Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Embedded AI for the semantic interpretation of a probabilistic environment model


Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Infrastructure et Ateliers Logiciels pour Puces




The perception and modelling of an environment is a major issue when developing autonomous vehicles. How to model the surroundings of a vehicle? How to detect and identify the various obstacles? What about free spaces, and areas safe to drive on? Which sensor combination is the most appropriate to reach an exhaustive description and modelling of the environment? Those questions all have beginnings of answers, but still remain open and not yet solved. There also is a strong constraint regarding the need for embedding systems, which is one of the CEA focuses. Which processing and analysis can be considered while targeting embedded systems? Occupancy Grid is a model used to represent the surroundings of a vehicle and present various advantages. Several sensors of various modalities are used to compute the grid: each modality brings a specific information. For instance, infra-red is efficient by night, LIDAR offers a 360° field of view but is not robust to bad weather conditions, in which case a radar would be preferable. Ultrasound sensors on the contrary are used to analyse very short distances. CEA has developed approaches based on Bayesian fusion to produce SigmaFusion library. SigmaFusion is a tool to fuse the information of different sensors to produce an occupancy grid, which evolves with time. A strong point of SigmaFusion is the computing optimization: the technology is particularly efficient and competitive under strong embedded constraints (low cost integration with low energy consumption on micro-controller certified for critical task for the automotive market). An issue currently addressed is the use of EdgeAI methods to gain a semantic interpretation of an occupancy grid. A typical question is the level of knowledge and interpretation that can be reached while respecting the embedding constraint. Is it possible to detect the object evolving in a grid automatically, in real time and at low energetic cost (pedestrians, cyclists, cars, etc.)?

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