Integrating Spline Curves in Road Constraint Object Tracking

Including road map information in the tracking of ground moving objects is a challenging problem. While many self localizing algorithms base on a modelling in global Cartesian coordinates a few approaches prefer a modelling in a local map coordinate frame. Throughout this contribution both strategies are compared theoretically and in numerical simulations. To achieve robust tracking results current onboard sensor information is utilized in the tracking framework. Commonly available on cars or trains are absolute position information, estimated e.g. by a GPS unit, and relative velocity measurements, e.g. measured by an odometer. To integrate road map knowledge with this sensor information time- and measurement-update equations are derived for both modelling strategies. Roads or tracks are often composed by a sequence of geometric primitives. Approximating this progression of geometric elements with smooth piecewise defined polynomials yields an accurate model, which can easily be integrated in the tracking framework. General preconditions using curves for tracking purposes are presented. In particular, cubic Hermite spline curves are chosen and implemented into the tracking framework.