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Photo by Chua, Yeong Jiun
Liza Dixon, M.Sc. is a recent graduate of the Hochschule Rhein-Waal University of Applied Sciences Usability Engineering program, where she specialized in human-machine interaction of assistive and autonomous vehicle systems.

Previously, she worked as a Graphic Designer and Social Media Coordinator for luxury fine jewelry brand, LAGOS, was part of the Anthropologie Visual Display team and livestreamed to over 300k+ users on Meerkat & Periscope. She has been featured by BehanceMashable, Venture Beat and AngelList

She is an honors graduate of Flagler College where she received a B.A. in Graphic Design, and minored in Psychology and Fine Art. 


When she’s not thinking about autonowashing, you’ll find her traveling, cooking with plants, doing yoga or biking through the woods. She was born in Miami, Florida and now resides just outside of Düsseldorf, Germany.




Trust in Automation:
An On-Road Study of Trust in Advanced Driver Assistance Systems

Master’s Thesis


Appropriate user trust is critical in ensuring the acceptance and safe use of Advanced Driver Assistance Systems (ADAS). Despite the prevalence of ADAS on-road today, there is a limited understanding of how trust is affected by a user’s first contact with the system on-road. Participants without prior experience were introduced to a level 2 system and completed an on-road test drive. Utilizing a mixed-methods approach including Facial Emotion Recognition (FER), trust in the system was measured at key milestones.

Experiment Proposal, Planning & Design, Moderation, Interviewing, Data Science, Statistical Analysis



Methods Participants were screened and selected in order to maintain homogeneousness. They completed interviews and were introduced to the vehicle systems via an introductory video, in order to ensure consistency in the information they received. Abiding by ethical and legal standards, each participant completed an on-road test drive on a pre-determined route, along with the moderator in the passenger seat.
Verbal trust scores were captured at multiple intervals. Questionnaires were administered to the participants before and after the driving experience. Multiple cameras were utilized, including roadway and driver-facing cameras for later Facial Emotion Recognition analysis.






Coding in R
Data AnalysisQualitative data, including participant behavior and commentary was recorded and aligned with factors of Trust in Automation. Questionnaire data and verbal trust scores were statistically analyzed in R. Driver-facing camera footage was analyzed by a Facial Emotion Recognition algorithm in MATLAB and further analyzed with R.


Event specific Facial Emotion Recognition (FER) Analysis
Results Trust in Automation (TiA) scores increased in a majority of participants, and a significant shift in the factor Reliability/Competence (p=0.02) was observed post-drive. According to FER, participants with a gain in TiA and those with a loss in TiA post-drive, more frequently displayed the emotions happy and angry, respectively.
While emotional states varied greatly between drivers, patterns emerge that revealed several different types of drivers. Some showed high emotional variability while others’ emotional states remained more consistent. Driving events, for example, autonomous braking, had an effect on the driver’s emotions, giving insight into whether or not the vehicle was behaving as they expected in a given scenario.

 


Feature in AutoVisionen, Herbrand Mercedes-Benz Magazine



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Autonowashing

[ aw-ton-uh-wosh-ing ]


verb. The practice of making unverified or misleading claims which misrepresent the appropriate level of human supervision required by a partially or semi-autonomous product, service or technology.

Autonowashing makes something appear to be more autonomous than it really is.


Autonowashing: The Greenwashing of Vehicle Automation

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Urban Dictionary

See Definition ︎





       © 2019 Liza Dixon