• Asia pacific journal of information systems
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• v.19 no.2
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• pp.117-137
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• 2009

Recently, intelligent traffic information systems have enabled people to forecast traffic conditions before hitting the road. These convenient systems operate on the basis of data reflecting current road and traffic conditions as well as distance-based data between locations. Thanks to the rapid development of ubiquitous computing, tremendous context data have become readily available making vehicle route planning easier than ever. Previous research in relation to optimization of vehicle route planning merely focused on finding the optimal distance between locations. Contexts reflecting the road and traffic conditions were then not seriously treated as a way to resolve the optimal routing problems based on distance-based route planning, because this kind of information does not have much significant impact on traffic routing until a a complex traffic situation arises. Further, it was also not easy to take into full account the traffic contexts for resolving optimal routing problems because predicting the dynamic traffic situations was regarded a daunting task. However, with rapid increase in traffic complexity the importance of developing contexts reflecting data related to moving costs has emerged. Hence, this research proposes a framework designed to resolve an optimal route planning problem by taking full account of additional moving cost such as road traffic cost and weather cost, among others. Recent technological development particularly in the ubiquitous computing environment has facilitated the collection of such data. This framework is based on the contexts of time, traffic, and environment, which addresses the following issues. First, we clarify and classify the diverse contexts that affect a vehicle's velocity and estimates the optimization of moving cost based on dynamic programming that accounts for the context cost according to the variance of contexts. Second, the velocity reduction rate is applied to find the optimal route (shortest path) using the context data on the current traffic condition. The velocity reduction rate infers to the degree of possible velocity including moving vehicles' considerable road and traffic contexts, indicating the statistical or experimental data. Knowledge generated in this papercan be referenced by several organizations which deal with road and traffic data. Third, in experimentation, we evaluate the effectiveness of the proposed context-based optimal route (shortest path) between locations by comparing it to the previously used distance-based shortest path. A vehicles' optimal route might change due to its diverse velocity caused by unexpected but potential dynamic situations depending on the road condition. This study includes such context variables as 'road congestion', 'work', 'accident', and 'weather' which can alter the traffic condition. The contexts can affect moving vehicle's velocity on the road. Since these context variables except for 'weather' are related to road conditions, relevant data were provided by the Korea Expressway Corporation. The 'weather'-related data were attained from the Korea Meteorological Administration. The aware contexts are classified contexts causing reduction of vehicles' velocity which determines the velocity reduction rate. To find the optimal route (shortest path), we introduced the velocity reduction rate in the context for calculating a vehicle's velocity reflecting composite contexts when one event synchronizes with another. We then proposed a context-based optimal route (shortest path) algorithm based on the dynamic programming. The algorithm is composed of three steps. In the first initialization step, departure and destination locations are given, and the path step is initialized as 0. In the second step, moving costs including composite contexts into account between locations on path are estimated using the velocity reduction rate by context as increasing path steps. In the third step, the optimal route (shortest path) is retrieved through back-tracking. In the provided research model, we designed a framework to account for context awareness, moving cost estimation (taking both composite and single contexts into account), and optimal route (shortest path) algorithm (based on dynamic programming). Through illustrative experimentation using the Wilcoxon signed rank test, we proved that context-based route planning is much more effective than distance-based route planning., In addition, we found that the optimal solution (shortest paths) through the distance-based route planning might not be optimized in real situation because road condition is very dynamic and unpredictable while affecting most vehicles' moving costs. For further study, while more information is needed for a more accurate estimation of moving vehicles' costs, this study still stands viable in the applications to reduce moving costs by effective route planning. For instance, it could be applied to deliverers' decision making to enhance their decision satisfaction when they meet unpredictable dynamic situations in moving vehicles on the road. Overall, we conclude that taking into account the contexts as a part of costs is a meaningful and sensible approach to in resolving the optimal route problem.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.53-59
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• 2022

After the 3rd level autonomous driving vehicle, the 4th and 5th level of autonomous driving technology is trying to maintain the optimal condition of the passengers as well as the perfect driving of the vehicle. However current autonomous driving technology is too dependent on visual information such as LiDAR and front camera, so it is difficult to fully autonomously drive on roads other than designated roads. Therefore this paper proposes a Braking Strength Calculation System (BSCS), in which a vehicle classifies road conditions using data other than visual information and calculates optimal braking strength according to road conditions and driving conditions. The BSCS consists of RCDM (Road Condition Definition Module), which classifies road conditions based on KNN algorithm, and BSCM (Braking Strength Calculation Module), which calculates optimal braking strength while driving based on current driving conditions and road conditions. As a result of the experiment in this paper, it was possible to find the most suitable number of Ks for the KNN algorithm, and it was proved that the RCDM proposed in this paper is more accurate than the unsupervised K-means algorithm. By using not only visual information but also vibration data applied to the suspension, the BSCS of the paper can make the braking of autonomous vehicles smoother in various environments where visual information is limited.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.139-148
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• 2006

