Why Edge Computing Could Transform Autonomous Cars

What’s the state of autonomous cars? To find out, the Autotech Council—a Silicon Valley-based ecosystem of automobile industry players—held an event on April 13th. The half-day event, hosted by Western Digital, brought together 300 leaders in the autonomous cars industry. We partnered with SiliconANGLE, a leading digital media platform, to spend a few minutes talking with a select group of these leaders.

Chris Bergey is one of the busiest guys around in the autonomous cars space.

When he’s not busy breaking down his company’s embedded automotive solutions, he’s driving adoption of mobile, compute, connected homes, smart cities, and industrial IoT technologies. It’s all about finding ways to create environments for data to thrive, so that people and relationships can thrive.

As Vice President of Product Marketing at Western Digital, Chris sees the challenges underlying fully autonomous cars from an infrastructure perspective. With autonomous cars, we might be able to have safer transportation and automate road maintenance. Luckily, we were able to take a few minutes out of his busy day to find out his insights. Chris spoke at the Autotech Council Autonomous Cars event with Jeff Frick, host of theCUBE, SiliconANGLE’s online show. Here’s what Chris had to say.

Unique Challenges of Autonomous Vehicles

* Video clip from full interview

Key Insights:

  • Autonomous cars are made possible by placing a network of sensors on the car.
  • Sensors generate a tremendous amount of data, between 0.75 GB and 1 GB per second.
  • Artificial intelligence is pulling insights from sensor data to make real-time driving decisions.

Find out how the automotive industry is evolving to keep pace with autonomous cars.

Pulling Out Insights from Raw Data

* Video clip from full interview

Key Insights:

  • Metadata and context are the two keys to pulling insights from raw data.
  • Common behaviors can be identified from raw data through context and metadata.
  • Autonomous cars “fuse” the images and data from their sensors to understand driving conditions.

Keeping Data Closer to the Edge 

* Video clip from full interview

Key Insights:

  • The rise of edge computing has made cars more capable of processing and finding patterns in sensor data.
  • Keeping sensor data closer to autonomous vehicles means less time spent waiting for data to make a driving decision.
  • With 0.75 GB of data produced by autonomous vehicles each second, edge computing means faster storage and transfer of data.

Chris made it clear that moving from human-assisted autonomous cars to fully autonomous cars means keeping data at the edge to make real-time decisions. To get there, it’ll take seamless communication between smart sensors, embedded storage solutions, and car driving components. But, major consumer electronic tradeshows have shown how people are embracing new automotive trends.

Learn more about the storage challenges of automotive edge and smart driving here.


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