• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1377-1399
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• 2020

In search and rescue mission, micro aerial vehicles (MAVs) are typically used to capture image and video from an aerial perspective and transfer the data to the ground station. Because of the power limitation, a cluster of MAVs are required for a large search area, hence an ad-hoc wireless network must be maintained to transfer data more conveniently and fast. However, the unstable link and the intermittent connectivity between the MAVs caused by MAVs' movement may challenge the packet forwarding. This paper proposes a delay tolerant packet forwarding algorithm based on location estimation for MAV networks, called DTN_est algorithm. In the algorithm, ferrying MAVs are used to transmit data between MAVs and the ground station, and the locations of both searching MAVs and ferrying MAVs are estimated to compute the distances between the MAVs and destination. The MAV that is closest to the destination is selected greedy to forward packet. If a MAV cannot find the next hop MAV using the greedy strategy, the packets will be stored and re-forwarded once again in the next time slot. The experiment results show that the proposed DTN_est algorithm outperforms the typical DTN_geo algorithm in terms of packet delivery ratio and average routing hops.

• Knowledge Management Research
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• 제25권1호
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• pp.217-233
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• 2024

The demand for two-wheelers has increased in recent years, driven by the growing delivery culture, which has also led to a rise in the number of two-wheelers. Although two-wheelers are economically efficient in congested traffic conditions, reckless driving and ambiguous traffic laws for two-wheelers have turned two-wheeler accidents into a significant social issue. Given the high fatality rate associated with two-wheelers, the severity and risk of two-wheeler accidents are considerable. It is, therefore, crucial to thoroughly understand the characteristics of two-wheeler accidents by analyzing their attributes. In this study, the characteristics of two-wheeled vehicle accidents were categorized using the K-prototypes algorithm, based on data from two-wheeled vehicle accidents. As a result, the accidents were divided into four clusters according to their characteristics. Each cluster showed distinct traits in terms of the roads where accidents occurred, the major laws violated, the types of accidents, and the times of accident occurrences. By tailoring enforcement methods and regulations to the specific characteristics of each type of accident, we can reduce the incidence of accidents involving two-wheelers in metropolitan areas, thereby enhancing road safety. Furthermore, by applying machine learning techniques to urban transportation and safety, this study adds to the body of related literature.

• Journal of Korean Society of Transportation
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• 제36권2호
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• pp.98-111
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• 2018

Truck platooning, which is a cluster of trucks in support of vehicle-to-vehicle communication and automated longitudinal vehicle control, is a promising method to both operational efficiency and prevent traffic crashes. Although a variety of studies have been conducted to identify the effects of vehicle platooning on traffic stream, we are not aware of any study attempting to identify promising road segments for vehicle platooning. This study aims to develop a methodology for determining the priority of freeway segments that would potentially lead to maximize the effectiveness of truck platooning. Evaluation measures derived in this study includes truck crash rates, the percentage of truck traffic, segment length, and the number of entry and exit points. Weighting values obtained from an analytical hierarchical process (AHP) method were applied to compute the proposed priority score to determine better freeway segment for truck platooning. Results suggested that a 46.9km freeway segment, from Sacheon IC to Sanin JC, was the most promising segment for maximizing the effectiveness of truck platooning. It is expected that the outcome of this study would be effectively used as a fundamental to establish operational strategies for truck platooning.

• Journal of Sensor Science and Technology
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• 제26권1호
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• pp.1-6
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• 2017

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.1141-1142
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• 2008

For intelligent vehicles, recognizing the sounding is an important task. In this paper we propose an road area detection system. This system uses u-disparity and v-disparity map. v-disparity map is used to find the road area. u-disparity is used to cluster the area that is an object. The test results and overall system is discribed in this paper.

• IEMEK Journal of Embedded Systems and Applications
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• 제7권1호
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• pp.41-52
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• 2012

Recently, FlexRay was developed to replace controller area network (CAN) protocol in chassis networking systems, to remedy the shortage of transmission capacity and unsatisfactory real-time transmission delay of conventional CAN. FlexRay network systems require correct synchronization and complex scheduling parameters. However, because platform configuration register (PCR) setting and message scheduling is complex and bothersome task, FlexRay is more difficult to implement in applications than CAN protocol. To assist a network designer for implementing FlexRay cluster, this paper presents an analysis of FlexRay platform configuration register and automatic generation program of PCR. To demonstrate the feasibility of the automatic generation program, we evaluated its performance using experimental testbed.

• Industrial Engineering and Management Systems
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• 제10권4호
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• pp.288-291
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• 2011

Recommendations for older driver-friendly automobile interior design have been determined by taking into account older people's physical and cognitive characteristics. Twenty three older people (aged from 54 to 78) and five younger people (from 20 to 29) performed several tasks in actual driving conditions, in which their reaction times and performance errors were recorded. Some design factors were found to be related to older drivers' visibility and controllability. Several design recommendations were proposed in terms of cluster color and font, display location, and HVAC control type. Proposed recommendations are expected to satisfy a wider range of older drivers as these will facilitate automobile interior designs which are fitter to older drivers' visual, cognitive, and manual capabilities.

• International Journal of Naval Architecture and Ocean Engineering
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• 제11권1호
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• pp.202-210
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• 2019

The obstacles modeling is a fundamental and significant issue for path planning and automatic navigation of Unmanned Surface Vehicle (USV). In this study, we propose a novel obstacles modeling method based on high resolution satellite images. It involves two main steps: extraction of obstacle features and construction of convex hulls. To extract the obstacle features, a series of operations such as sea-land segmentation, obstacles details enhancement, and morphological transformations are applied. Furthermore, an efficient algorithm is proposed to mask the obstacles into convex hulls, which mainly includes the cluster analysis of obstacles area and the determination rules of edge points. Experimental results demonstrate that the models achieved by the proposed method and the manual have high similarity. As an application, the model is used to find the optimal path for USV. The study shows that the obstacles modeling method is feasible, and it can be applied to USV path planning.

• Advances in aircraft and spacecraft science
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• 제9권5호
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• pp.401-414
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• 2022

In the present paper, the optimal selection of batteries for an electric pump-fed hybrid rocket engine is analyzed. A two-stage Mars Ascent Vehicle, suitable for the Mars Sample Return Mission, is considered as test case. A single engine is employed in the second stage, whereas the first stage uses a cluster of two engines. The initial mass of the launcher is equal to 500 kg and the same hybrid rocket engine is considered for both stages. Ragone plot-based correlations are embedded in the optimization process in order to chose the optimal values of specific energy and specific power, which minimize the battery mass ad hoc for the optimized engine design and ascent trajectory. Results show that a payload close to 100 kg is achievable considering the current commercial battery technology.

• IEMEK Journal of Embedded Systems and Applications
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• 제18권5호
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• pp.225-231
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• 2023

With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.