Valeo.ai

Since 2017, our artificial intelligence research center has been at the forefront of AI research in the automotive industry, especially in the fields of assisted and autonomous driving. Twelve years ago there was no real AI in cars. Today, most new cars are packaged with software, much of it AI-related.

Connected to the whole academic world worldwide, our Artificial Intelligence Research Center is committed to cutting-edge automotive applications. We are spearheading ambitious research in AI, especially in assisted and autonomous driving. Leveraging state-of-the-art AI, we pioneer advances that redefine the future of automotive.

Scientific research

Valeo.ai tackles the key challenges that autonomous vehicles encounter in everyday driving. Advanced Driving Assistance Systems sometimes fall short in accuracy and reliability, particularly under complex scenarios of malfunctioning traffic lights, missing lane markings, adverse weather conditions, and other road users behaving abnormally.

Our mission is to overcome those obstacles and enhance automated driving, enabling safer and more efficient autonomous travel in any environment, worldwide.

Scene understanding through multiple arrays of sensors
 

Autonomous vehicles are equipped with various sensors, including cameras, LiDARs (Light Detection And Ranging), radars, ultrasonic sensors, and inertial measurement units, which collectively provide a comprehensive understanding of the environment.

The data from these sensors are fused to create a map of the surroundings, crucial for the vehicle to perceive and understand its environment.

Data & annotation efficient learning
 

Collecting and annotating large datasets is costly and time-consuming. Our researchers are exploring alternatives to traditional fully-supervised learning, thus alleviating the annotation costs.

Research in open-world perception is also concerned with building models that can detect and adapt to novel objects and situations, while still providing safe and consistent operations within real-world dynamic environments.

Dependable models
 

Autonomous vehicles are mission-critical devices that require the utmost care in their design for a safe and robust deployment.

Self-driving vehicles must drive with confidence in contexts that are new or unexpected in comparison to their training scenarios, a goal assisted by domain generalization. That involves building systems that can adapt their learning to new environments, with reliable results in practical scenarios.

Our research also comprises methods to provide clear explanations for the decisions made by those complex systems, with the ultimate goal of providing transparency about their behavior in both normal and abnormal scenarios. We aim to improve the trust in those systems by, for example, anticipating, explaining, and eliminating biases that could lead to incidents.

Team presentation

The valeo.ai center spearheads AI research and applications applied to the automotive industry. With excellent skills, our teams include experts in generative AI and multimodal understanding, computer vision and scene interpretation, machine learning (Core Machine Learning) and predictive and uncertainty modeling.

Meet our team

  • R&I Technical Engineer Florent Bartoccioni

    R&I Technical Engineer

    Perception | Scene understanding | Dynamic forecasting

    ENS Rennes | CTU Prague | INRIA

    Pragmatic dreamer

  • Research Scientist Victor Besnier

    Research Scientist

    Deep Learning | Computer Vision | Image Synthesis

    Sorbonne Université | ENPC

  • Research Scientist Alexandre Boulch

    Research Scientist

    Computer vision | Deep Learning | Geometry processing

    X | MVA | ENPC | ONERA

    3D perceiver

     

  • Senior Research Scientist Andrei Bursuc

    Senior Research Scientist

    Machine Learning | Computer Vision | Reliability | Self-supervised learning

    Politehnica | Mines | Inria | Safran

    Random walker

       

  • PhD student Amaia Cardiel

    PhD student

    Deep learning | Vision and Language

    SciencesPo | SorbonneU | UGA

    Language learner

  • Ph.D. student Loick Chambon

    Ph.D. student

    Deep learning |Computer Vision

    MVA | Sorbonne

    Climber

  • Research Scientist Mickaël Chen

    Research Scientist

    Generative Models | Forecasting

    Sorbonne Université

    Entropy producer

     

  • Scientific Director Matthieu Cord

    Scientific Director

    Deep Learning | Computer Vision | Vision and Language

    Enseirb | CergyU | KULeuven | Ensea | CNRS | SorbonneU | IUF

    Top chef

  • Research Scientist Spyros Gidaris

    Research Scientist

    Deep Learning | Computer Vision

    AUTH | Cortexica | ENPC

    Life-loving epicurean

     

  • Research Scientist David Hurych

    Research Scientist

    Machine Learning | Computer Vision | Generative Networks

    CTU-Prague | NII-Tokyo

    Curious

  • Ph.D student Victor Letzelter

    Ph.D student

    Deep Learning | Uncertainty Quantification | Signal processing

    Telecom Paris | MVA | EMSE

    Landscape explorer

  • Principal scientist Renaud Marlet

    Principal scientist

    Computer vision | Scene Understanding | 3D | Geometry Processing

    X | Inria | EdinburgU | Simulog | Inria | TrustedLogic | Inria | ENPC

    Persistent eclectist

     

