• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.123-133
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• 2024

Existing autonomous driving technology has been developed based on sensors attached to the vehicles to detect the environment and formulate driving plans. On the other hand, it has limitations, such as performance degradation in specific situations like adverse weather conditions, backlighting, and obstruction-induced occlusion. To address these issues, cooperative autonomous driving technology, which extends the perception range of autonomous vehicles through the support of road infrastructure, has attracted attention. Nevertheless, the real-time analysis of the 3D centroids of objects, as required by international standards, is challenging using single-lens cameras. This paper proposes an approach to detect objects and estimate the centroid of vehicles using the fixed field of view of road infrastructure and pre-measured geometric information in real-time. The proposed method has been confirmed to effectively estimate the center point of objects using GPS positioning equipment, and it is expected to contribute to the proliferation and adoption of cooperative autonomous driving infrastructure technology, applicable to both vehicles and road infrastructure.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.758-761
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• 2003

This paper introduces a method that can detect obstacles and corridor environments from the images captured by a CCD camera in an automobile or mobile robot is proposed. Processing the input dynamic images in real time requires high performance hardware as well as efficient software. In order to relieve these requirements for detecting the useful information from the images in real time, a "Moving Window" scheme is proposed. Therefore, detecting the useful information, it becomes possible to search the obstacles within the driving corridor of an automobile or mobile robot. The feasibility of the proposed algorithm is demonstrated through the simulated experiments of the corridor driving.

• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1399-1410
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• 2003

In this paper, we develop a joystick manual driving algorithm for an electric vehicle called Cycab. Cycab is developed as a public transportation vehicle, which can be driven either by a manual joystick or an automated driving mode. The vehicle uses six motors for driving four wheels, and front/rear steerings. Cycab utilizes one industrial PC with a real time Linux kernel and four Motorola MPC555 micro controllers, and a CAN network for the communication among the five processors. The developed algorithm consists of two automatic vehicle speed control algorithms for normal and emergency situations that override the driver's joystick command and an open loop torque distribution algorithm for the traction motors. In this study, the algorithm is developed using SynDEx, which is a system level CAD software dedicated to rapid prototyping and optimizing the implementation of real-time embedded applications on distributed architectures. The experimental results verify the usefulness of the two automatic vehicle control algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.115.2-115
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• 2002

A real-time vehicle simulation system is a key element of a driving simulator because accurate prediction of vehicle motion with respect to driver input is required to generate realistic visual, motion, sound and proprioceptive cues. In order to predict vehicle motion caused by various driving actions of the driver on board the simulator, the vehicle model should consist of complete subsystems. On this paper, a tracked vehicle dynamic model with high efficiency and effectiveness is introduced that has been implemented on a training driving simulator. The multi-body vehicle model is based on recursive formulation and has been automatically generated from a symbolic computation package develop...

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.489-490
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• 2018

As the number of electric vehicles (EV) increases, there is an increasing interest in the post-vehicle application of the EV batteries. For the second use application of EV batteries, the state of health (SOH) at the end of automotive service has to be evaluated differently from the automotive perspective. It will be helpful to consider the driving conditions of EVs in understanding the performance deterioration trend of the battery. In this paper, we acquired the battery status information in real time during driving and compared the characteristics by the driving routes. The SOH from the BMS can be rescaled to percentage ratio to give a more general idea about the performance degradation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.645-650
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• 2010

In this paper we present the Bimodal-tram simulator using the PXI embedded real-time controllers. The Bimodal-tram is developed in KRRI (Korea Railroad Research Institute). The vehicle can be automatically operated by navigation control system (NCS). For the automatic driving, the vehicle lanes will be marked with permanent magnets that are placed in the ground. The vehicle is controlled by NCS. NCS governs the manual mode and automatic mode driving. The simulator is designed by an identical conception with the real control condition. The dynamic motion of vehicle is simulated by the nonlinear dynamic model. The control computer calculates the control values. The signal interface is linked by CAN communication. The simulation is processed by real-time base. The test driver can see the graphic motion of vehicle and can operate the steering wheel, gas and brake pedal to control direction and velocity of vehicle during the simulation. At present, the simulator is only operated by manual mode. The automatic mode will be linked after the control algorithm is finished. We will use the simulator to develop the control algorithm in the automatic mode. This paper shows the simulator designed for Bimodal-tram using real-time based controller. The results of the test using the simulator are presented and discussed.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.9-14
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• 2002

The durability prediction of emission control components, especially 02 sensor and catalytic converter, is getting more important as emission regulation is getting stricter and vehicle durability mileage requirement is also extended from 80,000 ㎞ to 160,000 km in Korean market. And the duration of vehicle mileage accumulation to get vehicle exhaust emission deterioration factor for certification is required to be shorter in order to reduce the vehicle development time. Since most of the vehicle emission development tests are done on chassis dynamometer and aging bench by using vehicle aging modes, real road condition and in-use driving patterns must be reflected into them to predict the vehicle emission level and to meet emission regulation especially at high mileage. In order to get the frequent driving pattern of vehicle and the aging characteristic of emission components, a vehicle was tested by changing drivers and driving roads around Seoul. Real road driving patterns were analyzed and compared with those of the certification modes which are well known in automotive industry.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.908-911
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• 2004

In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.150-157
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• 2016

In this paper, a control system for E-bike based on IOT was developed, which collects and monitors information of states of E-bike and surrounding environments from several sensors and control devices in E-bike, and informs the possible dangers to rider when riding the E-bike. Developed electronic control system can manage battery efficiently, obtain battery's remaining power in real-time and provide possible riding distance to rider. It makes possible for rider to schedule near optimal riding route in terms of battery usage and respond quickly to battery discharge. Results of applying developed system to E-bike show that according to driving-mode, possible driving distance can be calculated efficiently and using user application App, real-time driver position marking and driving route searching functions lead to energy efficient E-bike driving. Later we will endeavor to integrate BMS, ECU, smart-phone and PC(server) to provide stable driving system based on various driving information of E-bike.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.723-728
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• 2022

This study is a method for evaluating and quantifying driver's dangerous driving behavior. The quantification method calculates various driving information in real time after starting the vehicle operation such as the time that the vehicle has been continuously driven without a break, overspeed, rapid acceleration, and overspeed driving time. These quantified risk of driving behavior values can be individually provided as a safe driving index, or can be used to objectify the evaluation of a group of drivers on roads, or vehicle groups such as cargo/bus/passenger vehicles.