Model-based dependability analysis : state-of-the-art, challenges and future outlook

Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis

Safety Proofs for Automated Driving using Formal Methods

The introduction of driving automation in road vehicles can potentially reduce road traffic crashes and significantly improve road safety. Automation in road vehicles also brings other benefits such as the possibility to provide independent mobility for people who cannot and/or should not drive. Correctness of such automated driving systems (ADSs) is crucial as incorrect behaviour may have catastrophic consequences.Automated vehicles operate in complex and dynamic environments, which requires decision-making and control at different levels. The aim of such decision-making is for the vehicle to be safe at all times. Verifying safety of these systems is crucial for the commercial deployment of full autonomy in vehicles. Testing for safety is expensive, impractical, and can never guarantee the absence of errors. In contrast, formal methods, techniques that use rigorous mathematical models to build hardware and software systems, can provide mathematical proofs of the correctness of the systems.The focus of this thesis is to address some of the challenges in the safety verification of decision and control systems for automated driving. A central question here is how to establish formal methods as an efficient approach to develop a safe ADS. A key finding is the need for an integrated formal approach to prove correctness of ADS. Several formal methods to model, specify, and verify ADS are evaluated. Insights into how the evaluated methods differ in various aspects and the challenges in the respective methods are discussed. To help developers and safety experts design safe ADSs, the thesis presents modelling guidelines and methods to identify and address subtle modelling errors that might inadvertently result in proving a faulty design to be safe. To address challenges in the manual modelling process, a systematic approach to automatically obtain formal models from ADS software is presented and validated by a proof of concept. Finally, a structured approach on how to use the different formal artifacts to provide evidence for the safety argument of an ADS is shown