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Human Operator-Autonomous Vehicle In...

Fu, Ernestine,

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Human Operator-Autonomous Vehicle Interactions with System Bias and Transitions of Control /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Human Operator-Autonomous Vehicle Interactions with System Bias and Transitions of Control // Ernestine Fu.
Author:	Fu, Ernestine,
Description:	1 electronic resource (147 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
Subject:	Failure. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29755741
ISBN:	9798357504685

Human Operator-Autonomous Vehicle Interactions with System Bias and Transitions of Control /
Fu, Ernestine,

Human Operator-Autonomous Vehicle Interactions with System Bias and Transitions of Control /Ernestine Fu. - 1 electronic resource (147 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Autonomous vehicles are considered the future of transportation. They offer several potential long-term benefits, including reduction in driving and navigation chores, enhanced mobility for individuals with disabilities, improved safety and decreases in traffic collisions, and reduced infrastructure costs. Currently, however, autonomous vehicle systems in development are still limited by their abilities to detect their surroundings, interpret sensory information, and fully navigate without human input. The human operator needs to both supervise the imperfect system and collaborate with it to navigate the driving environment. The research presented in this dissertation was aimed at better understanding human operator and autonomous vehicle interactions and how they affect system design and implementation.An immersive driving simulator was modified to develop experimental studies that analyzed interactions between the human operator and the autonomous vehicle system. The first part of this research focused on the inherent algorithmic biases present in designing autonomous vehicle systems, and the effects of changes in the system's sensitivity level and autonomy level were analyzed. The results show that a system biased towards lower sensitivity can improve operator vigilance and performance. The results further demonstrate that higher levels of autonomy in vehicles can result in reduced operator performance during potentially fatal events.Subsequent research focused on transitions of control from automation, in which the system hands control of the vehicle over to the human operator because there is a failure in the autonomous driving system. The effect of varying alert frequencies and levels of failure severity on the human operator's ability to successfully navigate a transition was analyzed. Significant differences in operator performance between the presence or lack of an alarm were observed, as well as the fact that an initial system failure's severity level did not seem to affect operator performance in a later failure event.Overall, the results of this work show that engineers and designers of autonomous vehicle systems need to consider a number of scenarios when specifying system actions and responses. This research provides a starting point for further exploration of the implications of system bias, particularly with varying levels of automation. It also provides insights that can help inform the design of transitions of control. A better understanding of human operator and autonomous vehicle interactions remains critical as vehicles with higher levels of automation become more prevalent on the road.

English

ISBN: 9798357504685Subjects--Topical Terms:

3561225
Failure.

Human Operator-Autonomous Vehicle Interactions with System Bias and Transitions of Control /
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520 $a Autonomous vehicles are considered the future of transportation. They offer several potential long-term benefits, including reduction in driving and navigation chores, enhanced mobility for individuals with disabilities, improved safety and decreases in traffic collisions, and reduced infrastructure costs. Currently, however, autonomous vehicle systems in development are still limited by their abilities to detect their surroundings, interpret sensory information, and fully navigate without human input. The human operator needs to both supervise the imperfect system and collaborate with it to navigate the driving environment. The research presented in this dissertation was aimed at better understanding human operator and autonomous vehicle interactions and how they affect system design and implementation.An immersive driving simulator was modified to develop experimental studies that analyzed interactions between the human operator and the autonomous vehicle system. The first part of this research focused on the inherent algorithmic biases present in designing autonomous vehicle systems, and the effects of changes in the system's sensitivity level and autonomy level were analyzed. The results show that a system biased towards lower sensitivity can improve operator vigilance and performance. The results further demonstrate that higher levels of autonomy in vehicles can result in reduced operator performance during potentially fatal events.Subsequent research focused on transitions of control from automation, in which the system hands control of the vehicle over to the human operator because there is a failure in the autonomous driving system. The effect of varying alert frequencies and levels of failure severity on the human operator's ability to successfully navigate a transition was analyzed. Significant differences in operator performance between the presence or lack of an alarm were observed, as well as the fact that an initial system failure's severity level did not seem to affect operator performance in a later failure event.Overall, the results of this work show that engineers and designers of autonomous vehicle systems need to consider a number of scenarios when specifying system actions and responses. This research provides a starting point for further exploration of the implications of system bias, particularly with varying levels of automation. It also provides insights that can help inform the design of transitions of control. A better understanding of human operator and autonomous vehicle interactions remains critical as vehicles with higher levels of automation become more prevalent on the road.
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