Human Factors and VII Human Factors and VII Enabled ApplicationsEnabled Applications

A Role for Naturalistic Data

Jim SayerUniversity of Michigan Transportation Research Institute

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

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.

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

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

Origins of the Naturalistic DataOrigins of the Naturalistic Data

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

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

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

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

Data Analysis and WarehousingData Analysis and Warehousing

Relational database to analyze and mine FOT data

Video and Visualization ToolsVideo and Visualization Tools

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

A Naturalistic Examination of A Naturalistic Examination of Windshield Wiper Usage Windshield Wiper Usage

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

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?

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

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

Naturalistic Use of High-Beam Naturalistic Use of High-Beam HeadlampsHeadlamps

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°

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

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%

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

Naturalistic ABS EventsNaturalistic ABS Events

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

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

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

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

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

Video SamplesVideo Samples

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

Questions?Questions?

jimsayer@umich.edu