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Abstract. Automotive radar and lidar sensors represent key components for next generation driver assistance functions (Jones, 2001). Today, their use is limited to comfort applications in premium segment vehicles although an evolution process towards more safety-oriented functions is taking place. Radar sensors available on the market today suffer from low angular resolution and poor target detection in medium ranges (30 to 60m) over azimuth angles larger than ±30°. In contrast, Lidar sensors show large sensitivity towards environmental influences (e.g. snow, fog, dirt). Both sensor technologies today have a rather high cost level, forbidding their wide-spread usage on mass markets. A common approach to overcome individual sensor drawbacks is the employment of data fusion techniques (Bar-Shalom, 2001). Raw data fusion requires a common, standardized data interface to easily integrate a variety of asynchronous sensor data into a fusion network. Moreover, next generation sensors should be able to dynamically adopt to new situations and should have the ability to work in cooperative sensor environments. As vehicular function development today is being shifted more and more towards virtual prototyping, mathematical sensor models should be available. These models should take into account the sensor's functional principle as well as all typical measurement errors generated by the sensor.

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Backing up a trailer can be a challenge, particularly for inexperienced recreational drivers. We therefore develop two feedback controllers, which support the driver with automatic steering inputs in various situations. Based on the kinematics of the general one-trailer system, we first derive an input/output-linearizing control law that asymptotically stabilizes a given curvature for the trailer. This enables the driver to directly steer the trailer, e.g., by means of a turning knob, such that the trailer will automatically be prevented from jackknifing. The control task is then modified and solved so that the vehicle can also take over the complete stabilization task along given paths. In combination with a path-planning algorithm, this enables automated parallel parking for example. The complete system is implemented on a rapid-prototyping environment and evaluated in real-world scenarios.