Modern Data Infrastructure for Autonomous Vehicles

Introduction

• Brief overview of the importance of data infrastructure in autonomous vehicles
• Key challenges unique to autonomous vehicle data

1. Data Collection

• Types of data collected (sensor, camera, lidar, radar, telemetry, etc.)
• On-vehicle data logging and edge processing

2. Data Ingestion

• High-throughput data transfer from vehicles to cloud/data centers
• Handling intermittent connectivity and bandwidth constraints

3. Data Storage

• Scalable storage solutions (object storage, distributed file systems)
• Data organization and indexing for efficient retrieval

4. Data Processing and Labeling

• Batch and real-time data processing pipelines
• Automated and manual data labeling workflows
• Quality assurance and feedback loops

5. Data Management and Governance

• Metadata management and data cataloging
• Data versioning and lineage tracking
• Privacy, security, and compliance considerations

6. Machine Learning Infrastructure

• Training data curation and dataset management
• Scalable compute for model training and validation
• Model deployment and monitoring

7. Best Practices and Lessons Learned

• Building for scalability and reliability
• Cost optimization strategies
• Collaboration between engineering, data, and research teams

Conclusion

• Summary of key takeaways
• Future trends in autonomous vehicle data infrastructure