Evaluation of the USDOT’s Intelligent Vehicle Initiative (IVI) Generation 0 Field Operational Tests Presented by John E. Orban (Battelle) Luigi “Gino”

Evaluation of the USDOT’sIntelligent Vehicle Initiative

(IVI)Generation 0 Field Operational TestsPresented by

John E. Orban (Battelle)

Luigi “Gino” Mastrippolito (U.S. Army ATC)NDIA 3rd Annual Intelligent Vehicle Systems Symposium and Exhibition

Traverse City, Michigan

June 11, 2004

Evaluation of IVI Generation 0 FOTs

Discussion Topics

John Orban Background – Intelligent Vehicle Initiative Safety Benefits Methodology Some Preliminary Findings

Gino Mastrippolito Nationwide FOT Data Acquisition Challenges Nationwide FOT Data Acquisition Solution


The Intelligent Vehicle Initiative Five Field Operational Tests (FOTs) of Intelligent Vehicle Safety

Systems (IVSS) Commercial Vehicles (Freightliner, Volvo, Mack) Snowplows/Specialty Vehicles (Minnesota) Transit Buses (Pennsylvania/California)

DOT Agencies ITS Joint Program Office National Highway Traffic Safety Administration Federal Motor Carrier Safety Administration

Battelle is the Independent Evaluator

ATC developed on-board data acquisition system for Volvo FOT


Five Separate Field Operational TestsVolvo: Collision Warning, Adaptive

Cruise Control, Elec. Controlled BrakesFreightliner: Rollover Advisor

Mack: Safety Advisor, Lane Departure Warning

Plus, other platforms

•Snow plows and emergency vehicles

•Transit


Mack Trucker Advisor/LDW Systems SafeTRAC Lane

Departure Warning

Trucker Safety Advisory

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#0 Terminals#S Destinations#S Truck Advisory Zones

700 0 700 1400 Miles

N

EW

S

Terminals & Destinations with TAZ

36 McKenzie Tanker Trucks- 9 with LDW only- 9 with TSA only- 9 with LDW and TSA- 9 control trucks


Volvo Collision Warning System with ACC and ECBS

100 U.S. Xpress Long-Haul Trucks- 50 with ACC & ECBS- 50 control trucks


Freightliner Roll Advisor and Control (RA&C) System

RSA

RSA

Severity

RISKY MANEUVER

DETECTED

Level 1 (Threshold Lateral Acceleration)

ROLLOVER RISK

DETECTED

REDUCE SPEED

AT LEAST 7 MPH

Level 2 (Higher Lateral Acceleration)

HIGH RISK OF

ROLLOVER DETECTED

REDUCE SPEED AT LEAST 12 MPH

Level 3 (Highest Lateral Acceleration Range)

6 Praxair Tanker Trucks- before & after study


Evaluation Goals

1. Assess BenefitsA. Safety (highest priority)B. Mobility

C. Efficiency and Productivity

D. Environmental Quality

2. Assess Driver Acceptance and Human Factors

3. Evaluate System Performance

4. Assess Product Maturity for Deployment

5. Identify Institutional and Legal Issues


Safety Benefits Methodology – Three Data Sources: Historical Data: National Automotive Sampling System (NASS)

General Estimates System (GES) Historical crash rates (by type) without IVSS Identify “driving conflicts” that precede crashes

Driving Data from Field Operational Tests Determine frequency of driving conflicts – by type of crash Quantify conditions (speed, following interval, etc) at time of

conflict Evaluate driving behaviors (with and without IVSS)

Track Tests Validate crash simulation models Demonstrate system performance


General Methodology for Estimating Safety Benefits

CrashDatabases

(GES, FARS)

EstimateConflict

&Crash

ReductionRates

Mack FOT

Volvo FOT

Freightliner FOT

HistoricalConflict

&CrashRates

EstimateFutureCrash

Reductions


Partition Data by Driving Conflicts

S1 S2S3

Crashes

(No conflict)(Driving conflict 1)

(Driving conflict 2)

Normal Driving


Estimating Reduction in Number of Crashes

DrivenMiles)]C(P)C(P[B wwo Simplest Form

“Prevention Ratio” “Exposure Ratio”FOT Data and

Analytical ModelsFOT Data

From GES

Population Statistics

Useful form

i iwoiwo

iwiwwowo SPSCP

SPSCPCSPNB

i]

)()|(

)()|(1[)|(


“Rear-End” Driving Conflicts

Conflict Number

(Si)

Conflict Definition Relative Frequency forHeavy Trucks

Rear-end.1 Truck is traveling at a constant speed and encounters a lead vehicle traveling at a lower speed.

