March 11th, 2021

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 March 11th, 2021 - March 12th, 2021 


Event submitted on Friday, August 21st 2020, approved by Content Team

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March 11th, 2021 - March 12th, 2021
United States  »  West, USA  »  California » 
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* Due to COVID, it is most likely that this event will be online - but please make sure! The situation is constantly changing...
This event is part of a series managed by We.Conect Global Leaders

 Our Review

This event is the merger of two fascinating subject-matters: Artificial Intelligence and the (Self-Driving) Automotive Industry. If you work in the space then yes, consider this a “must-attend”.

- Review written by Henry Dalziel on Friday, August 21st 2020.
- If you would like to edit or ammend facts in my review please either send us a message or connect with me via LinkedIn.

Conference Event Summary

The following description was either submitted by the Conference Organizer on Friday, August 21st 2020, or created by us.

The Auto.AI is America’s first platform bringing together all stakeholders who play an active role in machine learning, computer vision, sensor fusion, perception, and cognitive vehicle scene.

The event provides you with precise insights into new technical innovations and challenges regarding the integration of AI and machine learning-based systems into Level 4 & 5 vehicles.

Topics that will be discussed:

  • Vision geometry & deep neural networks – how to verify deep learning detection with vision geometric principles
  • Insights of AI to increase testing coverage of AVs – how to validate data using qualitative approaches
  • Reinforcement learning for vehicular path planning – how to make use of deep learning for AVs
  • Full-stack software suites for AI – how to develop hardware-agnostic & scalable solutions
  • AVs & simulation – how to enable deep learning with synthetic data
  • Ensuring the safety of self-driving cars – how to prove that AI systems and vehicles as a whole fulfill all safety goals
  • Behavior learning for AVs – how cars can learn from real-world human driving behavior
  • Self-supervised learning for AVs – how to collect & label data at scale
  • Data processing and AI – how to develop software architectures and overcome hardware challenges
  • Path planning and object recognition with AI – how to include deep learning in the mix of computer vision approaches
  • Interpretable learning for AVs – how to use visual explanations that causally influence a CNN’s output

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