  This paper presents a generic feature-based navigation framework for
autonomous vehicles using a soft constrained Particle Filter. Selected map
features, such as road and landmark locations, and vehicle states are used for
designing soft constraints. After obtaining features of mapped landmarks in
instance-based segmented images acquired from a monocular camera,
vehicle-to-landmark distances are predicted using Gaussian Process Regression
(GPR) models in a mixture of experts approach. Both mean and variance outputs
of GPR models are used for implementing adaptive constraints. Experimental
results confirm that the use of image segmentation features improves the
vehicle-to-landmark distance prediction notably, and that the proposed soft
constrained approach reliably localizes the vehicle even with reduced number of
landmarks and noisy observations.
