activity
20212026
most citedTrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2026

A Real-Time Generalized Nash Equilibrium Framework for Interaction-Aware Autonomous Driving in Mixed Traffic

Nouhed Naidja, Mohamed-Cherif Rahal, Steve Pechberti +3

Safe and efficient navigation in mixed-traffic environments remains a critical challenge for Autonomous Vehicles (AVs), primarily due to the complex interdependence between the AV'…

cs.RO2025

Evaluation of an Uncertainty-Aware Late Fusion Algorithm for Multi-Source Bird's Eye View Detections Under Controlled Noise

Maryem Fadili, Louis Lecrosnier, Steve Pechberti +1

Reliable multi-source fusion is crucial for robust perception in autonomous systems. However, evaluating fusion performance independently of detection errors remains challenging. T…

cs.RO2025

A Late Collaborative Perception Framework for 3D Multi-Object and Multi-Source Association and Fusion

Maryem Fadili, Mohamed Anis Ghaoui, Louis Lecrosnier +2

In autonomous driving, recent research has increasingly focused on collaborative perception based on deep learning to overcome the limitations of individual perception systems. Alt…

cs.CV2022★ 1 cited

Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors

Joseph Gesnouin, Steve Pechberti, Bogdan Stanciulescu +1

Pedestrian crossing prediction has been a topic of active research, resulting in many new algorithmic solutions. While measuring the overall progress of those solutions over time t…

cs.CV2021★ 1 cited

TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction

Joseph Gesnouin, Steve Pechberti, Bogdan Stanciulescu +1

Understanding the behaviors and intentions of pedestrians is still one of the main challenges for vehicle autonomy, as accurate predictions of their intentions can guarantee their…