AI Solutions for Automotive
Supercharge AI in automotive
The automotive industry is rapidly adopting artificial intelligence (AI) to expand into new markets, optimize workflows, and move ever closer to fully autonomous driving. Like Henry Ford did over 100 years ago, AI is revolutionizing the automotive industry.
The automotive industry is rapidly adopting artificial intelligence (AI) to expand into new markets, optimize workflows, and move ever closer to fully autonomous driving.
Like Henry Ford did over 100 years ago, AI is revolutionizing the automotive industry. Across every use case—from connected and autonomous vehicles to mobility as a service (MaaS) to smart manufacturing—AI is the motor, while a proper data infrastructure is the drivetrain.
Large datasets make it possible to gain critical insights into roadways, people, and processes. Many automotive leaders are already investing in AI for in-vehicle applications and more efficient design and production processes.
Workloads range from high-performance computing (HPC) and analytics for crash simulations to machine learning (ML) and deep learning (DL) for autonomous driving and supply chain optimization. However, these workloads tend to be siloed, each with its own infrastructure and budget.
Today's AI workflows must connect across the organization, link large volumes of data from multiple sources, and extend to the cloud. Unleashing the full power of AI requires a data pipeline that can seamlessly capture and move data from devices at the edge, core, and cloud.
Features and Benefits
The capabilities that set NetApp AI Solutions for Automotive apart.
Autonomous Vehicles and Mobility as a Service
Autonomous Vehicles
As AI pushes the transfer of responsibility from human to machine, autonomous vehicles are being developed for passengers, product delivery, and mass transit solutions. It's only a matter of time before AI algorithms deliver you and your groceries.
Mobility as a Service (MaaS)
More than ever, customer needs, behaviors, and expectations are changing. Customers now expect their cars to function as an extension of themselves and their homes. They want intelligent cars that can actively keep them safe on the road, make critical decisions, adapt to their preferences, and even drive for them. With the explosion of new services like ride-sharing apps, consumers—especially those in urban environments—have new needs for mobility on demand and expect pay-per-use service options.
Robotaxi services
Driverless taxis might seem futuristic, but they're already here. Various companies are striving to commercialize autonomous ride hailing at scale. Waymo, an autonomous vehicle MaaS company, is now offering fully autonomous rides in Phoenix and San Francisco. In December of 2021, Ford, Lyft, and Argo AI launched an autonomous rideshare service in Miami, and are looking to expand into Austin, Texas. Using an app, you can now order a fully autonomous ride-hailing car to take you where you need to go.
Delivery services
If Uber and Motional have their way, residents of Santa Monica, California will have their lunches delivered by AVs in 2022. But what about other consumer goods? They too will soon be delivered by autonomous trucks. TuSimple, a provider of autonomous freight semitruck solutions, has completed various road tests, and the company's technology is projected to be fully operational by 2024. In fact, a NetApp customer is currently using NetApp AI solutions to optimize its AI infrastructure for driverless-truck development.
Lane detection
NetApp and Run:AI, a company focused on faster AI experimentation with full GPU utilization, joined forces to offer a single unified experience in the Azure cloud. We put this future-proof platform to the test with lane-detection training experiments for AVs and it passed with flying colors.
Connected Vehicles and Services
Connected Vehicles
Vehicles are moving to Internet of Things (IoT) platforms, connecting to the internet for seamless integration of entertainment, navigation features, and service reminders. With AI-powered in-car personal assistants and dynamic maintenance, the future of connected vehicles is much more than battery-level alerts.
In-car personal assistants
Voice-controlled personal assistants have become increasingly popular among most major automotive brands. Natural language processing (NLP), powered by AI, makes it all possible. Beyond making phone calls for you and giving directions to recommended restaurants, these assistants can remember your preferred settings, and over time, proactively suggest changes.
Dynamic maintenance
AI can take the guesswork out of routine maintenance. Low tire pressure warnings or sensors that tell you when you're due for an oil change are convenient. But dynamic maintenance applications scan the vehicle for indications of large problems before they result in loss of safety or function. AI can even adapt maintenance timing based on how the vehicle is driven. Customers will know to schedule a repair before it's too late.
