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Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian Robert Gray

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Page 1: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Modeling and Measuring Cognitive Load to Reduce Driver Distraction

in Smart CarsTanvi Jahagirdar

April 21, 2015Committee:

Ashraf Gaffar, ChairArbi Ghazarian

Robert Gray

Page 2: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian
Page 3: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

What causes driver distraction ?

Listening to music, Talking, Interactive Speech System

Ears off the Road

Page 4: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Research• how frequently drivers engage in certain distracting activities,

• how long and under what conditions they usually engage in them,

• drivers’ subjective assessments of the degree of distraction imposed by particular devices and their perceived ability to cope with these distractions,

• risk associated with distracted driving tasks

• whether and how training and practice can minimize driver distraction.

Page 5: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

How to measure driver distraction?

• Measures e.g., SSS (Stanford Sleepiness Scale), KSS (Karolinska Sleepiness

Scale)

• Driver biological measures e.g., EEG (Electroencephalogram), ECG

(Electrocardiogram)

• Driver physical measures e.g., PERCLOS (proportion/percentage of time in a

minute that the eye is 80% closed), Gaze direction

• Driving performance measures/ Engineering based e.g., steering wheel angle,

yaw angle, reaction times etc.

• Hybrid measures

Page 6: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Technology Evolution

Page 7: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Why speech recognition is bad?

Page 8: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

DISTRACTION

DRIVING RELATED NON-DRIVING RELATED

EXTERNALINTERNALEXTERNALINTERNAL

Checking Car Information,Switching Lights, Indication,GPS 

Road Signs,Traffic Lights, Pedestrian Crossing

Media player, Wireless,Kids or Pets on board, Eating,Grooming

Commercials, Unlawful Pedestrians

States:-

1. Stable State

2. Dynamic State

3. Emergency State

Page 9: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Human – Car Interaction (HCaI)• HCaI is subjected to different constraints that generally do not apply

to HCI.

• Every task has precedence in car and driver has to share his attention between activities.

• Input and output devices demanding high attention is not feasible.

• Two-handed operations are unacceptable.

• Driving situations greatly affect the interaction.

Page 10: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Multi-modal Interaction• More Modes Better Interaction• Human – Human Interaction is Multi-Modal:

o Verbalo Para-Verbal o Non-Verbalo Lip Readingo Contexto Historyo …

• “Attaining Perfection in Speech Recognition” is not the answer. We DO need Multi-Modal Interaction.

Page 11: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

However is it safe?

Page 12: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Experiment

For this study, we tested an abstract layout of icons of varying sizes, orientation and number of icons while driving, to effectively identify driver distraction and time taken to perform the distractive task.

Our Hypothesis – Large & Simple icons Minimal Driver Distraction

Page 13: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Screen Holder

Page 14: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Abstract UI for testing

Page 15: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Land

scap

e

Portr

ait

4 Ico

ns

8 Ico

ns

Small

Scre

en

Larg

e Scr

een

Tota

l

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

Results

Page 16: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian
Page 17: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Conclusion• Thus, the most complex task using our prototype should take 2

seconds per screen x 4 screens = maximum 8 seconds.

• The change in screen sizes or orientation did not affect the driver.

• There was also no significant change in using 6 +/- 2 number of icons with an average of less than 2 second response time per command and even less than a second with more experience.

Page 18: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Future Work• This above work was conducted only on a limited small number of participants. More test cases and more

participants would show more accurate results.

• Evaluate our borderlines by testing non-minimalist design of 24 icons compared to the minimalist design of 6+/- 2,

• Also in this work, the order of icons was serialized. Research should be done on randomizing the placement of icons, a comparison between novice and expert drivers in using the In-Vehicle Interactive System.

• Future research can also be done on 5 times vs. 20 times the UI was tested, to study the effect of learning mode and the effect of experience.

• There is also a need to check the difference between the 3 driving states as mentioned in the model.

• Future researchers can change the position of the screen by placing it at the bottom or angling it more to the driver.

Page 19: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Thank you & Drive Safe

Page 20: Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars Tanvi Jahagirdar April 21, 2015 Committee: Ashraf Gaffar, Chair Arbi Ghazarian

Questions?