Naturalistic Driving Studies: Another tool to assess the impact of driver distraction.

builderanthologyAI and Robotics

Oct 19, 2013 (3 years and 11 months ago)

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Naturalistic Driving Studies: Another tool to
assess the impact of driver distraction.

Charlie Klauer, Ph.D.

What is ‘Naturalistic’ Driving?


No experimenter present


Data collected in privately
-
owned vehicle,
preferably


Instrumentation = unobtrusive


Hidden radar


Face camera behind smoked glass


Other cameras are 1” x 1”


Preferable to do ‘large
-
scale’


Collect crash data


Generalizability


What are the advantages of the
“Naturalistic” approach?


More detailed pre
-
crash/pre
-
near crash
information


Greater external validity


Information about driver behavior under normal
day
-
to
-
day pressures


Rich database to mine


GES type crash database


Video


Driving performance


Empirical Data
Collection

Large
-
Scale
Naturalistic Data
Collection


Proactive


Provides
important ordinal
crash risk info


Precise knowledge
about crash risk


Information about
important
circumstances and
scenarios that lead
to crashes


Imprecise, relies on
unproven safety
surrogate


Experimental
situations modify
driver behavior


Reactive


Very limited
pre
-
crash
information


“Natural” driver behavior in full
driving context


Detailed pre
-
crash/crash info
including driver performance/
behavior, driver error and
vehicle kinematics


Can utilize combination of
crash, near crash and other
safety surrogate data


Epidemiological
Data Collection

What are the disadvantages of the
“Naturalistic” approach?


Costly and logistically complex


No experimental control over driver experience


Large amounts of data that must be
organized/reduced

100 Car “Naturalistic” Approach


Data collection in a “naturalistic” setting to obtain
crash/pre
-
crash/near
-
crash/conflict data as well as
distributions of driver performance:


100 drivers in their own (or leased) cars with
specialized instrumentation, on public roads, as
close to unobserved as possible.


Subjects used instrumented cars for an extended
period (up to 13 months) without an experimenter
present.


Subjects were not coached or instructed to perform
any specific actions other than drive as they
normally would.


Instrumentation was unobtrusive and inconspicuous
to other drivers, but not invisible.




Are the 100 car participants representative of the
driving population?


241 total drivers; recruited from flyers and
newspaper ads


Ages 18
-
73


Range of very “safe” to very “unsafe”


Wide range of miles driven


Commonly drove on all road classes


Urban, suburban and a small amount of rural
driving


Both sedans and SUVs



To what degree are the 100 car participants
representative of the driving population?


Possible recruiting bias due to nature of the
study


Extreme age groups missing (i.e., 16
-
17
and 75+)


One location (metro DC/NoVA)


Relatively little rural driving


Fewer SUVs and light trucks than the
national average



To what degree did the presence of the
instrumentation affect natural behavior?


Answer: Immeasurably, after the first hour.


Numerous occurrences of extreme driving across the
sample, including: Impairment, traffic violation,
aggressive driving, “road rage.”


Other “behavioral indicators” were often present.


Performance data (e.g., reaction time, speed
selection) remained largely consistent with prior
research and across the 13 months of data collection.


A few, isolated instances of “showing off” among
younger drivers in the presence of other passengers
was the only counter
-
indication.


Naturalistic Data Collection Approach

Highly capable instrumentation (well beyond
EDRs)


Five channels of digital, compressed video


Four radar sensors front, rear (for all 100 cars),
and side (for 20 cars)


Machine vision
-
based lane tracker


Many other sensors: GPS, glare, RF,
acceleration, yaw rate, controls, etc.


Cell phone, wireless internet, or hardwire
download


Tie into vehicle network to obtain other sensor
information


Ruggedized, crash tested, all solid state


Crash detection, Fault detection


Remote Access


100 Car Instrumentation Mounted in
Trunk

100 Car Face View Camera (behind smoked glass) and


and Incident Push Button mounted above Rear View Mirror

100 Car Forward Camera and Glare Sensor

Mounted behind the Rear View Mirror

The 100 Car “Event” Database


Continuous data were initially reduced based on
“trigger” signatures from the electronic data that
are indicative of the presence of a crash, near
crash or conflict/incident event.


