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

E Lat

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