Can Autonomous Vehicles Become Profitable Without PlasmaENGINE®?

Can Autonomous Vehicles Become Profitable Without PlasmaENGINE®?

Last year, we talked about the Extreme Data Challenges of the connected car. In this post, we’ll examine the data challenges of Autonomous Vehicles (AVs).

Safety is the paramount concern for automakers and technology companies as they forge the autonomous driving path, but according to Michelle Avary, head of autonomous mobility at the World Economic Forum, creating a technology and business model that can make money also presents an enormous challenge for manufacturers

Estimates vary when it comes to daily autonomous vehicle data creation, but all estimates are substantial. Tuxera, a world-leading file systems and storage software company out of Finland, crunched public numbers made available by Waymo’s test cars and estimated that one test vehicle creates between 11 TB and 152 TB of data per day. Another study by EnterpriseAI estimated each autonomous vehicle will generate between 5 TB and 20 TB per day. According to Accenture, test AVs generate between 4 and 10 TBs of data per day, which is equivalent to the data generated by ~6,200 internet users. 

According to Accenture, test AVs generate between 4 and 10 TBs of data per day, which is equivalent to the data generated by ~6,200 internet users.

Considering all of this data must be received, stored, protected, and analyzed in real-time – while being retained for research and legal information – cost-efficient data processing is a fundamental obstacle for autonomous vehicle manufacturers, and makes PlasmaENGINE® a necessary tool for companies looking to maximize profits in the AV space. 

As the first data processing software architected to harness the power of GPUs, PlasmaENGINE® allows manufacturers to slash infrastructure costs by up to 75%. By writing incredibly efficient software that fully takes advantage of the thousands of cores on a single GPU, PlasmaENGINE® has transformed data processing from the old batch world of “collect → store → process” to the new real-time streaming paradigm of “collect → process → store”. 

Rival engines, most notably Apache Spark (Databricks), are built on CPUs, which are adept at handling multiple tasks, but can’t come close to matching the speed of GPUs. By porting PlasmaENGINE® onto Spark, FASTDATA.io allows companies to start cutting their infrastructure costs within minutes, saving them money while allowing them to realize new revenue opportunities. 

In the new world of autonomous vehicles, it doesn’t make sense for manufacturers to batch process data. PlasmaENGINE® provides a solution to that problem. 

In the new world of autonomous vehicles, it doesn’t make sense for manufacturers to batch process data. PlasmaENGINE® provides a solution to that problem.

The challenges for AV leaders are many:

  • Will they use on-premise or cloud infrastructure? 
  • If they leverage a hybrid infrastructure, how will they connect on-premise and cloud? 
  • How will they offload data from the data collection vehicles? 
  • How will they move data from the vehicles to their storage infrastructure? 
  • How will you secure the data at each stage of the collection, annotation and usage process? 
  • How will they understand the data that is usable and not usable? 

Autonomous vehicles aren’t far from tackling their first hurdle (safety). When they’re ready to hit the streets, they’ll need an efficient software built to handle the above challenges. That software is PlasmaENGINE®. 

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