72%

Rear-end.2Truck is decelerating and encounters a lead vehicle, already in this vehicle’s lane, traveling in the same direction.

7%

Rear-end.3 Truck is changing lanes or merging and encounters a lead vehicle traveling at a lower speed.

2%

Rear-end.4Truck encounters a stopped vehicle in lane.

2%

Rear-end.5 Truck is traveling at a constant speed and encounters a lead vehicle decelerating.

1%

Sum 84%


Potential Rear-End Conflict: A Cut-In

Position vs. Time


Some Preliminary Results

Freightliner FOT: Roll Advisor & Control SystemRSA

RSASeverity

RISKY MANEUVER

DETECTED

Level 1 (Threshold Lateral Acceleration)

ROLLOVER RISK

DETECTED

REDUCE SPEED

AT LEAST 7 MPH

Level 2 (Higher Lateral Acceleration)

HIGH RISK OF

ROLLOVER DETECTED

REDUCE SPEED AT LEAST 12 MPH

Level 3 (Highest Lateral Acceleration Range)


A Rollover Conflict: High Speed in Turn

-0.4-0.3-0.2-0.10.00.10.20.30.4

0 10 20 30 40

Time, s

Ay

, g

65% of static rollover threshold

-250

-200

-150

-100

-50

0

0 50 100 150 200 250

east (m)

no

rth

(m

)

Lateral Acceleration vs. Time


How to Apply Crash Reduction Equation

From GES “Prevention Ratio” “Exposure Ratio”

i iwoiwo

iwiwwowo ][

)S(P)S|C(P

)S(P)S|C(P1)C|S(PNB

i

1. Count conflicts

2. Simulate probability of a crash

3. Determine historical rates.


Estimated Exposure Ratio

Rol

love

r Ris

k R

atio

-1

0

1

2

3

Driver2010 2020 2030 2040 2050

Driver-Specific Ratios

(with 95% confidence limits)Without RA&C

With RA&C

Conflicts 90 70

VMT 225,000 250,000

Conflicts per 1,000 VMT

0.40 0.28

Exposure Ratio 0.70

Hypothetical Results


Estimating the Prevention Ratio

“Prevention Ratio”

i iwoiwo

iwiwwowo SPSCP

SPSCPCSPNB

i]

)()|(

)()|(1[)|(


Estimating the Prevention Ratio Approach:

Simulate crashes under actual conflict conditions (using time domain vehicle dynamic model)

Generate a statistical distribution of possible speeds

Select variance to match historical crash rates to simulated average w/o RA&C

Actual

Conflict

Speed

in Actual Conflict

(mph)

Simulated Roll Speed

(mph)

S1 50 53

S2 45 49

S3 43 45

. . .

. . .

. . .

S90 61 64

Example Simulation Results

i iwoiwo

iwiwwowo SPSCP

SPSCPCSPNB

i]

)()|(

)()|(1[)|(


Statistical Distribution of Possible Speeds for Each Conflict

Variance parameter selected to match FOT and historical

crash rates (w/o RA&C)

vjActual speed of this conflict

Variance = (σ · vi,j)2

vj,RSpeed where this

conflict leads to a roll

P(C|S1,j)

Probability that a crash occurs,

given this conflict

Hypothetical Result: Average Pw(C|S)/Pwo(C|S) = 1.2


Benefits Calculation (Hypothetical)

preventedyearcrashes /7

i iwoiwo

iwiwwowo ][

)S(P)S|C(P

)S(P)S|C(P1)C|S(PNB

i

]70.020.11[55.084

]16.0[46

Tank trailersToo fast in a curve

Similar calculations performed for all tractor trailers and all large trucks. These fleets account for 471 and 787 rollovers per year, respectively, without RA&C


