Advanced control architecture for autonomous vehicles

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Abstract
An advanced control architecture for autonomous vehicles is presented. The hierarchical architecture consists of four levels: a vehicle level, a control level, a rule-based level and a knowledge-based level. A special focus is on forms of internal representation, which have to be chosen adequately for each level. The control scheme is applied to VaMP, a Mercedes passenger car which autonomously performs missions on German freeways. VaMP perceives the environment with its sense of vision and conventional sensors. It controls its actuators for locomotion and attention focusing. Modules for perception, cognition and action are discussed.

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