No abstract

In industrial model-based development (MBD) frameworks, requirements are typically specified informally using textual descriptions. To enable the application of formal methods, these specifications need to be formalized in the input languages of all formal tools that should be applied to analyse the models at different development levels. In this paper we propose a unified approach for the computer-assisted formal specification of requirements and their fully automated translation into the specification languages of different verification tools. We consider a two-stage MBD scenario where first Simulink models are developed from which executable code is generated automatically. We (i) propose a specification language and a prototypical tool for the formal but still textual specification of requirements, (ii) show how these requirements can be translated automatically into the input languages of Simulink Design Verifier for verification of Simulink models and BTC EmbeddedValidator for source code verification, and (iii) show how our unified framework enables besides automated formal verification also the automated generation of test cases.

Density of the reachable states can help understand the risk of safety-critical systems, especially in situations when worst-case reachability is too conservative. Recent work provides a data-driven approach to compute the density distribution of autonomous systems' forward reachable states online. In this paper, we study the use of such approach in combination with model predictive control for verifiable safe path planning under uncertainties. We first use the learned density distribution to compute the risk of collision online. If such risk exceeds the acceptable threshold, our method will plan for a new path around the previous trajectory, with the risk of collision below the threshold. Our method is wellsuited to handle systems with uncertainties and complicated dynamics as our data-driven approach does not need an analytical form of the systems' dynamics and can estimate forward state density with an arbitrary initial distribution of uncertainties. We design two challenging scenarios (autonomous driving and hovercraft control) for safe motion planning in environments with obstacles under system uncertainties. We first show that our density estimation approach can reach a similar accuracy as the Monte-Carlo-based method while using only 0.01X training samples. By leveraging the estimated risk, our algorithm achieves the highest success rate in goal reaching when enforcing the safety rate above 0.99.

Traditionally, extensive vehicle testing is applied to assure the robustness and safety of automotive systems. This approach is highly challenged by increasing system complexity. Formal verification lends a powerful framework for model-based safety assurance, but due to the mixed discrete–continuous behavior of automotive systems, traditional tools for discrete program verification are helpful but not sufficient.In academia, during the last two decades new approaches arose for the formal verification of such mixed discrete-continuous systems. However, the industry is not fully aware of this development, the tools are seldom tried and their applicability is not well examined. In a Ford–RWTH research alliance project, we aimed at evaluating the potential of knowledge and technology transfer in this area.This paper has two main objectives. Firstly, we want to report on the state-of-the-art in the above-mentioned academic development in a generally understandable form, targeted to interested potential users. Secondly, we want to share our observations after testing different available tools for their applicability and usability in the automotive sector and as a conclusion devise some recommendations.