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DRIVER DISTRACTION DETECTION AND EVALUATION METHOD WITH COMPUTATIONAL INTELLIGENCE ALGORITHMS
Andrei Aksjonov1;2, Pavel
Nedoma1, Valery Vodovozov2,
Eduard Petlenkov2, Martin
Herrmann3
1 ŠKODA AUTO a.s., Mladá Boleslav,
Czech Republic.
2 Tallinn University of Technology,
Tallinn, Estonia.
3 IPG Automotive GmbH, Karlsruhe,
Germany.This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
ITEAM project
Motivation
Driver Distraction Detection and Evaluation Method
Data Collection
Case Study Results
Conclusions
Content
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
2
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
InTErdisciplinary trAining network in Multi-actuated ground vehicles (ITEAM): Consortium
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
https://iteam-project.net/
3
9 European countries
8 Partner organizations
11 Beneficiaries7 Nonacademic organizations
10 Universities
2 Research centers
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Motivation: Traffic accident fatalities
Sources:• NHTSA Foundation• European Commission, Directorate General for
Mobility and Transportation
4
USA
37 461
326 mln
EU
25 600
513 mln
Germany
3 154
83 mlnPopulation
Fatalities
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Motivation: Driver distraction (DD)
5
18% (4 698 deaths)of all road accidents with fatal outcomes are due to
driver distraction in EU alone
The aim of current research istraffic safety improvement viadetection and furtherminimization of driver distractioninduced by in-vehicle informationsystem applying computationalintelligence methods;
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Motivation: DD definition
6
Source: http://www.marycuryinsurance.com
“Driver distraction occurs when a driver is delayed in the recognition of information needed to safety accomplish the driving task because some event, activity, object or person within or outside the vehicle compelled or tended to induce the driver’s shifting attention away from the driving task” – J.R. Treat.[1]
“Driver distraction is the diversion of attention away from activities critical for safe driving towards a competing activity” – M.A. Regan.[2]
[1] Young, K.; Regan, M.; Hammer, M. Driver Distraction: A Review of Literature. Distracted Driver, Sydney, NSW: Australian College of Road Safety, 2007
[2] Regan, M.A.; Lee, J.D.; Young, K.L. Driver Distraction: Theory, Effects, and Mitigation. CRC Press Taylor & Francis Group, Boca Raton, FL, USA, pp. 31-40
Source: http://www.aaafoundationng.org
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Motivation: Sources of DD
7
Internal
External
Cell phone Eating and drinking Reading and writing Media and navigation
Accidents Wildlife Pedestrians Bicyclists
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Motivation: Forms of DD
8
“Taking ears off the road”
Visual Auditory Biomechanical Cognitive
“Taking eyes off the road”
“Taking hands off the road”
“Taking mind off the road”
9 IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
Performance-based
Psychological
Behavioral
Subjective
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Motivation: Evaluation methods
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
DD Detection and Evaluation Method: Performance-based measures
10
t0
t1
+
eΔveΔx
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
11
Current Method: Higher sensitivity to low distractions
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
DD Detection and Evaluation Method
12
Per
form
an
ce
erro
r
Per
form
an
ce
pre
dic
tio
n
Lane keeping
offset (Δx)
Vehicle speed
deviation (Δv)
Speed
limit (vl)
Curve
radius (cr)
Δvp
Δxp
eΔv
Fuzzy logic
reasoning
(Evaluator)
Level of
distraction
(DD)
Machine
learning
driver
model
(Predictor)
Error
calculation
vlt; cr
t; cdt
Δvt; Δxt
Preprocessed
training data
Curve
direction (cd)
eΔx
Information
about the
environment
Distracted driving
DD
eva
lua
tio
n
Non
-dis
tract
ed
dri
ving
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
13
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Data Collection: Participants
14
18 participantsExperience є [1 21]
• Mean 11
Age є [24 39]
• Mean 30
72.2%male
27.8%female
61.1%< 30
38.9%≥ 30
50.0%< 11
50.0%≥ 11
Special thanks to the volunteers from IPG Automotive GmbH (Karlsruhe, Germany) for participating in the driver-in-the-loop experiments.
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Data Collection: Apparatus
15
• Gas pedal
• Break pedal
• Steering wheel
• 2 LCDs
System Experience Platform driving simulator
• Real-time driver-in-the-loop simulation
• Head-up display
• MATLAB®/Simulink®
integration with IPG CarMaker®
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Data Collection: Procedure
16
50
Length 10626 m2 linesLine width 3.5 m
30
90
• Sample frequency 50Hz (0.02 s sample period)
• Free run trial• 2 laps free run• 1 lap driver distraction
with smartphone chat
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Case Study Results: Driver performance prediction by machine learning
17
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Case Study Results: Driver performance prediction vs real driving performance
18
Δxp
Δvp
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Case Study Results: Resultative drive performance
19
Δxr
Δvr
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Case Study Results: DD evaluation
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A. Aksjonov, P. Nedoma, V. Vodovozov, E. Petlenkov, and M. Herrmann, “Detection and evaluation of driver distraction using machine learning and fuzzy logic,” IEEE Transactions on Intelligent Transportation Systems, 2018.DOI: 1−12.10.1109/TITS.2018.2857222.
DD
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
A novel DD detection and evaluation methodology based oncomputational intelligence algorithms (i.e. machine learning and fuzzylogic combination) is developed, which includes:
• a model of normal driving (machine learning);
• a subsystem for measuring the errors from the secondary tasks;
• a module for total distraction evaluation (fuzzy logic).
An appropriate software (i.e. MATLAB) and hardware (i.e. IPG SystemExperience Platform driving simulator) set ups are proposed.
The method is verified in the driver-in-the-loop test with 18participants with an interaction with cellular phone as a secondaryactivity.
Conclusion
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
21
IPG Automotive GmbH: Apply and Innovate - Karlsruhe, Germany - 12.09.2018
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
Questions
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THANK YOU FOR YOUR ATTENTION
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675999.
CONTACT INFO:
Andrei Aksjonov, MSc
ŠKODA AUTO a.s.,
Tř. Václava Klementa 869,
293 01 Mladá Boleslav II,
Czech Republic
+420 734 249 830,
www.skoda-auto.com