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Direct metrics of driver
performance
Johan Engström
Volvo Technology Corporation
Driver Metrics Workshop
Ottawa, October 2-3, 2006
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Outline
•
•
•
Background of research (HASTE and AIDE)
Metrics
•
•
•
Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
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Humans System Integration
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Outline
•
•
•
Background of research (HASTE and AIDE)
Metrics
•
•
•
Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
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Humans System Integration
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Research in HASTE and AIDE
on performance metrics
•
•
•
•
•
Research conducted 2003-2006 in the HASTE and AIDE EU-funded
projects
General objective of the studies:
•
Investigate systematically the effects of visual and cognitive load on driving
performance -> define metrics for IVIS safety evaluation
Data collected in simulators (of varying grade) and field (HASTE)
Further analysed in AIDE
Work reported here performed in collaboration with VTI (Swedish National
Transport Research Institute)
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The HASTE WP2 data set
•
•
•
•
•
Collected in HASTE WP2 during 2003-2004
9 parallel studies at different sites in Europe and Canada
Same general methodology and experimental design
Varied mainly with respect to test set-up (desktop simulator, meduim-high-fidelity
simulators and field trials)
Secondary tasks:
Auditory
Continuous
Memory Task
(aCMT)
Visual: Arrows task
Auditory/cognitive: aCMT
3 difficulty levels each
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The HASTE WP2 data set (cont’d)
•
•
Three general driving scenarios: Motorway, Rural, and Urban
Present analyses based on data from three sub-studies
•
•
•
VTEC fixed-base simulator (rural and motorway)
VTI moving-base simulator (rural and motorway)
Volvo-VTI field study (instrumented Volvo S80 on motorway)
VTEC simulator
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VTI simulator
Volvo S80
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General result
Visual and cognitive load have
qualitatively different effects on
driving…
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Summary of results: Visual task
Parameter
Effect
Interpretation
Lane keeping variation
+
Reduced lateral control due to visual
time sharing
Steering wheel activity (e.g.
reversals)
++ (mainly large
reversals, 2-5
deg.)
Increased steering effort to correct lane
keeping errors
Speed
-
Compensation for reduced lateral control
to maintain safety margins
Headway and TTC
+
Compensation for reduced lateral control
to maintain safety margins
Glance frequency
++ (increased with Increased visual complexity -> more
task difficulty)
glances required
Glance duration
++ (increased with Increased visual complexity -> longer
task difficulty)
glances required
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Summary of results: Cognitive task
Parameter
Effect
Interpretation
Gaze concentration to road centre
++
Less cognitive resources available for
visual monitoring to the periphery
Lane keeping variation
-
Indirect effect of increased gaze
concentration
Steering wheel activity (e.g.
reversals)
++ (mainly small
reversals.)
More active and precise steering as a
result of more visual input
Speed
+- (inconsistent)
Dependent on test scenario
Mean Headway and TTC
No effect
No compensation
Headway variation
+
Less consistent car following
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General conclusion: Visual and cognitive load have
different effects
•
•
Visual
Visual diversion
•
Steering hold
•
Lane keeping error
•
•
Large corrective steering
movements
Slowing down & increasing
headway to compensate
Cognitive
• Interference with attention
selection mechanisms
•
•
•
•
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Gaze concentrates to road
centre
More visual control input than
during normal driving
More active and precise
steering
More accurate lane-keeping
Reduced visual
detection/
decision making
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Outline
•
•
•
Background of research (HASTE and AIDE)
Metrics
•
•
•
Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
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Humans System Integration
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Focus of this presentation
•
•
Metrics intended for task-based IVIS evaluation
Types of metrics covered:
•
•
•
Lane keeping
Steering
Eye movements
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Outline
•
•
•
Background of research (HASTE and AIDE)
Metrics
•
•
•
Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
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Humans System Integration
Lane position (m)
ion (m)
km/h) St. wheel angle (deg) St.
