Transcript Virtual Mirror - ITS Institute

Crash Mitigation
At Rural Unsignalized Intersections
Providing Intersection Decision Support
(IDS) for the Driver
Inter-Regional Corridors:
Hi-speed, hi-density roads
crossing
Low-speed, low-density roads
Agenda
Attendees: Please send name, affiliation, phone and
email address to: Rick Odgers: [email protected]
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1. Introductions (30 minutes)
2. Background (20 minutes)
3. MN Intersection Crash Data Analysis (30 minutes)
4. Minnesota IDS Project status (20 minutes)
5. Human Factors (20 minutes)
6. Plan for the state pooled funded project (20 minutes)
7. Feedback on plan and participation in deploying
IDS in respective state (20 minutes)
8. Sign-up for DII review panel (5 minutes)
9. Plans for next meeting (10 minutes), week of April 20
10. Wrap-up (5 minutes)
National Motivation
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1.72 million annual intersection related crashes involved
crossing or turning scenarios (1998 GES data)
Represents 27.3% of all 6.33 million police reported
crashes
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41.6% occurred at signalized intersections
58.4% occurred at unsignalized intersections (stop sign, no
controls, other sign).
Crossing path crashes at uncontrolled intersections had the
highest fatality rates
8,474 of 37,409 (22.6%) of fatal crashes were
intersection related ….. Traffic Safety Facts 2000
Intersection crashes
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NHTSA [Carter, 1999] has indicated that 85% of
intersection crashes were due to driver error,
with the following breakdown:
27% due to driver inattention
 44% due to faulty perception, and
 14% due to impaired vision.
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Intersection Decision Support (IDS)
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Emphasis on:
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Focus on driver error causal factors
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Cooperative Systems (infrastructure + in-vehicle)
Driver Decision Aids (gap, velocity)
Crossing-Path Collisions (Signalized and Unsignalized)
• 78.1% of Intersection Crashes (1998 GES)
For example: Provide the driver with information that will
improve judgment of gap clearance and timing
Cooperative systems:
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NHTSA sponsored work focus on in-vehicle systems
Infrastructure Consortium formed to focus on infrastructure
Crossing Path Crash Causal Factors at
Intersections (1998 GES)
From Najm W G, Koopmann J A, and Smith D L (2001) Analysis of Crossing Path Crash
Countermeasure Systems. Proc. 17th Intl Conference on Enhanced Safety of Vehicles
Left turn
Right angle
Right turn
Traffic Cntrl
Device
Signal
Stop Sign
No Controls
LTAP/OD: Left turn across path/
opposite direction
LTAP/LD: Left turn across path/
lateral direction
LTIP: Left turn into path
RTIP: Right turn into path
SCP: Straight crossing paths
Virtually no crashes in shaded cells.
Causal
Factor
Insuf. Gap
Signal Viol.
Insuf. Gap
Sign Viol.
Insuf. Gap
Crossing Path Pre-Crash Scenarios
LTAP/OD
LTAP/LD
LTIP
RTIP
SCP
193,000
13,000
31,000
52,000 15,000
6,000 178,000
15,000
113,000 26,000 25,000 173,000
1,000
12,000
7,000
3,000
62,000
92,000
25,000 10,000 11,000
35,000
The IDS Team:
IVI Infrastructure Consortium
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IVI Infrastructure Consortium Principals
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California DOT (Caltrans)
Minnesota DOT
Virginia DOT
USDOT (FHWA)
Universities conducting the IDS research
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U.C. Berkeley (California PATH, other units)
University of Minnesota (ITS Institute, other units)
Virginia Tech (VTTI)
Special Concentrations by State
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Virginia team
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Straight-crossing-path (SCP) crashes
Tests on ‘Smart Road’ intersection
Intentional and unintentional signal/stop violation
Focus: Warn violator (DVI and DII)
California team
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Left Turn Across Path/ Opposite Direction
(LTAP/OD)
Urban intersections
Tests on ‘Richfield Field Station’ intersection
Wireless communications for cooperative systems
Why Rural Intersections?
