Me Mental Mo Mode dels an and ADA DAS: A Summa mmary of CS - - PowerPoint PPT Presentation

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Me Mental Mo Mode dels an and ADA DAS: A Summa mmary of CS CSRC RC Hu Huma man-Center ered ed Re Resea earch ch an and d De Developme ment Activities

John K. Lenneman, Ph.D. Collaborative Safety Research Center Human-Technology Integration

Febr bruar ary 2 27, 7, 2 201 019

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A A BEH BEHAVIORAL EVAL EVALUATION O OF MEN ENTAL MODEL EL D DEVEL ELOPMEN PMENT

The Rockville Institute September 2017 – September 2019

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Our ur E End nd Goal

• al

Saf afety i is #1 #1 goal goal – an an appr appropriat ate m ment ental al model

• del fac

acilitat ates s saf afe behav behavior

• r/respo

pons nse/int nteraction

• n

Not understanding how these products work can have disastrous consequences

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Our ur E End nd Goal

• al

“Unde nderstand nding” ng” is not not the he end end goal goal O Outcome bas based ed appr approa

• ach

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Overal all P Proj

• ject

ct G Goal

• als
• With regard to ADAS/ADS technologies:
• What are the nature of, and variants of drivers’ mental models?
• How are drivers’ mental models formed?
• How do drivers’ mental models change over time?
• What are the sources of change?
• Specific events that occur while driving
• Exposure to other sources of information (vehicle manual, service people, friends,

Internet, ads, etc.)

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Ant ntici cipat pated O ed Out utcom comes es

• Taxonomy of drivers’ mental models
• Indicates the most important dimensions or aspects of users’ MM that can be used to

characterize and classify them.

• The taxonomy may help to explain differences between drivers’ behavior and

changes in their behavior over time.

• Description of the information and experiences that influence formation of

drivers’ mental models.

• Description of the information and experiences that influence changes in

drivers’ mental models over time.

• Links between drivers’ mental models and specific behaviors

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Thr hree ee Phas hases

Subjective/qualitative research Objective/behavioral research

12 12

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On On-road D

• ad Driver

ver Behav ehavior

• r S

Study udy

Methods

• 12 vehicle owners:
• 8 purchased in the last month; 4 with no restriction.
• Target vehicles that have Toyota Safety Sense or Lexus Safety System+
• 8 recruited. Target completion August 2019

Corolla LS Camry RAV4

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On On-road D

• ad Driver

ver Behav ehavior

• r S

Study: udy: Met Methods hods

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Appl pplicat cation

• n
• System design
• Consumer education
• Research findings Proof-of-concepts/demonstrations

Ideally, we design technology so consumer education is not needed.

Scouting

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Bey eyond H

• nd Human F

uman Fac actor

• rs

Promote the use of advanced statistical techniques and leverage methods in machine learning.

+

ANNOYANCE MODEL

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Bey eyond H

• nd Human F

uman Fac actor

• rs

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Be Behav haviou

• ura

ral Ad Adap aptat ation

• n Mod
• del

el to Lane ane Depar partur ure Wa Warn rning ngs

Rudin-Brown & Noy, 2002

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Human uman-Technol chnolog

• gy I

y Int ntegr egrat ation G

• n Grou
• up T

p Topi

• pics

Driver State Detection Mixed Automation Levels Human-in-the- Loop Personalization & Experience Road User Interaction Infrastructure Licensing/Education Driving Behavior/Culture

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Human uman-Technol chnolog

• gy I

y Int ntegr egrat ation G

• n Grou
• up T

p Topi

• pics

Driver State Detection Mixed Automation Levels Human-in-the- Loop Personalization & Experience Road User Interaction Infrastructure Licensing/Education Driving Behavior/Culture

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