Weather & Road Condition Product Improvements Enabled by Vehicle Infrastructure Integration (VII)
Human Factors and VII Enabled Applications
description
Transcript of Human Factors and VII Enabled Applications
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Human Factors and VII Human Factors and VII Enabled ApplicationsEnabled Applications
A Role for Naturalistic Data
Jim SayerUniversity of Michigan Transportation Research Institute
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UMTRIUMTRI
Established in 1966
Founding sponsors: vehicle manufacturers
Research oriented toward highway safety
$17 million per year research budget
About 120 staff members
Report to the Vice President for Research
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Combining Human Factors and Combining Human Factors and Engineering Domains of ResearchEngineering Domains of Research
HF ERD AADSBA TSA Biosci
UMTRI
Dynamics and control of the motor vehicle, modeling
Capabilities, limitations, driver behavior, test methodologies
Program on the Driving Control Process
KNOWLEDGE
The normal driving process, as controlled by people - manually and
with driver assistance systems.
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Driver Assistance Systems Driver Assistance Systems ResearchResearch
Much of this work is field operational tests (FOTs)
Fleets of instrumented vehicles (cameras, radars, DGPS, accelerometers, etc.)
Data acquisition systems Data archiving
Includes a wealth naturalistic baseline data
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Today’s DiscussionToday’s Discussion What naturalistic driving data currently
exist that could help guide/support VII weather related applications? Driving behavior in inclement weather What is the behavioral baseline?
A need to better understand driver visibility and the use of windshield wipers I need historical precipitation data
Sharing of previously unknown resources
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Origins of the Naturalistic DataOrigins of the Naturalistic Data
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UMTRI Naturalistic Data with Driver UMTRI Naturalistic Data with Driver Assistance SystemsAssistance Systems
1996 –
1997
108 subjects, 2-5 weeks each, passenger cars, manual, adaptive cruise control (ACC)
Naturalistic 114K mi
2000 –
2001
30 heavy truck drivers, 12 months, manual, roll stability advisory
Naturalistic 478K mi
2003 - 2004
96 subjects, 4 weeks each, passenger cars,manual, ACC, forward collision warning
Naturalistic 140K mi
2004 - 2005
78 subjects, 4 weeks each, passenger cars,manual, curve and lane departure warnings
Naturalistic 83K mi
2008 - 2009
120 drivers, 16 passenger cars and 10 heavy trucks
Naturalistic 250K mi
500K+ mi
All studies include baseline periodsAlmost 800K miles of naturalistic data collection with passenger cars and heavy trucksWith additional experience on test tracks, on-road and pilot testing
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Data Acquisition & Remote Data Acquisition & Remote MonitoringMonitoring
VideoComputer
RuggedizedDisks
Main
Computer
PowerController
End-of-trip data upload cellular modem: Webpage tracking vehicle and system
750K miles of naturalistic data with 350 drivers
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Recent FOT Data ScopeRecent FOT Data Scope Approx. 3200 hrs (312
weeks, 1250 trips) >400 signals About 700 GB
including video/audio 10 Hz, 1Hz, event-
triggered, histograms Cameras (forward and
face), in-cabin microphone
Objective data: vehicle motion and state, environment, driver activities, sensing and processing (vision, GPS/map, radar, constructed maps)
Remote system monitoring Linked objective data to
subjective responses/ demographic data
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Integrated Data CollectionIntegrated Data Collection
ACAS
SensorFusionThreatRadar...
ACASData DBMain Cpu
CAN
Visualization/Analysis Tools
ACAS Project database
Video Cpu
Forward camera
Face camera
DAS
...
