Reasoning About the Reliability of Multi-version, Diverse Real-Time Systems

This paper is concerned with the development of reliable real-time systems for use in high integrity applications. It advocates the use of diverse replicated channels, but does not require the dependencies between the channels to be evaluated. Rather it develops and extends the approach of Little wood and Rush by (for general systems) by investigating a two channel system in which one channel, A, is produced to a high level of reliability (i.e. has a very low failure rate), while the other, B, employs various forms of static analysis to sustain an argument that it is perfect (i.e. it will never miss a deadline). The first channel is fully functional, the second contains a more restricted computational model and contains only the critical computations. Potential dependencies between the channels (and their verification) are evaluated in terms of aleatory and epistemic uncertainty. At the aleatory level the events ''A fails" and ''B is imperfect" are independent. Moreover, unlike the general case, independence at the epistemic level is also proposed for common forms of implementation and analysis for real-time systems and their temporal requirements (deadlines). As a result, a systematic approach is advocated that can be applied in a real engineering context to produce highly reliable real-time systems, and to support numerical claims about the level of reliability achieved

Managing Well Integrity using Reliability Based Models

Imperial Users onl

Leveraging ASTM Industry Standard F3269-17 for Providing Safe Operations of a Highly Autonomous Aircraft

This paper expands upon the ASTM industry standard F3269-17 to outline a run-time assurance (RTA) network architecture for use in ensuring safe flight operations of a highly autonomous aircraft. An RTA network architecture is proposed and critical features discussed to implement functions where automation is primarily responsible for the safety of the aircraft instead of a pilot. This shift in responsibility, made possible by the proposed architecture, is key to highly resilient automation and is a core enabler for future pilotless transportation concepts. The findings in this paper stem from the researchers experiences with ASTM in the generation of the standard and some seven years of RTA system development on various flight programs leveraging the RTA concepts outlined in the ASTM standard

Enhanced Position Verification for VANETs using Subjective Logic

The integrity of messages in vehicular ad-hoc networks has been extensively studied by the research community, resulting in the IEEE~1609.2 standard, which provides typical integrity guarantees. However, the correctness of message contents is still one of the main challenges of applying dependable and secure vehicular ad-hoc networks. One important use case is the validity of position information contained in messages: position verification mechanisms have been proposed in the literature to provide this functionality. A more general approach to validate such information is by applying misbehavior detection mechanisms. In this paper, we consider misbehavior detection by enhancing two position verification mechanisms and fusing their results in a generalized framework using subjective logic. We conduct extensive simulations using VEINS to study the impact of traffic density, as well as several types of attackers and fractions of attackers on our mechanisms. The obtained results show the proposed framework can validate position information as effectively as existing approaches in the literature, without tailoring the framework specifically for this use case.Comment: 7 pages, 18 figures, corrected version of a paper submitted to 2016 IEEE 84th Vehicular Technology Conference (VTC2016-Fall): revised the way an opinion is created with eART, and re-did the experiments (uploaded here as correction in agreement with TPC Chairs

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Integrity Determination for Image Rendering Vision Navigation

This research addresses the lack of quantitative integrity approaches for vision navigation, relying on the use of image or image rendering techniques. The ability to provide quantifiable integrity is a critical aspect for utilization of vision systems as a viable means of precision navigation. This research describes the development of two unique approaches for determining uncertainty and integrity for a vision based, precision, relative navigation system, and is based on the concept of using a single camera vision system, such as an electro-optical (EO) or infrared imaging (IR) sensor, to monitor for unacceptably large and potentially unsafe relative navigation errors. The first approach formulates the integrity solution by means of discrete detection methods, for which the systems monitors for conditions when the platform is outside of a defined operational area, thus preventing hazardously misleading information (HMI). The second approach utilizes a generalized Bayesian inference approach, in which a full pdf determination of the estimated navigation state is realized. These integrity approaches are demonstrated, in the context of an aerial refueling application, to provide extremely high levels (10-6) of navigation integrity. Additionally, various sensitivities analyzes show the robustness of these integrity approaches to various vision sensor effects and sensor trade-offs

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions

Low speed propellers: Impact of advanced technologies

Sensitivity studies performed to evaluate the potential of several advanced technological elements on propeller performance, noise, weight, and cost for general aviation aircraft are discussed. Studies indicate that the application of advanced technologies to general aviation propellers can reduce fuel consumption in future aircraft an average of ten percent, meeting current regulatory noise limits. Through the use of composite blade construction, up to 25 percent propeller weight reduction can be achieved. This weight reduction in addition to seven percent propeller efficiency improvements through application of advanced technologies result in four percent reduction in direct operating costs, ten percent reduction in aircraft acquisition cost, and seven percent lower gross weight for general aviation aircraft

Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions

The approach for probabilistic rationale of artificial intelligence systems actions is proposed. It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling. The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.  The approach includes description of the proposed probabilistic models, optimization methods for rationale actions and incremental algorithms for solving the problems of  supporting decision-making on the base of monitored  data and rationale a robot actions in uncertainty conditions. The approach means practically a proactive commitment to excellence in uncertainty conditions. A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems