We are an open, international, research lab based in Paris

We aim

  • to conduct cutting-edge AI research for automotive applications
  • to nurture collaborations with world-class academic teams
  • to irrigate Valeo’s R&D

We work towards better, lighter, clearer & safer automotive AI

The team

Hedi Ben-younes (research scientist) scholar
Maxime Bucher (research scientist) scholar
Andrei Bursuc (research scientist) scholar
Charles Corbière (PhD student) scholar
Matthieu Cord (principal scientist) scholar
Spyros Gidaris (research scientist) scholar
David Hurych (research scientist) scholar
Himalaya Jain (research scientist) scholar
Maximilian Jaritz (PhD student) scholar
Renaud Marlet (principal scientist) scholar
Gabriel de Marmiesse (research engineer)
Arthur Ouaknine (PhD student)
Patrick Pérez (scientific director) scholar
Julien Rebut (research scientist)
Simon Roburin (PhD student)
Antoine Saporta (PhD student)
Marin Toromanoff (PhD student) scholar
Huy Van Vo (PhD student) scholar
Tuan-Hung Vu (research scientist) scholar


  • High profile research scientists in machine learning or in 3D scene understanding
  • AI research engineer

Please contact me

Some projects

Multi-sensor perception: Automated driving relies first on a diverse range of sensors, like Valeo’s fish-eye cameras, LiDARs, radars and ultrasonics. Exploiting at best the outputs of each of these sensors at any instant is fundamental to understand the complex environment of the vehicle. To this end, we explore various deep learning approaches where sensors are considered both in isolation and collectively.

Domain adaptation: Deep learning and reinforcement learning are key technologies for autonomous driving. One of the challenges they face is to adapt to conditions which differ from those met during training. To improve systems’ performance in such situations, we explore so-called “domain adaptation” techniques, as in AdvEnt, our project presented at CVPR 2019.

Uncertainty estimation and performance prediction: When the unexpected happens, when the weather badly degrades, when a sensor gets blocked, the embarked perception system should diagnose the situation and react accordingly, e.g., by calling an alternative system or the human driver. With this in mind, we investigate automatic ways to assess the uncertainty of a system and to predict its performance.

Code & data

On our github:

  • AdvEnt: Adversarial Entropy minimization for domain adaptation in semantic segmentation (CVPR 2019) - PyTorch

Our academic partners

Czech Technical University in Prague (Josef Sivic)
EPFL (Alexandre Alahi)
INRIA (Jean Ponce)
le CNAM (Nicolas Thome and Avner Bar-hen)
Max Planck Institute (Christian Theobalt)
Ponts ParisTech (Mathieu Aubry)
Sorbonne Université (Matthieu Cord)
Telecom ParisTech (Florence Tupin and Alasdair Newson)

Recent news

  • July 2019: Code of our CVPR19 paper “AdvEnt: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation” is available on github
  • July 2019: Marin Toromanoff (PhD student with Mines ParisTech, Valeo DAR and ranks 1st on Track 2 of Carla Challenge 2019, and 2nd on Track 1.
  • June 2019: Spyros Gidaris receives the Best Thesis Prize from Ponts Foundation.
  • June 2019: researchers to present 8 papers (including 4 orals) at CVPR. Also on Booth 1556. See you in Long Beach!
  • May 2019: Hedi Ben-younes defends his PhD at Sorbonne Université, committee: M. Cord, V. Ferrari, Y. LeCun, P. Pérez, L. Soulier, N. Thome, J. Verbeek, Ch. Wolf.
  • May 2019: Himalaya Jain receives the Best Thesis Prize from Rennes 1 Foundation.
  • May 2019: Valeo is proud to be part of Prairie, the new Paris Interdisciplinary Artificial Intelligence Institute. Stay tuned.


Human Resource Partner: Pascal Le Herisse
Assistant: Ouardia Moussouni
Location: 15 rue de La Baume, Paris