We present a Visualization, Simulation, And Graphical debugging Environment (vIsage) for distributed systems. Time-varying spatial data as well as other information from different sources can be displayed and superimposed in a single view at run-time. The main contribution of our framework is that it is not just a tool for visualizing the data, but it is a graphical interface for a simulation environment. Real world data can be recorded, played back or even synthesized. This enables testing and debugging of single components of complex distributed systems. Being the missing link between development, simulation and testing, e.g., in robotics applications, it was designed to significantly increase the efficiency of the software development process.

The 2007 DARPA Urban Challenge afforded the golden opportunity for the Technische Universität Braunschweig to demonstrate its abilities to develop an autonomously driving vehicle to compete with the world's best. After several stages of qualification, our team CarOLO qualified early for the DARPA Urban Challenge Final Event and was among only 11 teams from initially 89 competitors to compete in the final. We had the ability to work together in a large group of experts, each contributing his expertise in his discipline, and significant organizational, financial, and technical support by local sponsors, who helped us to become the best non-U.S. team. In this report, we describe the 2007 DARPA Urban Challenge, our contribution, “Caroline,” the technology, and algorithms, along with her performance in the DARPA Urban Challenge Final Event on November 3, 2007. © 2008 Wiley Periodicals, Inc.

We present a lane detection algorithm that robustly detects and tracks various lane markings in real-time. The first part is a feature detection algorithm that transforms several input images into a top view perspective and analyzes local histograms. For this part we make use of state-of-the-art graphics hardware. The second part fits a very simple and flexible lane model to these lane marking features. The algorithm was thoroughly tested on an autonomous vehicle that was one of the finalists in the 2007DARPAUrban Challenge. In combination with other sensors, i.e. a lidar, radar and vision based obstacle detection and surface classification, the autonomous vehicle is able to drive in an urban scenario at up to 15 mp/h.