Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

One of the major challenges of automated driving systems (ADS) is showing that they drive safely. Key to ensuring safety is eliciting a complete set of top-level safety requirements (safety goals). This is typically done with an activity called hazard analysis and risk assessment (HARA). In this paper we argue that the HARA of ISO 26262:2018 is not directly suitable for an ADS, both because the number of relevant operational situations may be vast, and because the ability of the ADS to make decisions in order to reduce risks will affect the analysis of exposure and hazards. Instead we propose a tailoring using a quantitative risk norm (QRN) with consequence classes, where each class has a limit for the frequency within which the consequences may occur. Incident types are then defined and assigned to the consequence classes; the requirements prescribing the limits of these incident types are used as safety goals to fulfil in the implementation. The main benefits of the QRN approach are the ability to show completeness of safety goals, and make sure that the safety strategy is not limited by safety goals which are not formulated in a way suitable for an ADS.

Automated Driving Systems (ADSs) show great potential to improve our transport systems. Safety validation, before market launch, is challenging due to the large number of miles required to gather enough evidence for a proven in use argumentation. Hence there is ongoing research to find more effective ways of verifying and validating the safety of ADSs. It is crucial both for the design as well as the validation to have a good understanding of the environment of the ADS. A natural way of characterizing the external conditions is by modelling and analysing data from real traffic. Towards this end, we present a framework with the primary ultimate objective to completely model and quantify the statistically relevant actions that other vehicles conduct on motorways. Two categories of fundamental actions are identified by recognising that a vehicle can only move longitudinally and laterally. The fundamental actions are defined in detail to create a set that is collectively exhaustive and mutually exclusive. All physically possible combinatorial actions that can be constructed from the fundamental actions are presented. To increase the granularity of the modelling the combinatorial actions are proposed to be analysed as sequences. Further, multi-vehicle interactions, which capture correlations between actions from multiple vehicles, are discussed. The resulting modularity of the framework allows for performing statistical analysis at an arbitrary granularity to support the design of a performant ADS as well as creating applicable validation scenarios. The use of the framework is demonstrated by automatically identifying fundamental actions in field data. Identified trajectories of two types of actions are visualised and the distributions for one parameter characterising each action type are estimated.