In order to incorporate substitution effects between different transport modes in optimal road Pricing, relating economic theories and models have been reviewed. It includes unconstrained optimization problem of maximizing separable and non-separable social net benefit functions of different substitutable urban transport modes. In doing that, the problem and limitations such as path-independent conditions with the asymmetric Jacobian of the objective function have been reviewed. Consequently, a plausible way of deriving optimal road price under interdependent market conditions has been suggested so that the idea can help identifying desirable and acceptable urban transport policy alternatives in a more comprehensive way.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.32-35
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• 1996

Traffic signal cycle optimization is one of the most efficient ways for reducing fuel consumption and improving vehicle waiting time of highsaturated traffic conditions. But most research focused on lowsaturated traffic conditions. Only a few studies have researched traffic control for highsaturated traffic conditions. In this paper reviews the problem of conventional traffic signal system and creates optimal traffic cycle of at the bottom traffic intersection using 27 fuzzy rules. Moreover, to prevent spillback, it can adapt control even though upper traffic intersection has a different saturation rate, road length, road slope and road width.

• Current Optics and Photonics
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• v.3 no.5
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• pp.423-428
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• 2019

Visibility of road-surface markings is one of the critical issues that should be secured for self-driving cars as well as human drivers. Glass beads are taking on the role of retroreflectors, and therefore are considered a necessity in modern pavements. In this context, retroreflectance is sensitively dependent not only on the refractive index of glass beads but also on that of the surrounding medium. This implies that the optimum refractive index of glass beads immersed in water, i.e. under wet conditions, is different from that of glass beads surrounded by air, i.e. under dry conditions. A refractive index of approximately 1.9, which is known to maximize retroreflectance under dry conditions, actually exhibits much poorer retroreflectance under wet conditions. This suggests that glass beads with optimal refractive index for wet conditions need to be installed together with those for dry conditions. We propose a facile but practical model capable of calculating retroreflectance of glass beads surrounded by an arbitrary medium, here water in particular, and experimentally verify its capability of assessing the refractive index of commercial glass beads. Changes in retroreflectance according to the mixing ratio of glass beads with different refractive indices are also discussed, in an effort to propose the proper use of glass beads produced for dry and wet conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.894-902
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• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.

• Journal of Energy Engineering
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• v.23 no.3
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• pp.163-173
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• 2014

In this study, a modeling method is based on representing a road generation system with several rigid bodies, i.e, pad, shaft, torsional damper, oneway-clutch, gear system, and electricity generator. The simulation software is developed to evaluate the performance of a road generation system. It is used to determine parametric dimension for optimal design with the theoretically calculated results from the simulation software. The parametric dimensions are included as capacity, length, and angle of equipment. The transient responses at the conditions of low and high vehicle speed are compared with the calculated results as torque, power, out energy etc. Consequently, before manufacturing system, the analysis of simulation results shows that the proposed concept and system has efficiency and confidence.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.467-471
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• 2009

A road is a fundammental public traffic facility for transporting people and goods. Road designer should determine the best route considering various conditions to minimize environmental bad effect due to road construction and show road functions sufficiently. In this research, we tried to select the optimal route location by comparing candidate routes considering weights of various items such as land use, slope, aspect, land price and so forth. The candidate routes were analyzed and visualized from built data using a GIS software and then compared with the existing route.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.10
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• pp.935-945
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• 2014

This paper presents a new road generative system that employs a pad of preventive overspeed or tollgate. The system consists of pad, shaft, torsional damper, oneway-clutch, gear system, and electricity generator components. When the car driven through the road generation system, it occurred to surplus energy in the DC power. In order to maximize the power of electricity energy harvester, the simulation software is developed. It is used to determine parametric dimension for optimal design with the theoretically calculated results from the simulation software. The transient responses at the conditions of low and high vehicle speed are compared with the calculated results as torque, impact force, power, out energy etc. Consequently, before design a road generation system, the analysis of simulation results shows that the proposed concept and system has efficiency and confidence.