Transforming AV/ADAS development at Continental with generative AI (AUT307)

Autonomous Mobility Development with AWS and Continental

Key Takeaways

  1. Challenges in Autonomous Mobility Development:

    • Unprecedented data volume (up to 100 PB per year)
    • Diverse data requirements across development teams
    • Scalability and cost challenges as development programs change
    • Integrating various tools and applications
  2. Continental's Collaboration with AWS:

    • Developed a platform called "CATCH" (Continental Automotive Edge Computers) on AWS
    • Leveraged AWS managed services to store and process data
    • Built a set of data services to enable data-driven development
    • Integrated with various partners and tools to reduce integration time
  3. Scene Search:

    • Developed a scene search feature to help developers and engineers quickly find relevant driving data
    • Leveraged state-of-the-art approaches like generative AI, machine learning, and semantic search
    • Provided a user-friendly interface for data curation and visualization

Challenges in Autonomous Mobility Development

  • Autonomous Mobility development involves a significant shift from level 2 to level 3 automation, where the vehicle takes more control.
  • Key challenges include:
    • Unprecedented data volume (up to 100 PB per year) from sensors like cameras, radars, and LiDARs.
    • Diverse data requirements across development teams (labeling, ML training, simulation, verification, etc.).
    • Scalability and cost challenges as development programs change (e.g., from robo-taxi to passenger vehicles).
    • Integrating various tools and applications required for different tasks.

Continental's Collaboration with AWS

  • Continental is a major tier-one supplier in the automotive industry, with experience in developing autonomous driving features for over 20 years.
  • To address their challenges, Continental collaborated with AWS to build a platform called "CATCH" (Continental Automotive Edge Computers), hosted on AWS.
  • CATCH provides the following capabilities:
    • Ingesting up to 1 PB of data per day and processing it.
    • Offering data services and workbenches for developers to access and use the data.
    • Enabling third-party connections for data labeling, simulation, and other tasks.
  • Key targets for the collaboration were:
    • Reducing operational costs by 60%.
    • Reducing the time developers spend on data curation and discovery.
    • Improving the ability to integrate new technologies and partner solutions.

Scene Search

  • One of the outcomes of the collaboration was the development of a scene search feature.
  • The scene search feature aims to help developers and engineers quickly find relevant driving data for their tasks, such as:
    • Semantic search using natural language queries (e.g., "find pedestrians in the rain").
    • Similarity-based search using images or video snippets.
    • Structured search combining various criteria (e.g., object type, weather conditions).
  • The scene search feature leverages state-of-the-art approaches like generative AI, machine learning, and semantic search to provide a comprehensive data discovery experience.
  • It also includes a user-friendly interface for data curation and visualization, allowing developers to quickly identify true positives and false positives in the search results.

Call to Action

  • Reach out to an AWS representative to learn more about the collaboration approach and how it can benefit your autonomous mobility development efforts.
  • Provide feedback on the session to help improve future presentations and demonstrations.

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