Arti˜cial Intelligence: The Brain of Autonomous Cars Data ... · 3._McKinsey & Compnay -...
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10 million lives per decade Machine learning is an application of AI that uses statistics to give computers the ability to independently learn and improve performance without being explicitly programmed Estimates predict that it will take around 14 to 16 million miles of driving to collect enough data to train the neural networks that will eventually make fully autonomous cars feasible and safe Rising demand for intelligent vehicles and improved user experiences will lead to massive market growth for automotive AI, predicted to reach up to $10.8 billion by 2025 4 39% growth rate Artificial Intelligence: The Brain of Autonomous Cars Machine Learning 4._Bizwit Research and Consulting LLC - Global Automotive Artificial Intelligence (AI) Market Study 2017-2025 1._National Highway Traffic Safety Administration - Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey 2. Brookings - Gauging Investment in Self-Driving Cars Big Data in cars will become a multi-billion industry by 2030 3 2._Gartner - By 2020, a Quarter Billion Connected Vechiles Will Enable New In-Vehicle Services and Automated Driving Capabilities 3._McKinsey & Compnay - Monetizing Car Data Data: The Driver Behind AI Massive amounts of data is used to create AI algorithms through analytics that mimic human cognition The US National Highway Traffic Safety Administration (NHTSA) estimates 94% of all traffic accidents are a result of driver error 1 $750B By 2020, 250 million vehicles will be connected to each other and the surrounding infrastructure. 2 These vehicles will share information about changes in traffic, road conditions, weather, and more With connected devices increasing across multiple industries, more data than ever is now being collected Autonomous cars collect data from multiple sensors on the exterior and interior of the vehicle and also exchange information with other cars and infrastructure while on the road Automakers have ambitious plans to produce fully autonomous (SAE Level 5) cars requiring zero human intervention in the next three to five years Major automakers have invested over $80 billion in autonomous vehicles over the last three years 2 Autonomous Cars In 2017, 250 exabytes of data was generated by the nascent autonomous car industry Deep learning is a type of machine learning that uses artificial neural networks layered on top of each other to analyzing massive data sets and retrieve structured information Deep learning will be particularly useful for autonomous cars to tell the difference between vehicles, pedestrians, and city infrastructure Autonomous cars are predicted to save up to Products like Airbiquity OTAmatic TM will play a critical role in facilitating how automotive AI contines to evolve. Information from car senors, other vehicles, surrounding infrastructure, and more is sent to the cloud for data analytics, and improved algorithms are transmitted back down to the vehicle, updating the code on a continual basis. Being able to update software over-the-air (OTA) also has a wide range of automotive applications and will be integral for cybersecurity protection, edge analytics, and data management. OTA will power how a vehicle’s AI “brain” develops OTA Updates will Power how Future Models of Autonomous Vehicles Learn and Develop © Airbiquity 2018. Airbiquity is a trademark of Airbiquity Inc. All other products and brand names may be trademarks or registered trademarks of their respective owners. Specifications are subject to change without notice. Corporate Headquarters 1191 Second Avenue - Suite 1900 Seattle, WA 98101 Web www.airbiquity.com Email contact @ airbiquity.com Phone +1 206 219 2700 AI can determine what data is valuable and leveraged to improve system functions, including what data should be placed into storage for future access
Transcript of Arti˜cial Intelligence: The Brain of Autonomous Cars Data ... · 3._McKinsey & Compnay -...
10 million lives per decade
Machine learning is an application of AI that uses statistics to give computers the ability to independently learn and improve performance without being explicitly programmed
Estimates predict that it will take around 14 to 16 million miles of driving to collect enough data to train the neural networks that will eventually make fully autonomous cars feasible and safe
Rising demand for intelligent vehicles and improved user experiences will lead to massive market growth for automotive AI, predicted to reach up to $10.8 billion by 20254
39% growth rate
Arti�cial Intelligence: The Brain of Autonomous Cars
Machine Learning
4._Bizwit Research and Consulting LLC - Global Automotive Arti�cial Intelligence (AI) Market Study 2017-2025
1._National Highway Tra�c Safety Administration - Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey2. Brookings - Gauging Investment in Self-Driving Cars
Big Data in cars will become a multi-billion industry by 20303
2._Gartner - By 2020, a Quarter Billion Connected Vechiles Will Enable New In-Vehicle Services and Automated Driving Capabilities3._McKinsey & Compnay - Monetizing Car Data
Data: The Driver Behind AI
Massive amounts of data is used to create AI algorithms through analytics that mimic human cognition
The US National Highway Tra�c Safety Administration (NHTSA) estimates 94% of all tra�c accidents are a result of driver error1
$750B
By 2020, 250 million vehicles will be connected to each other and the surrounding infrastructure.2
These vehicles will share information about changes in tra�c, road conditions, weather, and more
With connected devices increasing across multiple industries, more data than ever is now being collected
Autonomous cars collect data from multiple sensors on the exterior and interior of the vehicle and also exchange information with other cars and infrastructure while on the road
Automakers have ambitious plans to produce fully autonomous (SAE Level 5) cars requiring zero human intervention in the next three to �ve years
Major automakers have invested over $80 billion in autonomous vehicles over the last three years2
Autonomous Cars
In 2017, 250 exabytes of data was generated by the nascent autonomous car industry Deep learning is a type of
machine learning that uses arti�cial neural networks layered on top of each other to analyzing massive data sets and retrieve structured information
Deep learning will be particularly useful for autonomous cars to tell the di�erence between vehicles, pedestrians, and city infrastructure
Autonomous cars are predicted to save up to
Products like Airbiquity OTAmaticTM will play a critical role in facilitating how automotive AI contines to evolve. Information from car senors, other vehicles, surrounding infrastructure, and more is sent to the cloud for data analytics, and improved algorithms are transmitted back down to the vehicle, updating the code on a continual basis. Being able to update software over-the-air (OTA) also has a wide range of automotive applications and will be integral for cybersecurity protection, edge analytics, and data management.
OTA will power how a vehicle’s AI “brain” develops
OTA Updates will Power how Future Models of Autonomous Vehicles Learn and Develop
© Airbiquity 2018. Airbiquity is a trademark of Airbiquity Inc. All other products and brand names may be trademarks or registered trademarks of their respective owners. Speci�cations are subject to change without notice.
Corporate Headquarters
1191 Second Avenue - Suite 1900Seattle, WA 98101
Webwww.airbiquity.com
Email [email protected]
Phone+1 206 219 2700
AI can determine what data is valuable and leveraged to improve system functions, including what data should be placed into storage for future access
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