Cooperative Cognitive Automobiles

Safety requirements are among the most ambitious challenges for autonomous guidance and control of automobiles. A human-like understanding of the surrounding traffic scene is a key element to fulfill these requirements, but is a still missing capability of today's intelligent vehicles. Few recent proposals for driver assistance systems approach this issue with methods from the AI research to allow for a reasonable situation evaluation and behavior generation. While the methods proposed in this contribution are lend from cognition in order to mimic human capabilities, we argue that in the long term automated cooperation among traffic participants bears the potential to improve traffic efficiency and safety beyond the level attainable by human drivers. Both issues are major objectives of the Transregional Collaborative Research Centre 28 'cognitive automobiles,' TCRC28 that is outlined in the paper. Within this project the partners focus on systematic and interdisciplinary research on machine cognition of mobile systems as the basis for a scientific theory of automated machine behavior

Description Logic for Scene Understanding at the Example of Urban Road Intersections

Understanding a natural scene on the basis of external sensors is a task yet to be solved by computer algorithms. The present thesis investigates the suitability of a particular family of explicit, formal representation and reasoning formalisms for this task, which are subsumed under the term Description Logic

Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.

A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results in an annotation that is significantly more accurate than what would be obtained by frame-by-frame evaluation of the classifier output. The framework has been implemented and applied successfully to the analysis of team sports with a single camera. Key words: Visua