Detection and tracking of boundary of unmarked roads

This paper presents a new method of detecting and tracking the boundaries of drivable regions in road without road-markings. As unmarked roads connect residential places to public roads, the capability of autonomously driving on such a roadway is important to truly realize self-driving cars in daily driving scenarios. To detect the left and right boundaries of drivable regions, our method first examines the image region at the front of ego-vehicle and then uses the appearance information of that region to identify the boundary of the drivable region from input images. Due to variation in the image acquisition condition, the image features necessary for boundary detection may not be present. When this happens, a boundary detection algorithm working frame-by-frame basis would fail to successfully detect the boundaries. To effectively handle these cases, our method tracks, using a Bayes filter, the detected boundaries over frames. Experiments using real-world videos show promising results.

[1]  Amnon Shashua,et al.  Off-road Path Following using Region Classification and Geometric Projection Constraints , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Sim Heng Ong,et al.  Robust extraction of shady roads for vision-based UGV navigation , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[4]  H. Neumann,et al.  Multiple Cue Data Fusion with Particle Filters for Road Course Detection in Vision Systems , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[5]  Ernst D. Dickmanns,et al.  Recursive 3-D Road and Relative Ego-State Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Sebastian Thrun,et al.  Self-supervised Monocular Road Detection in Desert Terrain , 2006, Robotics: Science and Systems.

[7]  Laurent Itti,et al.  Mobile robot monocular vision navigation based on road region and boundary estimation , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[9]  Paul E. Rybski,et al.  Fast feature detection and stochastic parameter estimation of road shape using multiple LIDAR , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  J. Little,et al.  Inverse perspective mapping simplifies optical flow computation and obstacle detection , 2004, Biological Cybernetics.

[11]  Camillo J. Taylor,et al.  Stochastic road shape estimation , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  U. Franks,et al.  Lane Recognition on Country Roads , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[13]  Guangming Xiong,et al.  Road detection using support vector machine based on online learning and evaluation , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[14]  Ludek Zalud,et al.  Robust detection of shady and highlighted roads for monocular camera based navigation of UGV , 2011, 2011 IEEE International Conference on Robotics and Automation.

[15]  Toby P. Breckon,et al.  Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis , 2009, ICVS.

[16]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Christian Wöhler,et al.  Road Boundary Detection and Tracking using monochrome camera images , 2013, Proceedings of the 16th International Conference on Information Fusion.

[18]  Ragunathan Rajkumar,et al.  Towards a viable autonomous driving research platform , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).