A decision network based frame-work for visual off-road path detection problem

This paper describes a decision network based frame-work used for path-detection algorithm development in autonomous vehicle applications. Lane marker detection algorithms do not work in off-road environments. Off-road trails have too much complexity, with widely varying textures and many differing natural boundaries. The authors have developed a general approach. Images are segmented into regions, based on the homogeneity of some pixel properties and the resulting regions are classified as road or not-road by a decision network process. Combinations of contiguous clusters form the path surface, allowing any arbitrary path to be represented

[1]  Christopher Rasmussen,et al.  Combining laser range, color, and texture cues for autonomous road following , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[2]  Josef Kittler,et al.  Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Bruce Abramson,et al.  Decision analytic networks in artificial intelligence , 1995 .

[4]  Michel Devy,et al.  Robot Visual Navigation in Semi-structured Outdoor Environments , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[5]  F. Chausse,et al.  A fast and robust vision based road following algorithm , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[6]  Alberto Broggi,et al.  An agent based evolutionary approach to path detection for off-road vehicle guidance , 2006, Pattern Recognit. Lett..

[7]  Alberto Broggi,et al.  The Single Frame Stereo Vision System for Reliable Obstacle Detection Used during the 2005 DARPA Grand Challenge on TerraMax , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[8]  Nicolaos B. Karayiannis,et al.  Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms , 2005, IEEE Transactions on Neural Networks.

[9]  Robert W. Lucky,et al.  The Grand Challenge , 1993 .

[10]  Sanjiv Singh,et al.  Obstacle detection using adaptive color segmentation and color stereo homography , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[11]  Massimo Bertozzi,et al.  GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection , 1998, IEEE Trans. Image Process..