1. A PHYSICAL
APPLICATION OF
STREAM PROCESSING
TO THE AUTOMOTIVE
INDUSTRY
Matthew Garren
CSIS 638 Final Presentation

2. Background
● Stream processing becoming common
● Distributed stream processing

3. The Automobile as a Distributed
System
Sensors
● MAF Sensor

4. The Automobile as a Distributed
System
Sensors
● Oxygen Sensors

5. The Automobile as a Distributed
System
Sensors
● ABS

6. The Automobile as a Distributed
System
● Sensor data = Streams
● Continuous/polled data
● ECU current computing solution

7. Proposed Automotive Stream
Computing Platform
● Hardware
○ Inexpensive
○ Adequate Storage
○ NetTop computers OR 1 physical machine with
multiple logical machines (virtualization)
○ Multiple to make it distributed

8. Proposed Automotive Stream
Computing Platform
● Software
○ CENTOS
○ S4 Streams
○ Hadoop/HBase

9. Why NoSQL?
● Data from sensors deposited as “blobs”
● Analytics can be written to display/compute
data with no transformation
● New sensors would require system
modification to add new tables

10. Analytics
● Streaming Analytics
○ Better error messages
○ Causal problem analysis
○ Driving efficiency analysis
● Batch Analytics
○ Precise drive times (based on historical data)
○ Long term problem identification
○ Car History logging

11. Feasability
● May not work for ALL vehicles
● Good fit for performance vehicles
● Fleet vehicles
● Trucking companies

12. Enterprise Possibilities
● “App Store” for automobile analytics
● Receive updates via phone
● Contains risks

13. Future Additions
● Expand on ideas like Park Assist
● Collision avoidance
● Distracted driver assistance
● Overcorrection avoidance
● Eventual autopilot

14. Related Work
● Flexible In-Vehicle Stream Processing with
Distributed Automotive Control Units for
Engineering and Diagnosis
○ Logging is too expensive
○ Stream processing/Engine Management

15. Questions?