Toyota Motor Europe

Camlin Italy S.r.l.

Ucl Business Plc

Toyota Motor Europe Nv/sa

Toyota Jidosha Kabushiki Kaisha

Lund University

Mindvisionlabs Limited

Malm, Henrik

Oskarsson, Magnus

Warrant, Eric

Jonas Ambeck-Madsen

Luca Ascari

Nilli Lavie

Hiromichi Yanagihara

Ichiro Sakata

Luke Palmer

Gabriel J Brostow

Luca Mussi

Roberto Ugolotti

Marcello Mastroleo

Alina Bialkowski

Maria Del Vecchio

Giovanni Vecchiato

Pietro Avanzini

Paolo Dario

Gabriel Othmezouri

Matteo Sacchi

Daniel Olmeda Reino

Cecilia Laschi

Paolo Corradi

Federico Sassi

Sergey Antopolskiy

Alessia Colucciello

Anna Marchenkova

Merit Bruckmaier

Ilias Tachtsidis

Luca Kubin

Alessandro Bacchini

Andrea Bellotti

Ulisse Bertocchi

Frederico Sassi

Henrik Malm

Aymeric Rateau

Eric Warrant

Magnus Oskarsson

Ichiro Sakato

Fabio Deon

Giacomo Rizzolatti

Petar Palasek

A method of determining the perceptual load of a human brain. The perceptual load is induced by a predetermined task carried out by the human. The method includes the steps of: measuring with a functional near-infrared spectroscopy (fNIRS) sensor device a metabolic activity of the human brain while the task is carried out and determining the perceptual load as a function of a measured change in metabolic activity. The invention further relates to a method of determining the level of stimulus perception corresponding systems.

System and method for determining the perceptual load and the level of stimulus perception of a human brain

A control device for a vehicle for determining perceptual load of a visual and dynamic driving scene, the control device being configured to: receive an image sequence representing the driving scene, extract a set of scene features from the image sequence, the set of scene features representing static and/or dynamic information of the driving scene, calculate a time-aggregated representation of the image sequence based on the extracted set of scene features, calculate an attention map of the driving scene by attentional pooling of the time-aggregated representation of the image sequence, and determine the perceptual load of the driving scene based on the attention map. The invention further relates to a corresponding method.

Control device, system and method for determining perceptual load of a visual and dynamic driving scene in real time

A method for determining a driving intention of a user in a vehicle using electroencephalography (EEG) signals, comprising: acquiring () a plurality of EEG signals on a user in the vehicle, determining () a plurality of features of the EEG signals, for each feature of the EEG signals, performing (') a respective preliminary soft classification process, so as to obtain a plurality of preliminary soft predictions of driving intention each based on a feature of the EEG signals, performing () a soft classification process based on the plurality of preliminary soft predictions so as to obtain the driving intention of the user.

Method and system for determining a driving intention of a user in a vehicle using EEG signals

A system for brain-based digital menu selection includes an acquisition device to obtain one or more electrical signals corresponding to an operator's brain response, a stimulation generation device to generate a first stimulus with one or more target options based on the operator triggering the system, a first database including a first classification model, and processor. The processor receives a first response from the acquisition device corresponding to an operator's brain response to the first stimulus with the one or more target options, determines a probability of intended target, when the probability of intended target option is higher than a predefined confidence threshold value, selecting the intended target option and presenting a second stimulus, or executing a final action, according to the selected target option, and storing the first response and the selected target option in the first database with a classification identifier identifying the operator.

Neural menu navigator and navigation methods

The invention relates to a control device () for a vehicle for determining the perceptual load of a visual and dynamic driving scene. The control device is configured to: &#x22c5;receive a sensor output () of a sensor (), the sensor () sensing the visual driving scene, &#x22c5;extract a set of scene features () from the sensor output (), the set of scene features () representing static and/or dynamic information of the visual driving scene, &#x22c5;determine the perceptual load () of the set of extracted scene features () based on a predetermined load model (), the load model () being predetermined based on reference video scenes each being labelled with a load value, &#x22c5;map the perceptual load to the sensed driving and &#x22c5;determine a spatial and temporal intensity distribution of the perceptual load across the sensed driving scene. The invention further relates to a vehicle, a system and a method.

Control device, system and method for determining the perceptual load of a visual and dynamic driving scene

A method for determining the intention of a user of a vehicle to brake or accelerate, comprising:

Method and system for determining the intention of a user of a vehicle to brake or accelerate

A computer-based method for simulating a human-like decision in an environmental context, comprising: capturing environmental data with at least one sensor, realising a computer based method for realising a bi-directional compression of high dimensional data by compressing the data into a lower-dimensional map, if environmental data are captured during a learning phase of a computer-based model, evaluate the map of compressed data by determining the quality of the map by how well it separates data with different properties, the captured data corresponding to known pre-recorded data that have been pre-evaluated, if environmental data are captured after the learning phase, add new point to the compressed data and generate a signal indicating which human-like decision to use to correspond to the state of the operator.

System and computer-based method for simulating a human-like control behaviour in an environmental context

This invention relates to an artificial memory system and a method of continuous learning for predicting and anticipating human operator's action as response to ego-intention as well as environmental influences during machine operation. More specifically the invention relates to an architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the network implementation comprising an artificial hierarchical memory system.

Artificial memory system and method for use with a computational machine for interacting with dynamic behaviours

The invention relates to a control device () for a vehicle for determining the perceptual load of a visual and dynamic driving scene. The control device is configured to: The invention further relates to a system and a method.

A method () of processing signal outputs of a plurality of topologically distinct sensors in response to stimuli is described. The method comprises obtaining () a plurality of temporal sensor outputs in parallel. Thereafter, features are extracted (), the features having dynamic behavior pattern. The extraction is performed in a topology consistent way by arithmetic processing in parallel of neighboring temporal sensor outputs. Furthermore, a quality of the extracted features is being determined.

Methods and systems for data processing and their applications

Method for processing a sequence of at least two image datasets each having at least two pixels, each pixel having an intensity value by calculating a structure tensor for each pixel in an image dataset included in the sequence of image datasets; calculating values in a summation kernel based on the structure tensor for each pixel in the image dataset; calculating a weighted intensity value for each pixel in the first image dataset, using as weights the values in the summation kernel; storing the weighted intensity value for each pixel in the image dataset as a processed intensity value for each corresponding pixel in a processed output image dataset; rotating a local coordinate system in which the summation kernel is described so that the coordinate axes of said local coordinate system coincide with the directions of the eigenvectors of the structure tensor.

Method, an apparatus and a computer-readable medium for processing a night vision image dataset

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Brussels, Belgium

Jonas Ambeck-Madsen