AI in Vehicles

Next-gen vehicles can achieve significant performance gains through AI, but must overcome challenges in managing immense data volumes while preserving privacy. Edge AI solutions that process large amounts of data on-vehicle in a privacy-preserving way will be critical to realizing the transformative potential of AI-powered vehicles.

Why Federated Learning?

The logic of moving model-to-data instead of data-to-model

Data Privacy

Vehicle AI applications like personalization, chat assistants, and driver sentiment analysis involve privacy-sensitive data. Federated Learning supports these applications by processing data directly in the vehicle, aligning with data minimization principles of regulations like the GDPR.

Communication Efficiency

Today's vehicle cameras and radars generate vast amounts of data essential for AI. Due to the high data volume and limited bandwidth, next-generation vehicles should adopt a model-to-data approach, keeping data on-vehicle rather than transferring it off-vehicle.

Next generation on-vehicle learning

Federated ML use cases in the automotive industry

Autonomous driving

In the development of autonomous vehicles, massive amounts of data are collected through onboard sensors and cameras.

AI challenge

Edge processing in vehicles presents significant hurdles, often due to poor connectivity and limited processing power or the sheer volume of data generated.

FeDERATED LEARNING

Federated learning can significantly improve the efficiency and safety of autonomous driving systems by:

  • Allowing AI models to learn from data generated across a fleet of vehicles, enhancing the system's overall intelligence and adaptability.
  • By processing data locally on each vehicle, federated learning reduces the need to transmit large volumes of potentially sensitive data.
  • Training models on diverse data from various environments and driving conditions, directly in the vehicle, helps address the issue of data diversity.

Federated learning provides a scalable approach to refine autonomous driving algorithms, making real-time decisions safer and more reliable by leveraging collective learning without compromising privacy.

Predictive maintenance

Using AI for predictive maintenance in vehicles enables early detection of potential failures, enhancing reliability and safety.

AI CHALLENGE

Developing AI models for predictive maintenance in the automotive industry faces hurdles such as:

  • Fragmented and inaccessible data across various vehicle systems and models, connectivity issues, and large volumes of data.

FeDERATED LEARNING

Federated learning enhances predictive maintenance by:

  • Allowing models to learn from the vast and varied data generated by different vehicles, improving predictive accuracy.
  • Processing sensitive data locally within each vehicle, minimizing privacy risks.
  • Training on data from multiple vehicles and models helps overcome the challenges of limited data availability, ensuring a comprehensive understanding of vehicle health.

Personalization in vehicle systems

Machine learning personalizes in-vehicle experiences by customizing settings, entertainment, and monitoring driver state for enhanced safety and comfort.

AI CHALLENGE

The development of personalized vehicle systems faces significant challenges such as:

  • Strict data privacy legislation that restricts the use and sharing of sensitive personal data.
  • Concerns over the handling and processing of sensitive information like driver behavior, preferences, and biometric data.

FeDERATED LEARNING

Federated learning addresses these challenges by:

  • Enabling ML models to learn from data generated across different vehicles while keeping this data localized, thereby adhering to data privacy regulations and mitigating sensitive data exposure risks.
  • Processing personal data on the vehicle itself, ensuring that individual preferences and sensitive information do not leave the vehicle, which enhances privacy and security.
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