Wyniki wyszukiwania - BazTech

1

Vision-based positioning of electric buses for assisted docking to charging stations

Nowak Tomasz, Nowicki Michał R., Skrzypczyński Piotr

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 4

583--599

EN

We present a novel approach to vision-based localization of electric city buses for assisted docking to a charging station. The method assumes that the charging station is a known object, and employs a monocular camera system for positioning upon carefully selected point features detected on the charging station. While the pose is estimated using a geometric method and taking advantage of the known structure of the feature points, the detection of keypoints themselves and the initial recognition of the charging station are accomplished using neural network models. We propose two novel neural network architectures for the estimation of keypoints. Extensive experiments presented in the paper made it possible to select the MRHKN architecture as the one that outperforms state-of-the-art keypoint detectors in the task considered, and offers the best performance with respect to the estimated translation and rotation of the bus with a low-cost hardware setup and minimal passive markers on the charging station.

2

Lane Detection by Dynamic Origin Technique for Advanced Driver Assistance System

Maya P., Tharini C.

International Journal of Electronics and Telecommunications

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2021

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Vol. 67, No. 4

589--594

EN

Lane detection is one of the key steps for developing driver assistance and vehicle automation features. A number of techniques are available for lane detection as part of computer vision tools to perform lane detection with different levels of accuracies. In this paper a unique method has been proposed for lane detection based on dynamic origin (DOT). This method provides better flexibility to adjust the outcome as per the specific needs of the intended application compared to other techniques. As the method offers better degree of control during the lane detection process, it can be adapted to detect lanes in varied situations like poor lighting or low quality road markings. Moreover, the Piecewise Linear Stretching Function (PLSF) has also been incorporated into the proposed method to improve the contrast of the input image source. Adding the PLSF method to the proposed lane detection technique, has significantly improved the accuracy of lane detection when compared to Hough transform method from 87.88% to 98.25% in day light situations and from 94.15% to 97% in low light situations.

3

Traffic Sign Classification based on Neural Network for Advance Driver Assistance System

Hannan M. A., Wali S. B., Pin T. J., Hussain A., Samad S. A.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 11

169--172

EN

Traffic sign is utmost important information or rule in transportation. In order to ensure the transportation safety the automotive industry has developed Advance Driver Assistance System (ADAS). Among the ADAS system, development of TSDR is the most challenging to the researchers and developers due to unsatisfying performance. This paper deals with, automatic traffic sign classification and reduces the effect of illumination and variable lighting over the classification scheme by using neural network according to the traffic sign shape. There are three main phase of the classification scheme such as; pre-processing using image normalization, feature extraction using color information of 16-point pixel values and multilayer feed forward neural network for classification. An accuracy rate of 84.4% has been achieved by the proposed system. Overall processing time of 0.134s shows the system is a fast system and real-time application.

PL

W artykule opisano metodę automatycznego rozpoznawania I klasyfikacji znaków drogowych z przenaczeniem do inteligentnych systemów wspomagania kierowcy ADAS. Do tego celu wykorzystano sieci neuronowe przeprowadzając normalizację obrazu, ekstrakcję cech i klasyfikację. Osiągnieto dokładność rozpoznawania rzędu 84% przy przeciętnym czasie rozpoznawania około 0.13 s.

4

Standardization of ADAS functionalities for fully electric vehicles

Matysiak A., Niezgoda M., Kamiński T., Kruszewski M.

Archives of Transport System Telematics

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2013

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Vol. 6, iss. 4

31--36

EN

EcoGem Project was conducted in scope of ICT Green Cars Initiative under the Seventh Framework Program, during 2010-2013 period. EcoGem Consortium’s aim was to provide ICT-based solutions increasing the mobility of Fully Electric Vehicles (FEV). A FEV-dedicated Advanced Driver Assistance System was developed in scope of the Project. It included suitable monitoring, analysis, reasoning and management capabilities, which increased the autonomy and energy efficiency of FEVs. One of the Project’s goals was to deliver a significant contribution into standardization activities concerning management of external information provided to ADAS systems. During the project development, a contact with various standardization organizations was established. We contributed to the standard development activities of OGC and TISA. The article presents the proposed solutions with regard to EcoGem functionalities. Described in the article standardization contribution includes new FEV oriented propositions of specifications for transmission of multi-modal traffic and travel information as well as the attributes defining charging stations.