ABSTRACT: Advanced driver assistance systems (ADASs) can greatly enhance road safety by providing real-time warnings to drivers in imminent crash situations. However, the provided warning time may deviate from its designed time. There is limited research on how warning uncertainties influence drivers’ behavior, safety performance, and trust. This study conducted a driving simulator study to examine how uncertainties in warnings impact driving behaviors and trust using a roundabout driving scenario. Two warning error distributions were constructed to represent low and high warning uncertainty levels. Thirty-six participants were recruited and randomly divided into two groups under the two uncertainty levels in a driving simulator experiment. The betweengroup analysis shows that the lower warning uncertainty level group results in higher trust and that trust increases (or decreases) over time under low (or high) uncertainty levels. The within-group analysis shows that higher warning errors downgrade drivers’ trust and safety performance when the errors are high. 

Heavy-haul railways are critical for transporting freight. However, prolonged wheel–rail interactions cause frequent rail defects, particularly in small-radius curve sections. Ultrasonic A-scan signals are essential for the non-destructive evaluation of internal rail defects. In real heavy-haul environments, these signals suffer from strong non-Gaussian coupled noise. Such noise includes structural noise, low-frequency irrelevant components, and high-frequency electrical noise. Noise aliasing obscures defect echoes and increases the risk of missed detections. Conventional denoising methods are limited by poor noise–signal separability, mode mixing, and inadequate adaptability to complex non-Gaussian signals. To address these challenges, an A-scan signal model under noise-coupled conditions is constructed by analyzing the statistical and time–frequency characteristics of different noise components.

The replication package contains the questionnaire, datasets and analysis process used for the study.

The basic code and date  of DLE

STPredictor is an explainable ship trajectory prediction framework powered by large language models. It reformulates trajectory prediction as a language modeling task using natural-language prompts and integrates Chain-of-Thought reasoning for more transparent and reliable predictions. Experiments on two large-scale AIS datasets show strong performance and improved interpretability over existing baselines.