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21st Southern California Nonlinear Control Workshop
Optimization-based
Road Curve Fitting
Sheng Zhao, UCR
Sheng Zhao, UCR
Advisor: Jay Farrell
Various advanced driver
assistance systems (ADAS) are under development that intend to provide improved
road safety. These
systems require precise road models. In particular, accurate
curvature is
important for some ADAS applications such as curve over
speed and lane departure
warning. Existing
road models often employ spline functions that are fit by least squares
to roadway position data. The curvature calculated for
such spline curves may not accurately reflect the curvature of the
underlying roadway. This article addresses this
problem in an unified framework, using optimization with l1-norm
regularization. In this approach, known roadway
characteristics can be enforced optimally with respect to a cost function
which finds the best tradeoff between the match to the available data
and the number of changes in curvature. Experimental results with show
that the proposed method chooses a sparse set of
curvature switching points (i.e., piecewise constant curvature) and
achieves a high accuracy fit to the roadway dataset.