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Artykuły w czasopismach na temat "Véhicules autonomes – Fiabilité"
Apvrille, Ludovic, Tullio Tanzi, Yves Roudier i Jean-Luc Dugelay. "Drone "humanitaire" : état de l'art et réflexions". Revue Française de Photogrammétrie et de Télédétection, nr 213 (26.04.2017): 63–71. http://dx.doi.org/10.52638/rfpt.2017.201.
Pełny tekst źródłaRozprawy doktorskie na temat "Véhicules autonomes – Fiabilité"
Assioua, Yasmine. "Méthodologie de développement de véhicules autonomes sûrs à partir d'exigences fonctionnelles et non fonctionnelles". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT012.
Pełny tekst źródłaThe automotive industry is changing, digital is replacinggradually the mechanical systems. The advent of autonomous and connected cars increases the number and complexity of electronic and computer systems embedded in them, which poses new challenges and requires new processes to develop them.Indeed, compared to conventional vehicles, these highly technological objects have an increased role in the safety of their passengers and their environment. The requirements in terms of reliability and safety are increased. To approach this new era, manufacturers must improve and find better methods of production.The thesis proposes a method to meet certain challenges related to the imperative of reliability and security, that the limitationsof the traditional development approach do not solve satisfactorily. It consists of introducing validation as early as possible in the software development life cycle. The method lays the foundations of an iterative approach for the validation and verification of requirements and textual statements in order to detect any errors, omissions or inconsistencies before implementation. This requirement qualification process is based on modeling and formal verification techniques. It also uses simulations for trace and scenario analysis. They are largely automated
Neggaz, Mohamed Ayoub. "Accélérateurs Matériels pour l'Intelligence Artificielle. Etude de Cas : Voitures Autonomes". Thesis, Valenciennes, Université Polytechnique Hauts-de-France, 2020. http://www.theses.fr/2020UPHF0017.
Pełny tekst źródłaSince the early days of the DARPA challenge, the design of self-driving cars is catching increasing interest. This interest is growing even more with the recent successes of Machine Learning algorithms in perception tasks. While the accuracy of thesealgorithms is irreplaceable, it is very challenging to harness their potential. Realtime constraints as well as reliability issues heighten the burden of designing efficient platforms.We discuss the different implementations and optimization techniques in this work. We tackle the problem of these accelerators from two perspectives: performance and reliability. We propose two acceleration techniques that optimize time and resource usage. On reliability, we study the resilience of Machine Learning algorithms. We propose a tool that gives insights whether these algorithms are reliable enough forsafety critical systems or not. The Resistive Associative Processor accelerator achieves high performance due to its in-memory design which remedies the memory bottleneck present in most Machine Learning algorithms. As for the constant multiplication approach, we opened the door for a new category of optimizations by designing instance specific accelerators. The obtained results outperforms the most recent techniques in terms of execution time and resource usage. Combined with the reliability study we conducted, safety-critical systems can profit from these accelerators without compromising its security
Bouchouia, Mohammed. "Multi layered Misbehavior Detection for a connected and autonomous vehicle". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT018.
Pełny tekst źródłaIn recent years, the vehicular field has undergone significant advancements with the development of autonomous vehicles and smart cities. These advancements have brought about a modernization of human life, where everything is interconnected - from individuals through smartphones to infrastructure, cars, and motorcycles. In such a system, information is exchanged and processed, and used to ensure the proper functioning of all entities. However, the increased reliance on V2X communication also makes it a target for security attacks, which could lead to the dissemination of false or manipulated information from malicious sources. This could pose a threat to the proper functioning of the system and can potentially result in accidents. To address this problem, it is crucial to validate and verify the communication to ensure its accuracy and prevent malicious attacks. We aim to formulate misbehavior and misbehavior detection for connected and autonomous vehicles of level 4/5 automation. In our thesis, we propose a multi-layered architecture for the detection of abnormal behaviors with automatic learning to secure the connected and autonomous vehicles' communications, sensors, and internal components. The architecture allows us to propose a novel reinforcement learning based neural architecture for the detection of misbehaviors where we showed in a simulated environment, through evaluation, that the model is capable of detecting novel misbehaviors and performs better than current state-of-the-art algorithms. Furthermore, we tackle data leakage in V2X data and propose a cross-validation method to avoid said leakage in machine learning applications. We also developed a simulation for vehicular environments capable of injecting and detecting misbehaviors for the evaluation of our thesis results. The ideas developed in this research have resulted in several publications and have the potential to significantly enhance the security and reliability of vehicular systems
Leurent, Edouard. "Apprentissage par renforcement sûr et efficace pour la prise de décision comportementale en conduite autonome". Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I049.
