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Mitigation of CrashesAt Unsignalized Rural
IntersectionsIDS Quarterly Meeting
June 14-5, 2004
Providing Intersection Decision Support for the Driver:
Addressing Rural Intersection Safety Issues:
The primary problem at rural intersections involves a driver on the minor road selecting an unsafe gap 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.
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
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
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:
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
Task A: Crash Analysis
Analysis of present conditions and intersections
Identification of Experimental Site: Minnesota Crash Data Analysis
3,784 Thru-STOP Isxns in MN Hwy Systemwere evaluated Total > CR (% of total)
2-Lane - 3,388 | 104 (~ 3%)Expressway - 396 | 23 (~ 6%)
Location of Selected Intersection MN Hwy 52 & CSAH 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.
Enabling Research:Driver Infrastructure Interface (DII) Development
Human Factors
… Nic Ward System
interface development
Simulation development
System interface evaluation
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
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
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
Surveillance Technologies:Outline
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
Vehicle-detection Sensor Development
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
Experiments to determine radar accuracy
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
Experiment Objectives
Variable Definitions:Overall Schematic
//
Radar Beam
Lane 1
Lane 2
DLC
2
DLC
1
Radar Beam Range (440 ft)
= Radar Orientation Angle (w.r.t. lane)DLC1 = Sensor Distance from Lane-center 1DLC2 = Sensor Distance from Lane-center 2LCov1 = Theoretical Lane coverage (Lane 1)LCov2 = Theoretical Lane coverage (Lane 2)
BeamWidth(120)
ActualVehiclePosition
Sensor ReportedPosition
RadarStation
}}
L2Cov
L1Cov
Variable Definitions:Theoretical Lane Coverage– Measure of vehicle detection start and stop
Theoretical Lane Coverage:
Different for each lane
Lane Centers
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
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
Direction ofTravel
ELa
t
ELon
SensorReported
Actual
VehiclePositions
Experimental Setup
Experimental Setup:Orientation Calibration
Initial calibration to get the reference yaw angle with respect to North
N
Pole(Px,Py)
Radar(Rx,Ry)
RadarBeam
RifleScope
RadarAntennap
R
2
tan 1
pR
yy
xxP RP
PR
- Yaw angle w.r.t North
LaneCenter
Sensor Orientation Angle w.r.t lanecenter
Y
x
Experimental Setup
Experimental Setup:Signal Flow Diagram
RF Modem
GPSCorrections GPS Data
Pre-Processor
Corrections GPS Data
Vehicle Position(State planeCoordinates), Speed, Heading
data
TrimbleMS750 GPS
Unit
EVT-300Radar
Antenna
Radar DataPre-Processor
Target DataTarget Position (StatePlane Coordinates),
Target Velocity
Data CollectionComputer
Add Timestamp Add Timestamp
Radar Station
Probe Vehicle
Target Data:Position (StatePlane Coord),
VelocityVehicle Data:
Position (State PlaneCoord), Velocity,
Heading
Experimental Setup
Experimental Setup:Radar Now Picks up Vehicles at 440 ft.
Experimental Setup:Playing back experimental data
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)
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
Inside Lane – 14ft from Lane CenterOutside Lane – 26ft from Lane Center
Inside
Outside
A
A
T
T
Results– Lane Lateral Position Accuracy
Lane lateral position error lower when sensor closer to lane
Lane lateral position error 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)
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)
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
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)
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
Lidar detectorsLIDAR - LIght Detection And Ranging
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
Development of vision-based detectors
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
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)
Vehicle-classification sensor testing
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
Vehicle Tracking Estimator
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
Tracking Estimator Validation SystemEsitmation
Error Region of Interest
Camera
Camera Fieldof View
Lane2
Lane1
Actual VehiclePosition
EstimatorReportedPosition
MN Test Intersection Final Design
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
Mainline Radar Sensor
MN Test Intersection- Mainline Sensors
Radar
Camera Suite(for evaluation)
Camera FOV53’x36’
Radar to track vehicles past isxn (primarily for minor road trajectory recording)
Intersection Crossroads- Vehicle Trajectory
C4 FOV
C3 FOV
Cameras at intersection capture trajectory of vehicles entering isxn from minor roads. Mn/DOT advised that median-based sensors won’t survive.
