In this paper, we introduce the problem of en-route destination changing for a self-driving car, and we study the effectiveness of human-robot {em copilot} systems as a solution. The copilot system is one in which the autonomous vehicle not only handles low-level vehicle control, but also continually monitors the intent of the human passenger in order to respond to dynamic changes in desired destination. We specifically consider a vehicle parking task, where the vehicle must respond to the user's intent to drive to and park next to a particular roadside sign board, and we study a copilot system that detects the passenger's intended destination based on gaze. We conduct a human study to investigate, in the context of our parking task, {em (a)} if there is benefit in using a copilot system over manual driving, and {em (b)} if copilot systems that use eye tracking to detect the intended destination have any benefit compared to those that use a more traditional, keyboard-based system. We find that the answers to both of these questions are affirmative: our copilot systems can complete the autonomous parking task more efficiently than human drivers can, and our copilot system that utilizes gaze information enjoys an increased success rate over one that utilizes typed input.

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In Proceedings of the 27th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN2018), Nanjing, China, August 2018.

@inproceedings{ROMAN18-Jiang, title={A Study of Human-Robot Copilot Systems for En-Route Destination Changing}, author={Yu-Sian Jiang and Garrett Warnell and Eduardo Munera and Peter Stone}, booktitle={Proceedings of the 27th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN2018)}, month={August}, address={Nanjing, China}, url="http://www.cs.utexas.edu/users/ai-lab?ROMAN18-Jiang", year={2018} }

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