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Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering

Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Sensors 2016, 16(8), 1276;
Received: 29 March 2016 / Revised: 25 July 2016 / Accepted: 8 August 2016 / Published: 12 August 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)
PDF [26361 KB, uploaded 12 August 2016]


Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters—the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)—are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle. View Full-Text
Keywords: lane detection; spatial-temporal knowledge; crossing point filter; structure triangle filter lane detection; spatial-temporal knowledge; crossing point filter; structure triangle filter

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Nan, Z.; Wei, P.; Xu, L.; Zheng, N. Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering. Sensors 2016, 16, 1276.

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