Autonomous driving in semi-structured environments: Mapping and planning

We consider the problem of autonomous driving in semi-structured environments (e.g., parking lots). Such environments have strong topological structure (graphs of drivable lanes), but maneuvers with significant deviations from those graphs are valid and frequent. We address two main challenges of operating in such environments: i) detection of topological structure from sensor data, and ii) using that structure to guide path planning. We present experimental results on both of these topics, demonstrating robust estimation of lane networks in parking lots and the benefits of using these topological networks to guide path planning.