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15|51 • 01-09-16 • 1
Program
14:00 Richard van der Weide, Intergo 14:15 Karel Brookhuis, Universiteit Groningen 14:45 Sarah Wiseman, University of London 15:15 Break 15:45 Chris Jansen, Universiteit Utrecht 16:15 Marc Woods, Mindhacker
Stella Donker, Universiteit Utrecht 17:00 Drinks
15|51 • 01-09-16 • 2
Marit Wilms
Richard vd Weide
Melcher Zeilstra
Diana Went
Gert-Jan Kamps
Kirsten Schreibers Fenneke Blommers
David de Bruijn
Alfred v Wincoop Henk Frieling
Colete Weeda
Deconducteurvandetoekomst
Program
14:00 Richard van der Weide, Intergo 14:15 Karel Brookhuis, Universiteit Groningen 14:45 Sarah Wiseman, University of London 15:15 Break 15:45 Chris Jansen, Universiteit Utrecht 16:15 Marc Woods, Mindhacker
Stella Donker, Universiteit Utrecht 17:00 Drinks
15|51 • 01-09-16 • 14
1
Karel Brookhuis (Traffic Psychology)
Traffic Psychology Group, Dept. Neuropsychology
Faculty Behavioural & Social Sciences
University of Groningen (Delft University of Technology) Colleagues: Dick de Waard, Janet Veldstra, Anselm Fürmaier, Arjan Stuiver
Human Factors in traffic Rijksuniversiteit Groningen
Faculty of Behavioural & Social Sciences Department of Psychology
Experimental & Work Psychology
Traffic in 1900
1900 Seat belt Patent by Gustave-Desire Levau
Experimental & Work Psychology
The rationale of Traffic Psychology
Accidents at least 95% “Human Factor” The driver makes errors. is not alert, distracted, tired, etc.
Experimental & Work Psychology
Development of fatal accidents in traffic in the Netherlands (SWOV)
Jaar1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 20000
500
1000
1500
2000
2500
3000
3500
0
1
2
3
4
5
6
1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
mln
05 September 2016 5
Research on in-vehicle ICT 25 years of driver support
• European projects, off 1989 • GIDS (Generic Intelligent Driver Support System) • AWAKE (Drowsiness warning)
• Dutch national initiatives (Ministry) • ISA (Intelligent Speed Assistance) • Belonitor (Rewarding good i.s.o. Punishing bad) • PAYD (Pay As You Drive, with Insurance cies)
• National Research Council (NWO) programs • PITA (Personal Intelligent Travel Assistant) • Amici (Advanced Multi-agent Information and Control for
Integrated traffic networks) • MDPIT (Multidisciplinary pricing policy)
05 September 2016 6
25 years of research on effects of driver support & information supply, EU start: Generic Intelligent Driver Support (European Framework, DRIVE 1989 - 1991)
• Philips (CARIN) & Bosch navigation systems • RDS – TMC (Radio Data System – Traffic
Message Channel)
• Simulator studies (VSC/TRC, Groningen) • Instrumented Vehicle (TNO, Soesterberg)
05 September 2016 7
GIDS: (early, 1990) Carin navigation system
05 September 2016 8
( GIDS in 1989 … simulator 2010)
05 September 2016 9
Results EU studies, GIDS … AutoVeh • Problems: attention, distraction, alertness • Accident likelihood:
• 2 a 2.5 second rule (within car) • 4 second rule (outside car) • 15 second rule (total processing time)
• Combination auditory and visual information • Improved route guidance systems
• GIDS book, Michon et al., T&F 1993 • Guidelines travel / traffic information
• HASTE Special Issue TR / Part F • In-Safety (Book, 2010) • Special Issue TR/F (History Traffic Psychology, 2015)
05 September 2016 10
Intelligent Speed Assistant ISA (1995, in advisory version)
05 September 2016 11
Intelligent Speed Assistant ISA (1995, in advisory version)
Results of Feedback
C
C
ISA
ISA
Compound opinions on ISA
-3 -2 -1 0 1 2
Pleasurable OLD
Practical OLD
Pleasurable YOUNG
Practical YOUNG
BeforeAfter
05 September 2016 15
ISA effects: 36% less injury accidents, up to 59% less fatal accidents
Longitudinal effects: wears off, but not completely
12 maart 2008 Symposium Synergie in Mobiliteit, Universiteit Twente
Ministry (NL) funded project
New concept: reward i.s.o. punish
• distance: 1.3 s
• local speed limit
• 62 lease-car drivers
• 4 weeks baseline
• 16 weeks test
• 4 weeks baseline
• subjective and objective measurements
Belonitor – Rewarding safe driving behaviour
12 maart 2008 Symposium Synergie in Mobiliteit, Universiteit Twente
Earned credits
Belonitor – Rewarding safe driving behaviour
12 maart 2008 Symposium Synergie in Mobiliteit, Universiteit Twente
Bad behaviour
Belonitor – Rewarding safe driving behaviour
12 maart 2008 Symposium Synergie in Mobiliteit, Universiteit Twente
Belonitor – Rewarding safe driving behaviour
Good behaviour
12 maart 2008 Symposium Synergie in Mobiliteit, Universiteit Twente
Belonitor - Conclusions General Attitude: 68% positive about system, 75% improved behaviour
