One of the key contributory factors for accident involvement is the misjudgment of vehicle approach. Past research has indicated that individuals can use the rate of visual "looming" in order to judge the time to arrival (TTA) of approaching vehicles. Although a large number of road traffic collisions occur at roadside junctions, very little research has focused on individuals' abilities to detect the onset of visual looming within a complex road scene at junction scenarios. In this research, computer generated scenes with photorealistic vehicle images, and a psychophysical staircase methodology, were used to explore drivers' ability to detect the approach of both motorcycles and cars within a contextually rich city scene. Across three experiments the effect of additional vehicular and observer motion on driver detection of vehicle approach was assessed. Results showed that individuals were significantly poorer at detecting the approach of the motorcycle stimulus compared with the car stimulus. Results also showed that additional vehicular motion within the scene had a negative effect on detection thresholds for the car stimulus. Finally, the results showed that introducing lateral global motion of the scene, such as might occur if the observer was moving steadily forward from a junction, negatively affected detection thresholds. The theoretical implications of the findings are discussed, including how vehicles traveling at high speed are often below the threshold for detecting visual looming. Practical implications for road design and layout are discussed that address the perceptual errors noted.
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Sci Rep
January 2025
Computational Learning Theory Team, RIKEN-Advanced Intelligence Project, Fukuoka, 819-0395, Japan.
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View Article and Find Full Text PDFPLoS One
January 2025
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China.
This study tried to focus on the older drivers' group and explore the impact factors of injury severity involving older drivers from geo-spatial analysis. To reach the goal, a spatial analysis was proposed employing geographic information systems (GIS) with a case study application to two counties in Nevada. First, crash clusters were explored using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach to investigate the spatial crash pattern for older drivers, and determine high risk locations of injury severity.
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View Article and Find Full Text PDFFront Robot AI
January 2025
School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom.
Introduction: The transition to electric vehicles (EVs) has highlighted the need for efficient diagnostic methods to assess the state of health (SoH) of lithium-ion batteries (LIBs) at the end of their life cycle. Electrochemical Impedance Spectroscopy (EIS) offers a non-invasive technique for determining battery degradation. However, automating this process in industrial settings remains a challenge.
View Article and Find Full Text PDFRev Cardiovasc Med
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Department of Anesthesiology, Intensive Care Medicine, Emergency Medicine, Pain and Palliative Therapy, Asklepios Klinikum Harburg, 21075 Hamburg, Germany.
Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide, with a low survival rate of around 7% globally. Key factors for improving survival include witnessed arrest, bystander cardiopulmonary resuscitation (CPR), and early defibrillation. Despite guidelines advocating for the "chain of survival", bystander CPR and defibrillation rates remain suboptimal.
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