Triggers include:


Radar
-
based time
-
to
-
collision


High lateral acceleration or yaw
-
rate change


Unplanned lane deviation


High longitudinal decelerations with short time
to collisions


Crashes

The Naturalistic “100 Car” Study


Once an event was triggered, its validity and
severity were determined via a review of 40
seconds of video.


Some events were triggered by normal driving
maneuvers or “false” radar targets (e.g., “flying
pass”).


Severity (i.e., crash, near crash or incident) was
determined using the following operational
definitions.


Video
-
Reduced Variables


Pre
-
Incident maneuver


Crash/Incident type


Precipitating factor


Contributing factor(s)


Evasive maneuver


Roadway/Traffic variables


Weather/Lighting


Driver’s state


Eye glance location


Observer rating of drowsiness


Fault assignment


Crash reconstruction

The Naturalistic “100 Car” Study:

Database Statistics


42,300 hours of driving data collected/ ~2 M miles of driving


82 Crashes and collisions


Defined as any contact between the subject vehicle and
another vehicle, fixed object, pedestrian, pedalcyclist,
animal.


761 Near crashes


Defined as a conflict situation requiring a rapid, severe
evasive maneuver to avoid a crash.


8295 Incidents


Conflict requiring an evasive maneuver, but of less


magnitude than a near crash.


20,000 Normal, baseline driving



Crash/Collision Category Definitions



Collision Category 1 (Police
-
reported
and/or contains an airbag or injury)

Collision Category 2 (Police
-
reported
with property damage only)

Left Turn Against Path

1

Lane Change

1

Rear
-
End Struck

2

Left Turn Against Path

1

Run
-
Off
-
Road

2

Rear
-
End Struck

2

Rear
-
End Strike

5





Run
-
Off
-
Road

2

Subtotal

5

Subtotal

11

















Collision Category 3 (Non
-
police
-
reported, physical contact/property
damage)

Collision Category 4 (Non
-
police
-
reported, physical contact/no
property damage)

Backing

2

Animal

2

Object

4

Backing

8

Rear
-
End Strike

6

Object

1

Rear
-
End Struck

6

Rear
-
End Strike

6

Run
-
Off
-
Road

6

Rear
-
End Struck

4

Sideswipe

1

Run
-
Off
-
Road

20

Subtotal

25

Subtotal

41

Total 82

Uses of Naturalistic Data


Detailed crash/near crash causation analysis


More pre
-
crash information than ever before
available


Safety surrogate validation


The relationship between crashes/near
crashes/incidents


The relationship to other surrogates like eye glances
and lane departures


Model development and validation


Crash benefits estimation


Crash countermeasure assessment


Modeling example from follow
-
on project work in
progress



100 Car Study Results


The capture of crash/collision events (included
minor, non
-
property
-
damage contact) provide
very valuable information and occur much more
frequently (i.e., 5 to 1) than more severe
crashes.


This has important implications for future
naturalistic driving studies aimed at assessing
driver
-
related crash causation.

100 Car Study Results

2.

This study allowed the capture and assessment
of near crash events in large numbers. Near
crashes provide valuable information as a
potential surrogate for crash events
and

as a
tool for the assessment of the factors that
contributed to the execution of a successful
evasive maneuver.

100 Car Study Results

3.

Inattention to the forward roadway
, which was
operationally defined as including: 1) secondary
task distraction, 2) driving
-
related inattention to
the forward roadway (e.g., blind spot checks), 3)
moderate to extreme fatigue, and 4) other non
-
driving
-
related eye glances, is the primary
contributing factor in most crashes and
collisions.

100 Car Study Results

4.

80% of
all
crashes and 65% of all near crashes
involved at least one form of driving inattention
just prior to (i.e., within 3 seconds) the onset of
the conflict.



5.

93% of the
conflict with lead vehicle

crashes and
minor collisions involved looking away. In 86%
of the lead vehicle crashes/collisions, the
headway at the onset of the event was greater
than 2.0 seconds.

Frequency of Events by Inattention Type

100 Car Study Results

7.

