valeo.ai

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
Alexandre Boulch (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
Gilles Puy (research scientist) 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

Openings

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

Please contact me

Some projects

(for not too technical updates, follow us on Medium)

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 and posts

On our github:

  • ConfidNet: Addressing failure prediction by learning model confidence (NeurIPS’19) - PyTorch
  • Rainbow-IQN Ape-X: effective RL combination for Atari games - PyTorch
  • DADA: Depth-aware Domain Adaptation in Semantic Segmentation (ICCV’19) - coming soon
  • AdvEnt: Adversarial Entropy minimization for domain adaptation in semantic segmentation (CVPR’19) - 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

  • 11/2019: Spyros Gidaris receives the Thesis Prize from Université Paris Est.
  • 10/2019: Valeo.ai researchers present 5 papers at ICCV in Seoul, Korea, and Valeo participates to associated workshops on Autonomous Driving and on Autonomous Navigation in Unconstrained Environments.
  • 10/2019: PRAIRIE research institute is officially launched, with a nice day of talks and pannels (inc. one on future mobility, program in French). Followed by PRAIRIE AI Summer School (PAISS), where Patrick Pérez delivered a lecture (slides).
  • 09/2019: Code of our NeurIPS’19 paper “Addressing failure prediction by learning model confidence” is available on valeo.ai github
  • 09/2019: Work of Marin Toromanoff et al. discussed by Andrew Ng in The Batch (deeplearning.ai newsletter)
  • 09/2019: Two papers accepted at NeurIPS (21% acceptance rate), including one on new problem of zero shot semantic segmentation (“Z3S”). Matthieu Cord among the top contributors according to conference stats.
  • 07/2019: Three papers accepted at ICCV19 (24% acceptance rate), including one oral (4.6% acceptance rate)
  • 07/2019: Code of our CVPR19 paper “AdvEnt: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation” is available on valeo.ai github
  • 07/2019: Marin Toromanoff (PhD student with Mines ParisTech, Valeo DAR and Valeo.ai) ranks 1st on Track 2 of Carla Challenge 2019, and 2nd on Track 1.
  • 06/2019: Spyros Gidaris receives the Best Thesis Prize from Ponts Foundation.
  • 06/2019: Valeo.ai researchers present 8 papers (25% acceptance rate), including 4 orals (5.6% acceptance rate), at CVPR. Patrick Pérez delivers keynote on “Sustainable supervision with application to autonomous driving” at the Safe AI for Automated Driving (SAIAD) CVPR 2019 workhsop.
  • 05/2019: 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.
  • 05/2019: Himalaya Jain receives the Best Thesis Prize from Rennes 1 Foundation.
  • 05/2019: Valeo is proud to be part of Prairie, the new Paris Interdisciplinary Artificial Intelligence Institute. Stay tuned.

Commnunication

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