Mitigation of Crashes At Unsignalized Rural Intersections IDS Quarterly Meeting June 14-5, 2004

1. Providing Intersection Decision Support for the Driver: Mitigation of CrashesAt Unsignalized Rural IntersectionsIDS Quarterly MeetingJune 14-5, 2004

2. Addressing Rural Intersection Safety Issues: • The primary problem at rural intersections involves a driver on the minor road selecting an unsafegap in the major road traffic stream. • Consider study of 1604 rural intersections (2-lane roadways, Thru/STOP intersection control only, no medians) over 2+ year period.

3. Addressing Rural Intersection Safety Issues • Analyzed 768 right angle crashes on 409 different intersections. • Nearly 60% occur after vehicle on the minor roadway stops • Approximately 25% involved vehicle running through the STOP sign. Source: Howard Preston CH2MHill … i.e. problem is one of gap selection, NOT intersection recognition

4. Recognized National Problem • NCHRP Report 500: Vol. 5 Unsignalized Intersections • Identifies objectives and strategies for dealing with unsignalized intersections • Objective 17.1.4 Assist drivers in judging gap sizes at Unsignalized Intersections • High speed, at grade intersections Guidelines for Implementation ofAASHTO Strategic Highway Safety Plan

5. Minnesota Focus • Rural unsignalized intersections: • High-speed corridors • Through stop intersections • Traffic surveillance technologies(& on-site validation) • Gap detection/estimation (& on-site validation) • Human interface design(& simulator evaluation) • Goal - Results from above to lead to next phase: • Approval of DII by MUTCD National Committee for DII • National Field Operational Test:

6. IDS Program • Tasks • A. Crash Analysis • B. Enabling Research • Surveillance systems: Test and eval at isxn • Experimental Intersection Design, Construction, and Implementation • Human Factors: Eval in driving simulator • C. Benefit:Cost Analysis • D. System Design

7. 3,784 Thru-STOP Isxns in MN Hwy Systemwere evaluated • Total> CR (% of total) • 2-Lane - 3,388 | 104 (~ 3%) • Expressway - 396 | 23 (~ 6%) Task A: Crash Analysis • Analysis of present conditions and intersections • Identification of Experimental Site: Minnesota Crash Data Analysis

8. Location of Selected Intersection MN Hwy 52 & CSAH 9

9. Task B: Enabling Research • Surveillance Technologies • Sensors – • Determine location and speed of high speed road vehicles • Determine type of vehicle on low speed road (signal timing) • Sensor placement, intersection design, etc. • Communications • Transmit data from sensors to IDS main processor (RSU) • Wired / Wireless options • Computational systems • Determine location, speed, and size of vehicle gaps • Performance issues: • Redundancy, reliability, range, power, cost, estimation vs. sensor coverage, etc.

10. Enabling Research:Driver Infrastructure Interface (DII) Development • Human Factors … Nic Ward • System interface development • Simulation development • System interface evaluation

11. TASK C: Benefit:Cost AnalysisDavid Levinson • Identify relevant technologies: Review of literature. • Develop benefit cost framework. • Estimate lifespan of technology. • Estimate costs of technology. • Estimate benefits of countermeasures. • Lifecycle analysis. • Recommend countermeasures • Analyze Inter-technology effects. • Determine performance metrics. • Develop cost:performance models • Analyze synergies. • Optimize counter-measure combination

12. Task D: System Requirements & Specification Definition • Functional Requirements • System Requirements • System Specifications • Experimental MUTCD Approval • Driver interface likely to fall outside the normal devices found within the MUTCD. Will work to gain MUTCD approval as soon as candidate interface is determined

13. Surveillance Technologies:Outline • Vehicle detection sensor development • Radar sensor development and testing • Lidar sensor development and testing • Vision-based sensor development and testing • Vehicle classification sensor development • Vehicle tracking estimator • Test intersection sensor configuration to validate installation • Experiments to be conducted at test intersection