Smart Manufacturing
Smart Manufacturing Overview
Industry 4.0 introduces the analytical and predictive power of AI to the factory floor. Trained algorithms can speed production, perfect product lifecycle management, and offer actionable insights for teams looking to maximize profit without sacrificing quality and customer satisfaction.
Process and efficiency management
AI-based automation, like robots on the factory floor, can help manufacturers increase production quality and yield, which ultimately results in revenue growth. Robots can improve workplace safety by performing dangerous tasks such as welding.
Quality control
Product quality can make or break your business. Quality-control processes span the entire manufacturing lifecycle—from design to final product. Computer vision and trained algorithms on the factory floor can be used to detect anomalies in production and performance that the human eye simply can't see.
Supply chain management
Automated planning helps you make more informed, agile business decisions. Instead of planning for a month at a time, you can use AI to make more accurate, real-time predictions such as raw material price forecasting or demand for specific products.
Intelligent maintenance
Intelligent maintenance uses AI to continuously monitor and predict when a machine failure might occur. This capability gives engineers time to order a part and schedule downtime to install it.
Four Keys to AI Success
More accurate models
Data is key to identifying patterns, developing predictive insights, and enabling increasingly accurate autonomous systems. Typically, the more data, the more accurate the model. But more data means larger AI models—some with millions or billions of parameters. Training models of this size can take weeks of compute time and require the best-of-the-best ML and DL frameworks.
Seamless data movement
Effective AI requires a data pipeline that spans the entire ecosystem, from ingest and data preparation all the way to analysis and tiering. Data must be able to flow quickly and freely throughout the pipeline at every step. If access to this data is limited by a siloed infrastructure, DL only scratches the surface. Not only does all this data need to be managed—it also needs to be protected within the strict parameters of internal and external compliance regulations.
Speed
AI infrastructures must be able to respond in a heartbeat. Applications such as in-car virtual assistants that use NLP must be able to ingest, process, and respond instantly. Whether you're training a vehicle to understand and respond to human language, or designing Automated Driving/Advanced Driver Assistance Systems (AD/ADAS) functionalities, fractions of a second matter.
Efficient utilization
With massive amounts of data, even a small change in efficiency can have a big impact on optimizing costs and performance. You need to be able to monitor and enact efficient utilization of resources and AI workload distribution. Extending the data center to the public cloud, as well as building automated AI pipelines and provisioning infrastructure for advanced workloads can help you maximize efficiency.
Why NetApp for AI in Automotive
NetApp and NVIDIA
NetApp has long partnered with NVIDIA, the leader in AI compute, to help speed your journey to AI. Our joint solutions combine NVIDIA DGX systems with NetApp cloud-connected all-flash storage to simplify, integrate, and accelerate your data pipeline for ML and DL.
Massive Data Volume Handling
Can you handle the massive volume of data from AVs? NetApp AFF A-Series systems sure can. These arrays hum under pressure and deliver consistent low latency.
Real-Time AI Processing
Is your AI processing data in real time? The NetApp ONTAP AI architecture, powered by NVIDIA DGX and NetApp cloud-connected storage, meets the most demanding AI training needs. Streamline the flow of data reliably; speed analytics, training, and inferencing; predict and respond to customer demand; and spot that faulty airbag before it leaves the factory.
Hybrid Cloud Data Access
Do you have instant, always-on access to data across the hybrid cloud? NetApp cloud data services and the NetApp DataOps Toolkit deliver instant productivity and management built for the complex multicloud world. Efficiently move data from vehicles across the globe to train your neural networks.
Optimize
Operate with greater efficiency and speed than you ever thought possible.
Protect
Keep data secure and remain compliant, without sacrificing availability or performance—whether data is at rest, in motion, in the data center, or in the cloud.
Innovate
Seize every opportunity to transform experiences and create new value by unlocking the power of all your data.
