The field of autonomous vehicles is rapidly advancing, with a focus on improving perception and human interaction. Recent research has highlighted the importance of estimating driver trust and comfort in autonomous vehicles, with the development of novel datasets and machine learning models to predict these factors. Additionally, there is a growing emphasis on creating holistic perception systems that integrate internal and external monitoring to optimize perception and experience on-board. The effects of communication delay on human performance and neurocognitive responses in mobile robot teleoperation have also been investigated, providing critical insights for the design of delay compensation strategies. Furthermore, research has explored the long-term variability in physiological-arousal relationships for robust emotion estimation, highlighting the need to account for temporal variability in these relationships. Noteworthy papers in this area include: The TRUCE-AV dataset, which enables the development of adaptive AV systems capable of dynamically responding to user trust and comfort levels non-invasively. The AutoTRUST paradigm, which introduces a holistic perception system for internal and external monitoring of autonomous vehicles, demonstrating a novel AI-leveraged self-adaptive framework. The study on the effects of communication delay on human performance and neurocognitive responses in mobile robot teleoperation, which provides the first evidence of perceptual and cognitive delay thresholds during teleoperation tasks in humans. The work on decoding perceived risk in automated vehicles through 140K+ ratings, which presents a novel method to time-continuously measure and decode perceived risk, leveraging a large dataset to train deep neural networks that predict moment-by-moment perceived risk from vehicle kinematics.