Learning Latent Traits for Simulated Cooperative Driving Tasks
- URL: http://arxiv.org/abs/2207.09619v1
- Date: Wed, 20 Jul 2022 02:27:18 GMT
- Title: Learning Latent Traits for Simulated Cooperative Driving Tasks
- Authors: Jonathan A. DeCastro, Deepak Gopinath, Guy Rosman, Emily Sumner,
Shabnam Hakimi, Simon Stent
- Abstract summary: We build a framework capable of capturing a compact latent representation of the human in terms of their behavior and preferences.
We then build a lightweight simulation environment, HMIway-env, for modelling one form of distracted driving behavior.
We finally use this environment to quantify both the ability to discriminate drivers and the effectiveness of intervention policies.
- Score: 10.009803620912777
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To construct effective teaming strategies between humans and AI systems in
complex, risky situations requires an understanding of individual preferences
and behaviors of humans. Previously this problem has been treated in
case-specific or data-agnostic ways. In this paper, we build a framework
capable of capturing a compact latent representation of the human in terms of
their behavior and preferences based on data from a simulated population of
drivers. Our framework leverages, to the extent available, knowledge of
individual preferences and types from samples within the population to deploy
interaction policies appropriate for specific drivers. We then build a
lightweight simulation environment, HMIway-env, for modelling one form of
distracted driving behavior, and use it to generate data for different driver
types and train intervention policies. We finally use this environment to
quantify both the ability to discriminate drivers and the effectiveness of
intervention policies.
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