• Computers and Concrete
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• v.19 no.6
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• pp.725-736
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• 2017

This paper presents the two-, three-, and four-lane transverse reduction factor based on FEA method, probability theory, and the recently actual traffic flow data. A total of 72 composite girder bridges with various spans, number of lanes, loading mode, and bridge type are analyzed with time-varying static load FEA method by ANSYS, and the probability models of vehicle load effects at arbitrary-time point are developed. Based on these probability models, in accordance to the principle of the same exceeding probability, the multi-lane transverse reduction factor of these composite girder bridges and the relationship between the multi-lane transverse reduction factor and the span of bridge are determined. Finally, the multi-lane transverse reduction factor obtained is compared with those from AASHTO LRFD, BS5400, JTG D60 or Eurocode. The results show that the vehicle load effect at arbitrary-time point follows lognormal distribution. The two-, three-, and four-lane transverse reduction factors calculated by using FEA method and probability respectively range between 0.781 and 1.027, 0.616 and 0.795, 0.468 and 0.645. Furthermore, a correlation between the FEA and AASHTO LRFD, BS5400, JTG D60 or Eurocode transverse reduction factors is made for composite girder bridges. For the two-, three-, and four-lane bridge cases, the Eurocode code underestimated the FEA transverse reduction factors by 27%, 25% and 13%, respectively. This underestimation is more pronounced in short-span bridges. The AASHTO LRFD, BS5400 and JTG D60 codes overestimated the FEA transverse reduction factors. The FEA results highlight the importance of considering span length in determining the multi-lane transverse reduction factors when designing two-lane or more composite girder bridges. This paper will assist bridge engineers in quantifying the adjustment factors used in analyzing and designing multi-lane composite girder bridges.

• Journal of Korean Society of Transportation
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• v.9 no.1
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• pp.5-18
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• 1991

Two-Lane, two-way roads account for approximately 92% of total road length in Korea and accomplish the majority of regional transport activities. Nevertheless not too many research have been made on two-lane roads particularly efficiency related topics such as capacity and travel time studies. In this study a full scale data collection was conducted using video equippments on rural two-lane roads to determine capacity Passenger Car Equivalents(PCE) and Level of Service criterion. Various PC programs were utilized to reduce traffic data and Walker ME? and Headway Method were employed to determine PCE's for heavy vehicles. The reseach has shown that capacity and PCE's for two-land two-way roads in Korea are 3200 pcph and 1.1∼1.9 resectively. In addition percent time delay was used as the basis of developing Level of service criterion on two-lane roads in Korea.

• Journal of Korean Society of Transportation
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• v.11 no.3
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• pp.67-82
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• 1993

The effects of passing lanes on traffic flow on rural two-lane highways have been investigated in this research. Field study results were compared with the computer simulation model, TRARR, TRARR model appears to be in good agreement with field study results except that average speed and distribution of platoon sizes showed a small amount of discrepancy, which is believed to be caused by too large a headway definition of 6 second for a vehicle platoon. Using the TRARR model, 4 situations including the existing condition, installation of passing lanes, improvement of design speed, and expansion to a four lane highway were evaluated. Traffic volume levels of 500,1000, and 1500 vph and truck composition of 20 and 40% were also considered making a total of 24 computer runs. It is concluded in this research that the installation of passing lanes is the most effective method to improve traffic operation on two-lane high ways except the alternative of four lane highway expansion.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.932-937
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• 2003

In this paper, we will explain about the unmanned vehicle control and modeling for combined obstacle avoidance and lane tracking. First, obstacle avoidance is considered as one of the important technologies in the unmanned vehicle. It is consisted by two parts: the first part includes the longitudinal control system for the acceleration and deceleration and the second part is the lateral control system for the steering control. Each system uses to the obstacle avoidance during the vehicle moving. Therefore, we propose the method of vehicle control, modeling and obstacle avoidance. Second, we describe a method of lane tracking by means of vision system. It is important in the unmanned vehicle and mobile robot system. In this paper, we deal with lane tracking and image processing method and it is including lane detection method, image processing algorithm and filtering method.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.359-370
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• 2007

