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In 2012, Continental received an autonomous vehicle testing license from the US state of Nevada and has subsequently operated an automated driving vehicle on more than 15.000 miles of testing on public roads. The present paper describes the development of this vehicle by explaining the necessary system modules - structured along the signal processing chain from sensing and representing the vehicles environment up to a holistic actuator concept. Therefore, the functional specification for automated lateral and longitudinal vehicle guidance is addressed, also giving an answer on how to cope with challenging scenarios such as stop-and-go traffic and narrow road lanes. Focus is set on a hybrid environment representation, integrating model-based tracking for moving traffic participants and an occupancy grid for maneuvering space.

In recent years of research activities within the field of Advanced Driver Assistance Systems (ADAS), it became more and more obvious, that the present standard representation of the current state of a vehicle's environment is not sufficient anymore. Experience has shown that for functions addressed in the years ahead, there is a strong need for more dense environmental models, than the commonly used object lists. Many proposals have been made towards this direction; these are discussed in this work. Based on this overview, we propose a so called “Hybrid Environmental Model” approach for meeting requirements of future ADAS functions by complementing the existing solutions with a dense, grid-based representation. Additionally, methods are discussed which enhance the efficiency of data transport and therefore enable a potential series application of this approach.