Abstract
Autonomous vehicles, or self-driving vehicles are a brand new disruptive technology that
is often misunderstood by the general public. These autonomous systems utilize a variety of
cutting-edge technologies, including radar, lidar, cameras, and ultrasonic sensors, all integrated
with machine learning models. Autonomous cars are capable of perceiving their surroundings
with remarkable precision, navigating traffic, and making split-second decisions, all without
human intervention. Autonomous vehicles have the potential to completely reshape human
transportation, yet the technology that controls them is misunderstood. My STS research
examines the relationship between producers of autonomous vehicle systems and consumers of
these systems, specifically how understanding of these systems affects trust in these autonomous
systems.
My technical project is a small gesture driven robotic car. My group and I have created a
glove that uses gyroscopic sensors to interpret hand gestures and send those instructions to the
car. In turn, the car has a mounted camera and distance sensor that relay information about where
the car is through video and haptic feedback. This project is aimed to be a toy for the young teen
tech enthusiast. As a small toy, our car has rudimentary systems for preventing crashes compared
to large scale systems such as autonomous cars. Our project has only two sensors to mitigate
crashes, a simple ultrasonic distance sensor and a camera to monitor where the car is. Through
driving with such limited sensing apparatus, it becomes clear why self-driving cars have such an
intricate system of sensors. Through the usage of the car, people can understand some of the
limitations and benefits of sensors in a robotic system through a fun and engaging platform.
While the car remains fully under human control, one can experience the world similarly to an
autonomous robotic system, with a view of the real world narrowed down to what is perceivable
through sensors.
My STS research first examines the underlying machine learning algorithms as an
emergent technology. Through my research I learned how perceptions and understanding of
machine learning technologies in the general public influence perceptions of autonomous
vehicles. My STS research used Google Waymo as a case study, examining their place in the
autonomous vehicle market and how their service has affected autonomous vehicle perceptions.
Using the framework of producers and consumers, I examined the relationship between Waymo
and those using Waymo’s service. Through examining Waymo I learned how companies are
presenting their technologies to consumers in a way that fosters trust in their systems. Looking at
the relationship will provide insight for how producers shape trust in autonomous vehicles and
how consumers perceive these systems.