Traffic Safety • Estimated reduction lethal & severely injured 15% • Estimated reduction injured 9% Efficiency and Environmental • reduction accidents 10% – traffic jams 1.2%
• reduction fule use 5.5 %
Bron: MinVenW, 2006
12 maart 2008 Symposium Synergie in Mobiliteit, Universiteit Twente
USA 2013: statistics indicates a 4% to 5% increase in accident involvement
Side effects of Lane Departure Warning
05 September 2016 22
05 September 2016 23
2006: RouteLint HSM (Railway-Project Het Spoor Meester)
05 September 2016 24
Attention for action → Distraction
Multitasking ↔ Distraction
Multitasking ↔ Distraction
Thanks to Peter Hancock
27 Driver distraction and inattention in its various forms is thought to play a role in 20-30% of all road
crashes (Wang, Knipling & Goodman, 1996).
05 September 2016 28
Future ?? (Google stopped !!) =>ICT in traffic
http://www.youtube.com/watch?v=V8ofTlynWPo&feature=player_detailpage
So, no more human drivers ? Automated Vehicle !
Opleiding Psychologie/Faculteit gedrags- en
maatschappijwetenschappen
Summary of Levels of Driving Automation for On-Road Vehicles This table summarizes SAE International’s levels of driving automation for on-road vehicles. Information Report J3016 provides full definitions for these levels and for the italicized terms used therein. The levels are descriptive rather than no rmative and technical rather than legal. Elements indicate minimum rather than maximum capabilities for each level.
“System" refers to the driver assistance system, combination of driver assistance systems, or automated driving system, as appropriate.
The table also shows how SAE’s levels definitively correspond to those developed by the Germany Federal Highway Research Institute (BASt) and approximately correspond to those described by the US National Highway Traffic Safety Administration (NHTSA) in its “Pre liminary Statement of Policy Concerning Automated Vehicles” of May 30, 2013.
Lev
el
Name Narrative definition
Execution of steering and acceleration/ deceleration
Monitoring of driving
environment
Fallback performance of dynamic driving task
System capability (driving modes) B
AS
t le
vel
NH
TS
A
leve
l
Human driver monitors the driving environment
0 No Automation
the full-time performance by the human driver of all aspects of the dynamic driving task, even when enhanced by warning or intervention systems Human driver Human driver Human driver n/a
Driv
er
on
ly
0
1 Driver Assistance
the driving mode-specific execution by a driver assistance system of either steering or acceleration/decelerati on using information about the driving environment and with the expectation that the human driver perform all remaining aspects of the
dynamic driving task
Human driver and system Human driver Human driver
Some driving modes A
ssis
ted
1
2 Partial Automation
the driving mode-specific execution by one or more driver assistance systems of both steering and acceleration/deceleration using information about the driving environment
and with the expectation that the human driver perform all remaining aspects of the dynamic driving task
System Human driver Human driverSome driving modes P
art
ially
a
uto
mate
d
2
Automated driving system (“system”) monitors the driving environment
3 Conditional Automation
the driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task with the expectation that the human driver will respond
appropriately to a request to intervene System System Human driver
Some driving modes H
ighl
y a
uto
mate
d
3
4 High Automation
the driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a
request to intervene System System System
Some driving modes
Fully
a
uto
mat
ed
3/4
5 Full Automation
the full-time performance by an automated driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a
human driver System System System All driving
modes
-
Source: SAE Standard J3016 Report
Summary of Levels of Driving Automation for On-road Vehicles
› Simulator experiment in Groningen • Drivers of Hermes and Arriva companies • Trajectory Eindhoven CS to Eindhoven Airport
Automating Public Transport SAE level 2
Phileas (APTS bv)
› HOV in Eindhoven • Hybrid system and control: (half)automatic and normal • Phileas (Automatic Public Transport System)
› Simulator experiment in Groningen, • Bus drivers of Hermes and Arriva • Track Eindhoven CS to Eindhoven Airport
Automating Public Transport 1996 (Hoogwaardig Openbaar Vervoer)
Route
Incident 1: vehicle blocks the road
o Braking in time
o Bus back to non- or semi-automatic
Incident 2: a cyclist runs red light
o 1st time: 72% okay, but 28 % not !!!
