Abstract-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.
In the development of embedded hard real-time systems the ability to guarantee worst case execution times is gaining importance and complexity. The fast evolving processor acceleration techniques constantly increase the gap between a processor which uses the accelerations and one which does not. Modeling cache, pipelines and other parts of the processor gets increasingly difJicult and time consuming. To circumvent this problem especially in the domain of rapid prototyping of embedded hard real-time systems, we propose to lay more weight on measurement and less on modeling. By analyzing the controljow graph the compiler uses for Optimization, a reduced controljow graph can be generated, which limits the paths to be measured. Using this information the object code of the program is being instrumented and then measured. By measuring all paths the reduced control $ow graph indicates, predictability is achieved without using too pessimistic estimations.
The Vehicle in the Loop test setup has been developed for the safe, reproducible and resources-saving test of driver assistance functions for support in critical traffic situations. This setup combines the advantages of driving simulators and a real test vehicle by incorporating it into a traffic simulation. While driving, the synthetic outside traffic is visualized to the driver realistically by means of an optical see through Head Mounted Display. Thanks to the Vehicle in the Loop test setup, motion sickness is avoided.With the help of sensor models, driver assistance functions can react to synthetic outside traffic already in an early phase of development, and the function can thus be tested realistically and without danger for humans and machine.
I n the field of robot motion control, visual servoing has been proposed as the suitable strategy to cope with imprecise models and calibration errors. Remaining problems such as the necessity of a high rate of visual feedback are deemed to be solvable b y the development of real-time vision modules. However, human grasping, which still outshines its robotic counterparts especially with respect to robustness and flexibility, definitely requires only sparse, asynchronous visual feedback. W e therefore examined current neuroscientific models for the control of human reach-to-grasp movements with the emphasis lying on the visual control strategy used. From this, we developed a control model that unifies the two robotic strategies look-then-move and visual servoing, thereby compensating the problems that each strategy shows when used alone.
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