In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of
multiple data modalities has the potential to reshape various applications. This paper …

This paper presents objective metrics for how explainable artificial intelligence (XAI) can be
quantified. Through an overview of current trends, we show that many explanations are …

Automotive user interfaces constantly change due to increasing automation, novel features,
additional applications, and user demands. While in-vehicle interaction can utilize numerous …

Learning from active human involvement enables the human subject to actively intervene
and demonstrate to the AI agent during training. The interaction and corrective feedback …

Explanations in automated vehicles help passengers understand the vehicle's state and
capabilities, leading to increased trust in the technology. Specifically, for passengers of SAE …

Recently, in-car monitoring has emerged as a promising technology for detecting early-
stage abnormal status of the driver and providing timely alerts to prevent traffic accidents …

Multimodal sensors (visual, non-visual, and wearable) can provide complementary
information to develop robust perception systems for recognizing activities accurately …

Automotive user interface (AUI) evaluation becomes increasingly complex due to novel
interaction modalities, driving automation, heterogeneous data, and dynamic environmental …

In conditionally automated driving, traffic safety problems would occur if the driver does not
properly take over the control authority when the request of automated system arises …

By the year 2045, it is projected that Autonomous Vehicles (AVs) will make up half of the
new vehicle market. Successful adoption of AVs can reduce drivers' stress and fatigue, curb …