• Journal of Aerospace System Engineering
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• v.15 no.2
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• pp.36-44
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• 2021

Unmanned aerial vehicles (UAVs) are increasingly needed as they can replace manned aircrafts in dangerous military missions. However, because of their low autonomy, current UAVs can execute missions only under continuous operator control. To overcome this limitation, higher autonomy levels of UAVs based on autonomous situational awareness is required. In this paper, we propose an autonomous situational awareness software consisting of situation awareness management, threat recognition, threat identification, and threat space analysis to detect dynamic situational change by external threats. We implemented the proposed software in real mission computer hardware and evaluated the performance of situational awareness toward dynamic radar threats in flight simulations.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.162-170
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• 2022

Today, automobiles have become an indispensable element in people's lives, and the distribution of vehicles with various autonomous driving functions is expanding. Sensors such as cameras are used to recognize various situations on the road as an essential element for autonomous driving functions, but camera sensors have disadvantages that are vulnerable to bad weather. In this paper, we present a derivation process that defines external weather environment factors that negatively affect the performance of a camera for an autonomous vehicle. Through the proposed process, it is expected that it will contribute to securing the reliability of the camera and further improving the safety of autonomous vehicles.

• Electronics and Telecommunications Trends
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• v.36 no.2
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• pp.1-11
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• 2021

As sensors such as Inertial Measurement Unit, cameras, and Light Detection and Rangings have become cheaper and smaller, research has been actively conducted to implement functions automating micro aerial vehicles such as multirotor type drones. This would fully enable the autonomous flight of drones in the real world without human intervention. In this article, we present a survey of state-of-the-art development on autonomous drones. To build an autonomous drone, the essential components can be classified into pose estimation, environmental perception, and obstacle-free trajectory generation. To describe the trend, we selected three leading research groups-University of Pennsylvania, ETH Zurich, and Carnegie Mellon University-which have demonstrated impressive experiment results on automating drones using their estimation, perception, and trajectory generation techniques. For each group, we summarize the core of their algorithm and describe how they implemented those in such small-sized drones. Finally, we present our up to date research status on developing an autonomous drone.

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.29-34
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• 2024

This paper presents a multi-label lane detection method for autonomous vehicles based on deep learning. The proposed algorithm can detect two types of lanes: center lane and normal lane. The algorithm uses a convolution neural network with an encoder-decoder architecture to extract features from input images and produce a multi-label heatmap for predicting lane's label. This architecture has the potential to detect more diverse types of lanes in that it can add the number of labels by extending the heatmap's dimension. The proposed algorithm was tested on an OpenLane dataset and achieved 85 Frames Per Second (FPS) in end to-end inference time. The results demonstrate the usability and computational efficiency of the proposed algorithm for the lane detection in autonomous vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.284-298
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• 2022

It is necessary to acquire information on traffic safety facilities installed on the roadways specifically for the operation of autonomous vehicles. The purpose of this study is to prepare a Korean version of an integrated message-set design as a way to provide to autonomous vehicles standardized information on traffic safety facilities. In this study, necessary facilities are classified according to four criteria (no legal basis; not providing information to autonomous vehicles; providing duplicate information; not standardized, and too difficult to generalize) based on information that must be provided to operate autonomous vehicles. The priority of information delivery (gross negligence followed by behavior change) was classified according to the importance of the information to be provided during autonomous driving, and the form was defined for the classification code in the information delivered. Finally, the information location and delivery method of traffic facilities for compliance with SAE J2735 were identified. This study is meaningful in that it provides a plan for roadway operations by suggesting a method for providing information to autonomously driven vehicles.

• Journal of Auto-vehicle Safety Association
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• v.9 no.4
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• pp.7-13
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• 2017

As automotive industry develops, the demand for increasing traffic safety is growing. Lots of researches about vehicle convenience and safety technology have been implemented. Now, the autonomous driving test is being conducted all over the world, and the autonomous driving regulations are also being developed. Autonomous vehicles are being commercialized, but autonomous vehicle safety has not been guaranteed yet. This paper presents scenarios that assess the safety of autonomous vehicles by identifying the minimum requirements to ensure safety for a variety of situations on highway. In assessing driving safety, seven scenarios were totally selected. Seven scenarios were related to lane keeping and lane change performance in certain situations. These scenarios were verified by analyzing the driving data acquired through actual vehicle driving. Data analysis was implemented via computer simulation. These scenarios are developed based on existing ADAS evaluation and simulation of autonomous vehicle algorithm. Also Safety evaluation factors are developed based on ISO requirements, other papers and the current traffic regulations.

• Journal of the Korean Society of Systems Engineering
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• v.13 no.2
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• pp.18-25
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• 2017

The use of unmanned combat systems is of interest for future battlefield. Advanced techniques are being actively studied to build fully autonomous unmanned systems. However, there are technical, ethical and legal limitations for the fully autonomous unmanned combat systems. In addition, a remote controlled system is necessary so far in order to prepare for situations where fully autonomous unmanned systems fail to function properly. Thus, a procedure of developing operational requirements in system level is proposed and interface requirements of unmanned combat vehicles for remote control are described in this study.

• The Journal of Korea Robotics Society
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• v.5 no.4
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• pp.339-348
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• 2010

One of the requirements for autonomous vehicles on off-road is to move stably in unstructured environments. Such capacity of autonomous vehicles is one of the most important abilities in consideration of mobility. So, many researchers use contact and/or non-contact methods to determine a terrain whether the vehicle can move on or not. In this paper we introduce an algorithm to classify terrains using visual information(one of the non-contacting methods). As a pre-processing, a contrast enhancement technique is introduced to improve classification of terrain. Also, for conducting classification algorithm, training images are grouped according to materials of the surface, and then Bayesian classification are applied to new images to determine membership to each group. In addition to the classification, we can build Traversability map specified by friction coefficients on which autonomous vehicles can decide to go or not. Experiments are made with Load-Cell to determine real friction coefficients of various terrains.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.5
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• pp.240-248
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• 1999

This paper presents a lane-change collision avoidance control algorithm for autonomous vehicles that will be used in AHS(Automated Highway System). In the proposed control algorithm, nominal control inputs are generated by solving the inverse vehicle dynamic equations of motion for a lane-change maneuver. In addition, a corrective steering input from preview as well as DYC (Direct Yaw Moment Control) may be included to reduce unpredictable errors and to insure yaw directional stability, respectively. The performance of the algorithm is evaluated with an ABS HILS system which consist of 17 DOF vehicle model and real ABS hardware parts. The HILS simulation results show that the proposed algorithm may be used for emergency lane-change maneuvers for autonomous vehicles.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.803-812
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• 2017

This paper proposes a local path planning method for stable autonomous driving in rough terrain. There are various path planning techniques such as candidate paths, star algorithm, and Rapidly-exploring Random Tree algorithms. However, such existing path planning has limitations to reflecting the stability of unmanned ground vehicles. This paper suggest a path planning algorithm that considering the stability of unmanned ground vehicles. The algorithm is based on the genetic algorithm and assumes to have probability based obstacle map and elevation map. The simulation result show that the proposed algorithm can be used for real-time local path planning in rough terrain.