Smart fusion of data from diverse sensors enables safer, faster and more precise automated vision and recognition. Imec’s researchers develop next-generation sensor fusion through AI-based cooperative algorithms and dedicated neuromorphic hardware.
With cooperative fusion, imec introduces a method for combining the inputs of various sensors that significantly outperforms the standard algorithms.
In applications such as autonomous driving, surveillance and robotics, high reliability and security are constant concerns. From that point of view, combining sensor data has a lot of advantages:
It allows for a wider coverage of a scene, e.g. from various angles, with various depths, or in various spectra.
It provides more robust, redundant information.
Most importantly: it will result in an actionable view under any circumstances, even difficult ones such as night, rain, fog or backlight. This is because different types of sensors have different failure modes.
But here’s the challenge. A conventional fusion of raw sensor data will require a huge amount of memory and computational bandwidth. Late fusion on preprocessed data, in contrast, risks overlooking meaningful details or, because of its high latency, taking too long to notice them. This may compromise the reliability and safety of the applications.
Imec’s researchers are opening up new roads. And they tackle the challenge with both software and hardware:
Are you involved or interested in improving machine sensing and vision? Come and discuss to see how your application can benefit from the latest developments in these domains.