Nouvelle stratégie de contrôle éco-énergétique pour les véhicules hybrides et électriques – Gestion de la batterie et optimisation prédictive

2021

Deep Learning-Based Real-Time Object Detection in Inland Navigation

2019

International audience; Semi-autonomous and fully-autonomous systems must have knowledge about the objects in their environment to ensure a safe navigation. Modern approaches implement deep learning techniques to train a neural network for object detection. This project will study the effectiveness of using several promising algorithms such as Faster R-CNN, SSD, and different versions of YOLO, to detect, classify, and track objects in near real-time fluvial domain. Since no dataset is available for this purpose in literature, we first started by annotating a dataset of 2488 images with almost 35 400 annotations for training the convolutional neural network architectures. We made this data s…

Smart River : Towards Efficient Cooperative Autonomous Inland Navigation

2022

In recent years, inland waterway transport has witnessed increasing attention from France and many European countries. However, this mode of transport lacks flexibility, has an aging infrastructure, and the current ships are not adapted to an increase in transport capacity ensuring the safety of vessels and goods as well as reliable and constant delivery times. Therefore, inland transport must go through an organizational and technical renovation specific to its particular environment in order to hope to compete with land transport.In this thesis, we propose developing a smart river ecosystem that focuses on three principal axes: (i) automatic inland infrastructure, (ii) autonomous inland s…