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Crashes in rural areas are more severe than in urban areas
 While 70% of all crashes in Minnesota occur in urban areas,
70% of fatal crashes occur in rural areas.
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Along Minnesota’s Trunk Highway System, there are more rural
through/stop intersections (3,920) than all categories of urban
intersections (3,714) combined
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During a three-year period (1998-2000), 62% of all intersection-related
fatal crashes in Minnesota occurred at rural through/stop intersections
Minnesota Focus
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Rural unsignalized intersections:
 High-speed corridors
 Through stop intersections
Traffic surveillance technologies (& on-site validation)
Gap detection/estimation (& on-site validation)
Human interface design
All intersection crash types occur at IRC
intersections
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Study will indicate which are prevalent
IRC intersection selected for tests & on-site validation
based on crash analysis
Guiding Principles
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Effective countermeasures depend on:
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Capability to sense and predict the behavior of all vehicles
“within” the intersection’s Region Of Interest (ROI)
Ability to predict with high probability at the appropriate time
the gap positions
Ability to predict the time at which a vehicle CANNOT be
released at a rural high speed intersection
A means to effectively communicate with driver(s) (and
eventually vehicles) appropriate actions
Ability to cost effectively deploy needed technology to
infrastructure (and eventually to vehicles)
Addressing Rural Intersection Safety Issues:
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The primary problem at rural intersections involves a
driver on the minor road selecting an unsafe gap in
the major road traffic stream.
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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:
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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.
… i.e. problem is one of gap selection,
NOT intersection recognition
Source: Howard Preston
CH2MHill
Guidelines for Implementation of
AASHTO Strategic Highway Safety Plan
 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
MN Pooled Fund Project:
Towards a Multi-State Consensus
Minnesota is leading a state pooled fund project for
rural intersection IDS
Multiple goals of state pooled fund:
 Assistance/buy-in of DII design
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Goal: nationally acceptable designs
• Performance
• Maintenance
• Acceptability
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Increased data collection capability
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Test intersections in participating states
Regional vs. national driver behavior
MN Pooled Fund Project:
Towards a Multi-State Consensus
Premise behind pooled fund project
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States provide their perspective to rural intersection project
Nationally inter-operable systems will result
Work distribution
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Minnesota provides intersection instrumentation design
guidance/assistance to states
States provide $ and resources to instrument test intersection in
their state
States provide intersection instrumentation data to Minnesota for
analysis
• Insures sufficient data for statistically valid results and conclusions
• Regional driver variability/coherence can be quantified
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Minnesota analyzes states’ data, and provides results and
feedback to participating states
Successful Demonstration, June 2003
Turner Fairbanks Highway Research Center,
McLean, VA
 View simulation
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IDS Program
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Tasks
 Crash Analysis
 Enabling Research
 Benefit:Cost Analysis
 System Design
Task A: Crash Analysis
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Analysis of present conditions and intersections ….
Howard Preston, lead
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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 396 |
23 (~ 6%)
Location of Selected Intersection
MN Hwy 52 & CSAH 9
Sight distance restricted
on the W approach at
CSAH 9
Note differences in
N and S vertical alignments
Satellite
Image
Prediction of Countermeasure Effects
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Modeling of relationships between intersection
characteristics and crash propensity … Gary
Davis, Principal Investigator
Identify both atypically safe and unsafe intersections
 Associate characteristics with both
 Use info for deployment and benefit:cost analyses
 Predict accident reduction based on potential
deployment scenarios
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STATISTICAL MODELING
Gary Davis, Nebiyou Tilahun, Paula Mesa
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Objectives
Predict Accident Reduction Effect of IDS Deployment
on (All, Some) Stop-Controlled Rural Expressway
Intersections in Minnesota
 Determine if Older Drivers are Over-Represented at
Stop-Controlled Rural Expressway Intersections in
Minnesota
 Assess Sensitivity of Accident Reduction Effect on
Predicted Changes in Distribution of Driver Ages
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STATISTICAL MODELING: Predict effect of IDS
on accident reduction
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Research Approach
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Adapt Accident Prediction Methods Developed for FHWA's
Interactive Highway Safety Design Module (IHSDM) to StopControlled Rural Expressway Intersections in Minnesota; Use
'Standard' Extrapolation Methods to Forecast Changes in
Traffic Volumes
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Accident prediction model predicts number of total intersection
related accidents per year (Nint), after application of accident
modification factor (AMF, yet to be determined) to a base model
prediction (Nbi: predicted number of total intersection related
accidents per year for nominal or base conditions).
