AutoMedia

1. AutoMedia T. J. GiuliK. Venkatesh PrasadDavid Watson Ford RIC Jason FlinnMingyan Liu Brian Noble Michigan CSE

2. AutoMedia: Bridging Islands of Connectivity • The problem • each vehicle is an island unto itself • limited connectivity that exists: stove-piped solutions • lost opportunities within, between, beyond the vehicle • AutoMedia Vision: bridge the islands… • …between vehicle and mainland of home/office • …between vehicles on the road • Project contributions • technology: networking, storage, modeling • enable new, collaborative, community-based • applications

3. A collaboration with…

4. Traffic • From the 2002 Urban Mobility Report • 58% of US roads experiencing congestion • congestion responsible for 5.7B gallons wasted fuel • 62 hours/year wasted in delay by average peak driver • Census/USDOT forecasts, from 2000 to 2020 • 51% increase in annual vehicle miles • Unfortunately, roads are expensive • needed 1,780 additional lane-miles of freeway in 1999-2000 • one lane-mile of urban freeway: $300M • needed another 2,590 lane-miles of surface streets • We need to improve carrying capacity of what we have

5. More bad news, and a glimmer of hope • Freeway traffic monitoring more or less adequate • we know how to model these (mostly fluid-flow) • almost-real-time using human monitoring • But, only part of the story • surface streets barely modeled at all by our CivE friends • infrastructure to measure is prohibitively expensive • Instead, leverage the vehicles themselves… • increasingly equipped with location/communication facilities • vehicles report movement to measurement infrastructure • we look for anomalous traffic behaviors and report those • Real time, universal traffic monitoring (almost) for free

6. Obtain baseline for traffic day of week/time of day Determine thresholds for good/bad traffic conditions Observations compared within threshold: acceptable exceed threshold: bad news Report unexpected situations integrated with navigation The big picture

7. Example

8. Questions to answer • How do we represent observations efficiently and effectively? • How are new observations classified as good/bad traffic states? • Does our classifier actually work?

9. Representing observations: segmentation • First, we have to convert roads to segments • a segment connects two intersections • segments usually form “stopping points” • but not always, and not exclusively

10. Representing observations: time or distance? • Our initial data set: few students driving fixed route • brevity with accuracy proved harder than we expected • Goal: representation that • captures all salient features • is quantifiable and directly comparable

11. Observations: spatio-temporal plot • Temporal mean speed = Segment length/traversal time • Spatial mean speed = Average of instantaneous speeds • One observation  one point in the spatio-temporal plot

12. Separate the plot into quadrants • Establish a temporal threshold • a set of temporal mean speeds that appear “good” • accounting for the delay imposed by traffic signals • Establish a spatial threshold • a set of “smooth” spatial mean speeds for “good” traversals • These two thresholds establish a set of quadrants • bad temporal and spatial characteristics -> bad traffic

13. 1 2 4 3 Classifying new observations • Traffic metric: clipped distance from the “good” thresholds • Q1: all zero, Q2: ΔX, Q4: ΔY, Q3: ΔX + ΔY

14. Case Study: planned routes • Trace data set collected for preliminary study • road construction on June 1 blocking one lane (b) Traffic metrics (a) Time-distance plot

15. Evaluating Accuracy of Estimation • Subjected classifier to a bake-off • UMTRI data set: 11 vehicles measured for 10 months • in-vehicle data, forward/driver video, GPS, … • selected segment with 80+ traversals over the 10 months • human panel classified subset of 40 as “good” or “bad” • ground truth if four out of five panelists agree • 36 produced agreement: 2 bad, 34 good

16. Accuracy of quadrant estimator • Averages across many sets of baseline observations • False alarms with low history, but settles quickly

17. Bringing research into the classroom

18. Bringing research into the classroom Today: SYNC Voice control of Consumer electronics

19. Bringing research into the classroom Where we are going: Fully connected vehicle

20. Where we’re going: the connected vehicle • But, we don’t know what the “killer app” will be… • …so we’ll have the students try to invent some! • AutoMake: a project course for vehicular telematics • Location-based services: GIS/Mapping, routing, etc. • Mobile data access: network management/prediction • Vehicle data: engine performance, fuel consumption, etc. • Social Networking: relationships/messaging. • Cloud services: storage, computation, etc.

21. The Project Competition • Course broken up into small teams of students (3-5) • Each team designs, builds, demonstrates an application • Simulators, test vehicle for development • Panel of judges evaluates each project • Winning team takes project on a road trip to Maker Faire

22. Questions? • ?