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.

Abstract In the automotive domain, scenario-based development is the answer to the increasing complexity of highly automated driving functions. Scenario-based methods cluster the large scenario space by so-called abstract scenarios which can be used to sample an infinite number of concrete scenarios. Because abstract scenarios constrain driving maneuvers by excessive use of additional constraints, constraint solving techniques may be required to find concrete scenario instances. In order to guarantee correctness of simulation runs with respect to the abstract scenario, realistic vehicle dynamics need to be considered during the instantiation process. This work proposes an encoding scheme for abstract scenarios into linear constraint systems for the generation of correct-by-construction concrete scenarios. It covers both temporal and spatial aspects of the abstract scenario, as well as simple, but realistic vehicle dynamics. The applicability of the method is shown by demonstrating it on a set of basic driving maneuvers. This paper extends the previous version by presenting further experimental results and giving an overview over practical applications.

Abstract For developing safe automated systems, recognizing safety-critical situations in data from their complex operational domain is imperative. This capability is, for example, essential when evaluating the system’s conformance to specified requirements in test run data. The requirements involve a temporal dimension, as the system operates over time. Moreover, the generated data are usually relational and require additional background knowledge about the domain for correctly recognizing the situation. This fact makes propositional temporal logics, an established tool, unsuitable for the task. We address this issue by developing a tailored temporal logic to query for situations in relational data over complex domains. Our language combines mission-time linear temporal logic with conjunctive queries to access time-stamped data with background knowledge formulated in an expressive description logic. Currently, however, no tools exist for answering queries in such settings. We hence also contribute an implementation in the logic reasoner Openllet, leveraging the efficacy of well-established conjunctive query answering. Moreover, we present a benchmark generator in the setting of automated driving and demonstrate that our tool performs well when tasked with recognizing safety-critical situations in road traffic.

Automated driving systems are safety-critical cyber-physical systems whose safety of the intended functionality (SOTIF) can not be assumed without proper argumentation based on appropriate evidences. Recent advances in standards and regulations on the safety of driving automation are therefore intensely concerned with demonstrating that the intended functionality of these systems does not introduce unreasonable risks to stakeholders. In this work, we critically analyze the ISO 21448 standard which contains requirements and guidance on how the SOTIF can be provably validated. Emphasis lies on developing a consistent terminology as a basis for the subsequent definition of a validation strategy when using quantitative acceptance criteria. In the broad picture, we aim to achieve a well-defined risk decomposition that enables rigorous, quantitative validation approaches for the SOTIF of automated driving systems.

The homologation of automated vehicles, being safety-critical complex systems, requires sound evidence for their safe operability. Traditionally, verification and validation activities are guided by a combination of ISO 26262 and ISO/PAS 21448, together with distance-based testing. Starting at SAE Level 3, such approaches become infeasible, resulting in the need for novel methods. Scenario-based testing is regarded as a possible enabler for verification and validation of automated vehicles. Its effectiveness, however, rests on the consistency and substantiality of the arguments used in each step of the process. In this work, we sketch a generic framework around scenario-based testing and analyze contemporary approaches to the individual steps. For each step, we describe its function, discuss proposed approaches and solutions, and identify the underlying arguments, principles and assumptions. As a result, we present a list of fundamental considerations for which evidences need to be gathered in order for scenario-based testing to support the homologation of automated vehicles.

With recent advancements in supporting fields like Embedded Systems and Ambient Assisted Living (AAL), intelligent environments are becoming reality. Learning and adapting to user behaviors and gaining some basic knowledge about the underlying user intentions and activities are essential features of an intelligent system. Many systems use an underlying environment model with spatial and semantic information, and often the manual creation of such spatial semantic models is an error-prone and time-consuming task. Moreover, these approaches often neglect how people actually use their physical space. The concept of Activity Zones defines the environmental context by regions of similar user activities and can be learned by observing human behavior. In this paper, we present our approach to extend the notion of Activity Zones by applying a three-dimensional zone computation and visualization process that is independent from the present smart home infrastructure and that is able to autonomously adapt to changes in the environment as well as to shifts in the user behavior.