Radial
gaze
(deg)
wheel
angle
(deg) Radial gaze (
ion (m)
Lane position (m)
km/h)
Radial
gaze
(deg)
St. wheel angle (deg) St.
wheel
angle
(deg) Radial gaze
50
50
30
40
4030
20 Volvo Technology
20
3020
3020
20 10
20 10
Example
data:
Straight
1010
1010driving,
rural road,
Driver
straight
rural
road
7, 39,
straight
80 130
100
ht ruralDriver
road
120
140 rural road
940 760950
50
30
40
20
30
20
20 10
VTEC
10
10
simulator
760
780
Baseline
Baseline
Baseline
4
4
5 30
50
30
40 22
200
30
020
0
20 10
-2
10
-2
10
-5
80 130
100
120
140
80
100
120
130 780
140
760
4
5 30
50
30
40 2 2
200
30
020
0
2010
-2
10
-2
10
-5
760950
780
940
960
940760
950120780
960
14
1000 1010 120
1020
960
980
Cognitive
task,
level
Visual
task,
level
3 23
Cognitive
task,
level
4
4
5
50
30
30
402 2
20 0
30
20
0
0
2010
-2
10
-2
10
-5
120
14
1000
1020
120
1000
1010
1020 140
140
960 1010
980
0.6
4
0.50.6
54
0.4
0.4
22
00.2
0.2
0 00
00
-2
-0.5-0.2
-2
-0.2
-5
120
140
80 130
100
80
100
120
130 780
140
760
0.6
4
0.5
0.6
54
0.4
0.4
22
0 0.2
0.2
0 00
00
-0.5-0.2
-2-2
-0.2
-5
940
960
760950
780
940760
950120780
960
0.6
4
0.5
0.6
4
5
0.4
0.4
22
0 0.2
0.2
00 0
0 0
-2
-0.5-2
-0.2
-0.2
-5
1000
1020
120
14
120
1000
1010
1020 140
140
960 1010
980
120
120
0.6
0.6
85
0.5
Technology
0.4 Volvo
110
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0.4
110
80
780
960
120
140
Baseline
Baseline
4Cognitive task, level 3
120
120
0.6
0.6
85
0.5
0.4
110
0.4
110
80
120
120
0.6
0.6
85
0.5
0.4
110
0.4
110
80
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Individual subjects
Frequency analysis
0
10
-1
10
-2
10
-3
10
-4
1
Frequency content in st. wheel signal
10
-1
10
0
10
Frequency (Hz)
1
10
Driver 8, fixed-base simulator, straight rural road
All subjects
10
Baseline
Visual task
Task length frequency
1
10
10 -2
10
0
Baseline
Visual task
Task length frequency
1
10
0
10
-1
10
-2
10
-3
10
-4
10
10
10 -2
10
-1
-2
10
-2
10
-1
10
Frequency (Hz)
0
10
1
Frequency content in st. wheel signal
Frequency content in st. wheel signal
Baseline, fixed base simulator, motorway, joint sequence, all drivers
Baseline, fixed base simulator, rural straight, joint sequence, all drivers
Baseline, fixed base simulator, rural curved, joint sequence, all drivers
Baseline, moving base simulator, rural straight, joint sequence, all drivers
Baseline, moving base simulator, rural curved, joint sequence, all drivers
Baseline, field, motorway, joint sequence, all drivers
Task length frequency
Frequency content in st. wheel signal
Driver 1, fixed-base simulator, straight rural road
-1
10
0
1
10
10
Frequency (Hz)
Driver 5, fixed-base simulator, straight rural road
Baseline
Visual task
Task length frequency
1
10
0
10
-1
10
-2
10
-3
10
-4
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10 -2
10
-1
10
0
10
Frequency (Hz)
1
10
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Common types of lane keeping metrics
Focus
here
Position-based
TLC (Time-to-line-crosssing)
-based
e.g. standard
deviation of lane
position (SDLP)
e.g. proportion
of lane
exceedences
(LANEX)
e.g. mean of
TLC minima
(MN_TLC)
e.g. proportion
of TLC minima <
X s (PR_TLC)
Continuous
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Event-based
Non-normal distribution &
too few instances -> difficult
to use for task-based
evaluation
Less sensitive than
position-based metrics
and yield roughly similar
results -> no obvious
advantage for present
purposes
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(Modified) Standard deviation of lane position (SDLP) (1)
•
Operational definition (AIDE D2.2.5 – Östlund et al. 2006):
• ”Standard deviation of lateral position data, high-pass filtered with a cutoff frequency of 0.1 Hz, where lateral position is defined as the average
distance between the right side of the front or rear right wheel and the
inner (closest) edge of the right hand lane marking.”
