles needs to be expanded on highways and urban roads in light of the substantial commercialization of Level 3 autonomous vehicles. Therefore, this study developed a specific infrastructure autonomous vehicle-based cooperative driving service to ensure the driving safety of autonomous vehicles on city roads. The traffic operation efficiency, safety evaluation, and core evaluation indices for each service were selected and analyzed to study the effect of each service. The result of the analysis confirmed that the traffic operation efficiency and safety of autonomous vehicles were improved through the V2X communication-based autonomous cooperative driving service. On the whole, the significance of this study is in deriving the effect of the autonomous cooperative driving service based on V2X communication on urban roads with interrupting traffic flow.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.90-98
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• 2020

Modern vehicles are evolving from manual to automatic driving. As the ratio of electrical equipment and software increases inside the vehicle, vehicles that support autonomous driving are becoming another open computer system that can communicate with the outside. The safety of the vehicle means the safety of both the passenger and the non-passenger. It is not clear whether the safety problem of ultimate autonomous vehicles can be solved by the current solution of computer systems related to fault tolerance and security. Autonomous vehicles should not be dangerous to people after they are released to the market, so it is necessary to proactively diagnose all the risks that can be predicted with current technology. This paper examines the current developments of autonomous vehicles and analyzes their dangers that threaten driving safety, as well as their countermeasures.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.223-230
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• 2021

Currently, self-driving vehicles are on the verge of commercialization after testing. However, even though autonomous vehicles have not been fully commercialized, 81 accidents have occurred, and the driving method of vehicles to avoid accidents relies heavily on LiDAR. In order for the currently commercialized 3-level autonomous vehicle to develop into a 4-level autonomous vehicle, more information must be collected than previously collected information. Therefore, this paper proposes a Driving Situation Judgment System (DSJS) that accurately calculates the crisis situation the vehicle is in by useing the roughness of the road and the state of the passengers of surrounding vehicles including road information and weather information collected from existing autonomous vehicles. As a result of DSJS's PDM experiment, PDM was able to classify passengers 15.52% more accurately on average than the existing vehicle's passenger recognition system. This study can be a basic research to achieve the 4th level autonomous vehicle by collecting more various types than the data collected by the existing 3rd level autonomous vehicle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.368-376
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• 2021

With the recent development of deep learning technology, autonomous things technology is attracting attention, and DNNs are widely used in embedded systems such as drones and autonomous vehicles. Embedded systems that can perform large-scale operations and process multiple DNNs for high recognition accuracy without relying on the cloud are being released. DNNs with various levels of priority exist within these systems. DNNs related to the safety-critical applications of autonomous vehicles have the highest priority, and they must be handled first. In this paper, we propose a priority-based scheduling framework for DNNs when multiple DNNs are executed simultaneously. Even if a low-priority DNN is being executed first, a high-priority DNN can preempt it, guaranteeing the fast response characteristics of safety-critical applications of autonomous vehicles. As a result of checking through extensive experiments, the performance improved by up to 76.6% in the actual commercial board.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1197-1204
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• 2008

This paper presents an asymptotic formation control scheme for a group of underactuated autonomous underwater vehicles (AUVs) where only three control inputs - surge force, yaw moment and pitch moment are available for each vehicle's six degree of freedom (DOF) underwater motion. Usually, the dynamics agents applied in most of the formation algorithms presented so far have been modeled as particle systems, which is a simple double-integrator system. Therefore, these algorithms cannot be directly applicable to the practical systems, especially to the underwater vehicles whose dynamics are highly nonlinear. Moreover, the vehicles considered in this paper are underactuated. The formation control is derived using general potential function method, and the corresponding potential function consists of two parts: interactions between vehicles and virtual-leader following. Proposed formation scheme guarantees asymptotic local stability of closed-loop system. Numerical simulations are carried out to illustrate the effectiveness of proposed formation scheme.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.190-201
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• 2023

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.335-335
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• 2000

Applications such as unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs) and the time varying nature of their navigation, guidance and control systems motivate an integrated approach to trajectory general ion and trajectory tracking for autonomous vehicles. In this paper, an experimental testbed was designed for studying this integrated trajectory control approach. In this paper we apply the separating approach to an autonomous nonlinear vehicle system. A new linear matrix inequality based H$_{\infty}$ control technique for periodic time-varying systems is applied to the role of trajectory tracking. Trajectory general ion is accomplished by exploit ing the differential flatness property of the vehicle system; this at lows product ion of desired feasible nominal or reference trajectories from certain ″flat'system outputs. Simulation and experimental results are presented showing stable tracking of a periodic circular trajectory.

• Electronics and Telecommunications Trends
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• v.33 no.1
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• pp.78-88
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• 2018

As the traffic environment gradually changes to autonomous driving and intelligent transport systems, vehicles are becoming increasingly complicated and intelligent, and their connectivity is greatly expandinged. As a result, attack vectors of such vehicles increasing, and security threats further expanding. Currently, various solutions for vehicle security are being developed and applied, but the damage caused by cyber attacks is still increasing. In recent years, vehicles such as the Tesla Model S and Mitsubishi Outlander have been hacked and remotely controlled by an attacker. Therefore, there is a need for advanced security technologies to cope with increasingly intelligent and sophisticated automotive cyber attacks. In this article, we introduce the latest trends of autonomous vehicles and their security threats, as well as the current status and issues of security technologies to cope with them.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.171-181
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• 2004

An autonomous navigation algorithm for marine vehicles is proposed in this paper using fuzzy logic under COLREG guidelines. The VFF (Virtual Force Field) method, which is widely used in the field of mobile robotics, is modified for application to the autonomous navigation of marine vehicles. This Modified Virtual Force Field (MVFF) method can be used in either track-keeping or collision avoidance modes. Moreover, the operator can select a track-keeping pattern mode in the proposed algorithm. The collision avoidance algorithm has the ability to handle static and/or moving obstacles. The fuzzy expert rules are designed deliberately under COLREG guidelines. An extensive simulation study is used to verify the proposed method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.