research - chengxi li
Post on 23-Feb-2017
101 Views
Preview:
TRANSCRIPT
EXAMINE DRIVER MENTAL WORKLOAD USING EXTREME LEARNING MACHINE.
Mentor: Prof. Yan YangReporter: Chengxi Li
BACKGROUND.ADVANCED DRIVER ASSISTANCE SYSTEM.
• Detectandclassifydriverdistractioninreal-time
• Adaptivein-vehiclesystemsHOWTOEVALUATE
ANDDESIGN?In-vehicle
InformationSystems(IVIS)
SafetyconcernDriverdistraction
Images: http://www.automotiveworld.com/news-releases/continental-to-showcase-digital-companion-technologies-during-ces-2013/
PublicationCX.Li and Y.Yang. Mental Workload of Young Drivers duringCurve Negotiation. IEEE International Conference on ConnectedVehicles & Expo (ICCVE 2014).
Index: IEEE Xplore®/ EI/ INSPEC/ ISTP/ ISI etc.
Previous work
BACKGROUND.ADVANCED DRIVER ASSISTANCE SYSTEM.
MOTIVATION.ADVANCED DRIVER ASSISTANCE SYSTEM.
Purpose: Advanced Driving Assistant System (ADAS)
Monitor in real-time
Fig. Driver Information Processing And Attention
VEHICLEDrive performance dataATTENTIONECG data
ATTENTION
TASK -> WORKLOADN-back [1]
Sound counting task [2]
[1] Owen, A.M., et al., N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human brain mapping, 2005. 25(1): p. 46-59.[2] Healey, J.A. and R.W. Picard, Detecting stress during real-world driving tasks using physiological sensors. Intelligent Transportation Systems, IEEE Transactions on, 2005. 6(2): p. 156-166.[3] Huang, G.-B., Q.-Y. Zhu, and C.-K. Siew, Extreme learning machine: theory and applications. Neurocomputing, 2006. 70(1): p. 489-501 0925-2312.
REAL-TIME MONITORMachine learning – ELM [3]
Fig. Apparatus
• Experiment Design: 2*2*3
• Scenario: driving with secondary task- Primary task: simple highway (straight vs. curve )- Secondary task: N-back & Sound counting task (3 levels)
• Participants: 40 male drivers• Data sources: ECG & driving
performance
• Methods: Extreme Learning Machine (ELM)
Image: http://medcitynews.com/2012/05/continuous-monitoring-device-aims-for-increased-user-comfort/
Fig. Secondary Task Difficulty Fig. Secondary Task Accuracy
EXPERIMENT OVERVIEW.ADVANCED DRIVER ASSISTANCE SYSTEM.
SIMULATOR STUDY.EXTREME LEARNING MACHINE.
Extreme Learning Machine (ELM) [1]
•a training algorithm for single-hidden layer feed-forward neural networks (SLFNs)•the input weights and hidden layer bias are randomly set and need not to be tuned
Th1. If the number of hidden layer neurons was equal to training samples, SLFN can approximate the training samples for any w and b with zero training error .Th2. If the number of hidden layer neurons was less than training samples, SLFN can approximate the training samples with ε>0training error .
[1] Huang, G.-B., Q.-Y. Zhu, and C.-K. Siew, Extreme learning machine: theory and applications. Neurocomputing, 2006. 70(1): p. 489-501 0925-2312.
Fig. Extreme Learning Mach network
SIMULATOR STUDY.ESTABLISHMENT OF MODELS.Objective: To establish drivers' mental recognition model
•Featureselection•Outlierdetection
EstablishingFeatureSpace
•ELM•ELM-Kernel•SVM
EstablishingModels •Classification
accuracy•ComputationalCosts
EvaluatingPerformance
Driving performance : – Speed– Steering Wheel Angle– Accelerate– Brake
ECG measurement:– MeanIBI– SDNN– MeanHR– SDHR– RMSSD– VLF– LF– HF
Image: http://www.reddit.com/comments/28xmvphttp://research.vet.upenn.edu/smallanimalcardiology/ECGTutorial/tabid/4930/Default.aspx
SIMULATOR STUDY.FEATURE SELECTION.
Task EntropyBaseline 0.7084291-Back 0.8159842-Back 0.953102
Fig. Steering Wheel Angle Without Secondary Task
Fig. Steering Wheel Angle With Secondary Task
( ) ( 1) ( ( 1) ( 2))1/ 2(( ( 1) ( 2)) ( ( 2) ( 3)))p n n n n
n n n nθ θ θ θ
θ θ θ θ
= − + − − −
+ − − − − − − −
9log , ( 1,...,9)i iHp P P i= − =∑
FIG: Deviation Calculation Between Angle Of Steering Wheel Angle And Actual Value
1.