Audio
Video/Audiofiles
Phone DB
Analyst’s DBs
Participant DBsubjective
questionnairesCell phone
Ÿ SQL analyzerŸ DB managerŸ Data browserŸ Video viewerŸ Desktop databaseŸ Spreadsheet
DAS files to tables
Test Vehicle Data Archive/Server
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Data Analysis and WarehousingData Analysis and Warehousing
Relational database to analyze and mine FOT data
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Video and Visualization ToolsVideo and Visualization Tools
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Geographic Reference Map of ADAS warnings
Selected HPMS data spatially “joined” to road segments
Functional Class
AADT
Urban/Rural
Selected crash data spatially “joined” to road segments
Date of Crash
Gender of Driver
Weather condition
Functional Class
Overlaying Vehicle Data with Crash and Roadway Data
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A Naturalistic Examination of A Naturalistic Examination of Windshield Wiper Usage Windshield Wiper Usage
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The Data SetThe Data Set
96 drivers from Michigan, USA Urban, suburban and rural residents Ranged in age (20-70)
Drove 4 weeks each, instrumented vehicle replaced their personal cars
137,000 miles (220,000 km), 13,600 trips 325,000 wiper cycles on 1,700 trips
170 windshield cleaning events
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Wiper Utilization by MonthWiper Utilization by Month
0
2
4
6
8
10
12
14
16
Jan
Feb Mar
AprM
ay Jun Ju
lAug Sep Oct
Nov Dec
Month
% T
rave
l with
Wip
ers
On
.
0
1
2
3
4
5
6
Pre
cipi
tatio
n (i
nche
s) .
Wiper Utilization
Precipitation
Spray?
Rain?
Rain?
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Wiper Speed Selection: Wiper Speed Selection: Ambient Light LevelAmbient Light Level
0
5
10
15
20
25
30
2 6 10 16 22 34 (Low) 49 (High)
Cycles Per Minute
% W
iper
Set
ting
Use
Light
Transition
Dark
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SummarySummary
No relationship between wiper speed selected and road class or vehicle speed
Average wiper usage is 8.6% of the time the car is running, or 3.9 events/100 miles
Neither wiper use nor speed selected is readily predicted by precipitation Attempts to relate wiper use with rain rates
was very poor using hourly historic data
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Naturalistic Use of High-Beam Naturalistic Use of High-Beam HeadlampsHeadlamps
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High-Beam UsageHigh-Beam Usage
Drivers vastly under use high-beam headlamps Even in conditions when glare is not an issue
Data from 87 drivers ~ 100k miles, of which ~ 21k miles were driven
at night Night defined by a solar zenith angle ≥ 96°
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Headlamp UsageHeadlamp Usage
0
10
20
30
40
50
60
70
80
90
100
20 30 40 50 60 70 80 90 100 110 120 130 140 150 160
Solar zenith angle (degrees)
% o
f d
ista
nce Low beam
High beam
Civil twilight
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ResultsResults Best case scenario for using high beams: Rural
roads, no opposing traffic, not following a vehicle
0.010.020.030.040.050.060.070.080.090.0
100.0
Time of Day
% o
f N
ightt
ime
mile
s w
ith h
igh b
eam
s .
Mean = 25.4%
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SummarySummary
Collapsed across road types, high beams are used 3.1% of the mileage driven at night
Even under ideal conditions, high-beam usage averages ~ 25%
Drivers continue to under use high-beam headlamps
Automatic high/low switching could improve this situation
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Naturalistic ABS EventsNaturalistic ABS Events
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The Data SetThe Data Set
96 drivers from Michigan, USA Urban, suburban and rural residents
137K miles, 13,600 trips, ~ 10 driving yrs 851 ABS events
~ 1 every 161 miles ~ 1 every 16 trips ~ 85 per year
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ABS and Precipitation?ABS and Precipitation?
0
100
200
300
400
500
600
700
800
Off Delay5 Delay4 Delay3 Delay2 Delay1 Low High
Wiper Status
ABS E
vent
s .
81% of ABS events are without active precipitation
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ABS and TemperatureABS and Temperature
-20
-10
0
10
20
30
40
0 10 20 30 40 50 60 70 80 90 100
% ABS Events
Outs
ide T
em
pera
ture
(°C
)
.
70% of ABS events occur above freezing
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0
50
100
150
200
250
300
Road Class
AB
S E
vents
.
Actual ABS Events
Predicted ABS Events
ABS and Road ClassABS and Road Class
ABS events over rep. on dirt roads
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ABS and SpeedABS and Speed
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70 80 90 100
% ABS Events
Spee
d (m
/s) .
50% of ABS events are initiated under 25 mph
90% of ABS events are initiated under 40 mph
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Video SamplesVideo Samples
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What This Means for VII WeatherWhat This Means for VII Weather
There is a wealth of naturalistic data to be mined relative to baseline driving behavior Data can aid in assessing the probability of a
weather related event Data can aid in determining timing required
before issuing an alert Data can be used for assessing the relative
value of providing VII in certain locations