The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.
The Stadtpilot project aims at autonomous driving on Braunschweig's inner city ring road. For this purpose, an autonomous vehicle called “Leonie” has been developed. In October 2010, after two years of research, “Leonie's” abilities were presented in a public demonstration. This vehicle is one of the first worldwide to show the ability of driving autonomously in real urban traffic scenarios. This paper describes the legal issues and the homologation process for driving autonomously in public traffic in Braunschweig, Germany. It also dwells on the Safety Concept, the system architecture and current research activities. The Stadtpilot project aims at fully automated driving on Braunschweig's inner city ring road. The TU Braunschweig's research vehicle Leonie is one of the first vehicles having the ability of fully automated driving in real urban traffic scenarios. In this paper, we present our approaches for context representation and environment modeling for automated driving. The demonstrated approach allows to provide a simple and yet universal information storage layer for the development of complex driving applications. Moreover, we present our approach for aggregating and fusing information between dynamic traffic objects detected by the sensor systems and a-priori map information.