| Volume 23 Issue 4/5- Publication Date: 1 April-May 2004 |
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| Special Issue on the 8th International Symposium on Experimental
Robotics (ISER ’02) |
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| Vision In and Out of Vehicles:
Integrated Driver and Road Scene Monitoring |
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| N. Apostoloff Department
of Systems Engineering, Research School of Information Sciences and Engineering,
The Australian National University, Canberra, ACT, 2611, Australia
and A. Zelinsky Department of Systems Engineering, Research
School of Information Sciences and Engineering, The Australian National
University, Canberra, ACT, 2611, Australia |
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| One of the more startling
effects of road related accidents is the economic and social burden
that they cause. In OECD countries (the 23 leading economically developed
countries of the world) over 150,000 people are killed every year (44,000+
in the USA, 38,000+ in Europe and 11,000+ in Japan) at an estimated
cost of US$ 500 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. In this paper we present preliminary results
of an Intelligent Transport System project that has fused visual lane
tracking and driver monitoring technologies in the first step towards
closing the loop between vision inside and outside the vehicle. Experimental
results of a novel 15 Hz visual lane tracking system will be discussed,
focusing on the particle filter and cue fusion technology used. The
results from the integration of the lane tracker and the driver monitoring
system are presented with an analysis of the driver’s visual behavior
in several different scenarios. |
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| Multimedia Key |
= Video |
 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
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1 |
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Example
One: Overview of the vision systems on TREV. (4.6MB) |
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2 |
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Example
Two: Example faceLAB™sequence showing head pose and eye
gaze tracking. (3.9MB) |
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3 |
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Example
Three: Example lane tracking sequence from a high curvature
road showing the convergence of particles onto the lane location.
(10.3MB) |
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4 |
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Example
Four: The three lane tracking sequences. 1.Ahighway with clear
lane markings, shadows and several run-off lanes. 2. A high curvature
outer city road showing dramatic lighting changes, a significant
shadows and discontinuous lane markings. 3. A highway with poor
lane markings and strong shadows. (8.5MB) |
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5 |
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Example
Five: The integrated driver and road scene monitoring system
is shown in a 3 minute sequence around a high curvature outer city
road. (3.9MB) |
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6 |
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Example
Six: Example dataset from a realworld test as well as the visualization
software that integrates the lane tracking results with the head
pose and eye gaze vectors from faceLAB™(The face-LAB™Toolbox
for MatLAB was kindly supplied by Seeing Machines - which I have
extended, with the help of David Liebowitz, to use dynamic patch
data). Extract the data and code using your favorite archiving utility
and run the “run_viov”
script in a MatLAB shell to visualize the data. The data viewer
is a modified version of the threed_browser that comes with FAT
v1.0. (0.9MB) ZIP file |
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7 |
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Example
Seven: Example dataset from the highway lane tracking scenario
containing the baseline, the respective image set and a transformation
framework between the road-centric coordinate system and the image
coordinate system. Extract the data and code using your favorite
archiving utility and use am_skeleton.m as a template file for your
code. (8.1MB) ZIP file |
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Example
Eight: Lane tracking sequence along a high curvature outer city
road in medium rain. (9.4MB) |
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