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Summary: Robust Vision based Lane Tracking using Multiple Cues
and Particle Filtering 1
Nicholas Apostoloff Alexander Zelinsky
Department of Engineering Sciences Department of Systems Engineering, RSISE
University of Oxford Australian National University
Oxford, United Kingdom Canberra, ACT, Australia
nema@robots.ox.ac.uk alex@syseng.anu.edu.au
Abstract
One of the more startling effects of road related ac-
cidents is the economic and social burden they cause.
Between 750,000 and 880,000 people died globally in
road related accidents in 1999 alone, with an estimated
cost of US$518 billion [11]. One way of combating this
problem is to develop Intelligent Vehicles that are self-
aware and act to increase the safety of the transporta-
tion system. This paper presents the development and
application of a novel multiple-cue visual lane track-
ing system for research into Intelligent Vehicles (IV).
Particle filtering and cue fusion technologies form the
basis of the lane tracking system which robustly handles
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