  Urban environments offer a challenging scenario for autonomous driving.
Globally localizing information, such as a GPS signal, can be unreliable due to
signal shadowing and multipath errors. Detailed a priori maps of the
environment with sufficient information for autonomous navigation typically
require driving the area multiple times to collect large amounts of data,
substantial post-processing on that data to obtain the map, and then
maintaining updates on the map as the environment changes. This dissertation
addresses the issue of autonomous driving in an urban environment by
investigating algorithms and an architecture to enable fully functional
autonomous driving with limited information.
