It’s also costly in terms of time and money. This is especially true in the automotive industry, which has traditionally relied on physical prototypes and complex simulations to test new safety and design features. The cost of building a vehicle prototype can be as high as $1 million dollars, making this approach to product development too expensive for many companies, especially startups, working to invent new vehicle and transportation technologies.
However, with the cloud, automotive companies are accelerating engineering development to achieve quicker design turnaround times at lower costs. One organization at
the forefront of automotive design is Formula 1. In 2021, the organization set out to create a more aerodynamic vehicle to increase wheel-to-wheel racing between cars by reducing loss of downforce.
For the redesign process, F1 needed to run detailed simulations of over 550 million data points to model the impact of one car’s aerodynamic wake on another. To reduce the time required for each simulation, FI moved away from the on-premises environment it had used to design and test aerodynamic properties and shifted to high-performance computing (HPC) on AWS. This helped reduce the average time to run these simulations by 80%—from 60 hours to 12 hours—and lower costs by 30%.
F1 has started using its next-generation cars this year with a design that, for the first time, features 18-inch wheels with low-profile tires. The car also includes wheel wake control devices, a brand-new bodywork design with a new front wing shape, simplified suspension, a new rear-end layout and underfloor tunnels. The result is significantly reduced downforce loss when following another car closely, which leads to more intense racing and excitement on the track.
Research and development (R&D) is critical for any business looking to invent and improve its products.
Accelerating Innovation While Lowering Design Costs
Moving to the cloud helped F1 reduce the average time to run redesign simulations by 80%—from 60 hours to
12 hours—and lower costs by 30%.
Like F1, the electric vehicle maker Rivian has moved its data-intensive, advanced modeling and simulations to cloud-based HPC for vehicle engineering. For Rivian, being able to render and test new features in the cloud not only delivers greater speed, but it also drastically reduces development costs—which is critical to a startup working to reinvent how vehicles are designed, manufactured and operated.
Rivian is well on its way to implementing a modern data strategy, using AWS to
Automotive companies maintain manufacturing operations in over 100 countries, producing more than 80 million vehicles each year.
Operating an efficient, effective global supply chain is critical to the Volkswagen Group, one of the world’s largest automotive companies. The company manufactures about 11 million cars per year and brings 200 million parts per day into its factories. To improve productivity while lowering costs, Volkswagen is working to completely digitize its manufacturing operations and create the Volkswagen Industrial Cloud.
With this project, Volkswagen has already connected more than 25 of its factory sites to the Industrial Cloud and is continuing to move its entire global portfolio of over 120 plants into the cloud and onto a single global architecture, built on AWS, with the goal of significantly improving productivity. Volkswagen’s suppliers, manufacturing robots, machines and logistics systems will all be connected to the Volkswagen Industrial Cloud.
With the data it collects from these multiple sources, Volkswagen can rapidly innovate and build new applications using ML and AI—for things like quality control, predictive maintenance and process optimization. It can then make these applications available in the cloud for all its factory sites to download, scaling out new applications and updates across the world almost instantly.
Volkswagen also uses cloud technology to improve its manufacturing processes and quality control by breaking down language barriers. With Internet of Things (IoT) and ML technology, Porsche, a Volkswagen Group brand, has built an intelligent Sign Inspection (iSI) solution that automatically checks vehicle labels containing country-specific safety, usability and specification data. iSI detects incorrect, damaged or missing labels and helps Porsche maintain safety standards and ensure regulatory compliance.
On the Porsche assembly line, automated cameras photograph labels and send them to the cloud for analysis. If an issue is detected, the system notifies an in-line worker, who takes the vehicle to a quality check station for manual inspection. The inspection results are translated using ML and displayed to the worker in their local language so they can take appropriate action to fix detected errors.
iSI is helping bring Volkswagen closer to its goal of zero defects, and the company is working to expand this solution to all its manufacturing facilities around the world.
Like many industries, the automotive industry extends across a complex worldwide network of manufacturing facilities, suppliers and distribution channels.
Improving Manufacturing Efficiency And Quality
Automakers are using data on vehicle performance and driver behavior to make cars safer, more comfortable and more enjoyable to drive and own.
Toyota Connected North America, a division of Toyota Motor Corp., leads the development of Toyota’s cloud-based digital Connected Mobility Intelligence Platform. Toyota Connected supports Toyota’s overall transition from an automotive to a “mobility services” business that addresses not only conventional vehicle ownership but also growing customer needs, like car sharing. Toyota Connected uses cloud-based data tools to invent new ways of enhancing the vehicle experience.
For example, the company recently announced its new Cabin Awareness concept technology that uses sensors and ML to detect vehicle occupants, including certain pets, who are left in the car after the driver exits. This technology is designed to alert the driver and passersby that a passenger may still be in the vehicle, a safety feature that could help prevent heatstroke deaths.
For Aurora Innovation, a leader in self-driving vehicles, data is helping the company transform the movement of goods and people, improve safety and expand access to transportation. Aurora is testing the first product that it will bring to market, an autonomous truck, in Texas and is piloting its technology with companies like FedEx and Schneider to make the logistics industry more efficient. Aurora plans to accelerate the commercialization and adoption of self-driving vehicles through its scalable self-driving product, the Aurora Driver, which is designed to integrate into a variety of vehicles for different use cases.
In addition to improving business processes, data can help organizations better understand their customers, which can help them improve customer experiences.
Reimagining Customer Experiences
Vehicles have come a long way since my first car, a 1992 Opel Corsa hatchback with a manual transmission and a 44-horsepower engine. Compared to how customers interact with modern automobiles, which are mechanically sophisticated, cloud-connected microprocessor networks on wheels, my relationship with my Opel was one-sided: I drove the car and learned its idiosyncrasies, but the car didn’t learn from or respond to me.
What’s more, unlike today’s vehicles, data wasn’t central to how my car operated or to my family’s experience of owning it. Now, by using the cloud to manage and scale its data, the automotive industry offers customers a reimagined picture of what automobile ownership and mobility can mean.
Learn more about how AWS helps a range of industries grow their business, how it can help organizations put data to work with the cloud, and find perspectives on innovating at AWS Executive Insights.
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connect its full value chain, from vehicle design to manufacturing to the vehicles themselves, and bringing all its data together from multiple sources. This has helped the company foster a collaborative culture of “adventurous thinkers” who are reimagining every aspect of the customer experience, right down to creating vehicle interiors with end-of-life recyclability. The company’s data strategy also enables Rivian to bring new vehicles to market quickly and to iterate and fine-tune vehicles after delivering them to customers.
Automotive manufacturers usually require about four years to design, build and release a new car. With the cloud, Rivian has been able to shorten the timeline. For example,
the company first began working with Amazon to design custom delivery vehicles in 2019 and now plans to put thousands of new Amazon electric delivery vehicles on the road in 100 cities by the end of 2022.
Before the Aurora Driver ever attempted its first unprotected left-hand turn on a physical road, it had completed over 2 million turns in simulation—which equals about 20,000 hours of real-world driving practice.
Powered by AWS's high-performing infrastructure, Aurora Innovation uses ML and millions of cloud-based simulations, running trillions of data points, to safely and quickly train, test, and validate the Aurora Driver. Using virtual testing, Aurora can train its Driver to safely navigate complex situations, such as unprotected left-hand turns and road construction. Before the Aurora Driver ever attempted its first unprotected left-hand turn on a physical road, it had completed over
2 million turns in simulation—which equals about 20,000 hours of real-world
driving practice.