  With the recently increasing capabilities of modern vehicles, novel
approaches for interaction emerged that go beyond traditional touch-based and
voice command approaches. Therefore, hand gestures, head pose, eye gaze, and
speech have been extensively investigated in automotive applications for object
selection and referencing. Despite these significant advances, existing
approaches mostly employ a one-model-fits-all approach unsuitable for varying
user behavior and individual differences. Moreover, current referencing
approaches either consider these modalities separately or focus on a stationary
situation, whereas the situation in a moving vehicle is highly dynamic and
subject to safety-critical constraints. In this paper, I propose a research
plan for a user-centered adaptive multimodal fusion approach for referencing
external objects from a moving vehicle. The proposed plan aims to provide an
open-source framework for user-centered adaptation and personalization using
user observations and heuristics, multimodal fusion, clustering,
transfer-of-learning for model adaptation, and continuous learning, moving
towards trusted human-centered artificial intelligence.
