• Journal of Auto-vehicle Safety Association
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• v.3 no.2
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• pp.28-33
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• 2011

This paper presents driving path estimation algorithm for adaptive cruise control system and advanced emergency braking system using multi-sensor fusion. Through data collection, yaw rate filtering based road curvature and vision sensor road curvature characteristics are analyzed. Yaw rate filtering based road curvature and vision sensor road curvature are fused into the one curvature by weighting factor which are considering characteristics of each curvature data. The proposed driving path estimation algorithm has been investigated via simulation performed on a vehicle package Carsim and Matlab/Simulink. It has been shown via simulation that the proposed driving path estimation algorithm improves primary target detection rate.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.2
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• pp.166-173
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• 2002

The wheel bearing hub unit is developed type of wheel bearing unified with the hub parts. It has advantage of reducing the weight and the number of components. And, it also improves uniformity of manufacturing quality, In order to design the wheel bearing hub units, many techniques are used such as load analysis, structure analysis and bearing characteristics analysis and so forth. These techniques need highly accurate load conditions founded on service conditions. In this study, to design the wheel bearing hub units used widespread in passenger cars, the service load was measured through driving tests on the public roads and in the special events. The public roads are classified into highway, intercity road, rural road, urban road, and unpaved road so as to know what the characteristics of the road loads are. The results of the tests showed that the wheel force was relative to the lateral acceleration, and also could be calculated from the lateral acceleration. The lateral acceleration was measured from 0.0G to 0.6G in general driving on the public roads, with different distributions in each road type. In special events, the maximum lateral acceleration was measured from 0.8G to 1.3G.

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.149-156
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• 2011

Considerable evaluation is needed to design a new longitudinal tunnel in advance because it damaged drivers' driving safety and heightened the possibility of traffic accidents with its physical characteristics. Specifically, considering traffic psychological and ergonomic factors was very important to prevent the difficulty of maintaining safe speed, the increase of the drowsy driving, the fatality of traffic accidents, and subjective feelings such as anxiety while driving a car through the tunnel, from design to construction. This study dealt with driving safety evaluation of an original road alignment design for the longitudinal tunnel (length: above 10km) with a driving simulator, and helped us to improve an original road alignment design and make an alternative road alignment design with presenting risky districts. The results of experiment showed that inflection points were revealed more risky districts, because they impaired driving safety and elevated driver workload while driving a car through around the inflection points of two-way route. Finally, the limitations and implications of this study were discussed.

• Journal of ILASS-Korea
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• v.22 no.4
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• pp.218-224
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• 2017

In order to improve the quality of air in urban areas, the emission regulations are being strengthened by the government. The on-road test of light-duty vehicles was started with PEMS because certification test mode does not sufficiently reflect on-road conditions. Therefore, The PEMS-based test was implemented from Sep. 2017 in Europe and Korea. However, this is lack of data on various on-road patterns in Korea. The purpose of the present study has analyzed the effect of speed per acceleration and acceleration on NOx emission on-road driving. The test route consisted of urban, rural, and motorway in Seoul. This study has been conducted by Euro-6 vehicles using on SCR system with PEMS. The on-road emission characteristics were evaluated by moving averaging windows (MAW) method. In results, RDE-NOx by severe driving pattern has been 1.4 times higher than soft driving pattern NIER Route 1.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.202-217
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• 2021

Following the fatal accident of pedestrians caused by Autonomous Vehicle by Uber, the world's largest ride-hailing company, two people were killed in a self-driving car accident by Tesla in April. There is a need to ensure the safety of road users. Accordingly, in order to secure the safety of Autonomous Vehicle driving, it is necessary to evaluate Autonomous Vehicle driving technologies in various situations based on the road and traffic environment in which the Autonomous vehicle will actually drive. Therefore, this study used UC-win/Road ver.14.0 based on general driver's license test questions to present a virtual reality-based Autonomous Vehicles driving ability evaluation tool among various driving ability test method. Based on this, it was intended to test driving ability for unexpected situations in complex and diverse driving environments, and to confirm its practical applicability as an optimal tool for Autonomous vehicle ability test and evaluation.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.53-59
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• 2022

