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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.
Using a visual attentional task and eye-tracking to investigate the effects of pre-takeover resource engagement on attention restoration in conditionally automated driving.
Traffic Inj Prev
January 2025
National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Centre, Beijing, China.
Objective: Attention forms the foundation for the formation of situation awareness. Low situation awareness can lead to driving performance decline, which can be dangerous in driving. The goal of this study is to investigate how different types of pre-takeover tasks, involving cognitive, visual and physical resources engagement, as well as individual attentional function, affect driver's attention restoration in conditionally automated driving.
View Article and Find Full Text PDFInferring 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 PDFIdentifying novice drivers in need of hazard perception ability improvement for takeover performance in Level 3 automated driving.
Accid Anal Prev
December 2024
School of Medicine, Tongji University, Shanghai 200092, China.
Takeover performance is a crucial constraint on deploying Level 3 automated driving. Not all drivers can adopt appropriate strategies to take over vehicle control during safety-critical situations. The hazard perception abilities of novice drivers may cause individual differences in urgent takeover performance.
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