Estimation and Navigation Methods with Limited Information for Autonomous Urban Driving
It addresses the problem of unreliable localization and map dependency for autonomous vehicles in urban settings, but appears incremental as it builds on existing limited-information approaches.
This dissertation tackles autonomous driving in urban environments where GPS is unreliable and detailed maps require extensive data collection and maintenance, by investigating algorithms and an architecture to enable fully functional autonomous driving with limited information.
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.