• Journal of Korea Game Society
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• v.15 no.4
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• pp.169-178
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• 2015

Providing various levels of difficulty of game play is one of important considerations in game development. In this paper, we propose a method for obtaining the challenges that will be placed on the track of an one-player car driving game. Herein challenges denote obstacles on the track, and the level of difficulty is represented by an estimated time needed for driving one lap of the track. In the proposed method, the problem for finding challenge placement is modeled as an IP(Integer Programming) one, and then LP relaxation and Simultaneous Annealing are employed to find a solution. To the experiment with the proposed method, we can obtain challenge placements to approximately meet given target driving times. Also, after practically driving on the track where those obtained challenges are being placed, it is seen that the average driving times approximate the target driving times of those challenge placements. Our method can allow game play with various levels of difficulty so that the users' interest and the level of immerse are expected to be raised.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.95-104
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• 2015

Many games for English vocabulary learning, such as hangman, cross puzzle, matching, etc, have been developed which are of board-type or computer game-type. Most of these computer games are adapting strategy-style game plays so that there is a limit on giving the fun, a nature of games, to learners who do not like games of this style. In this paper, a system for memorizing new English words is proposed which is based on a single-player car racing game targeting youths and adults. In the game, the core of our system, a learner drives a car and obtains game points by colliding with English word texts like game items appearing on the track. The learner keeps on viewing English words being exposed on the track while driving, resulting in memorizing those words according to a learning principle stating viewing is memorization. To our experiment, the effect of memorizing English words by our car racing game is good, and the degree of satisfaction with our system as a English vocabulary learning tool is reasonably high. Also, previous word games are suitable for the memory enforcement of English words but our game can be used for the memorization of new words.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1009-1011
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• 2021

게임에서 플레이어에게 시각적으로 제공되는 환경을 View라고 하는데, View의 전환만으로도 전혀 다른 게임의 경험이 가능하다. 본 논문에서는 car racing game에서 View의 전환에 따른 게임의 경험 차이를 비교하였다. 우리는 ARcore 라이브러리를 사용하여 AR car racing game을 구현하였고 virtual joystick을 사용한 Interaction 방법을 구현하였다. Top down view와 first person view의 차이점이 플레이어의 실감에 어떠한 영향을 미치는지 연구하기 위해 두 view을 구현하여 pilot study를 수행하였다.

• Journal of Korea Game Society
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• v.15 no.5
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• pp.131-142
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• 2015

A self-driving car is an autonomous vehicle capable of fulfilling the main transportation capabilities of a traditional car. It must be capable of sensing its environment and navigating without human input. In this paper, we design the agent that can simulate these self-driving cars and develop a prototype for it. To do this, we analyze the requirements for the self-driving car, and then the agent is designed to be suitable for traditional multi-agent system. The key point of the design is that agents move along the steering forces only. The prototype of the designed agent was implemented by using Unity 3D. From simulation results using the prototype, movements of the agents were very realistic. However, in the case of increasing the number of the agent the performance was seriously degraded, and so the alternatives of the problem were suggested.

• Journal of the Korean Society of Safety
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• v.22 no.1 s.79
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• pp.61-66
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• 2007

Car crashes are the leading cause of death for persons of every age. Specially, human-related factor has been known to be the primary causal factor of such crashes than vehicle-and environmental-related factors. There are various studies to analyze driver's behavior and characteristics in driving for reducing the car crashes in many areas of car engineering, psychology, human factor, etc. However, there are almost no studies which analyze mainly the human errors in driving and estimate their probabilities in terms of human reliability analysis. This study estimates the probability of human error in driving, i.e. driver error probability. First, fifty driver errors are investigated through DBQ (Driver Behavior Questionnaire) revision and the error likelihoods in driving are collected which are judged by skillful drivers using revised DBQ. Next, these likelihoods are converted into driver error probabilities using the results that verbal probabilistic expressions are changed into quantitative probabilities. Using these probabilities we can improve the warning effects on drivers by indicating their driving error likelihoods quantitatively. We can also expect the reduction effects of car accident through controlling especially dangerous error groups which have higher probabilities. Like these, the results of this study can be used as the primary materials of safety education on drivers.

