No abstract

This paper brings together a multidisciplinary perspective from systems engineering, ethics, and law to articulate a common language in which to reason about the multi-faceted problem of assuring the safety of autonomous systems. The paper's focus is on the "gaps" that arise across the development process: the semantic gap, where normal conditions for a complete specification of intended functionality are not present; the responsibility gap, where normal conditions for holding human actors morally responsible for harm are not present; and the liability gap, where normal conditions for securing compensation to victims of harm are not present. By categorising these "gaps" we can expose with greater precision key sources of uncertainty and risk with autonomous systems. This can inform the development of more detailed models of safety assurance and contribute to more effective risk control.

No abstract

Deploying advanced automated testing techniques, such as model-based testing, relies upon the development of rigorous models. Our extensive experience in trying to develop and deploy model-based testing within a large industrial setting has led us to the conclusion that developing requirement models is essential for good model-based testing practice. However, not only are requirements specifications generally incomplete, but it is also difficult to get system architects and designers to produce requirements with the rigor needed for automation. Hence, incentives are needed that tend towards the development of rigorous requirement models. To this end, we introduce the Mint tool that enables and helps automate the early detection of errors during requirements development and appraisal. The paper describes and discusses at length the semantic interpretation of scenariobased requirements and the various types of pathologies that can be detected. We also introduce a UML 2.0 profile for applying domain specific communication semantics that can be used to determine the relevance of these pathologies.

Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions. The consequences of failures and insufficiencies in such algorithms are severe and a convincing assurance case that the algorithms meet certain safety requirements is therefore required. However, the task of demonstrating the performance of such algorithms is non-trivial, and as yet, no consensus has formed regarding an appropriate set of verification measures. This paper provides a framework for reasoning about the contribution of performance evidence to the assurance case for machine learning in an automated driving context and applies the evaluation criteria to a pedestrian recognition case study.

No abstract

Background In March 2020, England went into its first lockdown in response to the COVID-19 pandemic. Restrictions eased temporarily, followed by second and third waves in October 2020 and January 2021. Recent data showed that the COVID-19 pandemic resulted in reduced transmission of some invasive diseases. We assess the impact of the COVID-19 pandemic on pertussis incidence and on the immunisation programme in England. Methods We assessed trends in pertussis cases from 2012 to 2020 by age group and month. Incidence from the time that England eased its initial lockdown measures in July 2020 through to summer 2021 was calculated and the incidence rate ratios of pertussis cases from five years prior to the pandemic (July 2014 – June 2019) compared to the same time period during the pandemic (July 2020 – June 2021). Vaccine coverage estimates for pertussis containing vaccines were reviewed for the maternal and childhood programmes. Results A substantial decline in pertussis cases was observed from April 2020 onwards, marking the lowest number of cases in the last decade. Pertussis incidence dropped in all age groups, particularly among infants less than one year old (0.50 / 100,000 during July 2020 to June 2021 compared to 24.49/ 100,000 from July 2014 to June 2019). The incidence rate ratio was 0.02 (95% CI 0.01 to 0.02) for July 2014 to June 2019 (pre-pandemic) compared to the pandemic period of July 2020 to June 2021. None of the cases had a co-infection with SARS-CoV-2. Vaccine coverage for infants born between January to March 2020 with three doses of pertussis vaccine by 12 months of age decreased by 1.1% points compared to infants born between January to March 2019 (91.6% and 92.7%, respectively). Prenatal pertussis coverage for the 2020 to 2021 financial year was 2.7% points lower than the year prior to the pandemic (70.5% and 76.8%, respectively). Conclusions Lockdown measures due to the COVID-19 pandemic have had a significant impact on pertussis transmission. With the easing of restrictions it is important to continue monitoring pertussis cases in England alongside coverage of the maternal and childhood immunisation programmes.

No abstract