Sciences informatiques

Prediction and anticipation of futures states of movable objects

Future prediction is a key component of human intelligence. It allows human to act safely in an uncertain environment. We approach the future prediction by modelling the uncertainty and the multi-modality of the future states of objects. We aim for prediction and anticipation of future states of movable objects, using IMRA’s thousands of hours of labelled driving video data.

Creation of algorithms for 3D spatial-temporal data modeling

Recent sensor technologies enables us to obtain accurate 3D spacial data and high temporal resolution data, like lidar and event camera.
These kinds of data give us many ideas, but on the other hand, we often face difficulties how to deal with them because of their sparseness and data structure, which are completely different from RGB image processing.
IMRA is working on "3D spatial-temporal data modeling", especially, how to use the features of these data, how to represent these data as "like an image data" in order to use them into deep learning, etc.

Methods to copy human thinking

Next generation of automotive driving support requires the ability to anticipate driving hazards better than humans. Such function would allow a safer control of vehicle trajectory. We are preforming research to establish a novel method for hazard anticipation in every scene by embedding human experience. We collect this experience from driving psychology questionnaires and confirming it by bio signals analysis. Our method to reproduce this danger ranking experience is inspired from humans which by combining information from obstacles, motion, distance, possible trajectories, focus and anticipation are able to reason about the context of new scenes to rank the danger in the scene. One challenging task of this research is to train Deep Neural Network from few examples of hazard (only 5% of scene contain hazards).

Advanced computer cluster for research

Deep Learning is a burgeoning topic which requires some material : the data. For Deep Learning problematic, we often find Big Data subjects. Here, we developed a tool completely automatize to easily use deep learning on big data.
Starting from our 1.000 h of driving video database, we developed this tool to manage all this database and all our models of deep learning. We finally reached the level of a fully automated tool for all steps of deep learning and big data : from Automatic Training to Benchmark Analysis