Accurate assessment of human trust in driver assistance systems is crucial for enhancing user acceptance and system safety. While prior research has explored physiological signals like skin conductance and heart rate to gauge trust, the link between these signals and trust remains insufficiently understood. Here, we present a novel approach to interpreting driver trust in intelligent warning systems by integrating subjective measures from questionnaires with objective electroencephalography (EEG) data. We develop TrustNet, a model leveraging separable convolution to capture the spatiotemporal dynamics of EEG signals and class activation mapping (CAM) to identify trust-relevant features. TrustNet achieves superior performance in trust assessment and classification, with accuracy and F1 score both exceeding 87%. CAM analysis reveals that EEG beta- and gamma-wave changes in the occipital and frontal regions are strongly associated with trust dynamics. Misclassification analysis highlights sensor noise and individual differences in response variability as key factors affecting performance. These findings demonstrate the feasibility of EEG-based trust assessment, offering new avenues for adaptive driver assistance systems responsive to human trust.

Regularity is a shared memory consistency condition that has received considerable attention. Lamport's original definition of regularity assumed a single-writer model, however, and is not well defined when the shared register may have multiple writers. In this paper, we consider four possible definitions of multiwriter regularity. The definitions are motivated by variations on a quorum-based algorithm schema for implementing them. We study the relationships between these definitions and a number of other well-known consistency conditions, and we give a partial order describing the relative strengths of these consistency conditions. Finally, we provide a practical context for our results by studying the correctness of two well-known algorithms for mutual exclusion under each of our proposed consistency conditions.