Track Detection for Autonomous Trains

This paper presents a way to efficiently use lane detection techniques - known from driver assistance systems - to assist in obstacle detection for autonomous trains. On the one hand, there are several properties that can be exploited to improve conventional lane detection algorithms when used for railway applications. The heavily changing visual appearance of the tracks is compensated by very effective geometric constraints. On the other hand there are additional challenges that are less problematic in classical lane detection applications. This work is part of a sensor system for an autonmous train application that aims at creating an environmentally friendly public transportation system.

[1]  G. Jiang,et al.  Lane and obstacle detection based on fast inverse perspective mapping algorithm , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[2]  C. Lipski,et al.  A Fast and Robust Approach to Lane Marking Detection and Lane Tracking , 2008, 2008 IEEE Southwest Symposium on Image Analysis and Interpretation.

[3]  Sovira Tan,et al.  Inverse perspective mapping and optic flow: A calibration method and a quantitative analysis , 2006, Image Vis. Comput..

[4]  Dinggang Shen,et al.  Lane detection and tracking using B-Snake , 2004, Image Vis. Comput..

[5]  Chien-Cheng Tseng,et al.  Environment classification and hierarchical lane detection for structured and unstructured roads , 2010 .

[6]  Yong Zhou,et al.  A robust lane detection and tracking method based on computer vision , 2006 .

[7]  Frédéric Maire,et al.  Vision based anti-collision system for rail track maintenance vehicles , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[8]  Kah Phooi Seng,et al.  Lane Detection and Kalman-Based Linear-Parabolic Lane Tracking , 2009, 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics.

[9]  Shigang Wang,et al.  Low-Level Image Processing for Lane Detection and Tracking , 2009, ArtsIT.

[10]  Sergiu Nedevschi,et al.  Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision , 2009, IEEE Transactions on Intelligent Transportation Systems.