A Light on Physiological Sensors for Efficient Driver Drowsiness Detection System

International audienceThe significant advance in bio-sensor technologies hold promise to monitor human physiologicalsignals in real time. In the context of public safety, such technology knows notable research investigations toobjectively detect early stage of driver drowsiness that impairs driver performance under various conditions.Seeking for low-cost, compact yet reliable sensing technology that can provide a solution to drowsy stateproblem is challenging. While some enduring solutions have been available as prototypes for a while, many ofthese technologies are now in the development, validation testing, or even commercialization stages. Thecontribution of this paper is to assess current progress in the development of bio-sensors based driver drowsinessdetection technologies and study their fundamental specifications to achieve accuracy requirements. Existingmarket and research products are then ranked following the discussed specifications. The finding of this work isto provide a methodology to facilitate making the appropriate hardware choice to implement efficient yet lowcostdrowsiness detection system using existing market physiological based sensors

Transformation-adversarial network for road detection in LIDAR rings, and model-free evidential road grid mapping.

International audienceWe propose a deep learning approach to perform road-detection in LIDAR scans, at the point level. Instead of processing a full LIDAR point-cloud, LIDAR rings can be processed individually. To account for the geometrical diversity among LIDAR rings, an homothety rescaling factor can be predicted during the classification, to realign all the LIDAR rings and facilitate the training. This scale factor is learnt in a semi-supervised fashion. A performant classification can then be achieved with a relatively simple system. Furthermore, evidential mass values can be generated for each point from an observation of the conflict at the output of the network, which enables the classification results to be fused in evidential grids. Experiments are done on real-life LIDAR scans that were labelled from a lane-level centimetric map, to evaluate the classification performances

Classification crédibiliste d'objets LIDAR en monde ouvert, par apprentissage profond

National audienceWe propose an evidential end-to-end deep neural network to classify LIDAR objects. The system is capable of classifying ambiguous and incoherent objects as unknown, while only having been trained on vehicles and vulnerable road users. This is achieved thanks to an evidential reformulation of generalized logistic regression classifiers, and an online filtering strategy based on statistical assumptions.Nous proposons un système de classification crédibiliste d'objets LIDAR par apprentissage profond. L'algorithme proposé est basé sur une reformulation crédibiliste d'un perceptron multi-couche, ainsi que sur un mécanisme de filtrage statistique simple. Le système, qui n'est entraîné que sur une base d'usagers routiers, est cependant capable de classifier des objets incohérents vis-à-vis de ce jeu d'entraînement comme étant des objets inconnus

Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles

International audienc

Systeme evolutif de perception en robotique mobile

Available from INIST (FR), Document Supply Service, under shelf-number : AR 16459 (1); AR 16459 (2) / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEMinistere de la Recherche et de l'Espace (MRE), 75 - Paris (France)FRFranc