On Provably Correct Decision-Making for Automated Driving

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 several other benefits such as the possibility to provide independent mobility for people who cannot and/or should not drive. Many different hardware and software components (e.g. sensing, decision-making, actuation, and control) interact to solve the autonomous driving task. Correctness of such automated driving systems is crucial as incorrect behaviour may have catastrophic consequences. Autonomous vehicles operate in complex and dynamic environments, which requires decision-making and planning at different levels. The aim of such decision-making components in these systems is to make safe decisions at all times. The challenge of safety verification 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, which are techniques that use rigorous mathematical models to build hardware and software systems can provide a mathematical proof of the correctness of the system. The focus of this thesis is to address some of the challenges in the safety verification of decision-making in automated driving systems. A central question here is how to establish formal verification as an efficient tool for automated driving software development.A key finding is the need for an integrated formal approach to prove correctness and to provide a complete safety argument. This thesis provides insights into how three different formal verification approaches, namely supervisory control theory, model checking, and deductive verification differ in their application to automated driving and identifies the challenges associated with each method. It identifies the need for the introduction of more rigour in the requirement refinement process and presents one possible solution by using a formal model-based safety analysis approach. To address challenges in the manual modelling process, a possible solution by automatically learning formal models directly from code is proposed

Guarded atomic actions and refinement in a system-on-chip development flow: bridging the specification gap with Event-B

Modern System-on-chip (SoC) hardware design puts considerable pressure on existing design and verification flows, languages and tools. The Register Transfer Level (RTL)description, which forms the input for synchronous, logic synthesis-driven design is at too low a level of abstraction for efficient architectural exploration and re-use. The existing methods for taking a high-level paper specification and refining this specification to an implementation that meets its performance criteria is largely manual and error-prone and as RTL descriptions get larger, a systematic design method is necessary to address explicitly the timing issues that arise when applying logic synthesis to such large blocks.Guarded Atomic Actions have been shown to offer a convenient notation for describing microarchitectures that is amenable to formal reasoning and high-level synthesis. Event-B is a language and method that supports the development of specifications with automatic proof and refinement, based on guarded atomic actions. Latency-insensitive design ensures that a design composed of functionally correct components will be independent of communication latency. A method has been developed which uses Event-B for latency-insensitive SoC component and sub-system design which can be combined with high-level, component synthesis to enable architectural exploration and re-use at the specification level and to close the specification gap in the SoC hardware flow

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