136 citations · 205 across the 6 of their papers we have counts for
8 papers
Automatic Image Annotation for Mapped Features Detection
Maxime Noizet, Philippe Xu, Philippe Bonnifait
Detecting road features is a key enabler for autonomous driving and localization. For instance, a reliable detection of poles which are widespread in road environments can improve…
Pole-based Vehicle Localization with Vector Maps: A Camera-LiDAR Comparative Study
Maxime Noizet, Philippe Xu, Philippe Bonnifait
For autonomous navigation, accurate localization with respect to a map is needed. In urban environments, infrastructure such as buildings or bridges cause major difficulties to Glo…
Map-aided annotation for pole base detection
Benjamin Missaoui, Maxime Noizet, Philippe Xu
For autonomous navigation, high definition maps are a widely used source of information. Pole-like features encoded in HD maps such as traffic signs, traffic lights or street light…
Collaborative Grid Mapping for Moving Object Tracking Evaluation
Rémy Huet, Antoine Lima, Philippe Xu +2
Perception of other road users is a crucial task for intelligent vehicles. Perception systems can use on-board sensors only or be in cooperation with other vehicles or with roadsid…
Fusion of evidential CNN classifiers for image classification
Zheng Tong, Philippe Xu, Thierry Denoeux
We propose an information-fusion approach based on belief functions to combine convolutional neural networks. In this approach, several pre-trained DS-based CNN architectures extra…
Lane level context and hidden space characterization for autonomous driving
Corentin Sanchez, Philippe Xu, Alexandre Armand +1
For an autonomous vehicle, situation understand-ing is a key capability towards safe and comfortable decision-making and navigation. Information is in general provided bymultiple s…
An evidential classifier based on Dempster-Shafer theory and deep learning
Zheng Tong, Philippe Xu, Thierry Denœux
We propose a new classifier based on Dempster-Shafer (DS) theory and a convolutional neural network (CNN) architecture for set-valued classification. In this classifier, called the…
Evidential fully convolutional network for semantic segmentation
Zheng Tong, Philippe Xu, Thierry Denœux
We propose a hybrid architecture composed of a fully convolutional network (FCN) and a Dempster-Shafer layer for image semantic segmentation. In the so-called evidential FCN (E-FCN…