Objectives: Driving simulation is an important platform for studying vehicle automation. There are different approaches to using this platform - with most using scripting or programmatic tools to simulate vehicle automation. A less frequently used approach, the Wizard-of-Oz method, has potential for increased flexibility and efficiency in designing and conducting experiments. This study designed and evaluated an experimental setup to examine the feasibility of this approach as an alternative for conducting automation studies.
Methods: Twenty-four participants experienced simulated vehicle automation in two platforms, one where the automation was controlled by algorithms, and the other where the automation was simulated by an external operator. Surveys were administered after each drive and the drivers' takeover performance after the automation disengaged was measured.
Results: Results indicate that while the kinematic parameters of the driving differed significantly for the two platforms, there were no significant differences in the perceptions of participants and in their takeover performance between the two platforms.
Conclusion: These results provide evidence for the use of alternative approaches for the conduct of human factors studies on vehicle automation, potentially lowering barriers to undertaking such experiments while increasing flexibility in designing more complex studies.
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http://dx.doi.org/10.1080/15389588.2020.1810243 | DOI Listing |
Front 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
January 2025
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.
View Article and Find Full Text PDFSensors (Basel)
January 2025
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH).
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Automation and Electrical Engineering, Beihang University, Beijing 100191, China.
Since the field of autonomous vehicles is developing quickly, it is becoming increasingly crucial for them to safely and effectively navigate their surroundings to avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods for precise obstacle identification, sophisticated path-planning algorithms that guarantee cars follow dependable and safe paths, and decision-making systems that allow for adaptable reactions to a range of driving situations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA.
Field implementations of fully underground sensor networks face many practical challenges that have limited their overall adoption. Power management is a commonly cited issue, as operators are required to either repeatedly excavate batteries for recharging or develop complex underground power infrastructures. Prior works have proposed wireless inductive power transfer (IPT) as a potential solution to these power management issues, but misalignment is a persistent issue in IPT systems, particularly in applications involving moving vehicles or obscured (e.
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