Selma Saidi

Mohamed Amine Khelassi, Felix Suchert, Abderaouf Amalou et al.

The adoption of high-performance multi-core platforms in avionics and automotive systems introduces significant challenges in ensuring predictable execution, primarily due to shared resource interferences. Many existing approaches study interference from a single angle-for example, through hardware-level analysis or by monitoring software execution. However, no single abstraction level is sufficient on its own. Hardware behavior, program structure, and system configuration all interact, and a complete view is needed to understand where interferences come from and how to reduce them. In this paper, we present a methodology that brings together several tools that operate at different abstraction levels. At the lowest level, PHYLOG provides a formal model of the hardware and identifies possible interference channels using micro-architectural transactions. At the program level, machine learning analysis locates the exact parts of the code that are most sensitive to shared-resource contention. At the compilation level, MLIR-based transformations use this information to reshape memory access patterns and reduce pressure on shared resources. Finally, at the system level, Linux cgroups enforce static execution constraints to prevent highly interfering tasks from running together. The goal of our approach is to reduce memory interference and improve the system's predictability, thereby easing the certification process of multi-core systems in safety-critical domains.

Selma Saidi, Omar Laimona, Christoph Schmickler et al.

Autonomous systems are becoming an integral part of many application domains, like in the mobility sector. However, ensuring their safe and correct behaviour in dynamic and complex environments remains a significant challenge, where systems should autonomously make decisions e.g., about manoeuvring. We propose in this paper a general collaborative approach for increasing the level of trustworthiness in the environment of operation and improve reliability and good decision making in autonomous system. In the presence of conflicting information, aggregation becomes a major issue for trustworthy decision making based on collaborative data sharing. Unlike classical approaches in the literature that rely on consensus or majority as aggregation rule, we exploit the fact that autonomous systems have different quality attributes like perception quality. We use this criteria to determine which autonomous systems are trustworthy and borrow concepts from social epistemology to define aggregation and propagation rules, used for automated decision making. We use Binary Decision Diagrams (BDDs) as formal models for beliefs aggregation and propagation, and formulate reduction rules to reduce the size of the BDDs and allow efficient computation structures for collaborative automated reasoning.

Aman Malhotra, Selma Saidi

This is a summary paper of a use case of a Robotdog dedicated to guide visually impaired people in complex environment like a smart intersection. In such scenarios, the Robotdog has to autonomously decide whether it is safe to cross the intersection or not in order to further guide the human. We leverage data sharing and collaboration between the Robotdog and other autonomous systems operating in the same environment. We propose a system architecture for autonomous systems through a separation of a collaborative decision layer, to enable collective decision making processes, where data about the environment, relevant to the Robotdog decision, together with evidences for trustworthiness about other systems and the environment are shared.