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Today's vehicle networks are mainly based on the event-triggered CAN-bus. In future FlexRay, which is a TDMA-based bus, will more and more be used for the implementation of safety-relevant real-time systems due to its determinism. In order to configure a CAN-based network the priorities of the messages sending via the external bus have to be defined. In this paper an approach will be presented allowing automated priority determination. Subsequently it will be shown how to adapt this method to automated cycle configuration in case of a FlexRay-based system. In order to ensure determinism not only in TDMA-based but also in event-triggered networks, a method will be presented adapting priorities of messages intending to exceed their deadline. This can be easily realized without changing the CAN protocol.

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.

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.

Bus systems like CAN or FlexRay allowed great advances in automotive electronics over the last 20 years. In order to function in an environment which requires the communication medium to tolerate one safety-relevant fault, these bus systems require a second, redundant bus to act as a backup for the original unit. With the network approach presented in this paper (SafeNet) it is possible to use the network intrinsic redundancy to keep the network fail-safe after at least one safety relevant fault in the network. To ensure this, messages are relayed to every node in the network. Even though the message delivery times in the network are not deterministic, it is shown that it is suitable for safety-relevant applications like drive-by-wire. Due to the simple point-to-point connections used to connect the nodes, high speeds can be achieved. The network approach is compared to both CAN and FlexRay under different aspects.

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.