A method for following unmarked roads
Authors:
- 1 January 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 19310587,p. 650-655
Abstract
Road-following for ground vehicles has been widely studied and the solutions normally tracks line markings or a salient road edge. Most of the presented approaches are thus based on geometric or spectral models and does a fit of image data to the model to ensure robustness. For operation on unmarked roads under highly varying weather and light conditions a strategy is presented which is based on a probabilistic generative model. The method is evaluated on a number of test scenes and compared to well known methods from the literature.Keywords
This publication has 15 references indexed in Scilit:
- Vision In and Out of Vehicles: Integrated Driver and Road Scene MonitoringThe International Journal of Robotics Research, 2004
- A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked RoadsAutonomous Robots, 2004
- The development of machine vision for road vehicles in the last decade2003
- Architectural Issues on Vision-Based Automatic Vehicle Guidance: The Experience of the ARGO ProjectReal-Time Imaging, 2000
- Rapidly adapting machine vision for automated vehicle steeringIEEE Expert, 1996
- A compact vision system for road vehicle guidance1996
- Where we look when we steerNature, 1994
- SCARF: a color vision system that tracks roads and intersectionsIEEE Transactions on Robotics and Automation, 1993
- Recursive 3-D road and relative ego-state recognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1992
- Vision and navigation for the Carnegie-Mellon NavlabIEEE Transactions on Pattern Analysis and Machine Intelligence, 1988