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.

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Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking - with corresponding multiple interpretations of ”edge computing” (e.g. device edge, network edge, distributed cloud, etc.). Considering the strong drivers for edge-computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g. avoiding specific gaps. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.

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 ADS or ADAS 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 literature review 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.

No abstract