AI infrastructure
Simplify procurement, configuration, installation, and support for AI workloads with NetApp ONTAP AI, powered by NVIDIA DGX systems and NetApp cloud-connected all-flash storage. With this unified AI environment, you can speed up analytics, training, and inference for faster return on investment.
Operations
Make it simpler and faster for your developers, data scientists, and data engineers to perform numerous data management tasks. These tasks include provisioning a new data volume or development workspace, cloning them almost instantaneously, and creating a NetApp Snapshot copy of them for traceability and baselining. It's all possible with the NetApp DataOps Toolkit.
Resource efficiency
Improve efficiency by simplifying and automating cloud infrastructure. With Ocean for Apache Spark (part of Spot by NetApp), you can continuously optimize Spark clusters, choosing the right infrastructure for an application, based on real-time requirements.
Cloud integration
Establish a hybrid cloud data layer for your data pipeline from edge to core to cloud. Increase data mobility and overall flexibility for your AI infrastructure needs.
Expert Guidance
Thrive with expert-led storage guidance
Get tailored advice on how NetApp AI Solutions for Automotive fits your environment — from sizing and deployment to long-term optimization.
Technical Specifications
Exhaustive hardware and software metrics extracted directly from official documentation.
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Global Automotive AI Market by 2025 (Deloitte)Approximately $27 billion total volume
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Connected Car Market Projection by 2026$56.3 billion
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U.S. Licensed Drivers in Connected Cars by 202570.1%
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U.S. MaaS Demand Estimate108 billion miles
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Data Per Test Car (Daily)Up to 60TB per day/test car
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Data Per Test Car (Yearly)Up to 1PB per test car/year
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Waymo Real-World and Simulated MilesOver 20 billion miles driven in AVs
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TuSimple Truck Real-World TestsAbout 3.7 million real-world miles
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Reduction in Commuter Time (U.S.)250 million hours of commuter time per year
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Lives Saved (U.S., 2035-2045)More than half a million lives
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Road Accidents Due to Human ErrorNearly 94% (per American National Highway Traffic Safety Administration)
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Annual Cost of Unplanned Downtime$50 billion each year for manufacturers
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Predictive Maintenance Failure ReductionEliminates 70% to 75% of failures
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Predictive Maintenance Production BoostBoosts production by 20% to 25%
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Logistics Cost Reduction with AI30% in supply chain management
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Data Pipeline ThroughputRun 5 times more data through your pipeline
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Dataset Copy TimeLess than 60 seconds (rather than hours or days)
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AI Infrastructure Configuration TimeApproximately 20 minutes with Ansible integration
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Level 3 Autonomous DrivingAllows drivers to take their hands off the wheel and eyes off the road by relinquishing control of steering, throttle, and brakes to the car's electronic control unit (ECU)
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First Level 3 Approval AutomakerMercedes-Benz, in partnership with NVIDIA - world's first automaker to gain approval for producing and selling passenger vehicles capable of Level 3 autonomous driving
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Mobility Levels SupportedLevels 4 and 5 for full MaaS opportunity
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Storage PlatformNetApp AFF A-Series systems with consistent low latency
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AI ArchitectureNetApp ONTAP AI powered by NVIDIA DGX and NetApp cloud-connected storage
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Data ManagementNetApp DataOps Toolkit
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Snapshot TechnologyNetApp Snapshot copy for traceability and baselining
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Cloud OptimizationOcean for Apache Spark (part of Spot by NetApp)
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Partner IntegrationNVIDIA DGX systems, Run:AI for GPU utilization, Azure cloud
Compare NetApp AI Solutions for Automotive Series
Select the right scale for your workload demands.
| Model Name | Max Capacity | Port Config | Action |
|---|---|---|---|
| NetApp AFF A-Series | Up to 1PB per test car/year | N/A | Get Quote |
| NetApp ONTAP AI | Cloud-connected all-flash storage | N/A | Get Quote |
| NetApp DataOps Toolkit | Hybrid cloud data layer | N/A | Get Quote |
| Ocean for Apache Spark (Spot by NetApp) | Continuously optimized Spark clusters | N/A | Get Quote |
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