• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.159-164
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• 1995

Driving maneuver in car following are affected by not only the factors related to road structure and traffic condition, but also the factors related to driver's cognition to them. So the aim of this research this to model the relation of driver's cognition for car-following distance considering driver's fuzziness for imformation cognition, As a result, driver's cognition of car-following distance model with fuzzy number is proposed. The 'width', which characterizes the fuzzy number can introduce car-following informtion into the model.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.5
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• pp.999-1011
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• 1994

Driving maneuvers in car-following are affected not only by the factors related to road and traffic conditions, but also by factors related to drivers' cognition to them. This paper attempts to analyse drivers' car-following distance tests on road characteristics. Driving maneuvers are directly observed by video recorder sets in the vehicle. At the same time drivers are interviewed about their cognitive degree of car-following distance according to a given level of cognitive language. As a result, linguistical cognition of car-following distance is well fitted in with Weber-Fechner's law. This paper suggests a modeling method that takes into account the driver's cognition for acquiring traffic visible information on driving maneuver.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.15-23
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• 2011

Driving directly in front of the driver of a vehicle driving in front of the vehicle and it is commonly known is affected. Responding to the car in front of the driver and the vehicle in front, and these follow the model is known as Three-Car-Following Model. Platoon vehicles to follow behind the driver's visibility is affected by the a tinted vehicle, and Parameters of the model is estimated to be affected also. In this study, in Three-Car-Followng Model parameters were estimated. and the parameter values differ about whether and how analysis was performed by the level of Visible Light Transmission Percentage. RTK GPS receiving data through field experiment analyzed based on sensitivity of three car by Visible Light Transmission Percentage and ${\gamma}$. And With statistical verification of driving directly in front of the driver in front of the vehicle and that the moving vehicle is influenced also confirmed. Also Visible Light Transmission Percentage is lowered, the vehicle in front of the driver's behavior showed sensitive reactions. In the further need to research for influence analysis of traffic flow capacity by the level of VLT.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.53-64
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• 2013

The car-following model is one of core models in Advanced Vehicle & Highway Systems (AVHS). The car-following model has been developed in aspects such as human factor and reduction error rates. However, the consideration of safety depending on weather condition has not been completed yet. In this paper, therefore, changes of driving condition for car-following due to different road condition were dealt with, and optimal safety distance corresponding to road condition such as dry, wet and snowy were computed. The GMIT(GM Model with Instantaneous T) model was picked over for simulation of adaptive cruise control applied the suggested optimal safety distance. As the results, the 1.7 times longer safety distance was required for wet road condition than dry road condition, and the 5.6 times longer safety distance was required for snowy road condition.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.911-914
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• 1993

This paper makes a trial to build the model of car-following in the state of starting to stable driving on the basic of driver's knowledge that is easily characterized by linguistical cognition. There are three main steps in building the model. Firstly, each driver's rule of three testees is studied in linguistical experssion by the interview and questionary surveys that are repeated once a day for ten days. Secondly, quantification of the linguistical expression is investigated by driving experiments that includes the questionary survey to the testee in the test vehicle, and the membership functions of variables of rule are obtained. Thirdly, implicaton and composition of fuzzy inference is made by Max-Min Methods and defuzzification by gravity method. It can be said that the proposed model of car-following based on driver's knowledge is practically allpicable to the estimation of drivering of car-following on trunk roads in urban area.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.121-133
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• 2008

Conventional car following models are controlled when the velocity of following vehicle is equal to preceding vehicle without consideration of relative distance. Also, since the car following models are hardly consider the driver's behaviors and the environmental factors in driving, the models can't be adopted in reality. Hence, we developed the car following model applying Human Factors to consider driver's safety and comfortness. We simulated to compare the suggested model with the existing model, GGM(General GM). As results of simulations, the GGM model followed the preceding vehicle when the velocity of following vehicle was equal to preceding vehicle without relation of relative range. The other side, when the relative range was less or over than safety range, the suggested model made the relative range equal to safety range. Accordingly, we could be sure that the model would decrease the driver's discomfort and intensify the safety on driving without unnecessary waste of road. We identified that the suggested model is more realistic than the conventional GGM model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.329-334
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• 2009

The various behaviors of vehicular traffic flow are generated through both car-following and lane-changing behaviors of vehicles. Especially lane-usage varies by lane-changing behaviors. In the area of microscopic vehicle simulation, a lane-changing model connected to a car-following model parallel is essential to generate both various traffic flows relationships and laneusages. In Korea, some studies on car-following models have been reported, but few studies for lane-changing models stay in the beginning stage. In this paper, a two-lane changing model for the simulation modeling of large freeway network is introduced. The lane-changing model is developed based on CA (Cellular Automata) model. The developed model is parallel combined with an existing CA car-following model and tested on a closed link system. The results of simulation show that the developed model generates the various behaviors of lane usage, which existing CA lane-changing models could not generate. The presented model is expected to be used for the simulation of more various freeway traffic flows.

• Proceedings of the Safety Management and Science Conference
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• 2001.05a
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• pp.325-329
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• 2001

This paper deals with a newly developed direction indicator system of a car which displays left turn and U- turn signal differently, so that the following driver can identify the Intent of the next car ahead easily. In general, when a car want to change its direction, the driver move the blinker lever below the steering wheel up or down. However, as the left turn and U-turn signal are the same, there always be the risk of rear-end collision by misinterpreting U-turn signal as left turn signal. In this paper, a new direction indicator system which differentiates left turn and U-turn signal is developed. The left turn signal is the same as before, but when a driver want to U-turn, an additional U-turn signal blinks at the rear of the car. By identifying the direction signals clearly, the developed system is expected to alleviate the risk of car accident.

• Journal of the Korea Safety Management & Science
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• v.3 no.2
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• pp.181-188
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• 2001

This paper deals with a newly developed direction indicator system of a car which displays left turn and U-turn signal differently, so that the following driver can identify the intent of the next car ahead easily. In general, when a car want to change its direction, the driver move the blinker lever below the steering wheel up or down. However, as the left turn and U-turn signal are the same, there always be the risk of rear-end collision by misinterpreting U-turn signal as left turn signal. In this paper, a new direction indicator system which differentiates left turn and U-turn signal is developed. The left turn signal is the same as before, but when a driver want to U-turn, an additional U-turn signal blinks at the rear of the car. By identifying the direction signals clearly, the developed system is expected to alleviate the risk of car accident.