Stackelberg Meta-Learning Based Shared Control for Assistive Driving
arxiv(2024)
摘要
Shared control allows the human driver to collaborate with an assistive
driving system while retaining the ability to make decisions and take control
if necessary. However, human-vehicle teaming and planning are challenging due
to environmental uncertainties, the human's bounded rationality, and the
variability in human behaviors. An effective collaboration plan needs to learn
and adapt to these uncertainties. To this end, we develop a Stackelberg
meta-learning algorithm to create automated learning-based planning for shared
control. The Stackelberg games are used to capture the leader-follower
structure in the asymmetric interactions between the human driver and the
assistive driving system. The meta-learning algorithm generates a common
behavioral model, which is capable of fast adaptation using a small amount of
driving data to assist optimal decision-making. We use a case study of an
obstacle avoidance driving scenario to corroborate that the adapted human
behavioral model can successfully assist the human driver in reaching the
target destination. Besides, it saves driving time compared with a driver-only
scheme and is also robust to drivers' bounded rationality and errors.
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