TU-Automotive Detroit is the world's largest auto tech conference and exhibition, shaping the future of connected auto mobility.

The road to vehicle autonomy is long and complex. Despite this, some technologies are emerging that hold the key to enabling autonomy level 4 by 2022:

  • AI will advance sensor perception beyond human levels
  • Semiconductor innovation will bring AI enhanced sensors to the automotive market for just a few cents
  • Multimodal connectivity will evolve as the only path to delivering mission critical communications for self-driving cars

Meet our team at the event to understand more about these technologies and discuss how CASE (Connected, Autonomous, Shared, Electric) vehicles, new market entrants and rapidly evolving business models are challenging the status quo.

Our booth will also include two of our latest break-through technology demos:

  • DeepRay™ – ground-breaking AI that creates clear, undistorted views of the real-world from a damaged or obscured moving image. DeepRay has the power to see clearly in difficult, unpredictable situations and could transform numerous machine vision applications, including autonomous driving
  • Sapphyre ML – our custom core design flow that can reduce power by 1,000x compared to an off-the-shelf processor. This points the way to semiconductor innovation that will bring AI enhanced sensors to the automotive market for cents

Panel discussion: AI and the brains behind the operation

Date | 6 June 2019
Time | 2:35 - 3pm
Speaker | Thomas Carmody, Head of Transport and Infrastructure

Thomas will join leaders from across the automotive industry to discuss:

  • How many brains do you want in the car? Weighing up the benefits of an intelligent sensor that is distributing intelligence through the vehicle
  • Anticipating data storage challenges to meet AV grade level requirements. What do we need to do, today, to have sustaining solutions for the future?

How integrated AI and networked smart sensors can together provide an optimum picture of surrounding objects and situations, adding layers of contextual information for making quicker, more accurate decisions

 

Going to TU Automotive Detroit?

 

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