Objective: The adaptive behavior of mobile phone-distracted drivers has been a topic of much discussion in the recent literature. Both simulator and naturalistic studies suggest that distracted drivers generally select lower driving speeds; however, speed adaptation is not observed among all drivers, and the mechanisms of speed selection are not well understood. The aim of this research was to apply a driver behavioral adaptation model to investigate the speed adaptation of mobile phone-distracted drivers.
Methods: The speed selection behavior of drivers was observed in 3 phone conditions including baseline (no conversation) and hands-free and handheld phone conversations in a high-fidelity driving simulator. Speed adaptation in each phone condition was modeled as a function of secondary task demand and self-reported personal/psychological characteristics with a system of seemingly unrelated equations (SURE) accounting for potential correlations due to repeated measures experiment design.
Results: Speed adaptation is similar between hands-free and handheld phone conditions, but the predictors of speed adaptation vary across the phone conditions. Though perceived workload of secondary task demand, self-efficacy, attitude toward safety, and driver demographics were significant predictors of speed adaptation in the handheld condition, drivers' familiarity with the hands-free interface, attitude toward safety, and sensation seeking were significant predictors in the hands-free condition. Drivers who reported more positive safety attitudes selected lower driving speeds while using phones.
Conclusion: This research confirmed that behavioral adaptation models are suitable for explaining speed adaptation of mobile phone distracted drivers, and future research could be focused on further theoretical refinement.
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http://dx.doi.org/10.1080/15389588.2017.1278628 | DOI Listing |
Biomed Opt Express
January 2025
Department of Robotics, University of Michigan, USA.
Conventional scanned optical coherence tomography (OCT) suffers from the frame rate/resolution tradeoff, whereby increasing image resolution leads to decreases in the maximum achievable frame rate. To overcome this limitation, we propose two variants of machine learning (ML)-based adaptive scanning approaches: one using a ConvLSTM-based sequential prediction model and another leveraging a temporal attention unit (TAU)-based parallel prediction model for scene dynamics prediction. These models are integrated with a kinodynamic path planner based on the clustered traveling salesperson problem to create two versions of ML-based adaptive scanning pipelines.
View Article and Find Full Text PDFMagn Reson Med
January 2025
Department of Radiology, Stanford University, Stanford, California, USA.
Purpose: To provide a fast quantitative imaging approach for a 0.55T scanner, where signal-to-noise ratio is limited by the field strength and k-space sampling speed is limited by a lower specification gradient system.
Methods: We adapted the three-dimensional spiral projection imaging MR fingerprinting approach to 0.
This Letter reports what we believe to be a novel schlieren approach with adaptive temporal resolution. The fundamental concept of this approach is to fuse an event-based camera and a low-speed frame-based camera to generate high-frame-rate videos by leveraging the strengths of both. Using a novel experimental setup, events and frames are accurately aligned in both space and time.
View Article and Find Full Text PDFJ Anat
January 2025
Center for Development Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, Washington, USA.
Geometric morphometrics is used in the biological sciences to quantify morphological traits. However, the need for manual landmark placement hampers scalability, which is both time-consuming, labor-intensive, and open to human error. The selected landmarks embody a specific hypothesis regarding the critical geometry relevant to the biological question.
View Article and Find Full Text PDFCurr Biol
January 2025
Norwegian Institute for Nature Research (NINA), Trondheim 7034, Norway.
Understanding the movements of highly mobile animals is challenging because of the many factors they must consider in their decision-making. Many seabirds, for example, are adapted to use winds to travel long distances at low energetic cost but also potentially benefit from targeting specific foraging hotspots. To investigate how an animal makes foraging decisions, given the inevitable trade-off between these factors, we tracked over 600 foraging trips of breeding Manx shearwaters (Puffinus puffinus; N = 218 individuals) using GPS accelerometers.
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