Claire Pagetti

Mélanie Ducoffe, Maxime Carrere, Léo Féliers et al.

As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data. Despite the strong practical and commercial interest in autonomous landing systems in the aerospace field, there is a lack of open-source datasets of aerial images. To address this issue, we present a dataset-lard-of high-quality aerial images for the task of runway detection during approach and landing phases. Most of the dataset is composed of synthetic images but we also provide manually labelled images from real landing footages, to extend the detection task to a more realistic setting. In addition, we offer the generator which can produce such synthetic front-view images and enables automatic annotation of the runway corners through geometric transformations. This dataset paves the way for further research such as the analysis of dataset quality or the development of models to cope with the detection tasks. Find data, code and more up-to-date information at https://github.com/deel-ai/LARD

Yassine Bougacha, Geoffrey Delhomme, Mélanie Ducoffe et al.

This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.

Benjamin Husson, Mohammed Belcaïd, Thomas Carle et al.

Convolutional neural networks (CNNs) require a large number of multiply-accumulate (MAC) operations. To meet real-time constraints, they often need to be executed on specialized accelerators composed of an on-chip memory and a processing unit. However, the on-chip memory is often insufficient to store all the data required to compute a CNN layer. Thus, the computation must be performed in several offloading steps. We formalise such sequences of steps and apply our formalism to a state of the art decomposition of convolutions. In order to find optimal strategies in terms of duration, we encode the problem with a set of constraints. A Python-based simulator allows to analyse in-depth computed strategies.

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.

Noémie Cohen, Mélanie Ducoffe, Christophe Gabreau et al.

Safety-critical applications are required to perform as expected in normal operations. Image processing functions are often required to be insensitive to small geometric perturbations such as rotation, scaling, shearing or translation. This paper addresses the formal verification of neural networks against geometric perturbations on their image dataset. Our method Super-DeepG improves the reasoning used in linear relaxation techniques and Lipschitz optimization, and provides an implementation that leverages GPU hardware. By doing so, Super-DeepG achieves both precision and computational efficiency of robustness certification, to an extent that outperforms prior work. Super-DeepG is shared as an open-source tool on GitHub.

Noémie Cohen, Mélanie Ducoffe, Ryma Boumazouza et al.

We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric. The approach has been implemented in an open source code, named IBP IoU, compatible with popular abstract interpretation based verification tools. The resulting verifier is evaluated on landing approach runway detection and handwritten digit recognition case studies. Comparisons against a baseline (Vanilla IBP IoU) highlight the superior performance of IBP IoU in ensuring accuracy and stability, contributing to more secure and robust machine learning applications.

Anthony Faure-Gignoux, Kevin Delmas, Adrien Gauffriau et al.

Deploying complex Convolutional Neural Networks (CNNs) on FPGA-based accelerators is a promising way forward for safety-critical domains such as aeronautics. In a previous work, we have explored the Versatile Tensor Accelerator (VTA) and showed its suitability for avionic applications. For that, we developed an initial stand-alone compiler designed with certification in mind. However, this compiler still suffers from some limitations that are overcome in this paper. The contributions consist in extending and fully automating the VTA compilation chain to allow complete CNN compilation and support larger CNNs (which parameters do not fit in the on-chip memory). The effectiveness is demonstrated by the successful compilation and simulated execution of a YOLO-NAS object detection model.

Nicolas Valot, Louis Fabre, Benjamin Lesage et al.

Machine Learning (ML) may offer new capabilities in airborne systems. However, as any piece of airborne systems, ML-based systems will be required to guarantee their safe operation. Thus, their development will have to be demonstrated to be compliant with the adequate guidance. So far, the European Union Aviation Safety Agency (EASA) has published a concept paper and an EUROCAE/SAE group is preparing ED-324. Both approaches delineate high-level objectives to confirm the ML model achieves its intended function and maintains training performance in the target environment. The paper aims to clarify the difference between an ML model and its corresponding unambiguous description, referred to as the Machine Learning Model Description (MLMD). It then refines the essential notion of semantics preservation to ensure the accurate replication of the model. We apply our contributions to several industrial use cases to build and compare several target models.

Cyril Cappi, Noémie Cohen, Mélanie Ducoffe et al.

This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Learning (ML) system. Relying on emerging certification standards, we describe the process for establishing ODDs at both the system and image levels. In the process, we present the translation of high-level system constraints into actionable image-level properties, allowing for the definition of verifiable Data Quality Requirements (DQRs). To illustrate this approach, we use the Landing Approach Runway Detection (LARD) dataset which combines synthetic imagery and real footage, and we focus on the steps required to verify the DQRs. The replicable framework presented in this paper addresses the challenges of designing a dataset compliant with the stringent needs of ML-based systems certification in safety-critical applications.

Mohammed Belcaid, Eric Bonnafous, Louis Crison et al.

In this paper, we present a development process of a drone detection system involving a machine learning object detection component. The purpose is to reach acceptable performance objectives and provide sufficient evidences, required by the recommendations (soon to be published) of the ED 324 / ARP 6983 standard, to gain confidence in the dependability of the designed system.

Hervé Delseny, Christophe Gabreau, Adrien Gauffriau et al.

Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exupéry de Toulouse (IRT), as part of the DEEL Project.

Arthur Clavière, Eric Asselin, Christophe Garion et al.

In this paper, we propose a system-level approach for verifying the safety of neural network controlled systems, combining a continuous-time physical system with a discrete-time neural network based controller. We assume a generic model for the controller that can capture both simple and complex behaviours involving neural networks. Based on this model, we perform a reachability analysis that soundly approximates the reachable states of the overall system, allowing to achieve a formal proof of safety. To this end, we leverage both validated simulation to approximate the behaviour of the physical system and abstract interpretation to approximate the behaviour of the controller. We evaluate the applicability of our approach using a real-world use case. Moreover, we show that our approach can provide valuable information when the system cannot be proved totally safe.