To train the AI in autonomous vehicles, manufacturers must use sophisticated sensors that create massive amounts of data.

How To Stay Competitive In The Autonomous Driving Revolution

May 21, 2018
Harnessing data’s true power for automotive industry breakthroughs.

The moving assembly line, automatic transmission, electronic fuel injection, and air bags—these innovations changed the automotive industry. Today they are vital to offering safe, efficient, affordable, and convenient vehicles to customers. In the digital age, there is a new opportunity to drive innovation with capabilities that will someday be as commonplace as the seat belt. The key to capturing this opportunity and remaining competitive in the digital age relies on manufacturers’ ability to shift their focus and investments into a new area entirely: data.

How Data Is Driving Automotive Innovation

Across nearly every industry, we’ve seen data go from a nice-to-have to a key differentiator that will determine a company’s survival. In the automotive industry, while some manufacturers are already embracing their data as the key for the future of their business, others have been slow to respond and need to seize on the opportunity their data holds to introduce new capabilities in three key focus areas: autonomous driving, connected cars, and smart manufacturing.

Autonomous Driving

The most high-profile way data is shaping automotive innovation is through the automation of the driving experience. Many manufacturers have begun to incorporate advanced driver-assistance systems (ADAS) into their vehicles to give customers a more convenient and safe experience. Ranging from automatic lane control and braking to (soon) full-fledged autonomy, the ability to develop, test, and continually improve these capabilities will be a major differentiator. The first company to enter the market with a safe, tested, fully-autonomous vehicle will be the king of the industry for years to come.

As with any new technology, research is vitally important. This is especially true in the automotive world, as products that are produced must provide passenger safety. Developing semi- and fully-autonomous vehicle capabilities requires auto manufacturers to deploy dozens or even hundreds of test vehicles with sophisticated sensors (e.g., 4K video, sonar, radar, LIDAR, etc.), capturing data on safety, user experience, traffic flow, and energy efficiency. Manufacturers must be able to quickly contextualize, correlate, and analyze hundreds of terabytes daily to extract value from data while test vehicles travel millions of miles.

A major focus area for automakers is crash avoidance. One example of research in this area involves wildlife incidents. For instance, if an autonomous test vehicle’s cameras or radar record a potentially hazardous situation involving a deer running in front of a car, the automaker needs to be able to take that data, analyze it, and teach the artificial intelligence governing the vehicle’s responses to take the right course of action when encountering similar incidents. Apply this same methodology to the vast array of situations that might occur on the road—construction, pedestrians, and erratic drivers, to name a few—and automakers will find themselves dealing with tons of data.

Automakers who have made investments in advanced data solutions can store and analyze all this data, automatically. These automakers tag metadata and automatically scan through their footage to determine whether a potentially dangerous scenario has happened before. Armed with data from multiple events, these automakers apply machine learning to run billions of potential permutations and make updates depending on that information to achieve the desired outcome. This approach will be constantly updated as new on-road scenarios are introduced into the data, creating a truly dynamic improvement process.

Ultimately, this level of research relies on the manufacturers’ ability to capture, store, analyze, and act on massive amounts of data. The earlier an automaker devotes resources to it, the greater their competitive advantage grows.

Looking ahead drivers will have new capabilities to interact with other vehicles and consume new services.

Connected Cars

There is also a push to improve driving experience with connected cars that share data in real-time through consistent connections to data centers and public cloud providers.

Much like the mobile devices we carry today, connected cars will give drivers the opportunity to receive nearby offers from retailers (such as coupons), along with advertisements delivered in real time and tailored to their buying habits. These opt-in programs could provide another level of convenience to customers and a potential revenue stream for manufacturers who partner with businesses.

In the case of fully autonomous vehicles, manufacturers will be able to provide customers with new in-car infotainment options such as streaming movies, video game systems, and access to any digital platform that requires an internet connection.

Eventually, connected cars will go beyond just offering drivers new ways to interact with their vehicles to enable interaction between the vehicles themselves. Interconnected vehicles capable of sharing data on road conditions, route alterations, and crash alerts will create a dynamic neural network, capable of simultaneously coordinating speeds, following distances, and braking processes for optimal travel speed and fuel efficiency.

Automakers will need to plan for building vehicles capable of accessing, gathering, and sharing data with each other to further increase safety, efficiency, and customer convenience.

Digitization of the auto industry will impact the manufacturing process in many areas.

Smart Manufacturing

For any automaker—whether an OEM or parts supplier—manufacturing is the biggest ongoing expense. The ability to make the manufacturing process more efficient, consistent, and cost-effective will lead to significant financial savings and improved customer satisfaction. Data and digitization are helping top auto manufacturers identify errors and faulty parts at an earlier stage, driving down manufacturing costs substantially and enabling them to reduce prices, reinvest in the business, or return money to shareholders.

The digitization of the auto industry will not only change how parts are manufactured but also what is made. Manufacturers specializing in traditional vehicle components will have the opportunity to change their business model to fit the demands of vehicles in the digital age. Manufacturers will be responsible for pioneering new sensors, braking mechanisms, onboard connectivity devices, and other components required to enable autonomous, connected vehicles.

Manufacturers that embrace and understand the impact of data in the manufacturing process can prepare for these changes ahead of their competition.

Why Are Some Automakers Lagging?

Automakers may understand the importance that their data capital holds, but their core competency is in building cars. Integrating disruptive technologies into the processes they’ve used for decades and managing big data is brand-new to them. In the digital age, disruption is coming from non-traditional, IT-focused companies that tend to thrive on leveraging data to drive results.

When an automaker decides to invest in information technology and data–like Volvo, Audi, Tesla, and others have–they have to create an entirely different culture. While traditional engineers are important, manufacturers will need to augment their workforce with new roles, such as developers and data scientists.

Manufacturers must also prepare for the effect this cultural shift will have on development and manufacturing cycles. The race to implement autonomous vehicles means forgoing the traditional multi-year development cycle and utilizing a more agile approach that improves rapidly and can deliver technology to retrofit existing in-market vehicles. This will put even more pressure on lagging manufacturers as leaders will continue to pull ahead as they accelerate releases of new models.

While the earliest investors in digitization, including ADAS and autonomous driving, have not yet achieved fully autonomous vehicles, they do hold a substantial advantage. Manufacturers who have not yet begun modernizing their IT to become a digital business need to start immediately if they wish to be competitive in the future.

Some companies may try to survive for a time by striking up partnerships with vendors who have gathered historical driving data, such as legacy fleet management or telematics providers. While partnerships can offer some stop-gap capabilities, it is crucial for manufacturers to start unlocking their data capital now so they can ensure they are not left behind.

It’s a lesson that applies well beyond the bounds of the automotive industry: If companies aren’t making serious investments to integrate data into their processes and offerings, there’s a chance their competitors likely are. And as uncomfortable as this might be to hear, it won’t be long before a latecomer is just a distant speck in the rearview mirror.

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