• Journal of the Ergonomics Society of Korea
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• v.29 no.1
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• pp.17-24
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• 2010

There has been recent interest in intelligent vehicle technologies, such as advanced driver assistance systems (ADASs) or in-vehicle information systems (IVISs) that offer a significant enhancement of safety and convenience to drivers and passengers. However, unsuitable design of HMI (Human Machine Interface) must increase driver distraction and workload, which in turn increase the chance of traffic accidents. Distraction in particular often occurs under a heavy driving workload due to multitasking with various electronic devices like a cell phone or a navigation system while driving. According to the 2005 road traffic accidents in Korea report published by the ROad Traffic Authority (ROTA), more than 60% of the traffic accidents are related to driver error caused by distraction. This paper suggests the structure of vehicle environment for real-time driving behavior monitoring system while driving which is can be used the driver workload management systems (DWMS). On-road experiment results showed the feasibility of the suggested vehicle environment for driving behavior monitoring system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1481-1484
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• 1997

The vehicle driving simulator expects vehicle motion with real-time simulation arise from driver's steering, accelerating, stopping and simulates motion of vehicl with visula, audio and washout algorithm. And it gives a vivid feeling to driver in reality. Vehicle driving simulator with vehicle integration control system is used for analysis of analysis of vehicle controllaility, steering capacity and safety in various pseudo environment alike. basides, it analyzeds vehicle safety factor dirver's reaction and promotes traffic safety without driver's own risks. The main proceduress of development of the vehicle driving simulator are classified by 3 parts. first the motion base system which can be generated by the motion queues, should be developed. Secondly, real-time vehicle software which can afford the vehicle dynamics, might be constructed. The third procedure is the integration of vehicle driing simulator which can be interconnected between visual systems with motion base. In this study, we are to study of the motion base for a vehicle driving simulator design and that of its real time control and using an extra gyro sensor and accelerometers to find a position and an orientatiion of the moving platform except for calculating forward kinematics. To drive the motion base, we use National Instruments corp's Labview software. Furthemore, we use analysis module for the vehicle motionand the washout algorithm module to consummate driving simulator, which can be driven by human in reality, so we are doing experimentally process about various vehicle motion conditon.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.3
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• pp.28-34
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• 2006

An ACC driving simulator is a virtual reality device which designed to test or evaluate vehicle control algorithm. It is designed and built based on the rapid control prototyping(RCP) concept. Therefore this simulator adopt RCP tools to solve the equation of a vehicle dynamics model and control algorithm in real time, rendering engine to provide real-time visual representation of vehicle behavior and CAN communication to reduce networking load. It can provide also many different driving test environment and driving scenarios.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.210-216
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• 2015

This paper proposes a method to determine vehicle driving state using the driving characteristics index. This index is a quantitative value to classify the driving state of a vehicle with its velocity and heading angle in that instant. It can classify driving state into straight driving, lane changing driving and curve driving in real time. In addition, the number of positional information is movably set up by designed region of interest. The proposed index is expressed on the stable driving states. Each driving state has characteristic tendency, and is compared with index distributional areas. The proposed method is verified by the actual driving experiment on the KATECH proving ground.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.6
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• pp.475-482
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• 2020

For electronic paper displays using electrophoresis, the response time and reflectivity of the image panel fabricated by filtering are analyzed. For the filtering process, a square wave and ramp wave are applied to white charged particles with a unique q/m value. We divide the sample panels into #1 to #4 according to the applied waveform in the filtering process. Step waves comprising two steps are used to drive the panel; therefore, we divide the driving conditions into D1~D4. The applied voltage at the first stage of the half cycle of the driving waveform moves the charged particles attached via the image force from the electrode, and the applied voltage at the second stage moves the floating charged particles by detaching. As mentioned, four types of driving conditions (D1 to D4) classified according to the half cycle of the driving waveform are applied to the samples #1 to #4), which are classified according to four types of filtering process. When driving condition D1 is applied to the four types of sample panels, the rise time of #1 is 1.59s, #2 is 1.706s, #3 is 1.853s, and #4 is 1.235s, resulting in #4 being relatively faster compared with other sample panels, and showing the same trend in other driving conditions. As a result, we confirm that applying the driving condition D1 causes abrupt movement of the white charged particles injected into the cell. When the same driving waveform (D1) is applied to each sample, reflectivities of 32.1% for #1, 31.4% for #2, 27.9% for #3, and 63.4% for #4 are measured. From the experiment, we confirm that the driving condition D1 (1s of 3.5 V, 9s of 3.0 V) and ramp wave #4 in filtering are desirable for good reflectivity and response time. Our research is expected to contribute to the improvement of the filtering process and optimization of the driving waveform.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.449-451
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• 2002

We reported the algorithm of driving scheme that enhances moving picture property by improving gray-to-gray response time. Here, we report result of simulation for estimation of driving voltage to reduce response time, and experimental result. We investigated optimization of algorithm so that minimum size of LUT can support to reducing the gray-to-gray response time within 1 frame period, and with single algorithm it is possible to apply the algorithm to various kinds of LC material. So in our system there is no external EEPROM.

• CDE review
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• v.21 no.2
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• pp.34-38
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• 2015

In case of the big city, choosing the adequate root of which we can reach the destination can affect the driver's condition and driving time. so it is quite important to find the optimal routes for arriving the destination as considering the factors, such as driving conditions or travel time and so on. In this paper, we develop route choice model with considering driving conditions and travel time, and it can search the optimal path which make drivers reduce their fatigues using genetic algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.39-47
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• 2020

This paper introduces a novel system-level framework that derives energy efficient electric vehicle (EV) driving speed profile to extend EV driving range without additional cost. This paper first implements an EV power train model considering forces acting on a driving vehicle and motor efficiency. Then, it derivate the minimum-energy driving speed profile for a given driving mission defined by the route. This framework first formulates an optimization problem and uses the dynamic programming algorithm with a weighting factor to derive a speed profile minimizing both of energy consumption and driving time. This paper introduces various weighting factor tracking methods to satisfy the driving time constraint. Simulation results show that runtime of the proposed scaling algorithm is 34% and 50% smaller than those of the binary search algorithm and greedy algorithm, respectively.

• Journal of Auto-vehicle Safety Association
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• v.12 no.1
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• pp.15-25
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• 2020

This paper presents a longitudinal control algorithm for ensuring takeover time of autonomous vehicle using V2V communication. In the autonomous driving of more than level 3, autonomous systems should control the vehicles by itself partially. However if the driver's intervention is required for functional safety, the driver should take over the control reasonably. Autonomous driving system has to be designed so that drivers can take over the control from autonomous vehicle reasonably for driving safety. In this study, control algorithm considering takeover time has been developed based on computation method of takeover time. Takeover time is analysed by conditions of longitudinal velocity of preceding vehicle in time-velocity plane. In addition, desired clearance is derived based on takeover time. The performance evaluation of the proposed algorithm in this study was conducted using 3D vehicle model with actual driving data in Matlab/Simulink environment. The results of the performance evaluation show that the longitudinal control algorithm can control while securing takeover time reasonably.