Mitigation of Crashes At Unsignalized Rural Intersections IDS Quarterly Meeting June 14-5, 2004 Providing Intersection Decision Support for the Driver:

Addressing Rural Intersection Safety Issues: u The primary problem at rural intersections involves a driver on the minor road selecting an unsafe gap in the major road traffic stream. u 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 u Analyzed 768 right angle crashes on 409 different intersections. u Nearly 60% occur after vehicle on the minor roadway stops u 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 u NCHRP Report 500: Vol. 5 Unsignalized Intersections v Identifies objectives and strategies for dealing with unsignalized intersections v Objective Assist drivers in judging gap sizes at Unsignalized Intersections v High speed, at grade intersections Guidelines for Implementation of AASHTO Strategic Highway Safety Plan

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

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

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

Location of Selected Intersection MN Hwy 52 & CSAH 9

Task B: Enabling Research u Surveillance Technologies u 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. u Communications Transmit data from sensors to IDS main processor (RSU) Wired / Wireless options u Computational systems Determine location, speed, and size of vehicle gaps u Performance issues: Redundancy, reliability, range, power, cost, estimation vs. sensor coverage, etc.

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

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

Task D: System Requirements & Specification Definition u Functional Requirements u System Requirements u System Specifications u Experimental MUTCD Approval v 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

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

u Eaton Vorad EVT300 radar to be used for high speed vehicle detection – have determined accuracy as a roadside sensor u SICK LMS221 lidar to be used for vehicle detection at low speed (on minor leg) – accuracy of vehicle detection algorithm to be determined u 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

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

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

Variable Definitions: Overall Schematic

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  E lat = Lane Lateral Position Error  E lon = 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

Experimental Setup

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

Experimental Setup

Experimental Setup: Signal Flow Diagram Target Data: Position (State Plane Coord), Velocity Vehicle Data: Position (State Plane Coord), 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 Center Outside 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 under 6 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 u Sensor Lane Coverage v Increases when sensor placed closer to lane v Increases with decreased sensor yaw angle v Better than specifications u Lane Lateral Position accuracy of the sensor v Better when sensor closer to lane v Better with lower sensor orientation u Lane Longitudinal Position accuracy of the sensor v Better when sensor closer to lane v Better with lower sensor orientation v Better for smaller vehicle u Speed measurement accuracy of the sensor v Better with lower sensor orientation v Error within 0.35 m/s (0.78 mph)

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

u Both visible-range and IR cameras will be tested u Vehicle detection algorithm developed to detect vehicles moving along a lane as well as making turns u 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

 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

u Eaton Vorad radar based system to be tested when installed at the Hwy52 test intersection u SICK LMS221 lidar based system to be developed – will be tested at test intersection u 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

u Estimator will be capable of tracking every vehicle in the system and predicting time to a pre-determined point at the intersection u 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 System

MN Test Intersection Final Design

Test Intersection Sensor Configuration: Major Leg – Hwy52  Radar sensors on 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 FOV 53’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 Intersection Mn/DOT updates at 10 Minute intervals. Data collected every 10 minutes

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

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

Information Available from Intersection (cont’d) u Maneuvers executed by drivers from minor road v Left turn in one stage or two? Variation in left and right gaps accepted for each maneuver type Cross-correlation with vehicle type v 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) u Response of mainline traffic v 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? v Reaction of drivers on major road if too small gap is accepted Braking? Lane change? Other?

Deliverables u As of June 14, most underground work complete; all posts installed; half power cabling completed. u By 1 st week of July, all contracted electrical work complete u July, 2004: Bring intersection on-line. u August, 2004: All tests on sensors and gap tracking estimators completed. u February, 2005: v Data from sensors on intersection analyzed and report delivered. v Cost-benefit study completed. v Driving simulator study completed.

MN Pooled Fund Project: Towards a Multi-State Consensus Minnesota 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: u Assistance/buy-in for DII design v Goal: nationally acceptable designs Performance, Maintenance, Acceptability Interoperability u Increased data collection capability v Identify site & design test intersections in participating states v Collect data at intersections (using minimized sensor suite) v 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, Basis of the Highway Design Manual for Older Drivers and Pedestrians, Publication No. FHWA-RD , May, 2001, U.S. Dept of Transportation. (See RECOMMENDATIONS, I. INTERSECTIONS (AT-GRADE) D. Design Element: Intersection Sight-Distance Requirement). See

Post-2005 Steps To FOT: u Simulator will be used to evaluate relative merits of DII and to downselect the needed features of DII u However, speed and gap size not perceived on road the same way as in simulator. u Safe vs unsafe gaps: Used general guidelines from NCHRP for older drivers, use 8 sec instead of 7.5 for left turn: 6.5 sec for right turn, etc. u 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. u What is the critical gap? For older drivers? For younger drivers? u Need control study of old/young drivers on test intersection. Use VehDAQ/eye gaze tracking. u Drivers take gaps confidently or not? Where to locate DII based on eye gaze study. Is DII intuitive? u Will know state of vehicles on expressway and minor leg.

Post-2005 Steps To FOT: u 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 u Cooperative Vehicle-Infrastructure systems: v Wireless communication to/from infrastructure v Vehicle data to RSU, then fused with other data to compute gaps v Driver (older, younger) and vehicle data to RSU, to determine safe vs unsafe gap v RSU to vehicle/driver DVI to inform driver v Nature and location of DVI in vehicle u Pilot FOT v DII Infrastructure only, DII and DVI u Large scale FOT v DII Infrastructure only, DII and DVI u Design Handbook/Warrants