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Autonomous driving functions (ADFs) in public traffic have to comply with complex system requirements that are based on knowledge of experts from different disciplines, e.g., lawyers, safety experts, psychologists. In this paper, we present a research preview regarding the validation of ADFs with respect to such requirements. We investigate the suitability of Traffic Sequence Charts (TSCs) for the formalization of such requirements and present a concept for monitoring system compliance during validation runs. We find TSCs, with their intuitive visual syntax over symbols from the traffic domain, to be a promising choice for the collaborative formalization of such requirements. For an example TSC, we describe the construction of a runtime monitor according to our novel concept that exploits the separation of spatial and temporal aspects in TSCs, and successfully apply the monitor on exemplary runs. The monitor continuously provides verdicts at runtime, which is particularly beneficial in ADF validation, where validation runs are expensive. The next open research questions concern the generalization of our monitor construction, the identification of the limits of TSC monitorability, and the investigation of the monitor's performance in practical applications. Perspectively, TSC runtime monitoring could provide a useful technique in other emerging application areas such as AI training, safeguarding ADFs during operation, and gathering meaningful traffic data in the field., Comment: In Proceedings FMAS2022 ASYDE2022, arXiv:2209.13181

This paper presents an approach for verifying the behaviour of nonlinear Artificial Neural Networks (ANNs) found in cyber-physical safety-critical systems. We implement a dedicated interval constraint propagator for the sigmoid function into the SMT solver iSAT and compare this approach with a compositional approach encoding the sigmoid function by basic arithmetic features available in iSAT and an approximating approach. Our experimental results show that the dedicated and the compositional approach clearly outperform the approximating approach. Throughout all our benchmarks, the dedicated approach showed an equal or better performance compared to the compositional approach., Comment: In Proceedings SNR 2021, arXiv:2207.04391