From enhanced personalization to safer driving, discover how AI is ushering in the age of Smart Mobility.
Automakers are moving away from merely building cars and towards being service providers. Powering this shift is artificial intelligence (AI), as companies in the auto industry leverage it to create and optimize services, from autonomous driving to a personalized user experience. Here at intive, our AI experts are helping OEMs train their own AI models and adapt existing ones to their needs.
The fuel for every AI model is data, which is used to train the models. How this data is collected, stored, and structured determines the quality of the AI models, says Alexander Bauer, AI Solutions Architect at intive.
Alexander: In the auto industry, artificial intelligence and machine learning are technologies for recognizing things, from sign recognition to speech recognition. In essence, AI models help make the car see and hear.
Many of the modern services and features are based on this recognition; autonomous driving, for example, requires the vehicle to be able to recognize its surrounding environment. In order for the vehicle and services to be personalized to the user, the car needs to be able to recognize that person.
AI-powered recognition is also important for safer driving. For example, driver gaze recognition can identify whether the driver has seen a pedestrian crossing the road, and if not, will cause the car to brake on its own. Pattern recognition of AI models is essential to developing these features.
A second area of application for AI and machine learning is within generative models. This is when a program generates new designs or features from an extensive database or text description. For example, the program can optimize the interior design of a car's modules by automatically evaluating collected customer feedback.
Another program can digitally test all parts of a car, essentially simulating crash tests with digital models based on real data. The AI model can test through hundreds or thousands of variants, finding patterns and optimizing components over time.
New business models are based on powerful and flexible data structures and processing pipelines, which determine how well AI systems learn and perform. However, we see in practice that there is often a lack of data organization. In many businesses, the data is unstructured and messy, and they don’t have the correct architecture and infrastructure set up.
This is of course something we offer – both the cleansing and the consolidation of data – but in the future, manufacturers will have to pay more attention to the structure and quality of their own data. In the end, this will determine what they can do with the data.
In addition, there are legal and regulatory questions: What do manufacturers do with vehicle data and user profiles? How is this data collected, where is it stored, how is it structured, and how can it be evaluated and used in compliance with data protection regulations? – in short, what does the data infrastructure look like? Manufacturers must address these questions if they want to make increased use of data in the future.
Personalization is becoming increasingly important for users. AI and machine learning help to recognize users and learn about their preferences, whether through voice and gesture recognition or by analyzing user data. In this way, offers are so well adapted to the user that they seem to think along with and even ahead of the user.
For many users, having access to a digital personal assistant is also indispensable. For example, ChatGPT has emerged as the next level up from intelligent assistants such as Siri, which are already commonly used. In the future, these AI-powered assistants will be standard in cars as well. They will enormously enhance the user experience, especially when drivers or passengers are too busy elsewhere.
With AI models, manufacturers can improve existing services, explore and create new ones, optimize parts, and build a more personal connection with customers. To do so, however, they need large volumes of data that must be structured, clean, and generated in compliance with all constraints. This presents a new challenge – but the added value is undeniable.
As a pioneer in automotive software, we at intive have helped shape the digital transformation of the automotive industry in Germany and use our extensive experience to give our customers a competitive edge.
We believe in a driver-centric approach to design and engineering to succeed in the Smart Mobility era.
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