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Redefining Transportation: The Impact of Digital Transformation on Autonomous Vehicle Development

There is a clear trend in the automotive industry. Autonomous vehicles are the future of transporation and companies big and small are vying for a piece of this new market. Nevertheless, new markets mean new difficulties. Digital transformation has already been critical to the few successful cases that exist. Companies like Tesla and GM have embraced digital engineering workflows with glowing results. Consider the challenges in designing autonomous vehicles and how going digital makes it easier to overcome them.

The Impact of Digital Transformation on Autonomous Vehicle Development

Redefining Transportation: The Impact of Digital Transformation on Autonomous Vehicle Development

The Autonomous Vehicle Industry Depends on Going Digital

Not that long ago, most car manufacturers still relied on paper plans and clay models to design a vehicle. While these tools are still used in some instances, they have been largely replaced by digital designs and models. This digital transformation was driven by the need to improve performance and value. The margins were already becoming thin in traditional internal-combustion engine (ICE) vehicles. There’s a practical limit to how aerodynamic or how efficient an ICE vehicle can be.

Autonomous vehicles push that limit forward a great distance, however, car manufacturers need to be able to work fast to close that gap. Digitalization makes it possible for these car companies to reach their goals and turn a profit. When Tesla started out, most automakers assumed they would fail. Nevertheless, their focus on digital development allowed them to overcome challenges and become one of the biggest automotive companies in the entire industry.

The Challenges in the Autonomous Automotive Industry

What makes designing an autonomous vehicle more difficult than the typical ICE car? Self-driving cars are essentially a completely new machine. For starters, the design of the vehicle itself has to change to accommodate a wide array of sensors and computers to enable autonomous features. Most manufacturers see electric vehicles (EVs) as the future, so these cars also have a unique drivetrain that radically changes the car’s design. Those are just the physical differences.

Next, consider the challenges related to the self-driving technology itself. In order to perform well, an autonomous vehicle needs extensive training to learn traffic rules, detect objects, and make decisions. This training would take decades to complete manually. Machine learning algorithms are necessary to accelerate the process, as well as huge volumes of data. Let’s examine these challenges in more detail and see how digital transformation solves them.

Brand New Vehicle Designs Benefit From Digitalization

Redesigning vehicles to allow for new technologies poses several challenges. Instead of iterating on a previous model and making small adjustments, engineers have to start from scratch. Tried-and-true layouts no longer work when you have to consider the placement of a massive battery pack. This means that many possible configurations exist, and it’s up to engineers to test them all until they find a suitable solution. Going digital makes this task much more agile.

Modern digital engineering tools allow automotive engineers to start drawing and simultaneously generate the components the vehicle needs. A sketch of a car model quickly becomes a series of fiberglass panels, a metal chassis, and interior parts. Algorithms can procedurally generate pieces of a car as a designer draws lines. This allows designers to experiment freely and quickly determine the cost of their design. This model-based engineering approach has become the gold standard for the automotive industry.

Model-Based Engineering Facilitates Design

Model-based engineering, or MBE, bases everything on a digital model. The digital model contains every component that will go into the vehicle. Each component has data points that can be modified on the fly. For example, an engineer might change the material or thickness of a component to shave weight or cut costs rapidly. The MBE system can determine if those changes create weaknesses or potential supply issues.

Another benefit of MBE is that it facilitates collaboration. Lead engineers can work together on the digital model, and changes are automatically pushed out to anyone who needs them. For instance, a change to a body panel might trickle down to the factory’s engineers so that they can modify the stamping tools used to produce that part. Adding a component could alert the company’s purchasers to seek a supplier for that part. Overall, this approach is much faster.

New Drivetrains Mean New Supplier Networks

Speaking of suppliers, autonomous vehicle makers face another challenge in getting parts for their vehicles. Traditional supplier networks for ICE vehicles have existed for over 50 years, making it easy to seek out new suppliers or get parts on short notice. For autonomous and electric vehicles, however, those networks are in their infancy. Although engineers strive to use parts they have easy access to, there will still be unique components such as sensors and electric motors.

Whenever there are new components, there is increased uncertainty. These new vehicles may change their design numerous times before launch, meaning that purchase orders will change, and suppliers will need to be flexible. Going digital has made it easier for both manufacturers and suppliers to communicate and fulfill each other’s needs more readily.

The Cloud Empowers Suppliers and Manufacturers

Digital platforms use the cloud to centralize data. A manufacturer can connect to suppliers in the cloud to quickly relay parts requests. Suppliers can see the digital model to understand how the part they must produce fits into the bigger picture. Changes made to the master design will immediately be updated from the supplier’s perspective as well. The model also dictates quantities and tolerances so that suppliers can tailor their production for the manufacturer without dozens of phone calls.

This is why automotive parts suppliers need to go digital. The manufacturer needs a supplier that can reliably use a digital environment. If your business is behind the times, don’t expect to secure the best contracts. Traditional companies move at a slower pace that autonomous vehicle development cannot tolerate. In addition, going digital cuts costs as you need fewer employees to handle purchase orders and communication with the manufacturer. Everything is in the cloud.

Testing Autonomous Systems Requires Digital Environments

Automated vehicles require extensive testing and training before even their simplest features can be released to the public. Already, Tesla has had to recall hundreds of thousands of vehicles for pushing self-driving capabilities too early, with several high-profile accidents as a result.

However, testing in the real world is not a viable option considering that it takes hundreds of thousands of hours of training for an autonomous vehicle to perform well. Companies that have already undergone a full digital transformation can use technology to greatly accelerate their progress.

Cloud Computing and Simulations Accelerate Development

Instead of getting data strictly from real-world tests, autonomous vehicle manufacturers use digital environments to test their vehicles. These simulations recreate intersections and traffic patterns and deliberately test the vehicle’s ability to detect pedestrians or other objects. By running many simulations in parallel, an automaker can squeeze thousands of hours of training into a single real-world hour.

However, this requires incredible processing power, the likes of which few businesses have on hand. Fortunately, cloud computing allows companies to upload their tasks to data centers where complex operations can be processed quickly. You only pay for the processing power that you actually use, which means more affordable access to high-performance equipment.

Big Data Drives Self-Driving Cars

As autonomous vehicles are an emerging technology, companies are constantly reevaluating how to design them according to user feedback and preferences. This is where big data merges with self-driving vehicles. For instance, most companies found that drivers were more likely to want autonomous features for long stretches of highway driving. Therefore, most companies focused on implementing those features first. Now, almost every car manufacturer has some form of automated cruise control and lane-keep assist.

For a company to turn that data into action, they need business intelligence. Business intelligence platforms can collect data from numerous sources and highlight useful insights that your team can turn into actionable items. Instead of paying analysts to comb through data, computers can process it for you.

The Time to Go Digital Is Now

If your company wants to keep pace with this fast-evolving industry, then you need to switch to a completely digital workflow. Major auto manufacturers have already done so, and they expect their suppliers to do the same. Anyone hoping to compete in an increasingly digital environment must follow suit.

To make sure your digital transformation goes smoothly, talk to the experts. Contact SAAB RDS to learn more about how we can help your company go digital and remain on the cutting edge of this emerging, lucrative market.