This article is the first in a six-part series on the topic of AI assistance in the vehicle interior. We shed light on what the motivation behind AI assistants in the vehicle interior is and where the challenges lie in order to implement intuitive, smart and useful AI assistants in the vehicle interior. For a quick overview, here are links to all arcticles in this series:
- The AI assistant in the vehicle interior (1/6): What is already possible today and what is a vision?
- The AI assistant in the vehicle interior (2/6): Technical challenges
- The AI assistant in the vehicle interior (3/6): machine vision as a key technology
- The AI assistant in the vehicle interior (4/6): intuitive, multimodal and proactive
- The AI assistant in the vehicle interior (5/6): market prospects and business models
- The AI assistant in the vehicle interior (6/6): Fraunhofer as your innovation partner
The idea of an intelligent, proactive AI assistant in the vehicle is fascinating. However, numerous technical hurdles must be overcome to implement a truly intuitive, safe and powerful assistant. Such an assistant should not only understand voice commands, but also recognize gestures, facial expressions, eye movements and context. It also has to respond in real time, ensure data protection and adapt flexibly to different users. What are the technological challenges that arise from this – and how can they be solved?
AI assistance requires real-time processing and computing power
An AI-based assistance system must respond to user input in milliseconds. Whether it’s a glance at the center console, an impromptu hand gesture, or a spoken command, delays would make the interaction unnatural and frustrating. This places enormous demands on the computing power in the vehicle and on an intelligent implementation of the interaction.
While many assistance systems rely on cloud-based solutions, strong on-board processing is essential for the vehicle interior. On the one hand, safety-critical functions must work reliably even without an internet connection. On the other hand, local processing significantly reduces latency times. Modern vehicle architectures therefore integrate special AI accelerators (e.g. NPUs or GPUs) to efficiently execute image processing and machine learning. The open source community is vigorously driving the development of small, specialized, and locally executable AI models.
More than language: multimodal sensor fusion
A powerful AI assistant must do more than just understand language. Humans communicate in a variety of ways: through facial expressions, gestures, posture and situational context. A pure speech assistance system would ignore many of these signals and thus lose natural interaction.
The solution lies in multimodal sensor fusion. Cameras, microphones, radar or infrared sensors capture various aspects of user interaction and combine them into an overall picture. For example, if the system detects that the driver is looking to the right while driving, it could recognize a possible interest in a local attraction and proactively provide information about it – without the need for a voice command.
Challenges in machine vision
Machine vision or computer vision plays a key role in capturing facial expressions, gestures and eye movements. However, the environmental conditions in the vehicle are challenging:
- Light conditions: strong sunlight, darkness or changing light conditions can affect camera systems.
- Varying user positions: Users move around, lean back or wear sunglasses – the algorithms must react flexibly to this.
- Occlusion: Hands, headrests or other objects can obscure relevant body parts, making interpretation more difficult.
To meet these challenges, we at Fraunhofer IOSB, among others, train AI models with large, diversified data sets that cover as many scenarios as possible. In addition, we work with a combination of RGB and infrared cameras to deliver reliable and stable results even in low-light conditions.
Personalization and adaptivity – important aspects of AI assistance
A good AI assistant adapts to its user. Every person has different speech patterns, gestures and preferences. Some users interact with the system a lot, others prefer a minimalist assistant.
Modern AI systems must be able to adapt individually without users having to go to the trouble of making settings. Machine learning makes it possible to recognize habits and optimize interaction accordingly. For example, the system can automatically set a preferred seat position or suggest frequently used navigation destinations.
Data protection and ethical aspects
An intelligent assistant requires a lot of personal data to function properly. But data protection is a critical factor: users must be able to trust that their data will be processed securely and not misused.
This is where privacy-by-design concepts come into play:
- On-board data processing reduces the need to send sensitive data to the cloud.
- Anonymous data aggregation prevents individual users from being traced.
- Explainability ensures that users understand why a particular recommendation is being made.
The automotive industry must meet high safety and data protection standards. Transparency and the ability to customize settings are essential for the acceptance of such systems.
Conclusion: The road to intelligent human-AI interaction
An AI-powered assistant in the vehicle interior can revolutionize the driving experience – but only if the technical challenges are overcome. Real-time processing, multimodal sensor fusion, robust computer vision and a high standard of data protection are the central building blocks for a successful implementation.
As a Fraunhofer Institute, we have been researching precisely these technologies for years and support our partners in developing future-oriented, innovative solutions. The challenge lies not only in the technology, but also in a user-friendly, trustworthy implementation. If you want to create a truly intelligent interior, you need a strong research base – and that is exactly where we come in. Numerous research and development projects testify to our experience:InCarIn, Karli, Salsa, Pakos, Initiative as well as many bilateral commissioned research projects with OEM and Tier1.