Driver Interaction Analytics and Learning (Project DIAL) Platform
Driver Interaction Analytics and Learning (Project DIAL) Platform
OVERVIEW
- Based in Detroit (United States), General Motors (GM) is an American multinational automotive manufacturing company that operates manufacturing and assembly plants and distribution centers throughout the United States, Canada, and many other countries.
- Its major products include automobiles, trucks, automotive components, and engines. It also provides automotive financing services through General Motors Financial Company, Inc.
- The GM team responsible for the design and development of in-vehicle display technology at General Motors needed a way to capture driver interactions and vehicle data for analysis and to improve the driving experience.
- One of the challenges that the client was facing was the lack of insight on the customer usage of infotainment features to make data-driven decisions.
- Lack of insight on customer usage of infotainment features to make data driven decisions.
- What are the most/least used infotainment applications/features?
- How does the vehicle speed influence the driver interaction?
- Is my Infotainment User Interface driver friendly, or is it difficult to use during the drive?
- How to validate market trends before deciding to invest or retire infotainment features/capabilities in the upcoming vehicle models?
- How do I reduce driver distraction?
- How can I create attractive infotainment option/features packages for the customers?
- Capturing this data along with vehicle diagnostic data would provide valuable insights into driver behavior and use of various vehicle features.
SOLUTION PROVIDED BY PEOPLE TECH GROUP
- People Tech Group partnered with AWS to develop a solution that extracts all driver interaction within the vehicle and pulls vehicle diagnostic information from the vehicle and sends it to the AWS IoT core service in the cloud for processing.
- The IoT Core services passes the data to the Kinesis Fire Hose and into an S3 bucket. There it is processed by Lambda functions and stored in an AWS Dynamo Db.
- Athena is used to query data from both S3 and Dynamo Db to generate analytic reports in QuickSight.
- AWS AWS SageMaker also processes the data with custom ML models for specific use cases.
BENEFITS
- For the first time ever, the HMI analytics team has the raw usage data they can use to make improvements to the HMI and improve the driver experience and the vehicle applications efficiency and performance.​
- Product planning can use this data to determine which features should be implemented as hard keys vs. soft keys in the vehicle.​
- Personalization features can be developed based on driver usage patterns (vehicle settings personalized based on driver, favorites bar made available on home screen)​.