14. Vehicle-detection Sensor Development • Eaton Vorad EVT300 radar to be used for high speed vehicle detection – have determined accuracy as a roadside sensor • SICK LMS221 lidar to be used for vehicle detection at low speed (on minor leg) – accuracy of vehicle detection algorithm to be determined • Vision-based vehicle detection algorithms being developed for low speed vehicle tracking (on minor leg and the intersection) and performance measurement of radar on major leg

15. Experiments to determine radar accuracy • Eaton Vorad radar is designed for use on vehicles, typically mounted on bumper • Determine radar’s performance while used as roadside sensor • Use probe vehicles with DGPS and compared vehicle position to radar detected position • Drove probe vehicles past radar • Varied radar orientation (yaw angle) • Varied distance from road (two different lanes) • Varied vehicle type (Mn/DOT truck and sedan) • Experiments performed at Mn/Road in October 2003

16. Experiment Objectives For each independent variable, determined : • Lane coverage • Lane classification accuracy of the sensor • Lane position accuracy of the sensor • Speed measurement accuracy of the sensor

17. Variable Definitions:Overall Schematic

18. Variable Definitions:Theoretical Lane Coverage– Measure of vehicle detection start and stop Lane Centers Theoretical Lane Coverage:Different for each lane

19. Variable Definitions:Lane Classification and Lane Position Accuracy • Lane Classification: In which lane is the vehicle? (Accuracy limited by lateral position error) • Lane Position Accuracy: Limited by longitudinal position error

20. Variable Definitions:Lane Classification and Lane Position Accuracy • Elat = Lane Lateral Position Error • Elon = Lane Longitudinal Position Error • Know that radar return does NOT come from center of front bumper • Tests will evaluate sensitivity of gap calculation to this effect

21. Experimental Setup

22. Experimental Setup:Orientation Calibration • Initial calibration to get the reference yaw angle with respect to North

23. Experimental Setup

24. Experimental Setup:Signal Flow Diagram Target Data: Position (StatePlane Coord),Velocity Vehicle Data: Position (State PlaneCoord), Velocity,Heading

25. Experimental Setup

26. Experimental Setup:Radar Now Picks up Vehicles at 440 ft.

27. Experimental Setup:Playing back experimental data

28. Results – Typical run with truck • Typical run: Truck at 45 mph • Error Curve for the entire run • RMS Values are used in evaluation • 10 m. max longitudinal error leads to 0.5 sec gap error at 45 mph (20m/sec)

29. Results – Actual vs Theoretical Lane Coverage for Varying Sensor Orientation • Both cases:Actual Lane Coverage  Theoretical(Predicted) Lane Coverage • Can use theoretical parameters to design sensor layout • 6 degrees gives best coverage for both lanes A Inside T A Outside T Inside Lane – 14ft from Lane CenterOutside Lane – 26ft from Lane Center

30. Results– Lane Lateral Position Accuracy • Lane lateral position error lower when sensor closer to lane • Lane lateral positionerror increases with increase in sensor orientation angle • Error within 1.2m for most runs (when under6 degrees • Lane classification threshold of 1.2m should be sufficient to place a vehicle in one lane (12ft / 3.7m)

31. Results– Lane Longitudinal Position Accuracy • Error increases with increase in orientation angle • Error lower when closer to lane • Error lower for smaller vehicle • When orientation angle is below 6 degrees, error is below 10m (equivalent to 0.5 sec error in gap; 45 mph)

32. Results – Speed Accuracy • Accuracy decreases with increase in orientation angle • Error is within 0.35m/s. Equivalent to 0.78 mph; for an 8 sec gap at 45 mph (20m/sec) equiv to 0.14 sec in gap

33. Experiment Conclusions • Sensor Lane Coverage • Increases when sensor placed closer to lane • Increases with decreased sensor yaw angle • Better than specifications • Lane Lateral Position accuracy of the sensor • Better when sensor closer to lane • Better with lower sensor orientation • Lane Longitudinal Position accuracy of the sensor • Better when sensor closer to lane • Better with lower sensor orientation • Better for smaller vehicle • Speed measurement accuracy of the sensor • Better with lower sensor orientation • Error within 0.35 m/s (0.78 mph)