Schedule for Completion of Final Reports Freightliner – June 2003

Volvo – Early 2004

Mack – Early 2005

DOT Release Dates TBD


Discussion Topics

John Orban Background – Intelligent Vehicle Initiative Safety Benefits Methodology Some Preliminary Findings

Gino Mastrippolito Nationwide Data Acquisition Challenges Nationwide Data Acquisition Solution


Intelligent Vehicle Initiative VOLVO Field Operational Test



• Acquire data from 100 Class-8 Trucks for 2 years

• 50 Trucks will have advanced safety systems• 50 Trucks will be “control” vehicles

• 30 with CWS, 20 without CWS (later converted)

• Trucks operated in normal revenue generating service by a nationwide commercial carrier.

• Record data relevant to potentially hazardous situations

• Get data from trucks and change test parameters WITHOUT interfering with the truck’s activity

Data Acquisition Challenge


Interfaces to sensors, GPS and multiple vehicle data buses. Record data in histogram format during all vehicle

operations.• Vehicle operation history and statistics of vehicle activity.

Record data in time history format when trigger conditions are met.

• Potentially hazardous situation record.• Pre-trigger collects 10 seconds prior then 5 seconds post

trigger. Transfers the status and data to a central facility regularly. Operating parameters may be modified during test. Provide access to data for review by several groups.


Data Acquisition Challenge


Instrumentation System


VISION (Versatile Information System

Integrated ON-line)

GPS SATELLITES

NATIONWIDE TRUCK FLEET

DATA ACQUISITION SYSTEM

CELLULAR TELECOMMUNICATIONSHIGH PERFORMANCE

COMPUTING

AUTOMATED DATA HARVESTING

ANALYST/CUSTOMER


On-Board Data Acquisition System PC-104 based embedded

computer system Real-time operating system Analog and digital interfaces Serial Bus Interfaces Removable solid-state

memory card User Programmable and

Re-programmable Custom Features

CWS interface Integrated cellular modem Integrated bi-axial accel. Front panel connectors



SAE J1939 Data Bus Road Speed Throttle Percentage Brake Pedal Percentage Service Brake Application Brake Pressure Cruise Control Status Odometer Ambient Temperature ABS Status

SAE J1708 Data Bus Vehicle Identification Number Fault Codes

GPS Receiver GPS Location GPS Time (Greenwich Mean)

VORAD Data Bus Following Distance Relative Velocity Relative Acceleration DDU Light Status DDU Audio Status Target ID Target Azimuth

Analog Sensors Steering Position Lateral Acceleration Longitudinal Acceleration

Derived Parameters Following Interval Time-to-Collision Kinetic Motion Steering Wheel Rate

Data Parameters Monitored



Lateral Acceleration > 0.2 g’s Fore/Aft Accel > 0.25g when brake applied Steering Rate > 120 °/sec when speed is > 30 mph ABS active Kinetic Motion Conditions (Battelle and Volpe derived) Time-to-Collision less than 4 seconds when road

speed > 20 mph (for minimum of .66 seconds) Following interval < 0.5 seconds when road speed >

11 mph (for minimum of 0.5 seconds)

Trigger Conditions



Data and Video Viewed from Database


• Active project acquiring, transferring and archiving data from a nationwide fleet of trucks.

• Programmable/Reprogrammable on-board acquisition system.

• Interface to many communications systems.

• Automated database loading.

• World Wide Web database interface.

• Graphical User Interfaces.

• Standard and evolving data mining tools.

• Valuable Test and Evaluation tool for many years/projects.


Summary


Acknowledgements

Safety Benefits Methodology Anne-Claire Christiaen, Jeff Hadden, Nancy McMillan,

Vangal Narendran, Michael Neighbor, Doug Pape, Steve Shaffer, Greg Stark (Battelle)

Wassim Najm (Volpe NTSC)

Data Collection Methodology VISION Team (U.S. Army ATC)

U.S. DOT Sponsors (FMCSA, NHTSA, FHWA) August Burgett, Tim Johnson, Jim Britell, Bob Miller

Documents

Evaluation of the USDOT’s Intelligent Vehicle Initiative (IVI) Generation 0 Field Operational Tests Presented by John E. Orban (Battelle) Luigi “Gino”