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SDLP depenency on data duration
High-pass filtering needed to overcome this problem (Östlund et al., 2006)
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Representative results from HASTE on SDLP
VTEC simulator, rural road
0,6
BL
0,4
SLv1
0,3
SLv2
0,2
SLv3
0,1
0
Straight
Visual task
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Curve
st_lp (m)
st_lp (m )
0,5
0,4
0,35
0,3
0,25
0,2
0,15
0,1
0,05
0
BL
SLv1
SLv2
SLv3
Straight
Curve
Cognitive task
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(M)SDLP pros and cons
•
•
•
Advantages
•
•
Easy to measure, at least in the simulator (feasible also in the field
using off-the-shelf lane-tracking systems)
Straightforward general interpretation as performance metric
Disadvantages
•
•
•
Only moderately sensitivite to secondary task task load
Strongly sensitive to environment factors (e.g. curvature, lane width)
Sensitive to discontinuities due to lane changes and exceedences
Relation to crash data
•
Open issue – no strong direct evidence of causal relation between
increased SDLP and crash risk (however, indirect evidence via visual
distraction)
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Outline
•
•
•
Background of research (HASTE and AIDE)
Metrics
•
•
•
Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
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Humans System Integration
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Example data: Straight driving, rural road, VTEC simulator
Radialgaze
gaze(deg)
(deg)
St. wheel
wheel angle
angle (deg)
(deg) Radial
St. wheel angle (deg)
St. wheel angle (deg) Radial gaze (deg)
ht rural
road
Driver
7, straight
rural road
Driver
39, straight
straight
rural
road
Driver
39,
rural
road
Driver
7,
straight
rural
road
ht rural road
Baseline
Baseline
Baseline
Baseline
Baseline
Baseline
50 3030
50 30
40 30
40 20
30 20
20
30 20
20 1010
20 10
10 10
10
80130
100
120
140
760
80
100
760
780
120
130 780
140
44
5 5 44
222
0 0 000
4 4
5 5 44
2222
00 0000
-2
-2-2
-2
-2
-2-2
n (m)
-5-5
80130
100
80
100
120
140
760
780
760
120
130 780
140
(m)
Cognitive
task, level 3
Baseline
Baseline
Baseline
Baseline
Cognitive
task, level 3
50 3030
50 30
40 30
40 20
30 20
20
3020
20 1010
20 10
10 10
10
760
780 140
940
950
960
780
120
940 760
950 120
960 140
0.6 Technology
0.6
0.5
0.50.6 Volvo
0.4 Humans System Integration
-5-5
760
780
780
940
950
960
120
940 760
950 120
960
0.6
0.6
0.6
0.5
0.50.6
0.4
0.4
Cognitive
task,
level
2 3
Visual
task,
level
3level
Cognitive
task,
Cognitive
task,
level
Visual task,
level
323
Cognitive
task,
level
5030 30
50 30
4030
40 20
3020
302020
2010 10
20
101010
10
120
14
1000
1020
960
980
9601010
980
1000
1010120
1020 140
4 4
55 4 4
22 2 2
0000 0 0
-2
-2-2-2
-5
-5
120
1000
1020
140
9601010
980
140
960
980
1000
1010120
1020
0.6
0.6
0.6
0.6
0.5
0.5
0.4
0.4
14
140
0
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Metrics investigated in AIDE
•
•
•
•
Standard deviation of steering wheel angle
High frequency steering – 3 versions
Steering entropy – 2 versions (Boer, 2000;Boer, 2005)
Steering wheel reversal rate – 2 versions (HASTE version; Modified version
developed in AIDE, Markkula and Engström, 2006)
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Results in sensitivity (effect size) – visual load
Visual
All metrics fairly sensitive in all
conditions except
Standard Deviation
1.6
Standardised effect size
1.4
1.2
Fixed, mw
Fixed, rural, straight
1
Fixed, rural, curve
0.8
Moving, rural, straight
0.6
Moving, rural, curve
0.4
Field
0.2
St
an
da
r
d
de
via
Re
tio
n
ve
rs
al
ra
Re
te
1
ve
rs
al
ra
te
HF
2
st
ee
rin
g1
HF
st
ee
rin
g2
HF
st
ee
St
ee
rin
rin
g3
g
en
St
tr o
ee
py
rin
1
g
en
tr o
py
2
0
Markkula and Engström (2006)
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Results in sensitivity (effect size) – cognitive load
Cognitive
Reversal Rate2 and Steering
entropy most sensitive
1.4
Standardised effect size
1.2
1
Fixed, mw
Fixed, rural, straight
0.8
Fixed, rural, curve
Moving, rural, straight
0.6
Moving, rural, curve
Field
0.4
0.2
St
an
da
r
d
de
via
Re
tio
n
ve
rs
al
ra
Re
te
1
ve
rs
al
ra
te
HF
2
st
ee
rin
g1
HF
st
ee
rin
g2
HF
st
ee
St
ee
rin
rin
g3
g
en
St
tr o
ee
py
rin
1
g
en
tr o
py
2
0
Markkula and Engström (2006)
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Steering entropy (1)
•
•
Operational definition
•
Entropy of the prediction errors made
by a linear predictive filter applied on
the steering wheel angle signal (see
Boer 2005 for detailed mathematical
definition)
Interpretation
•
”…increase in high frequency