o 2nd time: 100% okay !
Incident 1: vehicle blocks the road
o Braking in time
o Bus back to non- or semi-automatic
Experiment in driving simulator SAE level 3
Autonomous driving
Emergency situation on automated highway
Reactions to the unexpected event
Reactions Time slot Proportion participants No reaction - f 50 % Braked Late > 14 s 15 % Braked Not fast 9-14 s 30 % Braked Early < 9 s 5 %
Attitude towards fully automated system Before After › Usefulness 0.53 0.38 › Satisfying –0.31 –0.36
The road towards fully autonomous vehicles: via SAE levels 2 and 3 ??
Perhaps Chris Janssen will tell us
Mental Model, evidence form another field…. (thanks to Dietrich Manzey) According to Malinge (2011), the reduction of the annual accident rate of fourth generation airlines has stagnated, notwithstanding the inherent effectiveness. Although automated systems have evolved to reduce pilot workload, recent concerns exist about their safety effectiveness. Automation introduces a paradox: providing crews with necessary operational assistance simultaneously dissociates the crew from those operations. Unfortunately, this shift in pilot tasking exhibits itself in many forms of adverse crew behavior such as automation induced complacency (Manzey, Reichenbach & Onnasch, 2012), automation bias (Mosier, Skitka, Heers & Burdick, 1998), decision making errors (Orasanu, Martin & Davison, 1998), lack of (procedural and declarative) system knowledge and/or manual control skills (Potter et al., 2012), which are all in turn aggravated by overconfidence (Wood, 2004) and fatigue (Caldwell, 2012). These behaviors may contribute to the loss of situational awareness (SA).
18 March 2016 47 www.its.leeds.ac.uk
Survey: Safety and Priority?
So, automation… ?
When, how, where, who ?
Kyriakidis et al. “A Human Factors perspective on Automated Driving ” (i.e. European perspective), in preparation
Program
14:00 Richard van der Weide, Intergo 14:15 Karel Brookhuis, Universiteit Groningen 14:45 Sarah Wiseman, University of London 15:15 Break 15:45 Chris Jansen, Universiteit Utrecht 16:15 Marc Woods, Mindhacker
Stella Donker, Universiteit Utrecht 17:00 Drinks
15|51 • 01-09-16 • 64
Everything you always wanted to know about
numbers, but were afraid to ask
Dr Sarah WisemanGoldsmiths, University of London
Who am I? • Currently: !
• Research and teaching fellow at Goldsmiths!
• Public speaker on HCI and HF!
• In the past:!
• HCI PhD looking at number entry interfaces!
Who am I? • Currently: !
• Research and teaching fellow at Goldsmiths!
• Public speaker on HCI and HF !
• In the past:!
• HCI PhD looking at number entry interfaces!
• Daily Mail accredited “scientist” !
Why do you care about numbers?
Newtons Pounds per square inch
Newtons Pounds per square inch
x!
x!
x!x!
Number entry interfaces in the hospital
Electronic patient !records!
Infusion pump !devices!
Radiography / X-ray !equipment!
Why are number entry interfaces designed
like that?
7 8 9
4 5 6
1 2 3
0
1 2 3
4 5 6
7 8 9
0
Telephone Calculator
Expectations
all day long
55%
8%
7%
55%
But why are they laid out in a grid like
that?
Reality
“offered certain engineering advantages”
Desires
Can we improve the standard number entry interface?
Improving text entry
Improving text entry
URL email
Ok before we improve anything, what’s the most
common number?
Benford’s Law
hUp://www.thecleverest.com/benfords_law.html
When does Benford apply? “Naturally occurring cumulative numbers”
Number of twitter followers Length of rivers
Electricity bills First 652066 Fibonacci Numbers
When does Benford apply?
When does it not apply?
