Identification of performance limitations of sensing technologies for automated driving Todayu0027s automotive functions are more and more complex, because of the automation of driving assistance to different levels of automated driving. Functions like ExtendedAutomated Emergency Braking (EAEB) are implemented by Automated Driving System (ADS), which are intended to improve active safety of road vehicles such as cars, trucks, and buses. ADS functions are safetycritical in sense of Functional Safety according to ISO 26262, because any malfunction may lead to a harm of people. Limitations of sensor technologies and algorithms of ADS can lead to an unwanted hazardous situation, which is addressed in the first Public Available Specification (PAS) called Safety of the intended Functionality (SOTIF, ISOPAS 21448). This paper presents a work in progress of a systematic workflow to identify the socalled xe2x80x9ctriggering eventsxe2x80x9d of sensing units based on camera and Light Detection And Ranging (LIDAR). The workflow has been used for a systematic analysis to identify triggering events of relevant sensor technologies for an EAEB use case. In the last step, an evaluation of sensor modelling tools for simulation of sensor imperfections has been performed. Furthermore, the results will be used to derive simulation test cases for verification and validation of such sensing technology.

Comparing two systematic approaches for testing automated driving functions Thoroughly validating and verifying automated or autonomous driving functions is inevitable for assuring to meet quality criteria for safetycritical systems. In this paper, we discuss two system testing techniques that have been already used for detecting critical situations for the automated emergency braking function based on vehicle simulations. In particular, we introduce combinatorial testing and searchbased testing techniques and compare them. Whereas the first is for identifying interactions of parameters that lead to harmful situations considering predefined value domains, the latter is for finding parameter values that cause such critical situations. We discuss the underlying foundations behind the methods as well as their potential application areas. In addition, we summarize the results obtained when using these methods for testing automated emergency braking.