A System Architecture for Heterogeneous Signal Data Fusion, Integrity Monitoring and Estimation of Signal Quality
A large number of today’s automobiles, right down to the compact car segment, are equipped with vehicle dynamics control and driver assistance systems. In general, each one of these functions has been developed with its own dedicated set of sensors, and a
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A System Architecture for Heterogeneous Signal Data Fusion, Integrity Monitoring and Estimation of Signal Quality Nico Dziubek
10.1 Introduction 10.1.1 Motivation A large number of today’s automobiles, right down to the compact car segment, are equipped with vehicle dynamics control and driver assistance systems. In general, each one of these functions has been developed with its own dedicated set of sensors, and applied independently of other sensors installed in the same vehicle. As a result, redundant measurements are performed, the advantages of which are currently only utilized in a few cases. In light of the ever-increasing networking of functions, the differing measurement principles of the individual sensors must be borne in mind so that discrepancies or inconsistencies arising from different measuring errors can be resolved. In addition, the availability, sampling rates, resolution and delay times of the measurement signals generally differ and depend on external conditions. This is due to different types of sensors and function principles. The increasing powerfulness of microprocessors and the availability of bus systems in vehicles offer a basis for a central processing of the large quantity of data already available. An architecture created in this way for holistic processing of the measurement signals to cover all available data sources makes it possible to generate a consistent data record with increased accuracy. The quality of the data generated is evaluated based on this. This evaluation provides the user functions with additional information for further processing. Particularly in the case of safety-critical systems, the outlay for detecting measurement and sensor errors is very high. Centralized evaluation of the signal integrity offers the potential of relieving the functions of a considerable part of error detection, and of improving error detection through use of the redundancies (Table 10.1). N. Dziubek (B) Institute of Automotive Engineering, Technische Universität Darmstadt, Petersenstraße 30, D-64287 Darmstadt, Germany e-mail: [email protected] M. Maurer and H. Winner (eds.), Automotive Systems Engineering, DOI: 10.1007/978-3-642-36455-6_10, © Springer-Verlag Berlin Heidelberg 2013
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10.1.2 Goals The structure of a system architecture for centralized, consistent fusion of random pieces of data is shown and evaluated using an example. The requirements to be met by a fusion filter for processing data from random types of sensors are determined, and implemented for a set of sensors by way of example. The sensors used here are acceleration and yaw rate sensors produced using microelectro-mechanical system (MEMS) technology combined to an inertial measurement unit (IMU) with 3 degrees of freedom, a single-channel (L1) GPS receiver that issues raw data (pseudo ranges and carrier phase measurement), as well as odometry sensors measuring angle pulses from all four wheels and the steering wheel angle. In addition, an evaluation of the signal quality based on usage o
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