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In this paper two new approaches are presented how to partition an amount of functions distributed in automotive electronic systems. In contrast to common partitioning algorithms as Kernighan-Lin, Best-Gain-First, Simulated-Annealing, a.s.o., these algorithms are real multi-partitioning ones. With respect to ECU (electronic control unit) characteristics, the software functions to be partitioned will be allocated automatically onto the available hardware. Main motivation is the reduction of the resulting bus-load which is provoked by the communication between such functions. Moreover these algorithms optimize the final partitioning solution to achieve a reduced number of ECUs. Reducing bus-load and the number of ECUs can lead to significant cost reduction. In order to validate partitioning results, a CAN as well as a FlexRay model was developed in Matlab/Simulink determining the bus-load over time.

Electronic systems in vehicles characteristically consist of several distributed electronic control units (ECUs) from different suppliers. This situation hinders the integration of automotive systems and increases the overall costs due to individual solutions coming from each supplier. In order to get rid of these disadvantages, the French-German project OSEK/VDX was founded and is now drawing attention worldwide. OSEK/VDX worked out a respective specification to standardize services and protocols of communication, network management and a real-time operating system. An overview of the current state of OSEK/VDX including specifications and harmonization process with ISO is given. Furthermore a description of the Modistarc project working on methodologies and tools for conformance testing of commercial OSEK/VDX implementations is contained.

There are a number of tools available to assist the engineer during the automotive electronics design process, for example when transferring a graphical specification to a real time rapid prototyping environment. One step in this tool chain however is largely ignored by automated design tools: mapping a large monolithic model to a distributed system, more specifically the mapping of several functions on only a few electronic control units (ECUs) which are connected by a bus. In this paper we will present a method to analyze the underlying functional structure of a given model, partition it using a heuristic algorithm and verify the results with a model of the CAN bus. Based on a given functional model, we will show how to extract an algebraic representation of the communication behavior, the adjacency matrix. Using the adjacency matrix, the heuristic algorithm Best Gain First can be applied to map functions to ECUs.

In this paper the problem of fault detection in distributed systems is addressed. Due to the trend that these systems are incorporating an increasing number of subsystems from different suppliers fault detection is becoming an essential part of distributed system design. While meeting the typical constraints of the automotive industry there is the demand on increased safety and improved availability. Because of the connection of different subsystems, errors propagate through the system and may affect other subsystems where they can be detected. The key task which is dealt with in this paper is to detect the origin of these errors. Therefore, Hierarchical Colored Bayesian Petri-Nets are introduced to fulfill fault detection according to Bayesian networks. To reduce calculation efforts, the principle of clustering is included.

In this paper an approach is presented to determine an adequate number of clusters automatically in case of clustering a distributed automotive electronic system. Hereby, this approach is based on the ISODATA clustering algorithm. Its advantages are its flexibility and less computational effort in comparison to normally used partitioning algorithms. In order to cluster a distributed automotive electronic system with respect to a reduced external communication the input data normally used for partitioning algorithms has to be adapted. Besides, a new overall quality criterion is introduced to validate the results of clustering in reference to the busload before test stage.

In this paper an approach to clustering of complex electronic systems using Self-Ordering Maps (SOMs) is presented. SOMs are neural networks which learn through a competitive learning algorithm. In order to use SOMs for the clustering of electronic networks, a representation of the communication behavior in n-dimensional space is developed. The SOM is then used as a nonlinear projection of this space onto a two-dimensional plane. Two examples of clustering are given. The more complex of the two is verified by comparing the behavior of the clustered system and the unclustered system on a simple model of the CAN bus. It is shown that SOMs can be used to effectively cluster complex electronic systems.

A high speed serial communication link has been developed for interconnecting electronic control units within automobiles. The incorporation of object oriented communication in conjunction with acceptance filtering introduces a new level of message handling efficiency and flexibility. Powerful error handling techniques guarantee safe operation in noisy automotive environments.