2.
TABLE. THE ENTROPY OF THE STEERING WHEEL ANGLE UNDER DIFFERENT SECONDARY TASKS
SIMULATOR STUDY.ENTROPY ANALYSIS.
Outliers detection: T test
1 2, ,......, nx x x maxx minx
( )m pT T n>
( ) ( )21p pnT n t nn
= −−
''
mm
x xT
s−
=
( ) ( )21p pnT n t nn
= −−
For and
If
Then,
and
SIMULATOR STUDY.OUTLIERS DETECTION.
Road type
Straight Curve
Task level
0 1 2 0 1 2
Label 1 2 3 4 5 6
Nodes Instance of
Training
Instance
of Test
Training
time
Testing
time
Training
accuracy
Testing
accuracy
ELM
Straight
18 101 53 0.0312 0.0312 0.8922 0.8654
40 101 53 0.0468 0.0312 0.9020 0.8077
60 101 53 0.0468 0.0312 0.9804 0.7500
100 101 53 0.0468 0.0312 1 0.3654
Curve
18 101 53 0.0312 0.0312 0.8356 0.7027
40 101 53 0.0312 0.0312 0.9452 0.5676
60 101 53 0.0936 0.0312 1 0.5405
70 101 53 0.0468 0.0312 1 0.4324
Fig. The Effect Of Nodes Numbers On The Accuracy On Straight Road
Fig. The Effect Of Nodes Numbers On The Accuracy On Curve Road
TABLE. DRIVING WORKLOAD CLASSIFICATION AND LABELS
TABLE. RESULTS OF ELM ALGORITHM ON STRAIGHT AND CURVE ROAD
SIMULATOR STUDY.ESTABLISHMENT AND RESULTS OF ELM.
Comparison between ELM and ELM-Kernel
ELM-Kernel:ELM:
TABLE. RESULTS OF ELM-KERNEL ALGORITHM ON STRAIGHT AND CURVE ROAD
SIMULATOR STUDY.COMPARISON OF MODELS.
Method Kerneltype
Trainingnumber
Testingnumber
TrainingTime
TestingTime
Training Accuracy
TestingAccuracy
Straight
ELM-Kernel
RBF_kernel 101 53 0.0053 0.0045 0.9109 0.8491
ELM-Kernel lin_kernel 101 53 0.0053 0.0047 0.9307 0.8679
SVM - 101 53 0.28 - - 0.75817
Curve
ELM-Kernel
RBF_kernel 101 53 0.0053 0.0046 0.9007 0.7906
ELM-Kernel lin_kernel 101 53 0.0049 0.0047 0.91 0.8021
SVM - 101 53 0.04 - - 0.66055
• SupportVectorMachines(SVM)
Advantages:• Solvingtheproblemoflinearinseparable
• Priorknowledgebeforetrainingisunnecessary
•Minimizethe upperbound on the expected generalization error
Disadvantages:Difficultyincalculation
• ExtremeLearningMachine(ELM)
Advantages:• Lowcomputationalcosts
• Real-timedetection
Disadvantages:Difficultyinparametersdetermination
SIMULATOR STUDY.COMPARISON OF MODELS.
Trend:•Internet of Things (IoT)•Connected Vehicle•Advanced driver assistance systems•Pilotless Automobile
APPLICATIONS.ADVANCED DRIVER ASSISTANCE SYSTEM.
Fig. Bosch Advanced In-Vehicle Information Systems
TIMELINE.ADVANCED DRIVER ASSISTANCE SYSTEM.
2014 Q1 Q2 Q3 Q4 2015
Essay01/05/15
Model establishment
Data processing
01/03/15
01/01/15
Simulator experiment
01/02/15
Research plan 15/11/14
01/12/14
Review 01/11/14
Model optimization
Combined ECG and driving performance data in feature selection
Collected and analyzing a large amount of physical data under complexenvironment
Introduced secondary tasks and NASA-TLX rating in study
Combined subjective rating and objective data for research
Established ELM and ELM-Kernel in classifying driving workload
Collaboration, Interdisciplinary, High-quality, Application
INNOVATIVE FEATURES.ADVANCED DRIVER ASSISTANCE SYSTEM.
THANK YOU
top related