Transitioning automated driving systems to complex operational domains disproportionally increases demands on verification activities . In the worst case, the operational domain can not be covered by a manageable set of logical scenarios. An anticipated solution is to use abstract scenarios, which increase coverage while still enabling formal methods. However, established verification approaches must be adapted for abstract scenarios. In this work, we consider the generation of simulatable test suites from abstract scenarios. For this, we use Traffic Sequence Charts (TSCs), a visual yet formal scenario description language based on first order logic. We propose an SMT-based process for generating concrete test cases that can be simulated in e.g. CARLA. This theoretical framework is compiled into an architecture and a prototypical implementation called TSC2CARLA. An evaluation on a set of non-trivial examples yields initial evidence for the feasibility of our approach.

Artificial Intelligence (AI) plays an important role in managing the complexity of automated driving. Nonetheless, training and ensuring the safety of AI is challenging. The safe generalization from a known to an unknown situation remains an unsolved problem. Infusing knowledge into AI driving functions seems a promising approach to address generalization, development costs, and training efficiency. We reason that ascertaining the relevance of infused knowledge provides a strong indication of the correct execution of previous development phases of knowledge infusion. As a causal reason for AI performance, relevant knowledge is important for explaining AI behavior. This paper defines a novel notion of relevant knowledge in knowledge-infused AI and for requirements satisfaction in traffic scenarios. We present a scenario-based testing procedure that not only checks whether a knowledge-infused AI model satisfies a given requirement R but also provides statements on the relevance of infused knowledge. Finally, we describe a systematic method for generating abstract knowledge scenarios to enable an efficient application of our relevance testing procedure.

Autonomous driving functions (ADFs) are becoming more relevant and complex. Still, their safe and correct operation must be guaranteed. Scenario-based testing, i.e. confronting the ADF under test with other traffic in specified scenarios is an established approach for the validation and verification of ADFs, but tests currently often only consider simple technical requirements. Safe and correct operation is not only the absence of collisions but involves complex spatio-temporal requirements on the externally observable, functional driving behaviour in traffic. In this work, we consider Traffic Sequence Charts (TSCs) as a visual formalism for the specification of complex, functional ADF requirements. We define a monitoring problem for TSCs and finite, sampled observations of ADF behaviour and discuss how monitor verdicts contribute to requirements testing. We show that such monitors can effectively be constructed for realistic requirements and that they can contribute to efficient testing by assessing ADF behaviour at runtime.