• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.10
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• pp.1109-1116
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• 2014

The wheelchair mechanism for a car that is proposed in this study primarily consists of a transfer mechanism and storage mechanism. The wheelchair transfer mechanism consists of a manipulator installed in the roof of a car, and performs the function of transferring the wheelchair from the driver's seat to the trunk. The wheelchair storage mechanism consists of a lifting hoist installed in the trunk of car, and performs the function of storing the transferred wheelchair in the trunk and safely fastening it in place. This study analyzed and reviewed various manipulators, including a vertical type, Scara type, and telescopic type, with the goal of selecting the best type of manipulator for the wheelchair transfer mechanism. The telescopic type was selected and applied because of its good load support and storage capabilities. In addition, with regard to the wheelchair storage mechanism, a slide hoist type that used a slide rail and lift wire and a rotating link hoist type that used a rotating mechanism consisting of a worm gear and link were analyzed and reviewed. The slide hoist type was selected and applied because it had an advantage in relation to trunk space utilization. This study proposed a wheelchair transfer mechanism for a car to support a conventional wheelchair user's movements, and in order to conform to the structure of a domestic welfare car for the disabled.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.110-123
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• 2019

Traffic data is the most basic element necessary for transportation planning and traffic system operation. Recently, a method of estimating traffic flow characteristics using distance to a leading vehicle measured by an ADAS camera has been attempted. This study investigated the feasibility of the ADAS vehicle reflecting the distance error of image-based vehicle identification technology as a means to estimate the traffic flow through the normalized root mean square error (NRMSE) based on the number of lanes, traffic demand, penetration rate of probe vehicle, and time-space estimation area by employing the microscopic simulation model, VISSIM. As a result, the estimate of low density traffic flow (i.e., LOS A, LOS B) is unreliable due to the limitation of the maximum identification distance of ADAS camera. Although the reliability of the estimates can be improved if multiple lanes, high traffic demands, and high penetration rates are implemented, artificially raising the penetration rates is unrealistic. Their reliability can be improved by extending the time dimension of the estimation area as well, but the most influential one is the driving behavior of the ADAS vehicle. In conclusion, although it is not possible to accurately estimate the traffic flow with the ADAS camera, its applicability will be expanded by improving its performance and functions.