• Journal of Game and Entertainment
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• v.2 no.1
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• pp.35-44
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• 2006

Objective of this study is to develop a 3D car navigation system that shows the driving direction to a destination through real-time 3-D panoramic views of the route. For the purpose, a new searching process was established to find the optimal driving direction based on the driver's current location and the real-time traffic situation and the TIP (tour into the picture) method was extended to implement a wide virtual environment. A virtual environment was built up by applying the extended TIP method to the panoramic images taken at a constant distance from a real road, and then, displayed 3-D navigation as clear as the real images. The car navigation system developed in this study provides the optimal driving direction and real-time traffic situation using 2-D navigation module and 3D navigation module.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1089-1094
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• 2016

A simulation game represented the real world and situation as a video games, In the first time, this simulation game primarily applied to military practice areas and then it's applied areas were extended to strategy, flighting, racing, life areas. In this paper, we developed the driving simulator that operate the 3-axis sensor attached driver's handle which could forward/backward moving, control of direction, and transmit of gear in the virtual space. For more the realities of the situation and accuracy of the location and speed, we adopted the 3-axis sensing informations, Unreal engine4' and Blueprint.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.146-151
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• 2001

The Grand-Touring is a game motion simulator that simulates the race-car driving motion with three hydraulic cylinders which connect the platform and base in parallel. Its motion control system consists of the PC-based main controller and micro-controller based sub-controller. The former one process the dynamic image of race-car in response to the driver's action and computes the reference command for each cylinder and the latter one is designed for the tracking control of hydraulic cylinder and interfacing the auxiliary signals between various sensors/actuator and main controller. In this research, we developed the sub-controller that implements the required functions of Grand-Touring and prove the overall performance with experiments.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.409-420
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• 2012

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting down the number of policy decisions by agents. Macro-Actions were originally defined as combinations of the same primitive actions. Based on studies that showed the generation of Macro-Actions by learning, Macro-Actions are now thought to consist of diverse kinds of primitive actions. However an enormous amount of learning time and state space are required to generate Macro-Actions. To resolve these issues, we can apply insights from studies on the learning of tasks through Programming by Demonstration (PbD) to generate Macro-Actions that reduce the learning time and state space. In this paper, we propose a method to define and execute Macro-Actions. Macro-Actions are learned from a human subject via PbD and a policy is learned by reinforcement learning. In an experiment, the proposed method was applied to a car simulation to verify the scalability of the proposed method. Data was collected from the driving control of a human subject, and then the Macro-Actions that are required for running a car were generated. Furthermore, the policy that is necessary for driving on a track was learned. The acquisition of Macro-Actions by PbD reduced the driving time by about 16% compared to the case in which Macro-Actions were directly defined by a human subject. In addition, the learning time was also reduced by a faster convergence of the optimum policies.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1219-1228
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• 2020

In this study, the factors affecting the acceptance intention for level 4-5 of autonomous vehicles were investigated by applying TAM(Technology Acceptance Model). To this end, 332 ordinary persons interested in autonomous vehicle and experienced in driving car were analyzed by using SEM(Structural Equation Modeling). The results showed that self-efficacy and personal innovation had a positive effect on perceived usefulness. On the other hand personal innovation has been shown to have a negative effect on perceived usefulness. Perceived ease of use has a positive effect on perceived usefulness, perceived ease of use and perceived usefulness has a positive effect on acceptance intention. Safety and Privacy has been shown to have a positive effect on trust, trust has a positive effect on acceptance intention. Lastly, autonomous vehicles have a higher impact on their 20s and 30s. The result of this study is expected to be a very useful basic research for the development of target autonomous vehicles, the selection of targets, the direction of corporate marketing strategies, and the preparation of government policies.