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DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services

Published: 11 March 2024 Publication History

Abstract

A hybrid society is expected to emerge in the near future, with different mobilities interacting together, including cars, micro-mobilities, pedestrians, and robots. People may utilize multiple types of mobilities in their daily lives. As vehicle automation advances, driver modeling flourishes to provide personalized intelligent services. Thus, modeling drivers across mobilities would pave the road for future society mobility-as-a-service, and it is particularly interesting to predict driver behaviors in newer mobilities with traditional mobility data. In this work, we present takeover prediction on a micro-mobility, with car simulation data. The promising model performance demonstrates the feasibility of driver modeling across mobilities, as the first in the field.

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References

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cover image ACM Conferences
HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
March 2024
1408 pages
ISBN:9798400703232
DOI:10.1145/3610978
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 11 March 2024

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Author Tags

  1. multimodal data
  2. takeover
  3. transfer learning

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