Andrea Margheri

Sorin Grigorescu, Tiberiu Cocias, Bogdan Trasnea et al.

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

Andrea Margheri, Massimiliano Masi, Rosario Pugliese et al.

Access control systems are widely used means for the protection of computing systems. They are defined in terms of access control policies regulating the accesses to system resources. In this paper, we introduce a formally-defined, fully-implemented framework for specification, analysis and enforcement of attribute-based access control policies. The framework rests on FACPL, a language with a compact, yet expressive, syntax for specification of real-world access control policies and with a rigorously defined denotational semantics. The framework enables the automatic verification of properties regarding both the authorisations enforced by single policies and the relationships among multiple policies. Effectiveness and performance of the analysis rely on a semantic-preserving representation of FACPL policies in terms of SMT formulae and on the use of efficient SMT solvers. Our analysis approach explicitly addresses some crucial aspects of policy evaluation, as e.g. missing attributes, erroneous values and obligations, which are instead overlooked in other proposals. The framework is supported by Java-based tools, among which an Eclipse- based IDE offering a tailored development and analysis environment for FACPL policies and a Java library for policy enforcement. We illustrate the framework and its formal ingredients by means of an e-Health case study, while its effectiveness is assessed by means of performance stress tests and experiments on a well-established benchmark.