An international team based in Paris, which conducts AI research for Valeo automotive applications, in collaboraton with world-class academics. Our main research is towards better, clearer & safer automotive AI. See our papers, codes, posts and tweets.


Hedi Ben-younes, research scientist | scholar | twitter
Florent Bartoccioni, PhD student
Alexandre Boulch, research scientist | page | scholar | twitter
Maxime Bucher, research scientist | page | scholar
Andrei Bursuc, research scientist | page | scholar | twitter
Charles Corbière, PhD student | page | scholar | twitter
Matthieu Cord, principal scientist | page | scholar | twitter
Spyros Gidaris, research scientist | scholar
David Hurych, research scientist | scholar
Himalaya Jain, research scientist | page | scholar
Renaud Marlet, principal scientist | page | scholar
Arthur Ouaknine, PhD student | twitter
Patrick Pérez, scientific director | page | scholar
Gilles Puy, research scientist | page | scholar
Julien Rebut, research scientist
Simon Roburin, PhD student | page
Antoine Saporta, PhD student | scholar
Marin Toromanoff, PhD student | scholar
Huy Van Vo, PhD student | scholar
Tuan-Hung Vu, research scientist | page | scholar | twitter
Emilie Wirbel, research scientist | scholar
Eloi Zablocki, research scientist | scholar | twitter

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.

3D dynamic perception — Each sensor delivers information about the 3D world around the vehicle and its temporal evolution. Dectecting, segmenting and tracking important objects (road users, obstacles, street furnitures, etc.) in 3D, as well as forecasting their possible futures, is required for the driving system to plan and act in the safest and most confortable way. This encompasses a wide range of challenging tasks that our research tackles.

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’19 and DADA its extension presented at ICCV’19.

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, as in ConfidNet, our project presented at NeurIPS’19.

Learning under limited supervision — Collecting diverse enough data, and annotating it precisely, is complex, costly and time-comsuming. To reduce dramatically these needs, we explore various alternative and complements to fully-supervised learning, e.g, training that is unsupervised, self-supervised, semi-supervised, active, zero-shot or few-shot. We also investigate training with fully-synthetic data (in combination with unsupervised domain adaptation) and with GAN-augmenented data.

Code (PyTorch)

  • FLOT: Scene flow on point clouds guided by optimal transport (ECCV’20).
  • AdamSRT: Adam exploiting BN-induced pherical invariance of CNN (arXiv 2020)
  • LightConvPoint: Convolution for points (arXiv 2020)
  • xMUDA: Cross-modal UDA for 3D semantic segmentation (CVPR’20)
  • LearningByCheating: End-to-End driving using implicit affordances (CVPR’20)
  • ZS3: Zero-Shot Semantic Segmentation (NeurIPS’19)
  • BF3S: Boosting few-shot visual learning with self-supervision (ICCV’19)
  • ConfidNet: Addressing failure prediction by learning model confidence (NeurIPS’19)
  • Rainbow-IQN Ape-X: effective RL combination for Atari games
  • DADA: Depth-aware Domain Adaptation in Semantic Segmentation (ICCV’19)
  • AdvEnt: Adversarial Entropy minimization for domain adaptation in semantic segmentation (CVPR’19)

Academic partners

CNAM (Nicolas Thome)
CTU Prague (Josef Sivic)
EPFL (Alexandre Alahi)
INRIA (Jean Ponce, Karteek Alahari)
MPI (Christian Theobalt)
Ponts (Mathieu Aubry)
Sorbonne (Matthieu Cord)
Télécom (Florence Tupin, Alasdair Newson, Florence d’Alché-Buc)


  • 07/2020: Code for our ECCV’20 paper “FLOT: Scene flow on point clouds guided by optimal transport” (FLOT).
  • 06/2020: Seven papers accepted at ECCV’20 (27% acceptance rate)
  • 06/2020: Four team members (Alexandre, Andrei, Matthieu and Renaud) acknowledged as Outstanding Reviewers at CVPR’20
  • 06/2020: participates to (virtual) CVPR’20, presenting 5 papers in main conference, delivering tutorials on annotation-efficient learning, co-organizing the OmniCV workshop and presenting keynote at the SAIAD workshop.
  • 06/2020: Maximilian Jaritz defends his PhD at Inria Paris on “2D-3D Scene Understanding for Autonomous Driving” (reviewers: V. Lepetit and G. Brostow, external examiners: A. Dai and F. Jurie).
  • 06/2020: Code for our CVPR’20 paper “xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation” (xMUDA).
  • 04/2020: Eloi Zablocki receives the Second Best Thesis Prize from the French Association for AI.
  • 02/2020: Code for our NeurIPS’19 paper “Zero-Shot Semantic Segmentation” (ZS3).
  • 02/2020: Five papers accepted at CVPR’20 (22% acceptance rate), inc. one oral.
  • 01/2020: Spyros Gidaris, Andrei Bursuc and Karteek Alahari (Inria) to deliver a tutorial on Few-Shot, Self-Supervised, and Incremental Learning at CVPR’20.
  • 01/2020: Pedestrian monitoring demo at CES, Las Vegas.
  • 12/2019: Medium post: Is deep Reinforcement Learning really superhuman on Atari?
  • 12/2019: Code for our ICCV’19 paper “Boosting few-shot visual learning with self-supervision” (BF3S).
  • 12/2019: Code for our ICCV’19 paper “DADA: Depth-Aware Domain Adaptation in semantic segmentation” (DADA).
  • 11/2019: Spyros Gidaris receives the Thesis Prize from Université Paris Est.
  • 10/2019: researchers present 5 papers at ICCV’19 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” (ConfidNet).
  • 09/2019: Work of Marin Toromanoff et al. discussed by Andrew Ng in The Batch ( newsletter).
  • 09/2019: Two papers accepted at NeurIPS’19 (21% acceptance rate).
  • 09/2019: Matthieu Cord among NeurIPS’19 top cont.
  • 07/2029: Medium post: ADVENT: Adversarial entropy minimization for domain adaptation in semantic segmentation.
  • 07/2019: Three papers accepted at ICCV’19 (24% acceptance rate), including one oral (4.6% acceptance rate).
  • 07/2019: Code of our CVPR’19 paper “AdvEnt: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation” is available on github.
  • 07/2019: Marin Toromanoff (PhD student with Mines ParisTech, Valeo DAR and ranks 1st on Track 2 of Carla Challenge 2019, and 2nd on Track 1.
  • 06/2019: Spyros Gidaris receives the Best Thesis Prize from Ponts Foundation.
  • 06/2019: researchers present 8 papers (25% acceptance rate), including 4 orals (5.6% acceptance rate), at CVPR’19. Patrick Pérez delivers keynote on “Sustainable supervision with application to autonomous driving” at the Safe AI for Automated Driving (SAIAD) CVPR’19 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.


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


Maximilian Jaritz, PhD student (page, scholar), now at Amazon
Gabriel de Marmiesse, research engineer (github), now at EarthCube