September 20, 2019

By Matthias Bauhammer

As Industry 4.0 gives way to the smart factory, manufacturers are starting to make sense of the deluge of data from the internet of things (IoT) to digitize the supply chain and speed up development cycles. This is known as the industrial internet of things (IIoT), and it is changing the way vehicles are designed, used and operated.

Auto industry players saw it coming. They’ve been investing heavily in autonomous driving, lining up strategic partners and pouring resources into development. IoT sensors in manufacturing equipment give manufacturers real-time intelligence that could help improve and automate the development of autonomous vehicles in the future.

But as IIoT offers new possibilities and revenue streams, it also introduces new challenges. As the auto industry moves toward commercial deployment, there are three crucial challenges to overcome to master autonomous vehicle development in the age of Industry 4.0.

IIoT’s sidekick: Artificial intelligence

Many industry players such as fleet operators and logistics companies have ready supplies of vehicle data to offer for machine learning applications. Road tests and simulations are also already creating flows of data that can reach petabyte scale (1 million gigabytes), so artificial intelligence (AI) plays an important role in managing and making sense of it all.

However, AI alone is not enough. Companies need to merge AI with IIoT technologies to make the most out of all the information they are gathering. IIoT connects the physical machines and equipment in a factory, smart products and the whole supply chain to the digital world of cloud, data analytics and AI. IIoT and AI must be implemented together; without IIoT, AI technologies will not have enough data to truly learn and gauge trends from the data analyzed, and without AI, IIoT brings in massive amounts of information that cannot be efficiently analyzed or put to use.

Using IIoT data, AI allows the system to learn and adapt, enabling continuous system development. When AI technology is merged with IIoT, autonomous vehicles become a reality.

Data management and privacy

From the number of turns a vehicle takes, to where the customer buys groceries, automakers need to utilize the massive amount of IoT data autonomous vehicles collect to provide a superior customer experience, while also being privacy compliant.

When it comes to data management, it is important for manufacturers to work with research and development teams to map out the specific datasets that will help them test different scenarios. For example, if the research and development team is looking at how cars react to a pedestrian suddenly crossing the road from the right side, the team will need lidar data only for that specific situation — not the massive number of frames from the vehicles in all the other scenarios they are put through.

With the increased collection of personal data, it is crucial that manufacturers not overlook the importance of privacy. New approaches for cyber-physical systems are being developed that include authentication and encryption capabilities built into hardware, real-time analytics of network traffic, and IIoT gateways that serve as protective shields for factory equipment, similar to the way firewalls perform in IT networks. These are critical in keeping autonomous vehicles and passengers safe.

Producing autonomous vehicles to scale

Automakers are no longer worried about who produces an automated vehicle first; instead they are focused on who will be able to mass produce autonomous vehicles at scale first. Making this happen will require companies to identify the building blocks that can scale on an industrialized level.

Enterprises, and anyone interested in the mass manufacturing of autonomous vehicles, must realize that building at scale will require standardized data-gathering methods, a trusted system for exchanging data and algorithms for verification and validation, so the automakers and their development partners can deliver continuous improvements as the technology for autonomous cars advances.

The bottom line

To win the race to produce autonomous vehicles at scale, companies will need to extend their partner networks with AI and technology companies. Even automakers that have production experience will need to add new capabilities to their supply chains that sit not only in the car, but also in the cloud. Finding the right technology partner with an open and scalable platform approach is crucial in better understanding how to leverage IIoT for commercial deployment of autonomous vehicles.

Baumhammer Matthias Dxc, Industry Today
Matthias Bauhammer

Matthias Bauhammer is the worldwide offering leader for DXC Robotic Drive and the Automotive Center of Excellence Analytics lead, North and Central Europe, DXC Technology.

After studying industrial engineering in Karlsruhe (University) with a focus on computer science and operation research, Matthias Bauhammer started his career as a consultant in the area of Business Information Management. For the last 22 years Matthias Bauhammer has worked in various national and international projects in the field of analytics and data management, as a consultant, project manager and manager. As Head of Automotive COE Analytics, Matthias Bauhammer is responsible for the thematic development and strategic projects of Artificial Intelligence and the Analytics programs for the automotive industry.

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