steering corrections that result after
periods of diverted or reduced
attention (i.e., in response to a
perceived vehicle drift outside the
acceptable tolerance margins that
mounted during these periods of
degraded information)” (Boer, 2005)
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Steering Entropy pros and cons
•
•
Advantages
•
•
•
Disadvantages
•
•
•
•
Strongly sensitive to visual and cognitive load in a range of conditions
SW data easy to measure, also in the field
Relatively robust to differences in driving environment (road type, curvature,
test set-up etc.)
•
•
Fairly complex to compute (though straightforward)
Somewhat difficult to interpret, even in terms of performance (increased SE
may indicate both increased and reduced lateral control)
Interpretation of free parameters (alpha and re-sampling rate) not entirely
straightforward
Requires baseline data for computation of task condition data
”Normalisation” to baseline data makes BL and Task data somewhat dependent
Relation to crash data
•
No established relation to crash data (only indirectly via visual distraction)
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Steering Wheel Reversal Rate (SRR)
•
Operational definition
•
The number, per minute, of
steering wheel reversals
larger than a certain
angular value referred to as
the gap size (see Markkula
& Engström, 2006, for
detailed mathematical
definition)
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Representative results from HASTE: SRR1, 1 degree gap
size
VTEC simulator, rural road
16
14
14
12
10
SLv1
8
SLv2
6
SLv3
4
BL
10
BL
rr_st1
rr_st1
12
8
6
SLv1
4
SLv3
SLv2
2
2
0
0
Straight
Curve
Visual task
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Event
Straight
Curve
Event
Cognitive task
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SRR Sensitivity (effect size) as a function of gap size
1.6
Visual, fixed, mw
1.4
Cognitive fixed mw
Standardised effect size (σ)
1.2
Visual, fixed, rural, straight
1
Cognitive, fixed, rural,
straight
Visual, fixed, rural, curve
0.8
Cognitive, fixed, rural, curve
0.6
Visual, field
0.4
Cognitive, field
Visual, moving, rural,
straight
Cognitive, moving, rural,
straight
Visual, moving, rural, curve
0.2
0
0.1
0.5
1
2
3
-0.2
Gap size (degrees)
-0.4
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4
5
10
Cognitive, moving, rural,
curve
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SRR pros and cons
•
•
•
Advantages
•
•
•
•
Strongly sensitive to visual and cognitive load in a range of conditions
SW data easy to measure, also in the field
Easier to interpret than steering entropy
Does not involve normalisation of task data to baseline data (like Steering
Entropy)
Disadvantages
•
•
Sensitive to environment factors
Somewhat difficult to interpret in terms of performance - increased SRR may
indicate both reduced and increased lane keeping performance (however, can
be tuned by changing gap-size)
Relation to crash data
•
Like other steering wheel metrics, no established relation to crash data (only
indirectly via visual distraction)
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Outline
•
•
•
Background of research (HASTE and AIDE)
Metrics
•
•
•
Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
Volvo Technology
Humans System Integration
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Example data
ition (m)
St. wheel angle (deg) Radial gaze (deg)
ition (m)
St. wheel angle (deg) Radial gaze (deg)
Driver
7, straight
ruralrural
roadroad
Driver
39,
straight
ht rural road
Baseline
Baseline
Baseline
50
30
40 30
30 20
20
20
10
10 10
50
30
4030
3020
20
20
10
1010
120
140
80 130 780
100
760
5 44
22
0 00
-5
Baseline
Baseline
Cognitive
task, level 3
940760 950120780
960
50
3030
40
30
2020
20
1010
10
140
5 44
22
0 00
-2-2
120
140
80 130 780
100
760
0.6
0.50.6
0.4 Volvo Technology
0.4
Humans System Integration
-2-2
-5
940760 950120780
960
0.6
0.6
0.5
0.4
0.4
Visual
task,
levellevel
3 3
Cognitive
task,
Cognitive task, level 2
1000
1020 140
120980
960 1010
54 4
22
00 0
140
-2-2
-5
1000
1020 140
120980
960 1010
0.6
0.6
0.5
0.4
0.4
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Outline
•
•
•
Background of research (HASTE and AIDE)
Metrics
•
•
•
Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
Volvo Technology
Humans System Integration
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Factors to account for
•
•
•
•
Total time spent looking away from the road
Intensity (”how much looking away per time untit”)
Distribution of single glance durations
Eccentricity
On-road
A
Off-road
B
On-road
Off-road
C
On-road
Off-road