Nint = AMF x Nbi
where Nbi = exp (b0 + b1 ln ADTmaj + b2 ln ADTmin)
and b0, b1, b2 are to be determined for the intersection type
under consideration
STATISTICAL MODELING: Predict effect of IDS
on accidents involving older drivers
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Adapt Induced Exposure Methods to Estimate Relative Risk to
Older Drivers at Stop-Controlled Rural Expressway Intersections in
Minnesota
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Assumes that one can determine the at-fault and innocent drivers in 2
vehicle accidents. From the crash data base, use the proportion of
older innocent drivers at each intersection to estimate the relative
exposure of older drivers. The relative risk for older drivers can then
be estimated.
Results in rank ordering of intersections by risk to older drivers
Will try to adapt Exogenous Sampling Methods for Choice-Based
Samples to Develop Age-Specific Accident Base models, ie Nbi for
specific range of ages
Use Census Bureau's "Projections of the Population by Age, Sex
and Race for the United States“ to predict age-specific accident
frequencies.
Apply AMF to age-specific accident frequencies to estimate agespecific accident reductions.
References:
Vogt, A, and Bared, J., (1998) Accident Models for Two-Lane Rural
Roads: Segments and Intersections, Report FHWA-RD-98-133,
Federal Highway Administration, Washington, DC.
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Harwood, D., Council, F., Hauer, E., Hughes, W., and Vogt, A.
(2000) Prediction of the Expected Safety Performance of Rural TwoLane Highways, Report FHWA-RD-99-207, Federal Highway
Administration, Washington, DC.
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Davis, G. and Yang, S. (2001) Bayesian identification of high-risk
intersections for older drivers via Gibbs sampling, Transportation
Research Record, 1746, 84-89.
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Task B: Enabling Research
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Surveillance, Alec Gorjestani, Principal Investigator
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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.
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Communications
• Transmit data from sensors to IDS main processor
• Wire / Fiber Optic / Wireless options
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Computational systems
• Determine location, speed, and size of traffic gaps
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Performance issues:
• Redundancy, reliability, range, power, cost, estimation vs.
sensor coverage, etc.
Enabling Research
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Test Intersection
 Once Candidate Intersection selected, design test
infrastructure
• IDS System sensors, power, processors, and
associated cabinets within Mn/DOT R-O-W alongside
road, in advance of cross roads
• Test and validation system consisting of cameras,
and supporting structures (masts, power cabinets,
etc.).
Sensors
 Must provide at least 10 second warning at intersection
with vehicles traveling at 60 mph, need information from
at least 880 feet out (10 x 88 ft) at the Driver
Infrastructure Interface (DII) controller
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As vehicle speeds vary, gap sizes may change.
 Must track all gaps (‘safe’ and ‘unsafe’) as they
approach the Isxn.
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Need to determine location of sensors to provide
adequate advance preview.
