Researcher profile

Jérémie Guiochet

Jérémie Guiochet contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate

Artificial Intelligence Machine Learning Computer Vision Robotics eess.IV Software Engineering

Trust snapshot

Quick read

Trust 21 - Emerging

6works

0followers

6topics

4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

preprint2026arXiv

Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing

Runtime monitoring is essential to ensure the safety of ML applications in safety-critical domains. However, current research is fragmented, with independent methods emerging from different communities. In this paper, we propose a unified framework categorising runtime monitoring approaches into three distinct types: Operational Design Domain (ODD) monitoring, which ensures compliance with expected operating conditions; Out-of-Distribution (OOD) monitoring, which rejects inputs that deviate from the training data; and Out-of-Model-Scope (OMS) monitoring, which detects anomalous model behaviour based its internal states or outputs. We demonstrate the benefits of this categorization with a dedicated experiment on an aeronautical safety-critical application: runway detection during landing. This framework facilitates design of monitoring activities, with complementary categories of monitors, and enables evaluation and comparison of different monitors using common, safety-oriented metrics.

preprint2023arXiv

Out-Of-Distribution Detection Is Not All You Need

The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee their correct behavior. Runtime monitors are components aiming to identify unsafe predictions and discard them before they can lead to catastrophic consequences. Several recent works on runtime monitoring have focused on out-of-distribution (OOD) detection, i.e., identifying inputs that are different from the training data. In this work, we argue that OOD detection is not a well-suited framework to design efficient runtime monitors and that it is more relevant to evaluate monitors based on their ability to discard incorrect predictions. We call this setting out-ofmodel-scope detection and discuss the conceptual differences with OOD. We also conduct extensive experiments on popular datasets from the literature to show that studying monitors in the OOD setting can be misleading: 1. very good OOD results can give a false impression of safety, 2. comparison under the OOD setting does not allow identifying the best monitor to detect errors. Finally, we also show that removing erroneous training data samples helps to train better monitors.

preprint2022arXiv

Evaluation of Runtime Monitoring for UAV Emergency Landing

To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using on-board sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer.

preprint2022arXiv

Unifying Evaluation of Machine Learning Safety Monitors

With the increasing use of Machine Learning (ML) in critical autonomous systems, runtime monitors have been developed to detect prediction errors and keep the system in a safe state during operations. Monitors have been proposed for different applications involving diverse perception tasks and ML models, and specific evaluation procedures and metrics are used for different contexts. This paper introduces three unified safety-oriented metrics, representing the safety benefits of the monitor (Safety Gain), the remaining safety gaps after using it (Residual Hazard), and its negative impact on the system's performance (Availability Cost). To compute these metrics, one requires to define two return functions, representing how a given ML prediction will impact expected future rewards and hazards. Three use-cases (classification, drone landing, and autonomous driving) are used to demonstrate how metrics from the literature can be expressed in terms of the proposed metrics. Experimental results on these examples show how different evaluation choices impact the perceived performance of a monitor. As our formalism requires us to formulate explicit safety assumptions, it allows us to ensure that the evaluation conducted matches the high-level system requirements.

preprint2016arXiv

Hazard analysis of human--robot interactions with HAZOP--UML

New safety critical systems are about to appear in our everyday life: advanced robots able to interact with humans and perform tasks at home, in hospitals , or at work. A hazardous behavior of those systems, induced by failures or extreme environment conditions, may lead to catastrophic consequences. Well-known risk analysis methods used in other critical domains (e.g., avion-ics, nuclear, medical, transportation), have to be extended or adapted due to the non-deterministic behavior of those systems, evolving in unstructured environments. One major challenge is thus to develop methods that can be applied at the very beginning of the development process, to identify hazards induced by robot tasks and their interactions with humans. In this paper we present a method which is based on an adaptation of a hazard identification technique, HAZOP (Hazard Operability), coupled with a system description notation, UML (Unified Modeling Language). This systematic approach has been applied successfully in research projects, and is now applied by robot manufacturers. Some results of those studies are presented and discussed to explain the benefits and limits of our method.

preprint2015arXiv

A Model for Safety Case Confidence Assessment

Building a safety case is a common approach to make expert judgement explicit about safety of a system. The issue of confidence in such argumentation is still an open research field. Providing quantitative estimation of confidence is an interesting approach to manage complexity of arguments. This paper explores the main current approaches, and proposes a new model for quantitative confidence estimation based on Belief Theory for its definition, and on Bayesian Belief Networks for its propagation in safety case networks.