Detecting a looming object and its imminent collision is imperative to survival. For most humans, it is a fundamental aspect of daily activities such as driving, road crossing and participating in sport, yet little is known about how the brain both detects and responds to such stimuli. Here we use functional magnetic resonance imaging to assess neural response to looming stimuli in comparison with receding stimuli and motion-controlled static stimuli. We demonstrate for the first time that, in the human, the superior colliculus and the pulvinar nucleus of the thalamus respond to looming in addition to cortical regions associated with motor preparation. We also implicate the anterior insula in making timing computations for collision events.
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http://dx.doi.org/10.1098/rspb.2010.1895 | DOI Listing |
Traffic Inj Prev
December 2024
School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China.
Objective: Understanding pedestrians' pre-crash avoidance kinematics is crucial for improving the identification of potential collision areas in interactions with highly automated vehicles (HAVs). Age significantly influences pedestrian avoidance velocity and the subsequent crash risks. However, current active safety systems in HAVs often overlook the influence of pedestrians' avoidance velocity and age on imminent accidents.
View Article and Find Full Text PDFFront Neurosci
June 2024
Academy for Engineering and Technology, Fudan University, Shanghai, China.
Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers.
View Article and Find Full Text PDFSensors (Basel)
May 2024
Division of Systems and Automatic Control, Department of Electrical and Computer Engineering, University of Patras, Rio, 26504 Patras, Greece.
Multi-agent systems are utilized more often in the research community and industry, as they can complete tasks faster and more efficiently than single-agent systems. Therefore, in this paper, we are going to present an optimal approach to the multi-agent navigation problem in simply connected workspaces. The task involves each agent reaching its destination starting from an initial position and following an optimal collision-free trajectory.
View Article and Find Full Text PDFHeliyon
February 2024
Huazhong University of Science and Technology, School of Naval Architecture and Ocean Engineering, 1037 Luoyu Road, Hongshan District, Wuhan, Hubei, 430074, China.
The development of ship technology and information technology has been driving the continuous improvement of ship intelligence, with safety being an inevitable requirement in the shipping industry. A machine vision-based ship collision warning method is proposed for high monitoring system cost and limited information acquisition in safety design of autonomous ship navigation. The method combines machine learning with image algorithms.
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