Car manufacturers may be moving towards models that can autonomously arrange their own maintenance, taking automotive servicing to a new level.

Modern cars are now equipped with a dense network of sensors, yet the maintenance experience for most consumers remains outdated. However, this is set to change, as cars become a critical source of data that enriches a user's ecosystem of smart devices, creating a more connected and intelligent personal environment.

A new technical vision, outlined in the paper titled "Our Cars Can Talk: How IoT Brings AI to Vehicles", suggests a fusion of technologies that could revolutionize the automobile industry. By combining vehicle data with external factors like weather and road conditions, the system can make highly personalized and accurate predictions about a vehicle's performance.

The key to this transformation lies in integrating AI copilots that can understand both the vehicle's internal data and the driver's needs. For instance, a car frequently driven in extreme heat will experience different wear and tear than one in a colder climate, and an AI could adjust its maintenance predictions accordingly.

Moreover, AI could provide proactive alerts based on external factors like low temperatures and tire pressure drops. By analyzing long-term sensor data, AI algorithms can detect subtle anomalies and deviations from normal vehicle behavior, paving the way for predictive maintenance.

The proposed system centers on predictive maintenance, which uses machine learning models to identify early signs of performance degradation before a component fails. This proactive approach can save both time and money for drivers by preventing unexpected breakdowns.

The standardized OBD-II interface in vehicles provides a constant stream of valuable data, including real-time readouts of engine RPM, fuel system performance, battery voltage, tire pressure, and dozens of other key metrics. This data, when combined with personal context, can help AI assistants evolve into true copilots.

For example, the AI assistant integrated into the car can access a user's ecosystem of apps and devices, providing rich context such as home and work locations, typical driving routes, and even preferred service centers. This context can help the AI assistant provide tailored maintenance advice and service scheduling.

However, implementing such a system at scale presents challenges related to data variability and privacy. To address these issues, techniques like federated learning (FL) are suggested. Federated learning allows AI models to be trained locally without sending sensitive raw data to the cloud, ensuring privacy and security.

Moreover, techniques like differential privacy can further secure the process of training AI models with federated learning. These measures ensure that individual user data remains anonymous while still contributing to the overall improvement of the AI system.

The fusion of AI and vehicle data unlocks use cases beyond maintenance. For instance, real-time driving feedback for new drivers, optimized service schedules for fleet operators, and safer driving rewards for insurance providers are just a few examples of the potential benefits.

One company at the forefront of this revolution is ZF Friedrichshafen AG, which is developing a system that integrates artificial intelligence as a copilot in cars to transform car maintenance from reactive to proactive, analysing vehicle data to monitor function and predict issues in real time. AI might alert a driver about unusual brake usage and suggest a service center appointment.

As we move towards a more connected and intelligent world, the integration of AI into our vehicles promises to make our lives safer, more efficient, and more convenient. The future of car maintenance is here, and it's looking bright.

Car manufacturers may be moving towards models that can autonomously arrange their own maintenance, taking automotive servicing to a new level.