Pełny tekst źródłaIn this Ph.D. thesis, we study how autonomous vehicles can learn to act safely and avoid accidents, despite sharing the road with human drivers whose behaviors are uncertain. To explicitly account for this uncertainty, informed by online observations of the environment, we construct a high-confidence region over the system dynamics, which we propagate through time to bound the possible trajectories of nearby traffic. To ensure safety under such uncertainty, we resort to robust decision-making and act by always considering the worst-case outcomes. This approach guarantees that the performance reached during planning is at least achieved for the true system, and we show by end-to-end analysis that the overall sub-optimality is bounded. Tractability is preserved at all stages, by leveraging sample-efficient tree-based planning algorithms. Another contribution is motivated by the observation that this pessimistic approach tends to produce overly conservative behaviors: imagine you wish to overtake a vehicle, what certainty do you have that they will not change lane at the very last moment, causing an accident? Such reasoning makes it difficult for robots to drive amidst other drivers, merge into a highway, or cross an intersection — an issue colloquially known as the “freezing robot problem”. Thus, the presence of uncertainty induces a trade-off between two contradictory objectives: safety and efficiency. How to arbitrate this conflict? The question can be temporarily circumvented by reducing uncertainty as much as possible. For instance, we propose an attention-based neural network architecture that better accounts for interactions between traffic participants to improve predictions. But to actively embrace this trade-off, we draw on constrained decision-making to consider both the task completion and safety objectives independently. Rather than a unique driving policy, we train a whole continuum of behaviors, ranging from conservative to aggressive. This provides the system designer with a slider allowing them to adjust the level of risk assumed by the vehicle in real-time
Redondin, Maxime. "Approches de classifications à partir de données fortement censurées pour l'analyse de fiabilité et la définition de stratégies de maintenance : application aux marquages routiers dans un contexte de véhicules autonomes". Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1118/document.
Pełny tekst źródłaThe quality and reliability of road infrastructure and its equipment play a major role in road safety. This is especially true if we are interested in autonomous car traffic. Recent papers from VEDECOM Institut proves that a clear and reliable road marking is important in it decison making. Marking lanes are detected by camera. These markings need an accurate maintenance strategy to guarantee that the markings remain perceptible. This report proposes different solutions based on the reliabilty and maintenance theory. Today, the markings reliability is based on the retroreflective illuminance. A retroreflective marking reflects light from a vehicle headlight back in the direction of the driver. Marking retroreflectivity can be dynamically inspected using a retroreflectometer. The litterature of the last thirty years proposes degradation models for retroreflective marking based on a regression model. All of them have a common weakness: they are difficult to apply directly to a given road network. This report presents maintenance models who math with current maintenance actions. A marking lane is interpreted as multi-unit systeme. All unit are laid in parallel. The global maintenance strategy is based on four points. First, the whole inspection data is formalized into one monitoring base. If inspection data is missing or if the maintenance historic is unavailable else an estimation process based on the Agglomerative Hierarchical Clustering (AHC) is proposed. Second, to replace a whole markings lane is logistically difficult to work. Again, an AHC of the monitoring proposed several clusters. Each cluster presents it own degradation model. Clusters are geographically tracked and correlated to specific situation (interchange, urban area, bypass...). That's why a cluster is interpreted as a maintenance strategic area. Thirdly, a Weibull analysis of each cluster is done. Current retroreflectometers cannot detects the exact faillure moment. this information is statistically censored. Three cases are identified : left, right and interval censored. To parameter a Weibull model, an EM Algorithm is propoased as an alternative to the Maximum Likelihood Estimator. This algorithm is also an estimator to censored markings life time. Lastly, two classic preventive maintenance strategies are proposed : systematic according to the age and conditionned to the current degradation. Each one is credible according the current maintenance practice. The first prposed a passsive managament of the markings maintenance. The second ensures an advanced knowledge of the road network over the time. On a multi-unit system no-repairable and strongly censored, units which admit the same degradation model are identified by a clustering approach. Each cluster present it own Weibull analysis. Finally, an adapted maintenance strategy is done
Zermani, Sara. "Implémentation sur SoC des réseaux Bayésiens pour l'état de santé et la décision dans le cadre de missions de véhicules autonomes". Thesis, Brest, 2017. http://www.theses.fr/2017BRES0101/document.
