Data Intelligence

Ensures security, lowers cost and increases performance and efficiency

While thinking through data intelligence, please keep these critical questions in mind and how they apply to your needs.

  • How many vehicles are in your fleet?
  • How many cameras/LiDARs will be installed on each vehicle?
  • What is your HIL/SIL/Simulation strategy?
  • Will the vehicles be configured with the same sensor array?
  • How long will the data need to be stored on the vehicle before it is offloaded?
  • How long do you need to retain the data in high-performance storage before it is archived?
  • How many engineers will be accessing the uploaded data and what are their connection requirements?
  • How quickly do you need to retrieve the data from archive storage?
  • Do you collaborate with other sites?
  • What is your Disaster Report strategy?
Get started today

The development of ADAS sub-systems and autonomous vehicles generates vast amounts of data from the onboard sensors and systems. So, what do you do with all of the data? The team at AutonomouStuff has gained experience from several years of integrating components on autonomous vehicles and is now working with talented partners to provide a data ingest, storage and archive solution. We have put together information on several kits designed to fulfill common use cases, which is explored in more detail below. We will post further case studies and more information in the near future, so stay tuned.

Deployment Scenarios

Deployment Scenarios

Once you have determined your R&D roadmap and projected your data storage requirements you will need to determine the mechanism for storing the vast amounts of data.

Case Studies

Case Studies

Why do you need a data storage solution? View some of our Case Studies that have used our solutions and the success that they have had.

Coming Soon


We are working hard to bring you the best for Data Intelligence. Check out our plan to deliver value-added services to you

Typical Autonomous Vehicle Development Workflow

The diagram below shows data flow from Autonomous R&D vehicles back to the engineering teams. The vehicles typically go on a “mission,” gather data from the sensors and produce logs during autonomous operation. The raw sensor data, as well as onboard logs, is used by engineers to improve onboard autonomy systems including machine learning. This onboard data is typically transferred to a large scale storage device by either fiber optics or removeable/swappable drives. Once the data is processed by engineers, it is typically kept long-term and used later for regression testing and engineering.


  • Sensor Integration
  • Sensor Calibration
  • Sensor Logging
  • Image Capture
  • Storage


  • Manage Data Lifecycle
  • Tagging/Annotation
  • Sensor Truth
  • Deep Learning OD Training
  • Map Creation


  • Sub-System Development
  • Deep Learning OD Inferencing
  • Watchdog Application
  • Security
  • Compliance


  • SIL/HIL Full Simulation
  • Vehicle Integration
  • Validation

Autonomous Vehicle Sensor Data Generation Comparison (GB/Day)

Typical Autonomous R&D vehicles are fitted with a variety of sensors that are used by onboard autonomy systems and machine learning algorithms. It is important to understand the volume of data generated by these sensors. The raw data from these sensors is typically required to improve/train the onboard machine learning algorithms. The graph below shows that cameras and LiDAR generate the majority of the data onboard the vehicle.

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