AWS IoT FleetWise Lecture Notes

Instructor Information

Main Topics: AWS IoT FleetWise
- Industry trends and challenges in vehicle data
- Explanation and capabilities of AWS IoT FleetWise
- Applications in connected vehicle platforms
- Use cases and practical examples
- Features such as intelligent data collection and analysis
- Demonstration using an EV battery health monitoring example
- Available resources for getting started

Vehicle Data Workloads: Exponential growth in data from connected vehicles.
- By 2030, 95% of new vehicles will be connected (McKinsey).
- Increased sensors and richer data: terabytes per hour.
- Software-defined vehicles make data crucial for various features.
Challenges:
- Data Fragmentation: Silos due to multiple OEMs and proprietary signals.
- Data Tsunami: High data volumes, cost of cloud transfer, and storage.
- Data Delays: Slow access to data for diagnosing and fixing issues leads to high costs and potential recalls.

Definition: Fully managed service for standardizing, collecting, and transferring vehicle data to the cloud at scale.
Components:
- IoT FleetWise Edge Agent: Open-source software for communication between vehicles and the cloud.
- IoT FleetWise Cloud: Platform for creating cloud resources to model and collect vehicle data.
Benefits:
- Analyze standardized fleet-wide vehicle data.
- Improve data relevance with intelligent data collection.
- Detect and mitigate problems quickly.

Data Standardization: Virtual vehicle models and CAN bus data interpretation.
Intelligent Data Collection: Transfer high-value data based on specific rules and conditions.
Real-Time Health Monitoring: Near real-time anomaly detection for quick interventions.
Campaigns: Define data collection schemes and deploy to vehicles.
Architectural Components:
- Edge agent installation and vehicle connection.
- Data collection campaigns via AWS Management Console.
- Data storage in Amazon Timestream or Amazon S3.
- Analysis with AWS analytics services.

Use Cases:
- Autonomous driving and ADAS improvements
- Vehicle health monitoring
- EV battery health analysis
- Enhanced in-vehicle infotainment systems
- Fleet operations and driver safety
Customer Examples:
- Continental: Integrated AWS IoT FleetWise with their platform for CI/CD validation.
- LG Electronics: Developed vehicle data platforms and Mobility Services.

Steps:
- Create data models and signal catalogs using VSS standard.
- Deploy data collection campaigns (event-based and time-based).
- Analyze dashboard insights for vehicle health.
- Validate fixes using test fleet and conditions.
Highlights:
- Detect issues like ECU configuration bugs.
- Use dashboards to monitor and validate fixes.

Amazon Timestream:
- Time series database for near real-time analytics.
- Scalable, managed with built-in analytical functions.
Amazon S3:
- Object storage with high performance and durability.
- Supports Apache Parquet and JSON formats.
- Suitable for data lakes and integration with AWS analytics and ML services.

Main Elements:
- Vehicle onboarding and connectivity (AWS IoT Core, Greengrass)
- Data abstraction and vehicle insights (AWS IoT FleetWise)
- Fleet management and applications (AWS IoT Device Management, analytics tools)
- Security and privacy (AWS IoT Defender)
Architecture:
- Device layer, connectivity layer, abstraction layers, operations layer, applications layer.

Thank you for attending this lecture on AWS IoT FleetWise. I hope you found it informative!