In the rapidly expanding field of automotive technologies, the MUSE project stands out for its ambition to revolutionize the multimodal sensing environment for mobile applications. Through an advanced, integrated approach, MUSE aims to significantly improve the performance and safety of autonomous vehicles.
Cutting-edge technology for autonomous vehicles
The heart of the MUSE project lies in the development of decision support software designed to optimize the selection of sensors for autonomous cars. This optimization is made possible by a driving simulator capable of producing multimodal signals, including LIDAR, radar, cameras and OBD2. This technology creates a dynamic interaction loop, ensuring optimal sensor selection and positioning through realistic simulations.
The use of multi-modal transformer-based learning plays a crucial role in this process, offering accurate recognition in a variety of use cases. This approach enables the data collected to be analyzed and processed efficiently, guaranteeing rapid, reliable decision-making.
Use cases and benefits
The MUSE project is structured around several use cases, each making a significant contribution to improving automotive technologies:
- Collecting and analyzing data from passengers on the move: This application enables vehicle equipment to be adapted according to passenger data, offering enhanced comfort and safety.
- Testing external sensors: By optimizing the safety and performance of autonomous vehicles, this feature ensures greater reliability of accident detection and prevention systems.
- Simulation of V2X interactions: this technology improves communication and road infrastructure safety, particularly for priority vehicles such as ambulances. It ensures effective coordination and optimized management of emergency situations.
Partners and infrastructure
Le projet MUSE bénéficie du soutien d'un écosystème collaboratif comprenant plusieurs partenaires industriels et académiques renommés, tels que AISIN Europe, UCLouvain, UMONS, et Multitel. Chaque partenaire apporte une expertise unique, permettant d'atteindre les objectifs ambitieux du projet.
The OpenHub plays an essential and versatile role in the MUSE project. As well as providing the materials and equipment needed to develop and implement innovative technologies, the OpenHub facilitates collaboration by offering a workspace where researchers, engineers and developers can work together, share ideas and accelerate the innovation process.
By making advanced technical resources available, the OpenHub supports all phases of development, from prototyping to real-life testing. It also promotes an open innovation approach, providing access to a diversity of tools and technologies, encouraging experimentation and the discovery of new solutions.
Expected results
- Validation of multimodal sensors: validation in various scenarios guarantees the efficiency and reliability of the sensors used.
- Development of advanced AI models: these models, designed for decision support, ensure accurate recognition and analysis of situations, contributing to the safety and performance of autonomous vehicles.
- Demonstrating the effectiveness of V2X communication systems: This demonstration proves the effectiveness of vehicle-to-infrastructure communication systems, enhancing road safety.
All in all, the MUSE project represents a major breakthrough in automotive technologies, promising significant innovations for autonomous vehicles. Thanks to close collaboration between industrial and academic partners, MUSE is well positioned to transform current challenges into concrete opportunities, propelling the automotive industry to new heights of excellence and safety.