Test Intersection: Status, Results, Preparation for State Data Collection

1. Test Intersection: Status, Results, Preparation for State Data Collection Lee Alexander Pi-Ming Cheng Alec Gorjestani Arvind Menon Craig Shankwitz Intelligent Vehicles Lab University of Minnesota

2. Presentation • System Overview • Test Intersection Status • Construction • Sensing • Data collection • Analysis • Examples of Data Collected • Animations • Video • Database status • Installation in partner states • Design Documents • Cost

3. System Overview • Mainline surveillance • Radar based sensing • Provides position, speed, lane assignment, and time to intersection of each sensed vehicle • Minor Road Surveillance • Laser based system provides “profile” of stopped vehicle • Used for data analysis, timing of warnings (when DII deployed) • Crossroads Surveillance • Used to capture driver behavior (one step or two) • NOT part of deployed IDS system • Computation • Acquires driver behavior data now • Compute warning timing when IDS deployed

5. Construction - Mainline

6. Construction - Mainline

7. Construction- Vehicle Classification

8. Construction – Crossroads Surveillance

9. Construction- Control Cabinet

10. Test Intersection Status • Mainline surveillance • Construction Complete • All sensors operational • Series of Validation Experiments Complete

11. Mainline System Performance Results • Detection Rate: 99.990% (5 “misses” out of 51,942) • Miss defined as vehicle not within 40 meters of test zone • Results for a single sensor; multiple sensors decrease likelihood of a “miss”

12. Mainline System Performance Results • Lane Data Accuracy • Longitudinal Accuracy 8 meters • Lane assignment accuracy 90% • Ambiguity during lane changes, hanging near center line • Limitations of angular resolution of radar • Speed accurate to 1 MPH

13. Mainline System Performance Results • Vehicle Shadowing Performance: Range accuracy will be no worse than 75 feet if lateral shadowing occurs Performance: Can resolve 2 vehicles if separated by 50 or more feet

14. Vehicle Classification Validation Configuration DGPS Horizontal Camera Laser Vertical Laser Vehicle Classifying Radar Laser Presence Detector

15. Vehicle Classification Performance • Accuracy approximately 85% based on vehicle height • One sensor reduces cost substantially • Grouping conservative….classify as larger than actual

16. Vehicle Classification: Height Only Med. Truck Lt. Truck SUV Cars Semi. Truck

17. Crossroads Surveillance • Positions based on locating front of vehicle • Working definition of gap • Accuracy 1-2 meters • Larger concern 1 step or 2, time in crossroads • Performance • Left turns sensed, captured correctly 95% of time • Right turns sensed, captured correctly 95% of time • Open Issue • Straight through captured only 60% right now • Camera issues • Absolute vs. Relative thresholds (being tested today/tonight) • IR Illuminators • Cheaper ($2k system vs. $26K system) • We control illumination

18. Crossroads Trajectory Tracker Validation: Day with Visible Light Camera

19. Crossroads Trajectory Tracker Validation: Night with IR Camera

20. Crossroads Trajectory Tracker Validation: Night with IR Camera

21. Intersection Surveillance System:Visualization of all Data

22. Data Collection: Visualization

23. Data Acquisition – Control Cabinet

24. Data Acquisition – IV Lab Data Flow/Archival 120 Gbyte IDE Drive requires replacement once every 2 weeks. DOT will have to dispatch someone to swap out to mail to U of MN.

25. Batch program finds vehicles entering intersection from minor road (Vehicles of Interest (VOI) ) and consolidates tracking information to new table User specified queries User specified results Data Acquisition – IV Lab Analysis Processes

26. Data Acquisition and Analysis • Database system has been design • Initial automated queries have been completed. • Will be validating results next two weeks • Automated and specialized queries supported

27. Automated queries (can be run as frequently as desired). • Gaps as a function of vehicle classification • Gaps as a function of time of day • Gaps for Right, Straight, Left turns • Percentages of one step vs. two step maneuver • Identification of near misses/accidents • Other queries supported as well. • Add license plate reader, further refine data set.

28. Data Analysis – cont’d • Weird Observational Data • For every 100 Right turns, • 100 Straight-throughs • 5 left turns • 2 drivers have missed intersection approaching from west, none have missed from right • Both crashes, damages could have been much worse. • Last crash, no sensor damage, just mount damage

29. Can we build one for you?

30. FLASHBACK! Intersection Build Details from AP 2004 • Radar Stations • Vehicle Classification Stations • Vision Systems • Central Cabinet • Ethernet and Video Cable • $191,837

31. Cost Data • Electrical Contractor: $101K • Bought guys lunch last week in Cannon Falls, must be happy • Rethinking Laser for Vehicle Classification • Two Crash repairs: • #1, $2500 • #2, $800

32. Minnesota Radar Subsystem: $50K Video (Xroads): $70K Minor Roads: $48K Cabling (Power and Data): $10k Grand total (Contractor + HW): $314K Partner States Radar Subsystem: $50K Video (Xroads): $35K 2 masts, not 4 SDRC with IR Illumination, not IR Cam. Minor Roads: $35K (2 lasers, not 4) Cabling (Power and Data): $10k Computer Assy, parts procurement: $20K Estimated total (Contractor+HW): $275K MN vs. Partner States Cost

33. Build one for you? • Final Reports due 28 Feb 2005 • Master Design Document will be appendix • Can make available to states who wish to review/help them make decision to install equipment.