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Sensors 2013, 13(3), 3270-3298; doi:10.3390/s130303270

Robust Lane Sensing and Departure Warning under Shadows and Occlusions

Department of Electrical Engineering, Pontificia Universidad Cat´olica de Chile,Vicu˜na Mackenna 4860, Casilla 306-22, Santiago, Chile
Author to whom correspondence should be addressed.
Received: 19 February 2013 / Revised: 2 March 2013 / Accepted: 4 March 2013 / Published: 11 March 2013
(This article belongs to the Special Issue New Trends towards Automatic Vehicle Control and Perception Systems)
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A prerequisite for any system that enhances drivers’ awareness of road conditions and threatening situations is the correct sensing of the road geometry and the vehicle’s relative pose with respect to the lane despite shadows and occlusions. In this paper we propose an approach for lane segmentation and tracking that is robust to varying shadows and occlusions. The approach involves color-based clustering, the use of MSAC for outlier removal and curvature estimation, and also the tracking of lane boundaries. Lane boundaries are modeled as planar curves residing in 3D-space using an inverse perspective mapping, instead of the traditional tracking of lanes in the image space, i.e., the segmented lane boundary points are 3D points in a coordinate frame fixed to the vehicle that have a depth component and belong to a plane tangent to the vehicle’s wheels, rather than 2D points in the image space without depth information. The measurement noise and disturbances due to vehicle vibrations are reduced using an extended Kalman filter that involves a 6-DOF motion model for the vehicle, as well as measurements about the road’s banking and slope angles. Additional contributions of the paper include: (i) the comparison of textural features obtained from a bank of Gabor filters and from a GMRF model; and (ii) the experimental validation of the quadratic and cubic approximations to the clothoid model for the lane boundaries. The results show that the proposed approach performs better than the traditional gradient-based approach under different levels of difficulty caused by shadows and occlusions. View Full-Text
Keywords: road sensing; lane detection and tracking; lane departure warning; mean-shift clustering; gabor filters; Gaussian Markov Random Fields; RANSAC road sensing; lane detection and tracking; lane departure warning; mean-shift clustering; gabor filters; Gaussian Markov Random Fields; RANSAC

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Tapia-Espinoza, R.; Torres-Torriti, M. Robust Lane Sensing and Departure Warning under Shadows and Occlusions. Sensors 2013, 13, 3270-3298.

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