The automotive industry has a long-running track record for harnessing the latest technologies to bring efficient, innovative and safe vehicles to market, while continually working to cut manufacturing costs. Today, these technologies include artificial intelligence and high performance computing — two keys to automotive success.
While for many years the industry has invested heavily in HPC systems to power modeling, design and simulation applications, today this focus is broadening to include substantial investments in AI to drive autonomous and semi-autonomous vehicles. Auto makers around the world recognize that these smarter vehicles are clearly the future, and they know they can’t get there without AI.
AI is also being used for a wide range of predictive capabilities that personalize the driving experience and enable proactive maintenance. This personalization is enabled by connected vehicles that send data from on-board sensors to manufacturers, advertisers and insurance companies.
These advanced capabilities, coupled with rising consumer expectations, have pushed the automotive industry into a period of digital transformation. Today’s manufacturers know they need to harness new technologies — like AI, machine learning and deep learning — to reduce costs and give drivers more of what they want.
In a sign of the growing importance of emerging technologies to the industry, the automotive AI market is expected to grow at a compound annual growth rate (CAGR) of nearly 40 percent through 2025.1 This is on top of billions of dollars the U.S. government is spending each year to accelerate the acceptance of AI-driven autonomous vehicles.
Common AI use cases
Use cases for AI now cover the entire driving experience. Here are some of the diverse ways in which automobile manufacturers are using AI and the power of HPC to deliver a safer, more efficient driving experience while streamlining their processes to contain costs:
- Driver assist — With advanced driver-assist features, many of which are available in today’s cars and trucks, AI systems alert drivers to hazardous road conditions, monitor blind spots in the driver’s view, assist with steering, and take automated actions to help vehicles avoid accidents and dangerous situations.
- Autonomous vehicles — In the automotive industry, autonomous vehicles are the new holy grail. Manufacturers and their technology partners are working overtime to develop AI-driven systems to enable self-driving cars and trucks. These systems incorporate a wide range of AI-enabled technologies, such as deep learning neural networks, natural language processing and gesture-control features, to provide the brains for vehicles that can safely drive themselves, with or without a human driver on board.
- Connected vehicles — AI is an essential technology for connected vehicles. For example, AI can watch for and predict component failures, so vehicle manufacturers and owners can work proactively to avoid problems. It can also provide drivers with location‑based information and personalized advertising to help them find the things they need. Similarly, AI-enabled systems can send driving and accident data to insurance companies, which might offer incentives for safe driving habits.
- Manufacturing — AI enables applications that span the automotive manufacturing floor. Automakers can use AI-driven systems to create schedules and manage workflows, enable robots to work safely alongside humans on factory floors and assembly lines, and identify defects in components going into cars and trucks. These capabilities can help manufacturers reduce costs and downtime in production lines while delivering better finished products to consumers.
- Quality control — A study by McKinsey Global Institute highlights some of the unique advantages of using AI to inspect automotive component and products. In one of these advantages, AI systems get better over time at identifying defects. “The AI system constantly learns to improve its analysis based on feedback,” McKinsey notes. “Using these methods, AI-powered hardware can visually inspect and provide superior QC on various products, such as machined parts, painted car bodies, textured metal surfaces and more.”2
- Supply chain —In today’s global economy, automotive manufacturers have extremely complex supply chains that span many geographies. Any glitches or breakdowns in the supply chain can be extremely costly. With AI, manufacturers can gain greater control over their supply chains, including processes for planning, logistics, inventory tracking and management. For example, AI-driven systems can predict complex interactions between production units and automate requests for parts, labor, tools and repairs.
High performance computing
In addition to powering data-intensive AI applications, HPC systems remain essential tools for automotive design, engineering and testing processes. Here’s are a few examples of how HPC is being used in the day-to-day life of the industry:
- Engineering — For many years, HPC systems have been widely used to increase the performance of design, simulation and testing applications. Computer-aided engineering applications, for example, allow engineers to design, test and break components and assemblies in software — and to avoid the costs of building and testing physical prototypes. In the future, manufacturers may be able to use AI to automate, accelerate and improve the accuracy of these development processes.
- Safety — HPC‑powered simulation technologies are helping manufacturers to increase vehicle safety, while reducing design costs and timelines. Computer-generated simulations, for example, enable engineers to test new materials and evaluate their structural properties, strengths and breaking points before they are incorporated into a vehicle design. This wouldn’t be possible without the computational and visualization power of HPC systems that drive computer-aided design and engineering applications.
- Virtual desktop infrastructure (VDI) — The reach of HPC isn’t confined to servers and compute clusters in large data centers. It can be used to support remote visualization for multiple users on a single, virtualized server running a variety of computer‑aided engineering applications and supporting VDI software.
An ADAS and autonomous-driving case study
Zenuity is a small company with a large mission. This startup — launched by Volvo Cars and Veoneer, a subsidiary of automotive safety leader Autoliv — is developing advanced driver-assist systems (ADAS) and autonomous-driving (AD) technologies that promise to take vehicle safety systems to a new level. Zenuity is focused on the software side of the ADAS/AD problem. It enables smarter vehicles with a complete software stack, including algorithms for computer vision, sensor fusion, decision making and vehicle control, along with applications that run in the cloud.
AI is at the heart of the Zenuity’s solutions, according to Dennis Nobelius, the company’s CEO. In an interview with Automotive World, he notes that AI is one of the major trends driving advances in ADAS and AD systems. “We realize that we must be effective with the data that we generate and handle, and AI is really transforming the way we use that data and, therefore, how we operate software,” he says.3
A study by the McKinsey Global Institute predicts that, over the next two decades, AI will enable autonomous vehicles to become mainstream while transforming most aspects of the auto-manufacturing process, from research and design to project management and business support functions.
“These changes are fast approaching,” the firm notes. “Manufacturers should understand what the sources of value really are and then start developing the necessary analytical capabilities and establishing an AI-ready culture.”2
Ready to learn more?
For a look at Zenuity’s work to accelerate driver-assistance and autonomous-driving technologies, read the Dell EMC case study “Safer Driving” or watch the video “Zenuity: Making It Real.” And for a closer look at the ways enterprises are using AI to unlock the value of data, explore Dell EMC AI Solutions.
1 MarketResearch.com, “Automotive Artificial Intelligence Market by Offering (Hardware, Software), Technology (Deep Learning, Machine Learning, Computer Vision, Context Awareness and Natural Language Processing), Process, Application and Region – Global Forecast to 2025,” August 2017.
2 McKinsey Global Institute, “Building smarter cars with smarter factories: How AI will change the auto business,” October 2017.
3 Automotive World, “Zenuity CEO on the auto industry’s ‘fantastic future,’” June 6, 2018.