“Naturally occurring cumulative numbers”
Number of twitter followers Length of rivers
Electricity bills First 652066 Fibonacci Numbers
Lottery numbers
When does Benford apply?
When does it not apply?
“Naturally occurring cumulative numbers”
Number of twitter followers Length of rivers
Electricity bills First 652066 Fibonacci Numbers
Lottery numbers
Fraudulent invoices/expenses
When does Benford apply?
When does it not apply?
“Naturally occurring cumulative numbers”
Number of twitter followers Length of rivers
Electricity bills First 652066 Fibonacci Numbers
Lottery numbers
Fraudulent invoices/expenses
When does Benford apply?
When does it not apply?
“Naturally occurring cumulative numbers”
Number of twitter followers Length of rivers
Electricity bills First 652066 Fibonacci Numbers
Lottery numbers
Fraudulent invoices/expenses
Drug dosages in hospitals
Numbers used in the hospital
100, 5.5, 300, 1000, 750, 33.33, 999, 12.25, 150, 200, 40, 600…
Numbers used in the hospital
Numbers used in the hospital
Redesigning the interface
Redesigning the interface
Redesigning the interface
Significantly faster
No increase in error rate
Redesigning the interface
Significantly faster
No increase in error rate
✔
Is the interface design all there is to think about?
(You are a very smart audience)
Number entry is a 3-step process
1. Read the number
2. Memorise the number
3. Type the number
Number entry is a 3-step process
1. Read the number
2. Memorise the number
3. Type the number
Sarah Wiseman www.swiseman.co.uk
@oopsohno [email protected]
Program
14:00 Richard van der Weide, Intergo 14:15 Karel Brookhuis, Universiteit Groningen 14:45 Sarah Wiseman, University of London 15:15 Break 15:45 Chris Jansen, Universiteit Utrecht 16:15 Marc Woods, Mindhacker
Stella Donker, Universiteit Utrecht 17:00 Drinks
15|51 • 01-09-16 • 154
9/5/16&
1&
Automation and HCI: A case study of (semi-) autonomous car
Chris Janssen [email protected] www.cpjanssen.nl
H2020-MSCA-IF-2015, 705010, 'Detect and React'
http://jalopnik.com/teslas-autopilot-system-is-awesome-and-creepy-and-a-sig-1736573089
9/5/16&
2&
(semi-) Autonomous cars: Challenge? SAE level
Name Description
Human driver monitors driving environment
0 No Automation
1 Driver Assistance
2 Partial Automation
Automatic driving system monitors driving system
3 Conditional Automation
4 High Automation
5 Full Automation
SAE International (2014)
Designing for automated systems
Linda Boyle U Washington
Andrew Kun U New Hampshire
Lewis Chuang Max Planck
Wendy Ju Stanford
Initiated at Dagstuhl 16262
Driving requires varied behavior..
Strategic
Maneuvering
Control
After Michon (1985)
Lane position detection
Traffic detection
Change lanes
Driving requires varied behavior..
Lane position detection
Traffic detection
Change lanes ✔
✔
✔
9/5/16&
3&
Non-automated car
Change lanes Traffic detection Lane detection
Fully automated car (e.g., “ideal” google car)
Change lanes Traffic detection Lane detection
Change lanes Traffic detection Lane detection
Change lanes Traffic detection Lane detection
Change lanes Traffic detection Lane detection
Partial automated car (e.g. “Tesla”)
Change lanes Traffic detection Lane detection
Change lanes Traffic detection Lane detection
Change lanes Traffic detection Lane detection
Change lanes Traffic detection Lane detection
Potential dangers / issues • Transitions:
- How to inform human about transition? - How does driver know which state they
are in? - How to inform and enforce a shift in
responsibility?
9/5/16&
4&
Broader context: automation How do we.. • Communicate change? • Design for shared responsibility? • Assess system state? • Assess human state?
Example studies: asses human state
1. ‘Looking at the eyes’
2. ‘Sensing in the brain’
1. ‘Looking at the eyes’
Hidde van der Meulen MSc AI Utrecht
Andrew Kun U New Hampshire
Van der Meulen, Kun, Janssen (2016) Proceedings ACM AutoUI
Set-up
9/5/16&
5&
Eye-gaze
Figure 4. Top-figure: The percentage of looking at the road center (PRC) as a function of time. Bottom-figure: the average driving
speed over time. In both figures, the vertical dashed line shows the moment in time where both conditions have the same speed, with the standard deviation displayed as vertical grey bars. The error bars and ribbon show the standard error values of each
metric.
