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Active multi-focal vision systems are designed to perform visual tasks such as saccades and smooth pursuit movements. Saccades are fast movements to a given position, which is a time-optimal control problem. Smooth pursuit movement is used to follow moving objects and is a tracking control problem. Therefore we use a hybrid approach for the control of our vision system. For saccades we use sliding-mode control with a switching line, designed such that nearly time-optimal control performance is achieved. For smooth pursuit movement we use state-space control to attain adequate tracking performance. To widen the applicability of our multi-focal vision system, it is designed such that cameras are easily exchangeable. Thus system dynamics is subject to change, which implies the need for adaptive control. This paper presents the design of the resulting hybrid adaptive controller together with experimental results.

Precise perception of a vehicle's surrounding is crucial for safe autonomous driving. It requires a high sensor resolution and a large field of view. Active perception, i.e. the redirection of a sensor's focus of attention, is an approach to provide both. With active perception, however, the selection of an appropriate sensor orientation becomes necessary. This paper presents a method for determining the sensor orientation in urban traffic scenarios based on three criteria: the importance of traffic participants w.r.t. the current situation, the available information about traffic participants while considering alternative sensor orientations as well as sensor coverage of the vehicle's relevant surrounding area.

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