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It's About the Data, not Just Devices - Resilient Data for Self-Driving Vehicles (and Other Autonomous Systems)

Updated: Jun 6



The transportation landscape is on the cusp of a revolution led by self-driving cars. These vehicles hold the potential to dramatically improve safety, efficiency, and accessibility on our roads. However, this transformation hinges on the secure and reliable management of vast amounts of data.


Truth be told, autonomous systems of many types have the same challenges, though many may not have the same high-level of regulatory compliance requirements as vehicles that travel on our public roads, thus not making the spotlight in the same way.


Its the Data, Not Just the Devices

Self-driving cars and many other autonomous systems, rely on a constant stream of data. For self-driving cars data streams from systems providing data about:

  1. The car's environment (e.g. LiDAR, cameras, radar, GPS),

  2. The car’s operation (e.g. temperature, pressure, emissions, speed and many, many others),

  3. Other vehicles and infrastructure.


This real-time data serves as the lifeblood for the car's AI/ML systems, enabling critical functions like prediction, assessment, response and reaction.


Data security, integrity, and the ability to operate autonomously in disconnected environments are critical for safety. Additionally, these mobile data centers collect sensitive personal information like location, driving habits, and potentially financial data. This necessitates a robust data management solution that prioritizes security and resilience.


The Challenge of Data Resilience in Self-Driving Cars (and other autonomous systems)

Safe and effective navigation for self-driving vehicles requires a constant flow of accurate data. However, this data is susceptible to various vulnerabilities:

  • Data Corruption: Sensor malfunctions, software bugs, or physical damage can corrupt critical data.

  • Data Loss: Network issues, power outages, or security breaches can lead to data loss.

  • Data Manipulation: Malicious actors could attempt to inject altered data into the system.

  • Data Availability: Degraded networks or disconnected environments can disrupt data flow.


These vulnerabilities pose significant safety and security risks and recent incidents like GM's suspension of its Robo-Taxi pilot program highlight the importance of public trust, which can be easily eroded by data-related malfunctions. The same challenges existed for the “horseless carriage”. To mitigate these risks and ensure safe and reliable operation, self-driving car manufacturers require robust data resilience solutions.


Kinnami AmiShare: Secure and Resilient Data Mesh for Self-Driving Cars (and other ... Systems)



Kinnami AmiShare is a software-only data management solution designed to address the complexities of distributed environments, including disconnected and autonomous operation. It offers secure and resilient data management specifically tailored for the demanding needs of edge computing environments.

Here's how Kinnami AmiShare addresses the challenges of data resilience:

  • Intelligent Data Management: An AI-driven policy engine manages data prioritization and movement, ensuring it's available where and when needed. AmiShare even operates autonomously in disconnected environments, prioritizing urgent data for optimal real-time delivery.

  • Unstable or Degraded Networks: Connectivity disruptions are no longer a cause for concern. AmiShare seamlessly operates autonomously when connections are lost, ensuring continued safe operation of self-driving cars. It facilitates data movement between vehicles using ad-hoc networks, prioritizing data based on pre-defined policies and selecting the best available route.

  • Data Replication and Synchronization: AmiShare creates and maintains multiple copies of critical data across the distributed platform, ensuring data availability even in case of loss or corruption. Encrypted data replication utilizes the best available paths for high availability.

  • Data Authenticity and Accuracy: AmiShare empowers real-time data analytics and on-vehicle AI/ML decision-making by ensuring data authenticity and accuracy. It provides data provenance tracking, a clear history of data origin and modifications, facilitating easier identification and resolution of data integrity issues.

By comprehensively addressing data security, protection, and availability, Kinnami AmiShare offers a powerful solution for data resilience in autonomous systems and this technology paves the way for the safe, secure, and reliable future of self-driving vehicles. For more information please email patricia.friar@kinnami.com or info@kinnami.com

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