To demonstrate the power of our neuromorphic approach to machine intelligence, we are developing
an end-to-end event-based machine vision system for fast emergency pedestrian collision avoidance, for deployment in
automotive advanced driver assistance systems. The system will be
capable of the rapid Selection of objects of interest in the visual scene, predictive Tracking of
the objects to determine if they are moving into a danger zone, And accurate Recognition of the object to
decide the action required.
- Using Dynamic Vision Sensors (DVS) in combination with our neuromorphic algorithms and computing
hardware, our end-to-end neuromorphic system will be sufficiently fast to meet emergency response
robust to poor and high contrast lighting conditions, and highly energy efficent.
- The system will make use of low
(micro-second) latency, spatio-temporal motion event data from the DVS to detect
visual events of interest in the real world, select and focus attention on them, and by tracking their
spatial and temporal features and using these features to recognise the nature of these events, to make
rapid and accurate predictions about their potential to create dangerous situations.
- Experience-based learning on graphical knowledge bases will allow the system to achieve continouous and persistent
improvements in performance, whilst the use of validation techniques developed
for cryptocurrency applications, will ensure data integrity and ensure that stringent safety criteria are met.