Driving in action

Getting from sensory inputs to car control goes either through a modular stack (perception > localization > forecast > planning > actuation) or, more radically, through a single end-to-end model. We work on both strategies, more specificaly on action forecasting, automatic interpretation of decisions taken by a driving system, and reinforcement / imitation learning for end-to-end systems (as in RL work at CVPR’20).


LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation

Florent Bartoccioni, Éloi Zablocki, Andrei Bursuc, Patrick Pérez, Matthieu Cord, Karteek Alahari
Conference on Robot Learning, 2022

STEEX: Steering Counterfactual Explanations with Semantics

Paul Jacob, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, Matthieu Cord
European Conference on Computer Vision, 2022

Explainability of deep vision-based autonomous driving systems: Review and challenges

Éloi Zablocki*, Hédi Ben-Younes*, Patrick Pérez, Matthieu Cord
International Journal of Computer Vision, 2022

Raising context awareness in motion forecasting

Hédi Ben-Younes*, Éloi Zablocki*, Mickaël Chen, Patrick Pérez, Matthieu Cord
Computer Vision and Pattern Recognition (CVPR) Workshop, 2022

Driving behavior explanation with multi-level fusion

Hédi Ben-Younes*, Éloi Zablocki*, Patrick Pérez, Matthieu Cord
Pattern Recognition (PR), 2022

PLOP: Probabilistic poLynomial Objects trajectory Prediction for autonomous driving

Thibault Buhet, Emilie Wirbel, Andrei Bursuc and Xavier Perrotton
Conference on Robot Learning (CoRL), 2020

End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances

Marin Toromanoff, Emilie Wirbel, and Fabien Moutarde
Computer Vision and Pattern Recognition (CVPR), 2020

VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users

Adithya Ranga, Filippo Giruzzi, Jagdish Bhanushali, Emilie Wirbel, Patrick Pérez, Tuan-Hung Vu, Xavier Perotton
Electronic Imaging, 2020