Roadgraph Generation and Free-Space Estimation in Unknown Structured Environments for Autonomous Vehicle Motion Planning

Automotive manufacturers and customers wish to have fully automated driving functionality available in a huge set of locations, scenarios, and markets. This raises the need for universally applicable scene understanding and motion planning algorithms that do not rely on highly accurate maps or excessive infrastructure communication. In this paper we introduce two novel approaches for extracting a topological roadgraph with possible intersection options from sensor data along with a geometric representation of the available maneuvering space. Also, a search and optimization-based path planning method for guiding the vehicle along a selected track in the roadgraph and within the free-space is presented. We compare the methods presented in simulation and show results of a test drive with a research vehicle. Our evaluations show the applicability in low speed maneuvering scenarios and the stability of the algorithms even for low quality input data.