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Physiological fingerprinting of audiovisual warnings in assisted driving conditions: an investigation of fMRI and peripheral physiological indicators.
Brain Struct Funct
January 2025
Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, Aachen, Germany.
Physiological responses derived from audiovisual perception during assisted driving are associated with the regulation of the autonomic nervous system (ANS), especially in emergencies. However, the interaction of event-related brain activity and the ANS regulating peripheral physiological indicators (i.e.
View Article and Find Full Text PDFClassifying crash causation patterns in 2-vehicle collisions between autonomous and conventional vehicles.
Traffic Inj Prev
January 2025
School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha, Hunan, China.
Objective: This study aims to investigate the causes of 2-vehicle collisions involving an autonomous vehicle (AV) and a conventional vehicle (CV). Prior research has primarily focused on the causes of crashes from the perspective of AVs, often neglecting the interactions with CVs.
Method: To address this limitation, the study proposes a classification framework for crash causation patterns in 2-vehicle collisions involving an AV and a CV, considering their interactions.
Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics.
Virus Evol
November 2024
Department of Biology, University of Maryland, College Park, MD 20742, USA.
The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary-and largely uncharacterized-genetics of adsorption, injection, cell take-over, and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions among 51 strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days.
View Article and Find Full Text PDFTakeover and non-driving related task performance in conditional automated driving: EEG and behavior Parameters interaction.
Appl Ergon
February 2025
Department of Industrial Engineering, Tsinghua University, China. Electronic address:
In this study, a conditional automated driving scenario is simulated using virtual reality (VR) technology to explore whether office works presented through augmented reality (AR) affect task and takeover performance, and the neural mechanism was revealed. Sixty-four participants were recruited and their electroencephalography (EEG) was used to measure the brain activities. The results indicated that non-driving-related tasks (NDRTs) requiring higher internal attention focus resulted in poorer task and takeover performance.
View Article and Find Full Text PDFArticle Synopsis
- The study aimed to evaluate how well active inference models predict when drivers take over from automated vehicles and how these models relate to cognitive fatigue, trust, and situation awareness.
- Using a driving simulation, researchers developed a model that accurately predicted takeover times, finding that higher cognitive fatigue correlated with more uncertainty in taking control, while better situation awareness was linked to improved understanding of the driving environment.
- The findings support previous theories on trust in automation and indicate that active inference models can enhance the design and safety of automated driving systems by integrating human cognitive factors.
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