In modern vehicles, high communication complexity requires cost-effective integration tests such as data-driven system verification with in-vehicle network traces. With the growing amount of traces, distributable Big Data solutions for analyses become essential to inspect massive amounts of traces. Such traces need to be processed systematically using automated procedures, as manual steps become infeasible due to loading and processing times in existing tools. Further, trace analyses require multiple domains to verify the system in terms of different aspects (e.g., specific functions) and thus, require solutions that can be parameterized towards respective domains. Existing solutions are not able to process such trace amounts in a flexible and automated manner. To overcome this, we introduce a fully automated and parallelizable end-to-end preprocessing framework that allows to analyze massive in-vehicle network traces. Being parameterized per domain, trace data is systematically reduced and extended with domain knowledge, yielding a representation targeted towards domain-specific system analyses. We show that our approach outperforms existing solutions in terms of execution time and extensibility by evaluating our approach on three real-world data sets from the automotive industry.

With the introduction of IP/Ethernet networks in automobiles, video compression becomes mandatory for efficient video transfer. Lossy image and video compression standards, such as JPEG and H.264/AVC, achieve high compression efficiency, but also introduce irreversible modification to the video data. On the one hand, this can lead to perceivable image degradation, and on the other hand, affect the performance of image processing algorithms. The pedestrian detection application based on night vision technology, is one example of a driver assistance service using a video stream as input. In this paper, we examine the impact of image/video compression on pedestrian detection, which employs a far infrared (FIR) sensor. In order to measure application quality, metrics used for driver assistance system validation, are investigated through hardware in the loop (HIL) simulation.

Model-driven development has become the standard approach for the development of avionic and automotive embedded systems. When a semantically founded modeling language is employed, the implemented systems can be checked and further processed in an automated manner. To this end, the Component Language (COLA) was invented. It allows for the definition of all information needed during system development. For a better understanding, and also debugging, of the systems modeled there with, simulation at the level of the model is a welcome complement. In this paper we present a model-level simulator for COLA. It follows the language's semantics closely, thus guaranteeing the same behavior as specified in the model and implemented by other tools based on COLA. Furthermore, its modular nature allows for the use of different sources for input data. We will demonstrate the architecture and abilities of our simulator, using parts of a recent case study throughout the paper.