Pełny tekst źródłaAutonomous vehicles, such as drones, are used in different application areas to perform simple or complex missions. On one hand, they generally operate in uncertain environmental conditions, which can lead to disastrous consequences for humans and the environment. Therefore, it is necessary to continuously monitor the health of the system in order to detect and locate failures and to be able to make the decision in real time. This decision must maximize the ability to meet the mission objectives while maintaining the security requirements. On the other hand, they are required to perform tasks with large computation demands and performance requirements. Therefore, it is necessary to think of dedicated hardware accelerators to unload the processor and to meet the requirements of a computational speed-up.This is what we tried to demonstrate in this dual objective thesis. The first objective is to define a model for the health management and decision making. To this end, we used Bayesian networks, which are efficient probabilistic graphical models for diagnosis and decision-making under uncertainty. We propose a generic model based on an FMEA (Failure Modes and Effects Analysis). This analysis takes into account the different observations on the monitors and the appearance contexts. The second objective is the design and realization of hardware accelerators for Bayesian networks in general and more particularly for our models of health management and decision-making. Having no tool for the embedded implementation of computation by Bayesian networks, we propose a software workbench covering graphical or textual Bayesian networks up to the generation of the bitstream ready for the software or hardware implementation on FPGA. Finally, we test and validate our implementations on the Xilinx ZedBoard, incorporating an ARM Cortex-A9 processor and an FPGA
Dabboussi, Abdallah. "Dependability approaches for mobile environment : Application on connected autonomous vehicles". Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA029.
Pełny tekst źródłaConnected and Autonomous vehicles (CAV) must have adequate reliability and safety requirements in uncertain environments with complex circumstances. Sensor technology, actuators and artificial intelligence (AI) are constantly and rapidly evolving, thus enabling further development of self-driving vehicles, and increasing the automation of driving. CAV shows many benefits in human life such as increasing road safety, reducing pollution, and providing independent mobility to non-drivers. However, these advanced components create a new set of challenges concerning safety and dependability. Hence, it is necessary to evaluate these technologies before implementation.We study in this thesis the reliability of CAV as a whole, focusing on sensors and the communication system. For that purpose, a functional analysis was done for the CAV system.Our scientific approach for the analyzing the CAV reliability, was structured with methods that combine quantitative and qualitative approaches such as functional analysis for both internal and external, Preliminary Risk Analysis (PRA), and failure modes and effects criticality analysis (FMECA), in addition to other analysis techniques.To prove our results, a simulation was done using the Fault Tree analysis (FTA) probability in order to validate the proposed approach. The data (Failure ratio) used were from a professional database related to the type of components presented in the system. Using this data, a probabilistic model of degradation was proposed. A probability calculation was performed in relation to a reference time of use. Thereafter a sensitivity analysis was suggested concerning the reliability parameters and redesign proposals developed for the components.CAV provide several communication models: vehicles to vehicle (V2V), or with Road Side Infrastructure: vehicle to infrastructure (V2I). Dedicated Short Range Communication (DSRC) employs a multichannel approach to cater for a variety of safety and non-safety applications. Safety applications necessitate appropriate and reliable transmissions, while non-safety applications require performance and high speed. Broadcasting of Basic Safety Messages (BSM) is one of the fundamental services in today’s connected vehicles. For that, an analytical model to evaluate the reliability of IEEE 802.11 based V2V safety-related broadcast services in DSRC system on highway was proposed. Finally, an enhancement on the proposed model was made in order to increase the reliability of the V2V connection, taking into consideration many factors such as transmission range, vehicle density, and safety headway distance on highway, packet error rate, noise influence, and failures rates of communication equipment.Evaluating these problems leads to a sensitivity analysis related to reliability parameters, which helps further innovation in CAV and automobile engineering
Abci, Boussad. "Approche informationnelle pour la navigation autonome tolérante aux défauts : application aux systèmes robotiques mobiles". Thesis, Lille 1, 2019. http://www.theses.fr/2019LIL1I073.
