AI and Its Effects on Autonomous Vehicle Storage

The volume and velocity of data on our roadways globally have never been higher, and the ability to leverage and transform this massive amount of data into real-time intelligence and value is critical.
By Martin Booth

This form of machine learning (ML) is actually a subset of AI and was developed through science, so when applied to machines or devices, it will think and act almost like humans do. In AI, machines or devices execute tasks that humans consider smart. In ML, machines or devices are given data that they learn from and, through deep learning (DL) practices, enable many of the AI activities. Deep learning helps to break down tasks into manageable chunks and mimics the activities so the system can learn on its own. The more it learns, the more data it generates, and the more storage capacity that is required.

Data Center Connections
The connected car continually moves closer to enabling smartphone-like experiences and includes several categories of systems that cover infotainment, road and traffic warnings, vehicle diagnostics, navigation and more. AI-based infotainment systems enable drivers and passengers to receive and send emails, perform Internet searches and interact with smartphone applications—all through voice commands—and transmit many megabytes of data per second, depending on what application the vehicle is running at the time. As more automotive applications become available, and online-enabled, data will need to be quickly and efficiently moved between local vehicle storage (as soon as it is collected) and the cloud. This rise in automotive data generated will require considerable local storage and cloud gateway buffer coding.

Vehicle safety is one of the most important contributions that AI is making to connected cars. Through vehicle-to-vehicle technology driven by wireless connectivity, connected cars will have the ability to communicate with one another by informing other vehicles around them of what they are doing. For example, if a driver fails to slow down while approaching a red light, the connected car could alert cross traffic to avoid an accident. Additionally, connected cars will have the ability to interact with roadway infrastructures, such as traffic lights and signs, through vehicle-to-infrastructure technology. A simple example is a traffic light telling a connected car it is about to turn red so the car will know to slow down.

Flash-based Storage
Storage is a critical piece of the overall automotive solution and represents a significant part of the bill-of-materials. As the car increasingly becomes a data center on wheels, with multiple computers onboard and interconnecting through the cloud, storage optimization will become critical to ensure performance and reliability. In some of the recent infotainment systems, software updates can now be cached and infotainment apps can be buffered to reduce network bandwidth use at peak.

To address the need for higher performance, higher capacity, lower latency, and better reliability and endurance, many automobile makers are turning to flash-based storage for storing the operating system and advanced software applications, for collecting and analyzing drive data recordings, for buffering cloud communications (also for bandwidth optimization), and for storing local copies of infotainment data.

Now proven in the rigorous automotive environment, flash storage supports the high-capacity requirements of autonomous cars, and is available in highly compact packages that are smaller than a U.S. penny. In AI-enabled and autonomous vehicles in which the complexity of systems increase, yet the real estate is limited and every inch of the car counts, the flash-based storage form factors fit into small systems and consume minimal physical space within the vehicles themselves.

Local, on-board vehicle storage that supports connected cars and autonomous driving must be able to withstand harsh environments and perform reliably in the vehicle for long lifecycles. Given the varied environments in which automobiles operate (hot and cold extremes, wet and dry conditions, smooth and bumpy surfaces, shock and vibration challenges), local in-vehicle storage will soon have quality and reliability requirements that far exceed the storage requirements in smartphones or other mobile devices, and may soon approach the requirements found in mission-critical enterprise storage.

Final Thoughts
The reliable collection and storage of data are foundational in how automotive designers can incorporate AI and achieve safe and reliable autonomous drive in cars. Though we won't see driverless vehicles on our highways and byways this year, we will continue to see test cars, and a steady and dramatic progression among auto makers, technology companies and network infrastructure providers to put the pieces together prior to mass deployment. And though fully autonomous cars are still a ways off, and AI continues to infiltrate our daily lives in other ways, advancements in storage will go hand in hand with its success. It will become more apparent that data, and how data is used and stored in the connected car of the future, will be key in realizing the exciting AI visions of these manufacturers.

Martin Booth is the director of automotive solutions marketing at Western Digital Corp.

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