Lane tracking and obstacle avoidance are considered two of the key technologies on an unmanned ground vehicle system. In this paper, we propose a method of lane tracking and obstacle avoidance, which can be expressed as vehicle control, modeling, and sensor experiments. First, obstacle avoidance consists of two parts: a longitudinal control system for acceleration and deceleration and a lateral control system for steering control. Each system is used for unmanned ground vehicle control, which notes the vehicle's location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control strategy of the vehicle can detect obstacle and perform obstacle avoidance on the road, which involves vehicle velocity. Second, we explain a method of lane tracking by means of a vision system, which consists of two parts: First, vehicle control is included in the road model through lateral and longitudinal control. Second, the image processing method deals with the lane tracking method, the image processing algorithm, and the filtering method. Finally, in this paper, we propose a method for vehicle control, modeling, lane tracking, and obstacle avoidance, which are confirmed through vehicles tests.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.6
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• pp.627-634
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• 2016

In this paper, an augmented video generation method to evaluate the performance of lane departure warning system is proposed. In our system, the input is a video which have road scene with general clean lane, and the content of output video is the same but the lane is synthesized with contamination image. In order to synthesize the contamination lane image, two approaches were used. One is example-based image synthesis, and the other is background-based image synthesis. Example-based image synthesis is generated in the assumption of the situation that contamination is applied to the lane, and background-based image synthesis is for the situation that the lane is erased due to aging. In this paper, a new contamination pattern generation method using Gaussian function is also proposed in order to produce contamination with various shape and size. The contamination lane video can be generated by shifting synthesized image as lane movement amount obtained empirically. Our experiment showed that the similarity between the generated contamination lane image and real lane image is over 90 %. Futhermore, we can verify the reliability of the video generated from the proposed method through the analysis of the change of lane recognition rate. In other words, the recognition rate based on the video generated from the proposed method is very similar to that of the real contamination lane video.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.101-112
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• 2000

The Purpose of this Paper was to revise the method of USHCM and to establish new method for level of service analysis on two-lane rural highways. For this Purpose, total delay rate was selected as new MOE for level of service, replacing the present Percent time delay. This result showed that total delay rate was more effective for considering the effects of traffic flows, auxiliary lane, and vertical tirade. The application of total delay rate could resolve the Problems in the USHCM method, such as too wide ranges for level of service D and E, and the use of different Procedures for level of service analysis of general terrain segment and specific grade Procedures. The research results are as follows First, a new method for level of service analysis on two-lane rural highways was developed using the total delay rate. Second, a new classification for level of service was developed and a consistent method applicable for general terrain segment and specific tirade Procedures was developed. Third, the desired speed on two-lane rural highways was determined as 85km/h.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.773-780
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• 2015

Navigation in outdoor environments is a fundamental and challenging problem for unmanned ground vehicles. Detecting lane markings or boundaries on the road may be one of the solutions to make navigation easy. However, because of various environments and road conditions, a robust lane detection is difficult. In this paper, we propose a new approach for estimating virtual lanes on a traversable region. Estimating the virtual lanes consist of two steps: (i) we detect virtual road region through road model selection based on traversability at current frame and similarity between the interframe and (ii) we estimate virtual lane using the number of lane on the road and results of previous frame. To improve the detection performance and reduce the searching region of interests, we use a probability map representing the traversability of the outdoor terrain. In addition, by considering both current and previous frame simultaneously, the proposed method estimate more stable virtual lanes. We evaluate the performance of the proposed approach using real data in outdoor environments.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.185-223
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• 2022

The classical equation of Jonathan Homer Lane and Robert Emden, a nonlinear second-order ordinary differential equation, models the isothermal spherical clouded gases under the influence of the mutual attractive interaction between the gases' molecules. In this paper, the Adomian decomposition method (ADM) is presented to obtain highly accurate and reliable analytical solutions of a class of generalised Lane-Emden equations with strong nonlinearities. The nonlinear term f(y(x)) of the proposed problem is given by the integer powers of a continuous real-valued function h(y(x)), that is, f(y(x)) = h^m(y(x)), for integer m ≥ 0, real x > 0. In the end, numerical comparisons are presented between the analytical results obtained using the ADM and numerical solutions using the eighth-order nested second derivative two-step Runge-Kutta method (NSDTSRKM) to illustrate the reliability, accuracy, effectiveness and convenience of the proposed methods. The special cases h(y) = sin y(x), cos y(x); h(y) = sinh y(x), cosh y(x) are considered explicitly using both methods. Interestingly, in each of these methods, a unified result is presented for an integer power of any continuous real-valued function - compared with the case by case computations for the nonlinear functions f(y). The results presented in this paper are a generalisation of several published results. Several examples are given to illustrate the proposed methods. Tables of expansion coefficients of the series solutions of some special Lane-Emden type equations are presented. Comparisons of the two results indicate that both methods are reliably and accurately efficient in solving a class of singular strongly nonlinear ordinary differential equations.

• 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.