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Development cycles in Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) technologies pose various challenges even at the development stages, since they require extensive calibration, testing, and validation before they can be commissioned. Specifically, testing and verifying the expected vehicle behavior for ADAS/AD functions in various traffic scenarios is a major challenge. Unfortunately, the extent of real-life tests is often quite limited due to associated high costs. Therefore, it is a common practice to employ simulations for this. However, simulation-only verification also has limitations due to un-modeled dynamics. It is therefore in the interest of the developers and OEMs alike to ensure that ADAS/AD systems conform to reliability, compliance, and performance requirements in a cost-effective and efficient manner. Motivated by these, we introduce in this chapter two alternative testing/verification methods combining real-world testing with simulation for ADAS/AD development. The first concept involves a novel steerable chassis dynamometer and real-time camera stimulation in a co-simulation framework. The second involves a generalization of the co-simulation framework with a vehicle-in-the-loop concept and is named “hybrid testing” that was developed in the scope of the EU-H2020 funded project INFRAMIX, where a real vehicle can be combined with a virtual traffic scenario.

Development of Advanced Driver Assistance (ADAS) Systems and Automated Driving (AD) technologies require extensive calibration, testing and validation before such systems can be rolled out into the production line. It is in the interest of the OEMs to speed up these development cycles to ensure compliance with the expected performance requirements. In this paper, a novel steerable chassis dynamometer test bench with a corresponding methodology is introduced to develop and test ADAS/AD technologies. A use case example of tuning the camera-based implementations of the lane keeping assistant (LKA) and adaptive cruise control (ACC) functions on an AD demonstrator vehicle utilizing this test bench is described with preliminary performance results.

An approach to identify critical loops within connected systems especially in co-simulation frameworks is presented. Therefore a classification of the coupling signals is done to distinguish between different types of signals: continuous, discontinuous, discrete and flat. Based on this classification structural loops between the connected subsystems are identified and rated to get critical loops in sense of coupling effort. This information supports application engineers to identify critical signals from a coupling point of view and to set proper coupling methods to handle these coupling signals efficiently.