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Traditional (ISO 15007) glance-based metrics
Measure
Definition
Glance frequency
The number of glances to a target within a pre-defined time
period, or during a pre-defined task, where each glance is
separated by at least one glance to a different target (ISO
15007).
Single glance duration
Time from the moment at which the direction of gaze
moves towards a target to the moment it moves away from
it (ISO 15007).
Mean single glance duration
The average duration of the glances towards a target.
Number of glances > 2 seconds
The number of glances towards the system with a duration
longer than 2 seconds.
Total glance time (towards a
target)
Total glance time (or percentage of time) associated with a
target (e.g. in-vehicle device).
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Automating the ISO 15007 metrics: The VDM Tool
(Larsson, 2002; Johansson et al., 2006)
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Difficulties with automating the ISO 15007 metrics
•
•
•
•
Standard originally intended for manual transcription
Glance-based metrics are very sensitive to noise
Requires careful calibration and signal pre-processing
Much data still needs to be discarded (~30% in HASTE)
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An alternative: Road centre-based metrics
Road Centre
On-road glances
Off-road glances
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Percent Road Centre (Victor, 2005)
•
Operational definition:
•
•
PRC-Task: The percent of fixations directed towards the road centre (RC)
during a task. Represents intensity only.
PRC-Window: The percent of fixations directed towards the RC during a
moving time window of 1 minute. If the task is shorter than 1 minute, the
remaining time is completed with a constant PRC of 80%. The windowing
adds a weighting for task duration.
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Example data from HASTE (Victor, Harbluk and Engström, 2005)
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Pros and cons of PRC
•
•
•
Advantages
• Very sensitive to visual task difficulty
• Allows for baseline data (which glance-based metrics to do not)
• Should be more robust to measurement noise (focus measurement
where eye tracking accuracy is normally best, data order does not
matter)
Disadvantages
• PRC-Task measures only intensity
• PRC-Window accounts for task duration, but somewhat arbitrarily
• Does not account for eccentricity
Relation to crash data
• Strong empirical evidence on the relation between visual diversion from
the forward road scene and accident risk (e.g. Wierwille and Tijerina,
1995; Klauer et al., 2006)
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Further ideas
•
•
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RC-based versions of the ISO metrics (Kronberg et al., 2006)
Other ways to account for both intensity and duration
Weighting function for single glance duration
Account for eccentricity
For example:
VD   g
i
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i
 E ( ,  )
gi=single off-road glance duration
E=eccentricity weighting function
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Outline
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•
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Background of research (HASTE and AIDE)
Metrics
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•
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Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
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Measuring gaze concentration: Standard deviation of gaze
angle
•
Operational definition
• The standard deviation of the
combined horizontal and vertical
angles. The combined angle is the
square root of the sum of squared
vertical and squared horizontal
angles (Pythagoras theorem) and
thus is a one-dimensional angle
between the origin and a gaze point
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Effects of cognitive task on gaze concentration
Gaze angles (pich and yaw)
Baseline
Cognitive task (levels 1-3 aggregated)
VTEC simulator, rural road
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Example data from HASTE (Victor, Harbluk and Engström,
2005)
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Pros and cons of SD gaze angle
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•
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Advantages
• Sensitive to cognitive load (more than PRC) – good metric of gaze
concentration
• Robust to noise since data order does not matter
Disadvantages
• Only applicable to assessment of purely cognitive load
