TY - GEN
T1 - AutoPreview
T2 - 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021
AU - Shen, Yuan
AU - Wijayaratne, Niviru
AU - Du, Peter
AU - Jiang, Shanduojiao
AU - Driggs-Campbell, Katherine
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/5/8
Y1 - 2021/5/8
N2 - The behavior of self-driving cars may differ from people's expectations (e.g. an autopilot may unexpectedly relinquish control). This expectation mismatch can cause potential and existing users to distrust self-driving technology and can increase the likelihood of accidents. We propose a simple but effective framework, AutoPreview, to enable consumers to preview a target autopilot's potential actions in the real-world driving context before deployment. For a given target autopilot, we design a delegate policy that replicates the target autopilot behavior with explainable action representations, which can then be queried online for comparison and to build an accurate mental model. To demonstrate its practicality, we present a prototype of AutoPreview integrated with the CARLA simulator along with two potential use cases of the framework. We conduct a pilot study to investigate whether or not AutoPreview provides deeper understanding about autopilot behavior when experiencing a new autopilot policy for the first time. Our results suggest that the AutoPreview method helps users understand autopilot behavior in terms of driving style comprehension, deployment preference, and exact action timing prediction.
AB - The behavior of self-driving cars may differ from people's expectations (e.g. an autopilot may unexpectedly relinquish control). This expectation mismatch can cause potential and existing users to distrust self-driving technology and can increase the likelihood of accidents. We propose a simple but effective framework, AutoPreview, to enable consumers to preview a target autopilot's potential actions in the real-world driving context before deployment. For a given target autopilot, we design a delegate policy that replicates the target autopilot behavior with explainable action representations, which can then be queried online for comparison and to build an accurate mental model. To demonstrate its practicality, we present a prototype of AutoPreview integrated with the CARLA simulator along with two potential use cases of the framework. We conduct a pilot study to investigate whether or not AutoPreview provides deeper understanding about autopilot behavior when experiencing a new autopilot policy for the first time. Our results suggest that the AutoPreview method helps users understand autopilot behavior in terms of driving style comprehension, deployment preference, and exact action timing prediction.
KW - Agent Behavior Understanding
KW - Autonomous Vehicle
KW - Human Robot Interaction
KW - Imitation Learning
KW - Mental Model
KW - Preview
UR - http://www.scopus.com/inward/record.url?scp=85105780176&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105780176&partnerID=8YFLogxK
U2 - 10.1145/3411763.3451591
DO - 10.1145/3411763.3451591
M3 - Conference contribution
AN - SCOPUS:85105780176
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI EA 2021
PB - Association for Computing Machinery
Y2 - 8 May 2021 through 13 May 2021
ER -