Surveillance System - Overview
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System designed to record the location and velocity of every
vehicle at or approaching the intersection
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Surveillance system consists of an array of sensors
 Radar, Lidar (LIght Detection And Ranging)
 Vision – visible and infrared, image processing
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Sensor data transmitted to central processor
 Sensor data filtered and fused
 Intersection vehicle state matrix
 Gaps in traffic calculated
 Warnings can be generated for Driver Infrastructure Interface
(DII)
Eaton VORAD EVT-300 Radar
(24.725 GHz)
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Provides range, range rate, azimuth to
7 targets
Maximum range = 350 ft;
500 ft. when stationary
Maximum range rate = 120 mph
Beam geometry = 12 degrees azimuth
Elevation angle = 5 degrees
Strengths
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Weaknesses
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Long range, very accurate range rate,
weather insensitive
Targets ‘drop out’ at low relative speed
Performance better when close to road
Ideal for Hwy 52 main line traffic
Surveillance System – Radar
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Radar sensors placed along roadside to detect
high speed vehicles entering the intersection
Radar
Surveillance System - Lidar
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SICK LMS 221
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Strengths
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Very accurate range: +/- 10 mm
Good angle resolution: 0.25 degree
Can obtain vehicle profile for classification
Weaknesses
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2D scan, 180 degree
Range: 30 meters for 10% reflectivity target
No range rate provided, must calculate from successive scans
Low maximum range
Performance in snow unknown
Ideal for County Road 9
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Slow moving or stopped vehicles
Surveillance System - Lidar
Horizontal Alignment
Vertical Alignment
Surveillance System – Vision
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Visible light and IR cameras
 Visible light camera – records visible light to images
 IR camera – records temperature of radiating bodies as image
Unable to place hardware in median
 Likely to get hit by vehicle or snowplow blade
 Must monitor slow moving vehicles in median from distance
Strengths
 Can be placed further from road (zoom lens)
 Multiple vehicles detected at once
Weaknesses
 Image processing more complex
 Inconsistent lighting problematic (visible light)
Surveillance System - Vision
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IR image of Cadillac Escalade
Hot engine stands out against background
Surveillance System – Communication
Sensor data to central processing unit
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Use wired communication to test wireless’ suitability for
surveillance system
 Needed to trench for power anyway, therefore will
install wired network; enable us to compare various
wireless schemes against a wired network; cost vs.
performance tradeoffs
Wired Ethernet network
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Fast – 100 Mbit/sec; Reliable
Disadvantage: Must run cables long distances, need repeaters
Wireless
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Slower – 11 Mbit/sec for 802.11b
Not as reliable, interference, retransmission
Advantage: No need to run cables
Surveillance System – Evaluation
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System must be evaluated
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Collected data must be accurate
System must be reliable
Individual sensor accuracy tested using DGPS
equipped probe vehicles
Surveillance system accuracy also tested with probe
vehicles
Cameras will later be used to validate radar system’s
reliability
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Cameras placed on major legs
Images compared with radar data, will count misses
Radar Validation

Rada
r Bea
m Ra
nge
(440
ft)
DLC1
1
DLC2
2
LCov1
LCov2
= Radar Orientation Angle (w.r.t. lane)
= Sensor Distance from Lane-center
= Sensor Distance from Lane-center
= Theoretical Lane coverage (Lane 1)
= Theoretical Lane coverage (Lane 2)
DLC1
Radar
Station
Actual
VehiclePosition

DLC2
Radar Beam
Beam
Width
(120)
//
Sensor
Reported
Position
}
}
Lane 1
Lane 2
LCov2
LCov1
Determination of the Radar Yaw Angle
(w.r.t. North)
N
Pole
(Px,P y )
p
R
 P  tan
1
Radar
(Rx,Ry )
Rif leScope
R x  Px
Py  R y
R  p   2
-
Radar
Antenna
Yaw angle w .r.t North
Radar
Beam
 Sensor Orientation Angle
Lane
Center
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Radar Station
for Validation
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Experiments used to
determine optimal
location and angle for
radar
Locate radar 12 ft
laterally from road
Height: 15 inches
Sensor orientation
angle: 4.85 – 5.05
degrees
Surveillance System – Data Collection
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Intersection Data AcQuisition System (iDAQ)
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Stores all engineering data
• Vehicle states – X, Y, Speed, Class, Gap
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Stores images from cameras
• MPEG4 image capture board
• 4 channels
Engineering and video data time synchronized
 Removable hard drive
 R/WIS station within ½ mile of Hwy 52 and CSAH 9.
