Objectives: To characterize the real-world driving habits of individuals with traumatic brain injury (TBI) using naturalistic methods and to demonstrate the feasibility of such methods in exploring return to driving after TBI.
Methods: After passing an on-road driving assessment, 8 participants with TBI and 23 matched controls had an in-vehicle device installed to record information regarding their driving patterns (distance, duration, and start/end times) for 90 days.
Results: The overall number of trips, distance and duration or percentage of trips during peak hour, above 15 km from home or on freeways/highways did not differ between groups. However, the TBI group drove significantly less at night, and more during the daytime, than controls. Exploratory analyses using geographic information system (GIS) also demonstrated significant within-group heterogeneity for the TBI group in terms of location of travel.
Conclusions: The TBI and control groups were largely comparable in terms of driving exposure, except for when they drove, which may indicate small group differences in driving self-regulatory practices. However, the GIS evidence suggests driving patterns within the TBI group were heterogeneous. These findings provide evidence for the feasibility of employing noninvasive in-car recording devices to explore real-world driving behavior post-TBI.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/HTR.0000000000000410 | DOI Listing |
Clin Drug Investig
January 2025
Department of Medicine, Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
Purpose: The REDUCE-IT randomized trial demonstrated a cardiovascular benefit of icosapent ethyl (IPE) but also raised potential safety signals for atrial fibrillation (AF) and serious bleeding. We aimed to evaluate the real-world safety of IPE versus mixed omega-3 polyunsaturated fatty acid (OM-3) formulations.
Methods: This retrospective active comparator new-user cohort study compared rates of new-onset AF and major bleeding (MB) among adult new users of IPE versus OM-3 in 2020-2024 US Veterans Affairs data.
Sensors (Basel)
January 2025
Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan 442002, China.
Autonomous driving has demonstrated impressive driving capabilities, with behavior decision-making playing a crucial role as a bridge between perception and control. Imitation Learning (IL) and Reinforcement Learning (RL) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. On one hand, RL's policy networks often lack sufficient reasoning ability to make optimal decisions in highly complex and stochastic environments.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China.
Six degrees of freedom (6-DoF) object pose estimation is essential for robotic grasping and autonomous driving. While estimating pose from a single RGB image is highly desirable for real-world applications, it presents significant challenges. Many approaches incorporate supplementary information, such as depth data, to derive valuable geometric characteristics.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Electronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, Spain.
The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies.
View Article and Find Full Text PDFSensors (Basel)
December 2024
División de Sistemas e Ingeniería Electrónica (DSIE), Campus Muralla del Mar, s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
This paper presents a novel end-to-end architecture based on edge detection for autonomous driving. The architecture has been designed to bridge the domain gap between synthetic and real-world images for end-to-end autonomous driving applications and includes custom edge detection layers before the Efficient Net convolutional module. To train the architecture, RGB and depth images were used together with inertial data as inputs to predict the driving speed and steering wheel angle.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!