Pełny tekst źródłaOver the last years, autonomous navigation for mobile robot systems has known an increasing interest from the scientific community. This is mainly due to the diversity of its applications and the different challenges that it represents. Without any human intervention, autonomous navigation must be safe, reliable and accurate. Nevertheless, it may be subject to various degradations that could compromise its objective. Indeed, external disturbances, as well as sensor and actuator faults, may affect the different aspects of autonomous navigation, which are localization, path planning and trajectory tracking. This is why we are devoting this thesis to the design of new algorithms that contribute to make the navigation system robust against external disturbances and tolerant to sensor and actuator fauts. We have adopted a residual generation based fault-diagnosis strategy combined with a robust sliding mode controller that is robust against a certain class of perturbations that are not necessary uniformly bounded. The proposed diagnostic layer is purely informational. It is based on the use of two information filters with different evolution models, and the divergences of Bhattacharyya and Kullback-Leibler for residual design. These residuals are evaluated using statistical methods, in order to detect, isolate then exclude sensor and actuator faults from the navigation system. The proposed approach is applied to different differential drive mobile-robot systems. Experimental results obtained by using the CRIStAL robotic platform, so-called PRETIL, are presented and discussed
Brini, Manel. "Safety-Bag pour les systèmes complexes". Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2444/document.
Pełny tekst źródłaAutonomous automotive vehicles are critical systems. Indeed, following their failures, they can cause catastrophic damage to the human and the environment in which they operate. The control of autonomous vehicles is a complex function, with many potential failure modes. In the case of experimental platforms that have not followed either the development methods or the certification cycle required for industrial systems, the probabilities of failure are much greater. Indeed, these experimental vehicles face two problems that impede their dependability, which is the justified confidence that can be had in their correct behavior. First, they are used in open environment, with a very wide execution context. This makes their validation very complex, since many hours of testing would be necessary, with no guarantee that all faults in the system are detected and corrected. In addition, their behavior is often very difficult to predict or model. This may be due to the use of artificial intelligence software to solve complex problems such as navigation or perception, but also to the multiplicity of systems or components interacting and complicating the behavior of the final system, for example by generating behaviors emerging. A technique to increase the safety of these autonomous systems is the establishment of an Independent Safety Component, called "Safety-Bag". This system is integrated between the control application and the actuators of the vehicle, which allows it to check online a set of safety necessities, which are necessary properties to ensure the safety of the system. Each safety necessity is composed of a safety trigger condition and a safety intervention applied when the safety trigger condition is violated. This intervention consists of either a safety inhibition that prevents the system from moving to a risk state, or a safety action to return the autonomous vehicle to a safe state. The definition of safety necessities must follow a rigorous method to be systematic. To do this, we carried out in our work a study of dependability based on two fault prevention methods: FMEA and HazOp-UML, that respectively focus on the internal hardware and software components of the system and on the road environment and driving process. The result of these risk analyzes is a set of safety requirements. Some of these safety requirements can be translated into safety necessities, implementable and verifiable by the Safety-Bag. Others cannot be implemented in the Safety-Bag. The latter must remain simple so that it is easy to be validated. Then, we carried out experiments based on the faults injection in order to validate some safety necessities and to evaluate the Safety-Bag's behavior. These experiments were done on our robotic vehicle type Fluence in our laboratory in two different settings, on the actual track SEVILLE at first and then on the virtual track simulated by the Scanner Studio software on the VILAD testbed. The Safety-Bag remains a promising but partial solution for autonomous industrial vehicles. On the other hand, it meets the essential needs for the safety of experimental autonomous vehicles
Girault, Alain. "Contributions à la conception sûre des systèmes embarqués sûrs". Habilitation à diriger des recherches, 2006. http://tel.archives-ouvertes.fr/tel-00177048.
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