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Radar Installation with Respect to Road
The radar is 12 feet from the nearest drivable surface.
 If ditch slope is considered, radar is between 3 and 4.5 feet
from the ground.
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Contract Issues
U of MN will be bidding job
 Request for bids ready very soon
 Feb: Bidding process
 March: Select Contractor
 April: Work out contract
 May: Complete the job; bring intersection online.
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Human Factors Issues
 Misperceptions
of gap size and/or location
 Speed misjudgments
 Driver vigilance, situation awareness
 Learned Inattention
 Age related effects
 Communication to drivers
 Outlier behavior
Enabling Research
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Human factors…Nic Ward, Principal Investigator
Applying human-centered approach to problem of
intersection crashes because the state of the driver and
the actions of the driver are the most common factors
identified in police crash reports.
Consider the number of fatal crashes in MN between
1998 and 2000 that had one of the following crash
factors:
-
-
Driver (the state of the driver, eg impairment)
Driving (the actions of the driver, eg speeding)
Vehicle (faults or failures with vehicle and its components)
Environment (factors in the environment such as weather and
road conditions).
Human-Centered
Fatal Crashes (1998 - 2000)
1200
1000
800
Rural Crashes
600
Urban Crashes
400
200
0
Driver
Driving
Vehicle
Crash Factor
Environment
V.E.S.T.R
Task C: Benefit:Cost Analysis
Principal Investigator – David Levinson
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Three phased approach:
 Do Nothing (baseline values)
• Costs associated with intersection crashes
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Install Traditional Signals (or re-grade)
• Costs: Hardware, design, power, decreased traffic flow rates,
etc.
• Benefits: (possibly) fewer crashes, less severe crashes,
perceived value by motoring public
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Implementing an IDS System
• Costs: Hardware, design, power, public education, etc.
• Benefits: decreased crash rates, severity, maintenance of
traffic flow rates, surrogate economic benefits
Benefit:Cost Analysis
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To include:
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System Optimization
• Examine reliability, redundancy, sensor performance,
sensor coverage vs. gap estimation, cost:performance
sensitivity
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Deployment Modeling
• Identification of candidate intersections based on crash
numbers, crash severity, and other characteristics (based
on Gary Davis’ work).
• Development of deployment strategies and models based
on cost vs. performance vs. estimated effectiveness at rural
IRC intersections
Benefit Cost Analysis: Concept
Benefits
Costs
Fatality & Injury
Property Damage
Time Savings
Fuel Consumption
Service Charges
Other Costs
Service
Providers
Cost Savings
(O&M)
Efficiency
Enhanced Facility
Capital Costs
Operating and
Maintaining Costs
Society/
Community
Fatality & Injury
Property Damage
Emissions
Capital Costs
Other Costs
Users
Benefit Cost Analysis: Framework
Baseline
(Do-Nothing)
Scenario
Traditional
Engineering
Scenario
ITS Scenario
Identify SubMarket Impacts
Identify SubMarket Impacts
Identify SubMarket Impacts
Cost
Analysis
Benefit
Analysis
NPV
Cost
Analysis
Benefit
Analysis
NPV
Benefit Cost Ratio Comparison
Sensitivity Analysis
Recommendations
Cost
Analysis
Benefit
Analysis
NPV
Task D: System Requirements &
Specification Definition
Functional Requirements
 System Requirements
 System Specifications
 Experimental MUTCD Approval
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Driver interface likely to fall outside the normal
devices found within the MUTCD. Will need to work
to gain MUTCD approval as soon as candidate
interface is determined