We used a 24 (time after take-over: buckets of 5s) x 2 (situation: parking or autonomous) within-subjects ANOVA to determine the effect of time and condition on the PRC. The time after take-over had a significant influence on the PRC with F(1, 23) = 3.73, p < .001, there was no significant effect for driving type with F(1, 1) = 0.93, p = .542 and no significant interaction effect between time after take-over and the type of driving with F(1, 23) = 0.55, p = .954. As Figure 4 shows, the PRC gradually increases in the first 15 to 25 seconds and then stabilizes
Percent dwell time Figure 5 plots the PDT score for the autonomous driving condition (dark grey bars) and the parking condition (light grey bars) for the 100 seconds before taking over control (left two bars) and 2 minutes after taking over control (right two bars). A 2 (timing: before, after take-over) x 2 (situation: parking or autonomous) within-subjects ANOVA revealed that there was a main effect of timing, such that drivers looked more at the road after taking over, F(1, 15) = 78.75, p < .001. There was also a main effect of driving situation, F(1, 15) = 7.39, p = .016. This was influenced by an interaction effect, F(1, 15) = 7.48, p = .015.
As Figure 5 shows, the interaction was such that before the take-over, the participants looked almost twice as often at the road in the autonomous driving condition (M = 48%, SD = 9.4%) compared to the parking condition (M = 27%, SD = 6.22%), whereas after take-over both groups spent roughly a
similar amount of time gazing at the road (in both conditions M = 99%, SD = 0.5%).
Figure 5. Percentage of time spent looking at the road before
and after the take-over for both autonomous driving and parking conditions.
% g
aze
to tr
affic
sce
nario
2. ‘Sensing the brain’
Remo vd Heiden Promovendus
Leon Kenemans Chantal Merkx Rijkswaterstaat
Stella Donker
H2020-MSCA-IF-2015, 705010, 'Detect and React'
Data: BSc thesis Lotte Hardeman & Keri Mans
9/5/16&
6&
0 500 1000 1500
-50
510
P3a at FCz (Novel - Standard)
Time (ms)
Am
plitu
de (m
icro
Vol
t)
StationaryAutonomousDriving
(Active)
Preliminary results Do not cite
0 500 1000 1500
-50
510
P3a at FCz (Novel - Standard)
Time (ms)
Am
plitu
de (m
icro
Vol
t)
StationaryAutonomousDriving
(Passive)
Preliminary results Do not cite
Broader context: automation How do we.. • Communicate change? • Design for shared responsibility? • Assess system state? • Assess human state?
Program
14:00 Richard van der Weide, Intergo 14:15 Karel Brookhuis, Universiteit Groningen 14:45 Sarah Wiseman, University of London 15:15 Break 15:45 Chris Jansen, Universiteit Utrecht 16:15 Marc Woods, Mindhacker
Stella Donker, Universiteit Utrecht 17:00 Drinks
15|51 • 01-09-16 • 188
190
Why does “mindhacking” work?
191
Mindhacking in the “real” world
192
Mindhacking in the “real” world
Why does mindhacking work?
193
PercepFonislearned:BrainislazyPaUernrecogniFonEfficienttojumptoconclusions
DirecFngaUenFon
In/uitcheckenAmstelstaFon
DirecFngaUenFonin the “real” world
IncheckenamstelstaFon
DirecFngaUenFonin the “real” world
DirecFngaUenFonin the “real” world
Failureofawareness:theinformaFonistherebut(some)peopledonotseeit
198
Failure of awareness
199
Failure of awareness
Lookedbutfailedtosee(SMIDSY)
“Sorry mate, didn’t see you”
Failure of awareness
TheinformaFonisthere,butsFll(some)peopledonotseeit
Failure of awareness
Mindhackingisafact
203
Mindhacking:FactorFicFon?
204
Mindhacking:FactorFicFon?
istheresultofthemagnificantpowerofthebrain.
ThefactthatamindhackercanmakeficFonfeelreal,
Program
14:00 Richard van der Weide, Intergo 14:15 Karel Brookhuis, Universiteit Groningen 14:45 Sarah Wiseman, University of London 15:15 Break 15:45 Chris Jansen, Universiteit Utrecht 16:15 Marc Woods, Mindhacker
Stella Donker, Universiteit Utrecht 17:00 Drinks
15|51 • 01-09-16 • 206