Fatigue contributed to crashes/collisions at
much higher rates than is reported using
existing crash databases. Fatigue was a
contributing factor in 20% of all crashes and
12% of near crashes, while most current
database estimates place fatigue
-
related
crashes at approximately 2 to 4% of total
crashes.

Frequency of Fatigue
-
Related Crashes
and Near
-
Crashes Occurring During
Day vs. Night

100 Car Study Results

9.

The rate of inattention
-
related crash and
near crash events decreased dramatically
with age, with the rate being as much as
four times higher for the 18
-
20 year old age
group relative to some of the older driver
groups (i.e., 35 and up).

Distraction Analysis Results



Secondary tasks that are moderately to
very complex and driver drowsiness have
the highest associated crash risk. Very
simple secondary tasks do not appear to
have a crash risk that is greater than
normal driving.



Secondary Task Complexity Levels

(from Dingus, Antin, Hulse, & Wierwille, 1989).



Complex Secondary Task: Multi
-
step, multiple
eye glances away from the forward roadway,
and/or multiple button presses.


Moderate Complexity Tasks: At most two glances
away from the roadway and/or at most two button
presses.


Simple Secondary Tasks: Zero or one button
press and/or one glance away from the forward
roadway.

Calculation of Odds Ratio (Relative Risk)

Odds Ratio = (A x D)/(B x C)




A = the number of events where <inattention type> was present without
any other type of inattention


B = the number of baseline epochs where <inattention type> was present
without any other type of inattention


C = the number of events where < inattention type> was
not

present or
was present but in combination with other types of inattention


D = the number of baseline epochs where <inattention type> was
not
present or was present but in combination with other types of inattention


Interpretation of OR:


Greater than 1.0 = Increased crash risk


Equal to 1.0 = Risk is same as normal driving


Less than 1.0 = Decreased crash risk or ‘protective effect’.


Relative Risk Estimates (Odds Ratios) for Crash/Near
Crash Inattention Events



Type of Inattention

Odds Ratio

Lower CL

Upper CL

Complex Secondary Task

3.1

1.7

5.5

Moderate Secondary Task

2.1

1.6

2.7

Simple Secondary Task

1.2

0.9

1.6

Moderate to Severe
Drowsiness (in isolation)

6.2

4.6

8.5

Moderate to Severe
Drowsiness (all occurrences)

4.2

3.3

5.5

Driving
-
related Inattention to
the Forward Roadway > 2 s

0.5

0.2

0.8

Driving
-
related Inattention to
the Forward Roadway < 2 s

0.2

0.2

0.3

Non
-
Specific Eye Glance Away
from the Forward Roadway > 2
s

0.9

0.2

3.7

Non
-
Specific Eye Glance Away
from the Forward Roadway < 2
s


0.4

0.2

1.1

Population Attributable Risk (Estimate of the
Percentage of Total Crashes where Inattention is a
Contributing Factor)



Type of Inattention

Population
Attributable
Risk (%)

Lower CL

Upper CL

Complex Secondary Task

4.3

4.0

4.6

Moderate Secondary Task

15.2

14.6

15.8

Simple Secondary Task

3.3

2.7

3.9

Moderate to Severe
Drowsiness (in isolation)

22.2

21.7

22.7

Moderate to Severe
Drowsiness (all
occurrences)

