Cloud-native service technologies can empower autonomous driving, and a cloud-agnostic, fast platform is necessary for efficient development and scalability.
- Autonomous driving relies heavily on artificial intelligence algorithms and data, making a cloud-agnostic platform necessary for data privacy and development flexibility.
- The complexity of autonomous driving requires a fast platform with automatic scaling and efficient error handling.
- Open-source cloud-native technologies like Keta can provide the necessary features for a cloud-agnostic, fast platform.
- Efficient observability mechanisms and a robust system are crucial for troubleshooting and safety requirements.
- Package management and an easy-to-use interface wrapper can make the platform more powerful and user-friendly.
- An active and efficient maintenance team is necessary for the stability and prosperity of the community and product development.
Autonomous driving involves a series of AI technologies, such as environmental perception, pedestrian avoidance, and multi-vehicle cooperation. The amount of data generated by these technologies is far beyond the reach of traditional IoT devices, and the streaming analysis of large amounts of data requires a platform with great scalability. Additionally, the logic of data processing changes frequently based on rapid-changing industry requirements, different weather conditions, and business models, requiring a fast and flexible platform for efficient development.