Relation to crash data
• No empirical data on the relation between gaze concentration and
crash risk
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Outline
•
•
•
Background of research (HASTE and AIDE)
Metrics
•
•
•
Lane keeping
Steering
Eye movements
• Time sharing
• Gaze concentration
Conclusions, lessons learned and topics for further
research
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Conclusions and lessons learned
•
The metrics addressed here mainly relevant for evaluating visually
demanding tasks
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•
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Lateral control performance metrics somewhat problematic as surrogate safety metrics –
no clear link to crash data
Direct eye movement metrics seem to be the most promising (though still practical
difficulties with data collection and analysis)
For cognitive tasks, other metrics are needed to capture the main safetyrelevant effects (e.g. detection task metrics such as PDT)
Lack of agreed driver model – very little consensus on how to interpret
even the most common driving performance metrics
Little discussion and emprical work on the link between performance
metrics and safety (especially in Europe)
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Topics for further research
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Development of metrics representing sensory-motor coordination (e.g. correlation
between steering and eye movements)
More comprehensive visual demand metrics, taking into account both duration,
intensity and eccentricity
Establish relation between different performance metrics and crashes (using data
from naturalistic field studies) -> valid criteria for IVIS safety evaluation and ADAS
safety benefits analyses
Investigate how to incorporate exposure data (frequency of use) into the IVIS
evaluation methods (e.g. in a general formula for visual demand exposure)
Establish stronger theoretical foundation for driving performance assessment
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Multiple resource theory (Wickens, 2002) does not explain all variance in the data (e.g.
driver adaptation and effects of cognitive load in terms of gaze concentration, and
”improved” lane keeping)
Incorporate modern perception and attention (”active vision”) theories into driving
research (see Victor, 2005)
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References
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Engström, J., Johansson, E and Östlund, J. (2005). Effects of visual and cognitive load in real and
simulated motorway driving. Transportation Research Part F, pp. 97-120.
HASTE special issue (contains all papers from the WP2 studies): Transportation Research Part F: Traffic
Psychology and Behaviour, Volume 8, Issue 2
Johansson, E., Engström, J., Cherri, C., Nodari, E., Toffetti, A., Schindhelm, R., Gelau, C. (2004) Review of
existing techniques and metrics for IVIS and ADAS assessment. EU project AIDE, project IST-1-507674-IP,
Deliverable 2.2.1
Johansson, E., Kronberg, P., Victor, T., Martens, M., Chin, E. and Nathan, F. (2006). Visual demand
measurement tool development. AIDE Deliverable 2.2.2. EC contract No. IST-1-507674-IP.
Kronberg, P., Victor, T. and Engström, J. (2006). Road-centre-based measures of visual demand. Vision in
Vehicles, Dublin.
Larsson, P. (2002). Automatic Visual Behavior Analysis. Dissertation for a Master of Science Degree
Applied Physics and Electrical Engineering Control and Communication Department of electrical
engineering Linköping University, Sweden. LiTH-ISY-EX-3259.
Markkula, G. and Engström, J. In press. A Steering Wheel Reversal Rate Metric for Assessing Effects of
Visual and Cognitive Secondary Task Load. ITS World Congress, London 2006.
Östlund, J., Peters, B., Thorslund, B., Engström, J., Markkula, G., Keinath, A., Horst, D., Mattes, S. Foehl,
U. 2005. Driving performance assessment: Methods and metrics. AIDE Deliverable 2.2.5. European
Commission, IST-1-507674-IP.
Östlund, J. Carsten, O., Merat, N., Jamson, S., Janssen, W., & Brouwer, R., et al. (2004). Deliverable 2—
HMI and safety-related driver performance. Human Machine Interface And the Safety of Traffic in Europe
(HASTE) Project. Report No. GRD1/2000/25361 S12.319626.
Victor TW (2005) Keeping eye and mind on the road. PhD Thesis, Uppsala University, Sweden.
Victor, T. W., Harbluk, J. L. & Engström, J. (2005). Sensitivity of eye-movement measures to in-vehicle task
difficulty. Transportation Research Part F 8:167-190
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