34. Lidar detectorsLIDAR - LIght Detection And Ranging • SICK LMS221 sensors are used – works at 5Hz; low speed minor leg application • Developed roadside vehicle detection/classification algorithm • Experiments similar to those for radar to be performed in July 2004

35. Development of vision-based detectors • Both visible-range and IR cameras will be tested • Vehicle detection algorithm developed to detect vehicles moving along a lane as well as making turns • Experiments to be conducted in July 2004 to determine the performance of both types of cameras under different lighting conditions

36. Development of vision-based detectors • Data collected at the Washington Ave parking ramp exit to Union. • Thresholds set to ignore pedestrians and bicyclists • Algorithm sufficient to determine lane position and trajectory of vehicle

37. Vehicle-classification sensor testing • Eaton Vorad radar based system to be tested when installed at the Hwy52 test intersection • SICK LMS221 lidar based system to be developed – will be tested at test intersection • Both sensors will be used to cover the same area; the accuracy of the two sensors will be determined by comparing images captured of the vehicles with the radar data (for multiple vehicles)

38. Vehicle Tracking Estimator • Estimator will be capable of tracking every vehicle in the system and predicting time to a pre-determined point at the intersection • Two types of tests to be conducted to determine accuracy • Low-volume traffic using DGPS-based probe vehicles • High-volume traffic using a vision-based vehicle detection system

39. Tracking Estimator Validation System • Camera placed perpendicular to traffic direction • Accuracy of test system to be validated by processing video and comparing results with the radar’s reported results • Estimator error, false targets and missed targets will be determined

40. MN Test Intersection Final Design

41. Test Intersection Sensor Configuration:Major Leg – Hwy52 • Radar sensorson Hwy52 • Approximately 2100ft of lane coverage in each direction (17.2 secs at 85mph) • Average sensor spec’d orientation angle is 4.9º

42. Mainline Radar Sensor

43. MN Test Intersection- Mainline Sensors Camera Suite (for evaluation) Radar to track vehicles past isxn (primarily for minor road trajectory recording) Radar Camera FOV 53’x36’

44. C4 FOV C3 FOV Intersection Crossroads- Vehicle Trajectory Cameras at intersection capture trajectory of vehicles entering isxn from minor roads. Mn/DOT advised that median-based sensors won’t survive.

45. Test Intersection Sensor Configuration:Minor Leg – CSAH 9 • Radar and lidar sensors on CSAH9 • Radar to detect approaching traffic and lidar used for slow/stopped traffic • Vehicle classification radar and lidar also used

46. Test Intersection Sensor Configuration • Vision-based sensors for the median • Both IR and visible-range cameras will be tested

47. R/WIS Data from Intersection Mn/DOT updates at 10 Minute intervals. Data collected every 10 minutes

48. Experiments to be Conducted at Test Intersection • Determine effect of vehicle length, speed, lateral location on radar-based position and gap calculations • Determine accuracy of lidar-based and vision-based vehicle detection/tracking systems • Vehicle entering intersection from minor leg • Validation of vehicle classifier systems • Radar vs lidar • Determine accuracy and robustness of Gap Tracking Estimator

49. Information Available from Intersection • Distribution of gaps accepted by drivers • for right turns • for left turns • for crossing intersection (see next page) Cross-correlated with • Vehicle type / size • Driver age (macroscopic level, limited basis initially) • Driver gender (limited basis initially) • Weather effects (R/WIS 0.9 Mile away), with in-road sensors (collecting data already)

50. Information Available from Intersection (cont’d) • Maneuvers executed by drivers from minor road • Left turn in one stage or two? • Variation in left and right gaps accepted for each maneuver type • Cross-correlation with vehicle type • Crossing intersection in one stage or two? • Variation in left and right gaps accepted for each maneuver type • Cross-correlation with vehicle type