One of the more startling effects of road related accidents 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. One way of combating this problem is to develop Intelligent Vehicles that are self-aware and act to increase the safety of the transportation system. This paper presents the development and application of a novel multiple-cue visual lane tracking system for research into Intelligent Vehicles (IV). Particle filtering and cue fusion technologies form the basis of the lane tracking system which robustly handles several of the problems faced by previous lane tracking systems such as shadows on the road, unreliable lane markings, dramatic lighting changes and discontinuous changes in road characteristics and types. Experimental results of the lane tracking system running at 15 Hz will be discussed, focusing on the particle filter and cue fusion technology used.
Robust vision based lane tracking using multiple cues and particle filtering
2003-01-01
553536 byte
Conference paper
Electronic Resource
English
Robust Multi-Lane Detection and Tracking in Temporal-Spatial Based on Particle Filtering
SAE Technical Papers | 2019
|A robust lane detection and tracking method based on computer vision
Tema Archive | 2006
|Lane level positioning using particle filtering
IEEE | 2009
|British Library Online Contents | 2010
|Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
British Library Online Contents | 2004
|