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A Framework for Lane Prediction on Unstructured Roads

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 968))

Abstract

In this paper, we propose to address the issue of lane prediction on unstructured roads, i.e. roads where lane markings are not available. Lane prediction has received considerable attention in the last decade towards the development of ADAS (Advanced driver assistance systems) system. We consider lane prediction as a vision based problem and propose a learning based framework for lane prediction. We pre-process the data using adaptive thresholding to estimate ROI (Region of Interest) in an image. We develop a variant of Bayesian learning using the evidence based Cascaded Dempster Scafer Combination Rule to categorize the road and non-road sectors of the region of interest. We also propose to post-process the data with improved morphological operations to remove the outliers. Lane prediction finds applications in pothole detection, autonomous driving etc. We demonstrate the results on real datasets captured in different scenarios. We compare the results with different state-of-art techniques of lane prediction to validate the efficiency of proposed algorithm.

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Author information

Authors and Affiliations

  1. KLE Technological University, Hubballi, India

    Rohan Raju Dhanakshirur, Preeti Pillai, Ramesh Ashok Tabib, Ujwala Patil & Uma Mudenagudi

Authors
  1. Rohan Raju Dhanakshirur
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  2. Preeti Pillai
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  3. Ramesh Ashok Tabib
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  4. Ujwala Patil
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  5. Uma Mudenagudi
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Corresponding author

Correspondence to Preeti Pillai .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management, Kerala, India

    Sabu M. Thampi

  2. Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA

    Oge Marques

  3. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Sri Krishnan

  4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. University of Naples Federico II, Naples, Italy

    Domenico Ciuonzo

  6. Electrical Engineering Department, Indian Institute of Technology Patna, Patna, India

    Maheshkumar H. Kolekar

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Dhanakshirur, R.R., Pillai, P., Tabib, R.A., Patil, U., Mudenagudi, U. (2019). A Framework for Lane Prediction on Unstructured Roads. In: Thampi, S., Marques, O., Krishnan, S., Li, KC., Ciuonzo, D., Kolekar, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2018. Communications in Computer and Information Science, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-13-5758-9_16

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  • DOI: https://doi.org/10.1007/978-981-13-5758-9_16

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  • Print ISBN: 978-981-13-5757-2

  • Online ISBN: 978-981-13-5758-9

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