Artificial intelligence

In recent years, the automotive industry has taken a path marked by digital transformation , not only of its own operations, but also of its products. The vehicles that will hit the market throughout this decade will have numerous digital components that will form part of the increasingly evolved concept of the connected vehicle, laying the technological foundations for the future ecosystem of autonomous vehicles and driver services. This implies that huge amounts of data will be generated both in the manufacturing operations and later, throughout the life cycle of the vehicles.

For manufacturers, this data will be a true gold mine that will allow them to know like never before the customs and needs of their customers, and also the particularities of each of the processes they carry out in their factories. But taking advantage of this information is complex and requires technologies capable of understanding and contextualizing the data to extract valuable information, and for experts the answer lies in artificial intelligence.

In a report recently published by Capgemini , its researchers claim that AI will be the key to driving innovation in the automotive industry. Its usefulness in the design, manufacturing and distribution chain is more than evident, but it will also allow manufacturers to develop new business channels, taking advantage of all this knowledge to offer relevant services to end customers. This has been a difficult point for the industry, as brands are failing to take advantage of the digital ecosystem they have developed to provide value-added services to their customers, with subscription models that have not been well received.

But the digital journey begins much earlier, as customers demand a fully online shopping experience, from product research to final vehicle purchase. In addition, the researchers point out that the ownership model is going to change radically, and in the coming years the model of shared transport services, short-term rentals and community fleets will gain a lot of presence. This will completely change the relationship of manufacturers with their customers, expanding the scope of services and the need for OTA updates throughout the life of the vehicles.

To meet the challenges of a more digitized, shared and connected personal transportation model, manufacturers must capture as much insight as possible from the data generated in the modern vehicle ecosystem. Although experts point out that not everything should be based on inventing new things, but on recycling ideas that already exist to adapt them to the current and future context.

To achieve this, data and systems capable of extrapolating valuable information are needed to help direct innovation efforts in the right direction, and the key is artificial intelligence. There is a large amount of data that serves this purpose and there is, for example, that which is generated in the design and construction stages, but much of it has traditionally been discarded because it was not understood that it was truly relevant.

With a more holistic approach to the application of artificial intelligence, data from the entire life cycle of products can be connected to better understand the relationship that exists between all design and manufacturing processes with the result and the experience that the user obtains. end customer with the vehicle they have purchased. Capgemini researchers note that AI can help companies make the most of data in a wide variety of contexts, including several key environments.

AI embedded in vehicles

For many, the main utility of AI in the automotive context is in the capabilities and functionalities of the vehicles themselves. Beyond driving automation, which will come with future autonomous vehicles, driver assistance systems (ADAS) feed on information that the vehicle captures from the environment in which it moves and everything that happens in each journey. Applying AI to all this data can improve many vehicle capabilities, both in terms of safety and reliability and in the information that drivers can use to adapt to situations that occur while traveling.

Intelligence applied to production

The automation of production lines in vehicle factories is one of the clear use cases for artificial intelligence, achieving new levels of efficiency, productivity and sustainability. It is already applied to numerous processes today, such as the analysis of images captured by cameras that monitor manufacturing and assembly operations or the management of incoming material logistics. But other technologies that further support these operations, such as augmented reality , are beginning to be integrated.so that operators can work with more information about the processes. And in the field of design, AI has the potential to offer proposals that often go beyond the capacity of humans, such as recommendations based on generative design.

AI powered back office

Artificial intelligence has great potential to improve strategic planning, for example, helping to better choose the vehicle configurations that will be most accepted in the final market. It could also help optimize areas such as human resources at a time when talent shortages are complicating the development of new vehicle design, manufacturing and marketing paradigms.

Capgemini researchers highlight how certain relevant brands are betting heavily on artificial intelligence in different areas of their operations. For example, General Motors is exploring the use of AI-enabled pattern recognition technologies to speed up ADAS design. Along this path, they are exploring the capabilities of diversified deep learning networks and convolutional networks for pattern recognition between drivers that allow them to more precisely focus the development of ADAS to adapt its capabilities to the real needs of its customers.

In addition, Capgemini has been working with Volkswagen and Audi to demonstrate to the German government the value of its Mobility Data Space, with use cases such as real-world road hazard and incident information collected by vehicles on the roads themselves. . This would allow the development of a much more reliable and reactive alert system than the current ones, helping to improve road traffic and prevent accidents.

In their conclusions, the Capgemini experts point out that the industry must ensure that the data they need to feed artificial intelligence is available in the right way, in the right place, and at the right time. In this sense, they point out that several initiatives are already underway to this end, and advocate supporting them so that artificial intelligence can operate properly in an automotive industry that increasingly takes into account the needs of real life. when designing its products and offering added value both to individual drivers and to society as a whole.