• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• International Journal of Automotive Technology
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• v.6 no.6
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• pp.615-623
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• 2005

In this research, an effective technique was examined to improve the drift running performance. Concretely, the driver model by which the counter steer was done was assumed to the model by which the vehicle body slip angle (and the vehicle body slip angle velocity) was feed back. Next, the effectiveness of the system which added the assist steer angle corresponding to the steering wheel angle velocity to a front wheel steer angle was clarified as a drift running performance improvement technique of the vehicle. As a result, because the phase advances when the differentiation steer assistance is added, it has been understood to be able to cover the delay of the counter steer when the drift running. Therefore, it has been understood that the drift control does considerably easily. Moreover, it has been understood that the differentiation steer assistance acts effectively at the drift cornering by which the drift angle is maintained in cornering and the severe lane change with a drift at a situation. That is, it was understood to be able to settle to the drift angle of the aim quickly at the time of the drift cornering because the delay of the control steer angle of the counter steer was improved. Moreover, it was understood for the transient overshoot of the vehicle tracks to be able to decrease, and to return to the state of stability quickly at the severe lane change.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.329-336
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• 2006

This paper describes a lane change assistance system for the help of safe lane change, which detects vehicles approaching from the rear side by using a computer vision algorithm and notifies the possibility of safe lane change to a driver. In case a driver tries to lane change, the proposed system can detect vehicles and keep track of them. After detecting side lane lines, region of interest for vehicle detection is decided. For detection a vehicle, optical flow technique is applied. The experimental result of the proposed algorithm and system showed that the vehicle detection rate was 91% and the embedded system would have application to a lane change assistance system being commercialized in the near future.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.167-175
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• 2022

With the recent expansion of electric kickboards and bicycle sharing services, more and more people use them. In addition, the rapid growth of the delivery business due to the COVID-19 has significantly increased the use of two-wheeled vehicles and personal mobility. As the accident rate increases, the rule related to the two-wheeled vehicles is changed to 'mandatory helmets for kickboards and single-person transportation' and was revised to prevent boarding itself without driver's license. In this paper, we propose a wearable smart safety equipment, called SafetyHelmet, that can keep helmet-wearing duty and lower the accident rate with the communication between helmets and mobile devices. To make this function available, we propose a safe driving assistance function by notifying the driver when an object that interferes with driving such as persons or other vehicles are detected by applying the YOLO v5 object detection algorithm. Therefore it is intended to provide a safer driving assistance by reducing the failure rate to identify dangers while driving single-person transportation.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.79-85
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• 2017

In this paper, several optimizations are proposed to enhance the quality of lane detection algorithms in automotive applications. Considering the diagonal directions of lanes, the proposed limited Hough transform newly introduces image-splitting and angle-limiting schemes that relax the number of possible angles at the line voting process. In addition, unnecessary edges along the horizontal and vertical directions are pre-defined and removed during the edge detection procedures, increasing the detecting accuracy remarkably. Simulation results shows that the proposed lane recognition algorithm achieves an accuracy of more than 90% and a computing speed of 92 frame/sec, which are superior to the results from the previous algorithms.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.33-39
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• 2017

In this paper, we propose a method which implements warning to drivers through real-time analysis of risky and unexpected driver and vehicle behavior using only a smartphone without using data from digital tachograph and vehicle internal sensors. We performed the evaluation of our system that demonstrates the effectiveness and usefulness of our method for risky and unexpected driver and vehicle behavior using three information such as vehicle speed, azimuth and GPS data which are acquired from a smartphone sensors. We confirmed the results and developed the smartphone application for validate and conducted simulation using actual driving data. This novel functionality of the smartphone application enhances drivers' situational awareness, increasing safety and effectiveness of driving.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.637-639
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• 2021

As autonomous driving research is actively progressing, lane detection is an essential technology in ADAS (Advanced Driver Assistance System) to locate a vehicle and maintain a route. Lane detection is detected using an image processing algorithm such as Hough transform and RANSAC (Random Sample Consensus). This paper implements a linear shape detection algorithm using OpenCV on Raspberry Pi 3 B+. Thresholds were set through OpenCV Gaussian blur structure and Canny edge detection, and lane recognition was successful through linear detection algorithm.

• Current Industrial and Technological Trends in Aerospace
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• v.6 no.2
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• pp.133-142
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• 2008

We study the possibility of entering into helicopter market with respect to demand, industry structure and infrastructure. In conclusion, it is possible to enter into it based on domestic demand if we complement some technology, financial assistance system and helicopter operation related regulation. Strategy is made for helicopter industry to be a growth driver and then we suggest five projects to carry out it. Those are lasting creation of helicopter demand, possession of core part material and competitive technology, construction of airworthiness certification system and infrastructure for activation of helicopter operation, improvement of financial assistance system and finally strengthening policy modulation between civil, army, and government.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.3
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• pp.54-61
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• 2009

Lane keeping assistant system (LKAS) could save thousands of lives each year by maintaining lane position and is regarded as a promising active safety system. The LKAS is expected to reduce the driver workload and to assist the driver during driving. This paper proposes a model based predictive controller for the LKAS which requires cooperative driving between the driver and the assistance system. A Hardware-In-the-Loop-Simulator (HILS) is constructed for its evaluation and includes Carsim, Matlab Simulink and a lane detection algorithm. The single camera is mounted with the HILS to acquire the monitor images and to detect the lane markers. The simulation is conducted to validate the LKAS control performance in various road scenario.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.637-657
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• 2021

While the conventional vehicle's Head-Units played relatively simple roles (e.g., control of heating ventilation and air conditioning, the radio reception), they have been evolving into vehicle-driver interface with the advent of the concept of Connected Car on top of a rapid development of ICT technology. The Head-Unit is now successfully extended as an IVI (In Vehicle Infotainment) that can operate various functions on multimedia, navigation, information with regards to vehicle's parts (e.g. air pressure, oil gauge, etc.). In this paper, we propose a platform architecture for IVI devices required to achieve the goal as a connected car. Connected car platform (CoCaP) consists of vehicle selective gateway (VSG) for receiving and controlling data from major components of a vehicle, application framework including native and web APIs required to request VSG functionality from outside, and service framework for driver assistance. CoCaP is implemented using Tizen IVI and Android on hardware platforms manufactured for IVI such as Nexcom's VTC1010 and Freescale's i.MX6q/dl, respectively. For more practical verification, CoCaP platform was applied to an real-world finished vehicle. And it was confirmed the vehicle's main components could be controlled using various devices. In addition, by deriving several services for driver assistance and developing them based on CoCaP, this platform is expected to be available in various ways in connected car and ITS environments.