After the 3rd level autonomous driving vehicle, the 4th and 5th level of autonomous driving technology is trying to maintain the optimal condition of the passengers as well as the perfect driving of the vehicle. However current autonomous driving technology is too dependent on visual information such as LiDAR and front camera, so it is difficult to fully autonomously drive on roads other than designated roads. Therefore this paper proposes a Braking Strength Calculation System (BSCS), in which a vehicle classifies road conditions using data other than visual information and calculates optimal braking strength according to road conditions and driving conditions. The BSCS consists of RCDM (Road Condition Definition Module), which classifies road conditions based on KNN algorithm, and BSCM (Braking Strength Calculation Module), which calculates optimal braking strength while driving based on current driving conditions and road conditions. As a result of the experiment in this paper, it was possible to find the most suitable number of Ks for the KNN algorithm, and it was proved that the RCDM proposed in this paper is more accurate than the unsupervised K-means algorithm. By using not only visual information but also vibration data applied to the suspension, the BSCS of the paper can make the braking of autonomous vehicles smoother in various environments where visual information is limited.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.341-343
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• 1995

In this paper, an algorithm is presented to recogniz the driving direction of a vehicle and obstacles in front of it based on highway road image. The algorithm employs a neural network with 27 sub sets obtained from the road image as its input. The outputs include the direction of the vehicle movement and presence or absence of obstacles. The road image, obtained by a video camera, was digitized and processed by a personal computer equipped with an image processing board.

• International Journal of Highway Engineering
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• v.17 no.5
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• pp.57-66
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• 2015

PURPOSES: Nighttime driving is very different from daytime driving because drivers must obtain nighttime sight-distances based on road lights and headlights. Unfortunately, nighttime driving conditions in Korea are far from ideal due to poor lighting and an insufficient number of road lights and inadequate operation and maintenance of delineators. This study is conducted to develop new standards for nighttime road visibility based on experiments of driver perception for nighttime visibility conditions. METHODS : In the study, perception level and satisfaction of nighttime visibility were investigated. A total of 60 drivers participated, including 34 older drivers and 31 young drivers. To evaluate driver perceptions of nighttime road visibility, fuzzy set theory was used because the conventional analysis methods for driver perception are limited in effectiveness for considering the characteristics of perception which are subjective and vague, and are generally expressed in terms of linguistic terminologies rather than numerical parameters. RESULTS : This study found that levels of nighttime visibility, as perceived by drivers, are remarkably similar to their satisfactions in different nighttime driving conditions with a log-function relationship. Older drivers evaluated unambiguously degree of nighttime visibility but evaluations by young drivers regarding it were unclear. CONCLUSIONS : A minimum value of brightness on roads was established as YUX 30, based on final analyzed results. In other words, road lights should be installed and operated to obtain more than YUX 30 brightness for the safety and comfort of nighttime driving.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.430-443
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• 2013

The recognition of current position and road surrounding of car is very important to driver for safe driving. This paper presents the recognition technique of the road traveling environment using position information hiding and viewpoint transform that monitors the information of road furniture and signature and notifies them to driver. The proposed scheme generates the road images into which the position information are hided, from car camera and GPS module and provides the road information to driver through the viewpoint transformation and the road signature detection. The driving tests with camera and GPS module verified that the position information hiding takes about 66.5ms per frame, the detection rate of road signature is about 95.83%, and the road signature detection takes about 227.45ms per frame. Therefore, we know that the proposed scheme can recognize the road traveling environment on the road video with 15 frame rate.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.50-59
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• 2014

This paper proposes a method of localization of vehicle especially the horizontal position for the purpose of recognizing the driving lane. Through tracking road signs, the relative position between the vehicle and the sign is calculated and the absolute position is obtained using the known information from the regulation for installation. The proposed method uses Kalman filter for road sign tracking and analyzes the motion using the pinhole camera model. In order to classify the road sign, ORB(Oriented fast and Rotated BRIEF) features from the input image and DB are matched. From the absolute position of the vehicle, the driving lane is recognized. The Experiments are performed on videos from the highway driving and the results shows that the proposed method is able to compensate the common GPS localization errors.