  • PhD student Tetiana Martyniuk

    PhD student

    Deep learning | Computer Vision

    Mines Paris | INRIA

    Proud Ukrainian

  • Ph.D. student Björn Michele

    Ph.D. student

    Computer vision | Deep Learning | Frugal Learning

    DHBW | MVA | IRISA | UBretagne Sud

    Domain adapter

  • Project manager Serkan Odabas

    Project manager

    AI Norms, Regulations, Standardization | Management

    Sorbonne Université | Inria

    Gamer

  • Research Scientist Gilles Puy

    Research Scientist

    Computer Vision | Deep Learning

    Supélec | EPFL | INRIA | Technicolor

     

  • Research Scientist Nermin Samet

    Research Scientist

    Deep learning | Computer Vision

    METU | ENPC

    Book wanderer

  • Ph.D. student Corentin Sautier

    Ph.D. student

    Computer Vision | Deep Learning | Self-supervised Learning

    Mines Paris | MVA | ENPC

    Annotations hater

  • PhD student Sophia Sirko-Galouchenko

    PhD student

    Deep learning | Computer Vision

    DauphineU | MVA | SorbonneU

    Borscht-powered Cyborg

  • Senior scientist Eduardo Valle

    Senior scientist

    Computer Vision | Deep Learning | Generative AI

    CergyU | University of Campinas

    Neural optimizer

  • Research scientist Tuan-Hung Vu

    Research scientist

    Deep Learning | Computer Vision | Robustness | Generative AI

    Telecom | Inria | NEC

    Protein lover

      

  • Research Scientist Yihong Xu

    Research Scientist

    Deep Learning | Computer Vision | Motion and Tracking

    Telecom Bretagne | Inria | UGA

    Troublemaker

  • Research Scientist Eloi Zablocki

    Research Scientist

    Deep Learning | Computer Vision | Vision and Language

    X | MVA | SorbonneU

    Substancial learner

Collaborative projects


Confiance.ai

Confiance.ai is a technological research program aimed at securing, certifying, and enhancing the reliability of artificial intelligence (AI) systems. The program, launched by the Innovation Council, focuses on developing methods and tools for industrial players to engineer and deploy AI-based systems. With a strong ambition to break down barriers associated with AI industrialization, Confiance.ai addresses the scientific challenges of trustworthy AI and provides tangible solutions for real-world deployment. The program adopts a strategy of progressive advancement, starting with data-based AI solutions and gradually moving to more complex problems and industrial use cases.


Multitrans

The project MultiTrans aims to accelerate the development and deployment of autonomous vehicles (AVs) by addressing the challenges of perception, decision, and control in open environments. The project focuses on vision-based embedded systems and proposes a novel approach to transfer learning and domain adaptation, enabling AVs to operate safely and reliably in a wider range of situations. The expected impacts and benefits of the project include advances in transfer and frugal learning, multi-domain and multi-source computer vision, and the development of a robotic autonomous vehicle model demonstrator combined with a virtual world model.


ASTRA

Within the joint lab with Inria, we study 2D vision and 3D perception for robust scene understanding. Our research focuses on relaxing the use of abundant data and supervision, stepping towards weak-/un-supervised vision algorithms, while providing models that are more interpretable. We primarily address autonomous driving but our research expands to a variety of indoor and outdoor applications.


ELSA

The ELSA project aims to establish a virtual center of excellence on safe and secure AI technology to address fundamental challenges hindering the deployment of AI. The project will develop a strategic research agenda focusing on technical robustness, privacy, and human agency, and will tackle three grand challenges: robustness guarantees, private collaborative learning, and human-in-the-loop decision making. The initiative builds on the ELLIS network of excellence and will connect over 100 organizations and 337 fellows and scholars to drive the development and deployment of AI technology that promotes European values.


EXA4MIND

 

The EXA4MIND project aims to democratize access to and enable connectivity across EU supercomputing centers, allowing for innovative solutions to complex everyday problems and addressing challenges in data analytics, Machine Learning, and Artificial Intelligence at scale. The project will build an extreme data platform that combines large-scale data storage systems with powerful computing infrastructures, enabling integration with diverse data sources and supporting advanced data analysis pipelines for knowledge extraction.

Partners