Due to the rapid progress in the development of automated vehicles over the last decade, their market entry is getting closer. One of the remaining challenges is the safety assessment and type approval of automated vehicles, as conventional testing in the real world would involve an unmanageable mileage. Scenario-based testing using simulation is a promising candidate for overcoming this approval trap. Although the research community has recognized the importance of safeguarding in recent years, the quality of simulation models is rarely taken into account. Without investigating the errors and uncertainties of models, virtual statements about vehicle safety are meaningless. This paper describes a whole process combining model validation and safety assessment. It is demonstrated by means of an actual type-approval regulation that deals with the safety assessment of lane-keeping systems. Based on a thorough analysis of the current state-of-the-art, this paper introduces two approaches for selecting test scenarios. While the model validation scenarios are planned from scratch and focus on scenario coverage, the type-approval scenarios are extracted from measurement data based on a data-driven pipeline. The deviations between lane-keeping behavior in the real and virtual world are quantified using a statistical validation metric. They are then modeled using a regression technique and inferred from the validation experiments to the unseen virtual type-approval scenarios. Finally, this paper examines safety-critical lane crossings, taking into account the modeling errors. It demonstrates the potential of the virtual-based safeguarding process using exemplary simulations and real driving tests.

As the development of advanced driver assistance systems (ADAS) continues, more and more software functions and sensors are being introduced to the market. This is accompanied by an increase in the amount of data that has to be transmitted to multiple receivers in the vehicle under hard real-time requirements. The use of deterministic and non-deterministic Fieldbus protocols enables communication between sensor and actuator or ECUs. For the purpose of verifying and validating the developed software modules, but also for type approval, an objective and thus data-driven toolchain is mandatory. By using suitable middleware such as Robotic Operating System (ROS), the complexity of integrating multiple (reference) sensors as well as prototypical software functions can be broken down into subtasks and thus distributed to the hardware in a computationally efficient manner. Recording and manipulating sensor ECU communication while driving is also possible under certain circumstances. However, at least to our knowledge, there is no public ROS driver available to integrate automotive-specific Fieldbus protocols except for CAN. In the following paper, we introduce a generic and open-source framework for integrating on-board communication of various Fieldbus protocols and demonstrate the integration in ROS as a real-world use case. To validate the presented methodology, we perform a time analysis of the presented ROS node and compare it to a ROS-independent reference measurement system while performing a standardized vehicle dynamic driving test. In addition, we objectively compare two different on-board sensors from a series vehicle with two distinct reference sensors in a real-world scenario.