Spiking Neural Networks (SNNs) are increasingly explored for their energy efficiency and biological plausibility, offering a compelling alternative to conventional artificial neural networks in neuromorphic applications. However, even Spik-former a fully spiking adaptation of the Transformer model, can exhibit significant computational redundancy due to excessive spike activity, resulting in non-negligible energy consumption. In this paper, we introduce Masked Spikformer, a unified framework for regulating temporal spike activity in spiking Transformers through three complementary masking strategies: Random Spike Masking (RSM), Gaussian-Based Spike Masking (GSM), and Gaussian-Based Spike Weighting (GSW). These approaches encompass both binary masking and continuous, learnable temporal weighting. Our method is integrated into fully spiking architectures and applied consistently during both training and inference. Experimental results on neuromorphic benchmarks (CIFARIO-DVS and DVS128 Gesture) show that the proposed masking strategies significantly reduce energy consumption while maintaining high classification performance. Notably, the best accuracy on DVS128 Gesture is achieved by RSM with 70% masking, while GSW(i), the inverse Gaussian weighting variant, attains the highest accuracy on CIFARIO-DVS. RSM also provides the lowest energy consumption across both datasets, highlighting the effectiveness of temporal sparsity for energy-efficient spiking Transformers.

Positioning is a critical function in every intelligent vehicle application. Most applied system is a GNSS (Global Navigation Satellite System)-based receiver, cheap and offering a continuous meter-level accuracy. However accuracy strongly depends on the satellite signal reception state: LOS (Line of sight), that is direct, optimal signal or NLOS (Non LOS), i.e received without direct visibilty and after one or more reflections of the signal. Based on previous work, this paper summarizes different weighting schemes applied to mitigate these local effects on GNSS signals and enhance position accuracy in land transport environment. A database collected by ISAE is used for application and comparison of the different schemes. One of them not only relies on GNSS signals but also on satellite state identification thanks to the use of a fisheye camera. This state allows us to deweight degraded measurements without excluding them, in order to keep availability. The paper shows that considering this additional information allows the WLS (Weighting Least Square) to significantly increase accuracy in every types of environments. If state of the art weighting schemes already improve accuracy by 65 and 77% over ordinary least squares in the 2D plan, our weight further improves accuracy by 40% compared to the classical elevation-based weight.

This paper provides a comprehensive review of the integration of Spiking Neural Networks (SNNs) and Transformers, combining the energy efficiency of SNNs with the high performance of Transformer architectures. By leveraging the event-driven nature of SNNs and the powerful self-attention mechanism of Transformers, this fusion aims to address the challenges of high energy consumption in deep learning while improving task accuracy, especially for complex datasets. We introduce the core concepts of SNNs and Transformers, reviewing state-of-the-art methods for their combination, including hybrid architectures. The performance of each architecture is presented thanks to both static and neuromorphic datasets, highlighting their advantages and limitations. This review also discusses the challenges of integrating self-attention into spiking architectures and outlines future research directions to further enhance model performance and energy efficiency.

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the sky.Because the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy.

The emergence of autonomous transportation systems marks a significant milestone in modern mobility, promising enhanced safety, efficiency, and convenience. However, ensuring the safety of passengers remains a paramount concern in the development and deployment of such systems. Monitoring the interior of autonomous vehicles has emerged as a critical aspect to guarantee passenger safety, requiring robust on-board action recognition systems. This paper provides an overview of the challenges and advancements in on-board action recognition for interior monitoring in autonomous vehicles. A comprehensive review of datasets pertinent to interior monitoring is presented, encompassing diverse scenarios and conditions to facilitate the training and evaluation of on-board action recognition models. Furthermore, we explore the methodologies employed in the development of these systems, including traditional computer vision techniques, deep learning architectures, and multimodal approaches . By synthesizing insights from existing research and highlighting key challenges and advancements, this paper aims to contribute to the ongoing discourse on enhancing safety measures in future autonomous transportation systems (bus, metro , train, car) through effective interior monitoring and action recognition technologies.

This paper proposes a semi-automatic ground truth annotation software designed for the specific needs of the EVEREST project. The purpose of this project is to build an annotated and anonymized video database, and use it to evaluate algorithms in the task of detecting hazardous behavior in guided mountain transport. To do so, a ground truth annotation tool that disposes designed specifically for the EVEREST project was needed. Ski lifts safety based on intelligent video systems is a niche domain which has not yet been explored in depth, which means no annotation tool suited for this task was available. That is why, we decided to develop a user-friendly and flexible tool to allows the semi-automatic annotation of events and faces (for privacy purposes). We looked at existing tracking algorithms, chose an implementation of TLD, and designed a new tracking algorithm that could be used when TLD isn't effective. This led to a simple, lightweight tracking algorithm that is more practical to use than the original CAMshift algorithm, and a user-friendly and flexible annotation tool that is well adapted to the specific task of annotating hazardous behavior in guided mountain transport.

This paper is focused on the characterization of GNSS signals reception environment by estimating the percentage of visible sky. A new segmentation technique based on a color watershed using an adaptive combination of color and texture information is proposed. This information is represented by two morphological gradients, a classical color gradient and a texture gradient based on co-occurrence matrices. The segmented images are then used as input for a k-means classifier in order to determine the percentage of visible sky in fish-eye images. The obtained classification results are evaluated to demonstrate the effectiveness and the reliability of the proposed approach.

This paper presents a fusion method for objects tracking using laser sensory data and stereovision. Based on the extended Kalman filter, the tracking uses an oriented bounding box (OBB) representation for tracked objects. The representation model takes into account an inter-rays (IR) uncertainty concept, which is related to the fact that the laser raw data points representing the extremities of an extracted OBB do not coincide with the real objects extremities. To improve the objects state estimation, the tracking process integrates a fixed size (FS) assumption. The FS assumption allows to exploit the most precise object's size estimation, memorised during the tracking. To achieve data association, a threshold based laser points clustering provides satisfying results. However, there are many cases where, without additional information, it is impossible to cluster laser raw data points correctly. To discard clustering ambiguities, a fusion method combining laser sensory data and stereovision information is proposed. The stereovision information is extracted only within regions of interest, defined from laser points. The fusion method takes place in the early stage of the measurement extraction from laser raw data points. The proposed approach is tested and evaluated to demonstrate its reliability.