Combined AI Capabilities for Enhancing Maritime Safety in a Common Information Sharing Environment

The complexity of maritime traffic operations indicates an unprecedented necessity for joint introduction and exploitation of artificial intelligence (AI) technologies, that take advantage of the vast amount of vessels’ data, offered by disparate surveillance systems to face challenges at sea. This paper reviews the recent Big Data and AI technology implementations for enhancing the maritime safety level in the common information sharing environment (CISE) of the maritime agencies, including vessel behavior and anomaly monitoring, and ship collision risk assessment. Specifically, the trajectory fusion implemented with InSyTo module for soft information fusion and management toolbox, and the Early Notification module for Vessel Collision are presented within EFFECTOR Project. The focus is to elaborate technical architecture features of these modules and combined AI capabilities for achieving the desired interoperability and complementarity between maritime systems, aiming to provide better decision support and proper information to be distributed among CISE maritime safety stakeholders

Modèles de détection et de prédiction de la somnolence au volant pour des systèmes personnalisés d'aide à la conduite

La conduite automobile requiert la mobilisation des ressources cognitives et physiologiques. Ainsi, détecter si le conducteur est somnolent ou même prédire dans combien de temps il risque de le devenir sont des défis importants pour le développement de nouveaux systèmes d’aide à la conduite. La somnolence, état intermédiaire entre le sommeil et la veille, représente un état dégradé et affecte donc potentiellement les performances de conduite. Ces travaux s’intéressent à la modélisation de la somnolence au volant grâce à des réseaux de neurones artificiels (ANN) et à l’aide de mesures physiologiques (rythme cardiaque et respiratoire), comportementales (mouvement des paupières et de tête) et à l’activité et performances de conduite. La première étude a montré qu’un ANN peut détecter le niveau de somnolence compris entre 0 et 4 (alerte à extrêmement somnolent) avec une racine carrée de l’erreur quadratique moyenne (REQM) de 0,40, mais aussi prédire dans combien de temps un état dégradé risque d’arriver avec une REQM de 2,23 min. Le temps de conduite et les informations personnelles permettent d’accroître les performances. Puis, ces modèles ont été testés sur un nouveau conducteur, mais de mauvaises performances sont observés. Ainsi, Un ANN a été entraîné sur un ensemble de conducteurs, puis il a été adapté à un nouveau conducteur, jamais vu avant par le ANN, grâce à ces premières données d’enregistrement. Grâce à cette adaptation personnalisée du ANN, une amélioration des performances de 40 et 80% est observé pour la détection et la prédiction de la somnolence au volant respectivement. Cette adaptation est une première réponse au problème de la variabilité interindividuelle.Driving a car is a requiring full mobilization of physiological and cognitive resources to maintain performance. Detecting when the driver is drowsy but also predicting when the driver’s operational state begins to degrade has become one ambitious challenge for the development of new Advanced Driving Assistance Systems. Drowsiness, the intermediate state between sleep and awake, represents an impaired state for driving and its potential effect on the driving performance. This work focuses on developing a driver drowsiness model by using artificial neural networks (ANN) and physiological measures (heart and respiratory rate and their variability), behavioral (eyelids and head movement) and driving and performance activity (speed, time-to-lane-crossing, speed, steering wheel angle, position on the lane). The first study shows that a model can detect the level of drowsiness between 0 and 4 (alert and extremely drowsy) with a root mean square error (RMSE) of 0.4 and also predict when the impaired state will occur with a RMSE of 2.23 min. The driving time and personal information can enhance the performance. These models were then tested on a different participant, but in this case, we observe poor generalization performance. We then tested a personalized adaptation of this ANN, where the ANN was trained on a group of drivers and then adapted to a new driver. With this personalized adaptation of the ANN, we observe a performance improvement of 40% and 80% for the detection and the prediction of driver drowsiness respectively. This personalized adaptation process to the first data recorded is an initial response to the problem of inter-individual variability

Detection and prediction of driver drowsiness using artificial neural network models

International audienceNot just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110 min under conditions optimized to induce drowsiness. We measured physiological and behavioral indicators such as heart rate and variability, respiration rate, head and eyelid movements (blink duration, frequency and PERCLOS) and recorded driving behavior such as time-to-lane-crossing, speed, steering wheel angle, position on the lane. Different combinations of this information were tested against the real state of the driver, namely the ground truth, as defined from video recordings via the Trained Observer Rating. Two models using artificial neural networks were developed, one to detect the degree of drowsiness every minute, and the other to predict every minute the time required to reach a particular drowsiness level (moderately drowsy). The best performance in both detection and prediction is obtained with behavioral indicators and additional information. The model can detect the drowsiness level with a mean square error of 0.22 and can predict when a given drowsiness level will be reached with a mean square error of 4.18 min. This study shows that, on a controlled and very monotonous environment conducive to drowsiness in a driving simulator, the dynamics of driver impairment can be predicted

Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness

International audienceMonitoring car drivers for drowsiness is crucial but challenging. The high inter-individual variability observed in measurements raises questions about the accuracy of the drowsiness detection process. In this study, we sought to enhance the performance of machine learning models (Artificial Neural Networks: ANNs) by training a model with a group of drivers and then adapting it to a new individual. Twenty-one participants drove a car simulator for 110 min in a monotonous environment. We measured physiological and behavioral indicators and recorded driving behavior. These measurements, in addition to driving time and personal information, served as the ANN inputs. Two ANN-based models were used, one to detect the level of drowsiness every minute, and the other to predict, every minute, how long it would take the driver to reach a specific drowsiness level (moderately drowsy). The ANNs were trained with 20 participants and subsequently adapted using the earliest part of the data recorded from a 21st participant. Then the adapted ANNs were tested with the remaining data from this 21st participant. The same procedure was run for all 21 participants. Varying amounts of data were used to adapt the ANNs, from 1 to 30 min, Model performance was enhanced for each participant. The overall drowsiness monitoring performance of the models was enhanced by roughly 40% for prediction and 80% for detection

Etude des actions du quotidien pour l'induction de comportements écologiques

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Impact des facteurs démographiques sur l’étiologie des PID

L’impact de l’âge, du sexe et du tabac est bien connu dans certaines pneumopathies interstitielles diffuses chroniques (PIDc). Concernant l’origine géographique, alors que son rôle dans la sarcoïdose est avéré, peu de données sont disponibles dans les autres PIDc. L’objectif du travail était d’évaluer l’impact de l’origine géographique dans l’étiologie des PIDc en prenant en considération les autres facteurs démographiques confondants possibles. [Premier paragraphe