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Design and implementation of a real-time LDWS with parameter space filtering for embedded platforms

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Abstract

In this work, a lane departure warning system (LDWS) algorithm for embedded platforms which has restricted resources is proposed. An LDWS consists of two main sub-functions which are lane detection and lane tracking. Although sophisticated methods have been developed for both sub-functions, they usually require high processing power and even GPU processing power. Therefore, they are not applicable for hardware with limited resources. In this work, Hough Transform (HT)-based lane detection algorithm is applied. The vulnerability of HT-based methods against misleading images is eliminated by the proposed filtering algorithm. Main differences of the proposed filtering algorithm from the classical methods in the literature are that it is applied in the parameter space rather than the image, and it is specialized only for determining lanes. In the lane tracking stage, the K-means clustering algorithm has been modified to operate online. In this way, the parameters of the detected lane can be followed adaptively during lane changing or overtaking. Real-time test results on embedded hardware demonstrated that the processing time does not exceed 41.67 ms with an accuracy of over 91.5%.

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Acknowledgements

This project was financially supported by the BMC Automotive Industry Company and Ministry of Science, Industry and Technology under the title of “Lane Detection and Tracking System Design for Commercial Vehicles” and grant order No. 01229.STZ.2012–1.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Ege University, Izmir, Turkey

    Erman Selim & Musa Alci

  2. Department of Computer Engineering, Ege University, Izmir, Turkey

    Aybars Uğur

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  1. Erman Selim
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  2. Musa Alci
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  3. Aybars Uğur
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Correspondence to Erman Selim.

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Selim, E., Alci, M. & Uğur, A. Design and implementation of a real-time LDWS with parameter space filtering for embedded platforms. J Real-Time Image Proc 19, 663–673 (2022). https://doi.org/10.1007/s11554-022-01213-3

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  • DOI: https://doi.org/10.1007/s11554-022-01213-3

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