24.7

21.1

25.2

Preliminary Results from 100
-
Car Study

Type of Secondary Task

Odds Ratio

Lower CL

Upper CL

Reaching for a moving object

8.8

2.5

31.2

Insect in vehicle

6.4

0.8

53.1

Looking at external object

3.7

1.1

12.2

Reading

3.4

1.7

6.5

Applying make
-
up

3.1

1.3

7.9

Dialing hand
-
held device

2.8

1.6

4.9

Inserting/retrieving CD

2.3

0.3

17.0

Eating

1.6

0.9

2.7

Reaching for non
-
moving object

1.4

0.8

2.6

Talking/listening to hand
-
held device

1.3

0.9

1.8

Drinking from open container

1.0

0.3

3.3

Other personal hygiene

0.7

0.3

1.5

Adjusting radio

0.6

0.1

2.2

Passenger in adjacent seat

0.5

0.4

0.7

Passenger in rear seat

0.4

0.1

1.6

Combing hair

0.4

0.1

2.7

Child in rear seat

0.3

0.04

2.4

Preliminary Results from 100
-
Car Study

Type of Secondary Task

Population
Attributable
Risk %

Lower CL

Upper CL

Reaching for a moving object

1.1

0.97

1.3

Insect in vehicle

0.4

0.3

0.4

Looking at external object

0.9

0.8

1.1

Reading

2.9

2.6

3.1

Applying make
-
up

1.4

1.2

1.6

Dialing hand
-
held device

3.6

3.3

3.9

Inserting/retrieving CD

0.2

0.2

0.3

Eating

2.2

1.9

2.5

Reaching for non
-
moving object

1.2

1.0

1.5

Talking/listening to hand
-
held
device

3.6

3.1

4.1

Drinking from open container

0.04

-
0.1

0.2

Odds Ratios for Eyes Off the Forward
Roadway (
Excluding Mirror Glances
)

Total Time

EOR

Odds Ratio

Lower CL

Upper CL

Less than or equal to 0.5 seconds

1.1

0.7

1.9

Greater than 0.5 but less than or
equal to 1.0 seconds

1.1

0.8

1.6

Greater than 1.0 but less than or
equal to 1.5 seconds

1.1

0.8

1.7

Greater than 1.5 but less than or
equal to 2.0 seconds

1.4

1.0

2.0

Greater than 2.0 seconds

2.3

1.8

2.9

OR for EOR

1.6

1.3

1.9

Population Attributable Risk % for Eyes Off
the Forward Roadway (
Excluding Mirror Glances
)

Total Time

EOR

Population
Attributable
Risk %

Lower CL

Upper CL

Less than or equal to 0.5
seconds

0.7

0.4

1.1

Greater than 0.5 but less than or
equal to 1.0 seconds

1.5

1.0

2.0

Greater than 1.0 but less than or
equal to 1.5 seconds

1.6

1.1

2.0

Greater than 1.5 but less than or
equal to 2.0 seconds

3.8

3.4

4.3

Greater than 2.0 seconds

18.9

18.3

19.5

OR for EOR

18.3

17.5

19.0

Mean Total EOR Time

Mean # of Eye Glances Away From
the Forward Roadway


Mean Length of Longest Single
Glance

Summary


The 100 Car approach provides much greater
information regarding the pre
-
crash and crash
events than is currently available in crash
databases, even those containing detailed crash
investigation variables.


Also, this method provides externally valid data
when compared to that obtained on test tracks or
in simulators.

Summary


One of the primary contributions of the 100 Car
Study is the creation of an “event” database. This
database is similar in classification structure to an
epidemiological crash database, but with video,
driver, and vehicle data appended. The video and
electronic data can be replayed multiple times and
at varying frame rates in order to fully understand
the nature of each event. The resulting database
should be useful for a variety of investigations for
the next several years.

Summary


The 100 Car Study also marks the first time that
detailed information on a large number of near
-
crash events has been collected. Near crashes
have two important advantages over crashes.
First, they occur much more frequently (e.g., 15
times more often than crashes). Second, every
near crash event demonstrates a driver’s
successful performance of an evasive maneuver.
This may provide additional insight into effective
defensive driving techniques and factors, as well
as insight into potential countermeasures for
these driving situations.

Can large
-
scale naturalistic data
collection assess causation?


Yes…using epidemiological research
methods


Case
-
control studies have already been conducted


Use crash and near
-
crash involvement with baseline
driving epochs as control.


These results are conservative estimate because each
crash/near
-
crash has multiple baseline epochs to compare

»
All behaviors per vehicle are matched thus those
drivers who are over
-
involved in inattention
-
related C
and NC are ALSO over
-
represented in the baseline
database.


Case
-
crossover studies, that focus on individual
drivers, will be conducted in the future…our next
step.


Concluding Remarks


Epidemiological and empirical research will
always be valuable to driving safety research.


Technology has advanced to a level to give
researchers another tool to assess crash
causation and develop crash countermeasures in
a surface transportation environment.


This tool will be particularly important for the
assessment of the crash risk associated with
factors such as driver error, impairment, and
distraction.