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
Test Intersection Sensor Configuration
Vision-based sensors for the median
Both IR and visible-range cameras will be tested
R/WIS Data from IntersectionMn/DOT updates at 10 Minute intervals. Data collected every 10 minutes
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
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)
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
Information Available from Intersection (cont’d)
Response of mainline traffic Speed adjustment if stationary vehicle on minor
road• Do mainline drivers adjust speed if a vehicle is
spotted on minor road?• Will mainline drivers move to left lane (when
possible) to provide a lane for the minor road traffic?
Reaction of drivers on major road if too small gap is accepted
• Braking?• Lane change? • Other?
Deliverables
As of June 14, most underground work complete; all posts installed; half power cabling completed.
By 1st week of July, all contracted electrical work complete
July, 2004: Bring intersection on-line. August, 2004: All tests on sensors and
gap tracking estimators completed. February, 2005:
Data from sensors on intersection analyzed and report delivered.
Cost-benefit study completed. Driving simulator study completed.
MN Pooled Fund Project:Towards a Multi-State ConsensusMinnesota is leading a state pooled fund project for
rural intersection IDS, includes… MN, NV, NH, WI, MI, GA, IA, NC
Multiple goals for state pooled fund: Assistance/buy-in for DII design
Goal: nationally acceptable designs• Performance, Maintenance, Acceptability• Interoperability
Increased data collection capability Identify site & design test intersections in participating states Collect data at intersections (using minimized sensor suite) Regional vs. national driver behavior
Gap Acceptance Studies:Safe Gaps
Left turn from a minor road – 8.0secs + 0.5secs for each additional lane to be crossed
Right turn from a minor road – 7.5secs
Crossing maneuver – 6.5secs for passenger cars, 8.5secs for single-unit trucks and 10.5secs for combination trucks; Add 0.5secs for each additional lane
Source: National Cooperative Highway Research Program, Report 383, Intersection Sight Distance, National Academy Press, 1996. Basis of the Highway Design Manual for Older Drivers and Pedestrians, Publication No. FHWA-RD-01-103, May, 2001, U.S. Dept of Transportation.
(See RECOMMENDATIONS, I. INTERSECTIONS (AT-GRADE)D. Design Element: Intersection Sight-Distance Requirement).
See http://www.tfhrc.gov/humanfac/01103/chp1rec.htm
Post-2005 Steps To FOT: Simulator will be used to evaluate relative merits of DII and to
downselect the needed features of DII However, speed and gap size not perceived on road the same way as
in simulator. Safe vs unsafe gaps: Used general guidelines from NCHRP 383 - for
older drivers, use 8 sec instead of 7.5 for left turn: 6.5 sec for right turn, etc.
Should conduct series of studies at isxn to model and differentiate needs between older and younger drivers, rather than use Hwy capacity/safety manual’s “recommended” values.
What is the critical gap? For older drivers? For younger drivers? Need control study of old/young drivers on test intersection. Use
VehDAQ/eye gaze tracking. Drivers take gaps confidently or not? Where to locate DII based on
eye gaze study. Is DII intuitive? Will know state of vehicles on expressway and minor leg.
Post-2005 Steps To FOT: Baseline will not need communications to/from vehicles. Will be able
to test in MN plus 7 (?) additional states. If FOT only evaluates DII, can then proceed immediately
Cooperative Vehicle-Infrastructure systems: Wireless communication to/from infrastructure Vehicle data to RSU, then fused with other data to compute gaps Driver (older, younger) and vehicle data to RSU, to determine safe vs unsafe gap RSU to vehicle/driver DVI to inform driver Nature and location of DVI in vehicle
Pilot FOT DII Infrastructure only, DII and DVI
Large scale FOT DII Infrastructure only, DII and DVI
Design Handbook/Warrants