Simultaneous detection of lane and pavement boundaries usingmodel-based multisensor fusion
Ma, B.; Lakshmanan, S.; Hero, A.O., III
Intelligent Transportation Systems, IEEE Transactions on
Volume 01, Issue 3, Sep 2000 Page(s):135 - 147
Digital Object Identifier 10.1109/6979.892150
Summary:Treats a problem arising in the design of intelligent vehicles:
automated detection of lane and pavement boundaries using
forward-looking optical and radar imaging sensors mounted on an
automobile. In previous work, lane and pavement boundaries have always
been located separately. This separate detection strategy is problematic
in situations when either the optical or the radar image is too noisy.
We propose a Bayesian multisensor image fusion method to solve our
boundary detection problem. This method makes use of a deformable
template model to globally describe the boundaries of interest. The
optical and radar imaging processes are described with random field
likelihoods. The multisensor fusion boundary detection problem is
reformulated as a joint MAP estimation problem. However, the joint MAP
estimate is intractable, as it involves the computation of a notoriously
difficult normalization constant, also known as the partition function.
Therefore, we settle for the so-called empirical MAP estimate, as an
approximation to the true MAP estimate. Several experimental results are
provided to demonstrate the efficacy of the empirical MAP estimation
method in simultaneously detecting lane and pavement boundaries. Fusion
of multi-modal images is not only of interest to the intelligent
vehicles community, but to others as well, such as biomedicine, remote
sensing, target recognition. The method presented in the paper is also
applicable to image fusion problems in these other areas
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