Thursday, October 15, 1998
4:30-5:30 pm
1311 EECS
Abstract -
In this work we consider a problem arising in intelligent
vehicles and highway systems (IVHS), specifically in
automatic road following, collision avoidance, and maneuver
control. A millimeter-wave radar is placed on the front of
the vehicle and an image of the radar backscatter of the
terrain is acquired in polar coordinates. The radar sensor
acquires the scene in polar coordinates.
The obtained radar image contains both road and offroad
scatter components and we present a method to segment them
from each other. The variety and intesity of the offroad
scatter components, the requirement of reliability under
changing conditions (weather, visibility, road surface
etc.), make it necessary to pertinently use the available
constraints about the road geometry. These constraints are
naturally formulated in the cartesian cartographic domain.
Following [Lakshmanan96,Ma97], the road boudaries are
modeled using a pair of tightly coupled parabolic curves.
One novelty of our approach is to transform these
constraints in polar coordinates which allows us to estimate
the road edge parameters directly from recorded data.
Unfortunately, maximum likelihood estimation yields
estimates which are quite dependent on the off-road
scatters. Another novelty of ourmethod is to construct an
estimator which minimizes a cost criterion which is largely
insensitive to the off-road scatter components. This
criterion involves only the road pixels for which faithfull
information is available, while its evaluation needs a
reduced amount of calculations.
Our estimation procedure depends on a small number of
parameters for which pertinent contraints can be
established. This allows us to minimize our criterion by the
means of pseudo-exhaustive search.
Numerical results are given for real data (L band radar
images of a Southeast Michigan roadway) which indicate the
accuracy and robustness of our approach. Several
directions, concerning both the improvement of the numerical
complexity and the robustification of the method, are
actually under study.
Biosketch -
Mila Nikolova received the Ph.D. degree in Signal Processing from
the Universite de Paris-Sud in 1995. Currently, she is teaching and
research assistant at the Universite Paris V. Her research interests
are in inverse problems and image reconstruction.