AI & ML
the technology has the perceptive skills of a human driver . On top of this , machine vision has limitations in terms of camera sensor capture and the software which enables safe self-driving features .
“ When it comes to autonomy , there is a need for better camera sensors , with more depth , faster framerate , better resolution etc ., in addition to radar and lidar sensors . Manufacturers would need to leverage advanced software to manage and process the data from these sensors , while also ensuring the self-driving features in their vehicles are safe . This is underpinned by hardware , for example , the GPU or AI accelerator , meeting the safety requirements for autonomous vehicle deployment ,” explains Rodriguez .
Concluding – and despite acknowledging the benefits of this technology – Rodriguez shares a word of warning : it is important technologists recognise the challenges to ensure high safety levels are maintained .
“ There are multiple challenges for the successful implementation of machine vision . First , manufacturers need to have the right sensor technology ( different cameras with different dynamic ranges , frame rates and light sensitivity ). This is followed by the challenge of managing and using the sensor data . And , finally , we have the most complex part , which is how the car behaves and drives .”
“ In the automotive industry , it takes up to five years to get silicon into production , so a flexible hardware architecture is needed to enable support for continuous software development . At Imagination , we enable this software-defined evolution through our innovative GPU , NNA and EPP IP , which offers the compute and connectivity flexibility required for these implementations .”
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