Artificial Intelligence and Data Science in the Automotive Industry

1 Artificial Intelligence and Data Science in the Automotive Industry

Martin Hofmann1, Florian Neukart2,3, Thomas Bäck3 *1,2Volkswagen AG, 3Leiden University *1martin.hofmann@volkswagen.de; *2florian.neukart@vw.com; *3t.h.w.baeck@liacs.leidenuniv.nl

Abstract Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future. This article defines the terms “data science” (also referred to as “data analytics”) and “machine learning” and how they are related. In addition, it defines the term “optimizing analytics“ and illustrates the role of automatic optimization as a key technology in combination with data analytics. It also uses examples to explain the way that these technologies are currently being used in the automotive industry on the basis of the major subprocesses in the automotive value chain (development, procurement; logistics, production, marketing, sales and after-sales, connected customer). Since the industry is just starting to explore the broad range of potential uses for these technologies, visionary application examples are used to illustrate the revolutionary possibilities that they offer. Finally, the article demonstrates how these technologies can make the automotive industry more efficient and enhance its customer focus throughout all its operations and activities, extending from the product and its development process to the customers and their connection to the product. Keywords data science, big data, machine learning, automatic optimization, optimizing analytics, automotive industry 1 Introduction Data science and machine learning are now key technologies in our everyday lives, as we can see in a multitude of applications, such as voice recognition in vehicles and on cell phones, automatic facial and traffic sign recognition, as well as chess and, more recently, Go machine algorithms1 which humans can no longer beat. The analysis of large data volumes based on search, pattern recognition, and learning algorithms provides insights into the behavior of processes, systems, nature, and ultimately

1 D. Silver et. al.: Mastering the Game of Go with Deep Neural Networks and Tree Search, Nature 529, 484-489 (January 28, 2016). people, opening the door to a world of fundamentally new possibilities. In fact, the now already implementable idea of autonomous driving is virtually a tangible reality for many drivers today with the help of lane keeping assistance and adaptive cruise control systems in the vehicle. The fact that this is just the tip of the iceberg, even in the automotive industry, becomes readily apparent when one considers that, at the end of 2015, Toyota and Tesla’s founder, Elon Musk, each announced investments amounting to one billion US dollars in artificial intelligence research and development almost at the same time. The trend towards connected, autonomous, and artificially intelligent systems that continuously learn from data and are able to make optimal decisions is advancing in ways that are simply revolutionary, not to mention fundamentally important to many industries. This includes the automotive industry, one of the key industries in Germany, in which international competitiveness will be influenced by a new factor in the near future – namely the new technical and service offerings that can be provided with the help of data science and machine learning. This article provides an overview of the corresponding methods and some current application examples in the automotive industry. It also outlines the potential applications to be expected in this industry very soon. Accordingly, sections 2 and 3 begin by addressing the subdomains of data mining (also referred to as “big data analytics”) and artificial intelligence, briefly summarizing the corresponding processes, methods, and areas of application and presenting them in context. Section 4 then provides an overview of current application examples in the automotive industry based on the stages in the industry’s value chain –from development to production and logistics through to the end customer. Based on such an example, 2

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section 5 describes the vision for future applications using three examples: one in which vehicles play the role of autonomous agents that interact with each other in cities, one that covers integrated production optimization, and one that describes companies themselves as autonomous agents. Whether these visions will become a reality in this or any other way cannot be said with certainty at present – however, we can safely predict that the rapid rate of development in this area will lead to the creation of completely new products, processes, and services, many of which we

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