• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1964-1966
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• 2003

Lighting in the tunnel makes for safe driving of cars taking into account of change in perception arisen in the sight of drivers of cars entering into and passing tunnel, drivers' mental reaction and unique circumstances of a tunnel. There are many variables to satisfy safe driving in the tunnel. Since tunnels are mostly located in the mountainous area and illumination at the approach part differs largely depending on the surrounding circumstances, a standard different from that of common lighting is applied. An accidentin the tunnel, which involves same danger as in underground, may lead to a large mishap. Therefore, control by sensors inside and outside of tunnel ensures prevention of an accident through control of illumination at the approach part and inside of tunnel. It is very important in the aspect of saving energy since such control is available to reduce depreciation factor resulted from early excessive intensity of illumination.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.589-594
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• 2015

In reflection of road expansion and increasing use rates, interest has blossomed in predicting driving environment. In addition, a gigantic scale of big data is applied to almost every area around the world. Recently, technology development is being promoted in the area of road traffic particularly for traffic information service and analysis system in utilization of big data. This study examines actual cases of road management systems and road information analysis technologies, home and abroad. Based on the result, the limitations of existing technologies and road management systems are analyzed. In this study, a development direction and expected effort of the prediction of road information are presented. This study also examines regression analysis about relationship between guide name and traffic volume. According to the development of driving environment prediction platform, it will be possible to serve more reliable road information and also it will make safe and smart road infrastructures.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.1
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• pp.112-121
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• 2016

AFLS (Adaptive front lighting system) is being applied to improve safety in driving automotive at night. Safe embedded system design for controlling head-lamps is required to improve noise robust ECU hardware and software simultaneously by considering safety requirement of hardware-dependent software under severe environmental noise. In this paper, we propose an adaptive headlight controller with a newly-designed symmetric angle sensor compensator, especially based on the proposed steering-swivel angle lookup table to determine whether the current controlling target is safe. The proposed system includes an additional backup hardware to compare the system status and provides safe swivel-angle management using a controlling algorithm based on the pre-defined lookup table (LUT), which is a symmetric mapping relationship between the requested steering angle and expected swivel angle target. The implemented system model shows that the proposed architecture effectively detects abnormal situations and restores safe status of controlling the light-angle in AFLS operations under severe noisy environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.8
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• pp.1210-1215
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• 2012

Traction motors for driving and power conversion devices using semiconductor switch are equipped with a transportation systems such as an electrical railway system. Power conversion devices have the possibility of malfunction by external electromagnetic waves. As a result, these could affect the safe operation of the railway. Moreover, the electromagnetic waves above safe limits will be harmful to the passengers inside the railway vehicles or anyone working around the rail-track. For this reason, the importance and need about the reliability check and complement of electromagnetic waves generated from the IPT(Inductive Power Transfer) system have been suggested for the safe application of the IPT system to the railway system. In this study, prediction for the electromagnetic wave properties was conducted through FEM(Finite Element Method) analysis of 5kW-class IPT system design model. Next, the 5kW IPT system prototype was made and then the small-scaled railway vehicle was made to mount the IPT system and the energy management system. Finally, the electromagnetic waves generated from the real small-scaled IPT system were measured and analyzed, and then the reliability check of predictions by FEM analysis were carried out.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.225-231
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• 2023

With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.99-108
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• 2007

The bi-modal tram guided by the magnetic guidance system has two car-bodies and three axles. Each axle of the vehicle has an independent suspension to lower the floor of the car and improve ride quality. The turning radius of the vehicle may increase as a consequence of the long wheel base. Therefore, the vehicle is equipped with the All-Wheel-Steering(AWS) system for safe driving on a curved road. Front and rear axles should be steered in opposite directions, which means a negative mode, to minimize the turning radius. On the other hand, they also should be steered in the same direction, which means a positive mode, for the stopping mode. Moreover, only the front axle is steered for stability of the vehicle upon high-speed driving. In summary, steering angles and directions of the each axle should be changed according to the driving environment and steering mode. This paper proposes an appropriate AWS control algorithm for stable driving of the bi-modal tram. Furthermore, a multi-body model of the vehicle is simulated to verify the suitability of the algorithm. This model can also analyze the different dynamic characteristics between 2WS and AWS.

• Journal of the Korean Society for Railway
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• v.10 no.4
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• pp.444-450
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• 2007

The bi-modal tram guided by the magnetic guidance system has two car-bodies and three axles. Each axle of the vehicle has an independent suspension to lower the floor of the car and improve ride quality. The turning radius of the vehicle may increase as a consequence of the long wheel base. Therefore, the vehicle is equipped with the All-Wheel-Steering(AWS) system for safe driving on a curved road. Front and rear axles should be steered in opposite directions, which means a negative mode, to minimize the turning radius. On the other hand, they also should be steered in the same direction, which means a positive mode, for the stopping mode. Moreover, only the front axle is steered for stability of the vehicle upon high-speed driving. In summary, steering angles and directions of the each axle should be changed according to the driving environment and steering mode. This paper proposes an appropriate AWS control algorithm for stable driving of the bi-modal tram. Furthermore, a multi-body model of the vehicle is simulated to verify the suitability of the algorithm. This model can also analyze the different dynamic characteristics between 2WS and AWS.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.87-97
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• 2018

Bus, a major transportation mode, doesn't have a systematical evaluation system for dangerous driving behavior yet. This paper analyzes the characteristics and pattern of bus driving behavior using Digital Tacho Graph(DTG) data on express bus. 8 types of dangerous driving behavior were considered according to timeslot, the day of week and weather condition. As results, rapid acceleration, rapid left right turn and rapid deceleratio accounted for more than 97% and relatively high percentages were shown in dawn, on Friday and on the clear day, respectively. From the statistical analysis, correlation between the dangerous driving types and difference according the timeslot were found, and 3 groups considering the level of the dangerous driving were suggested. This study contributes to setting an efficient and reliable eduction system for using driving simulators.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• 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.