Background: Until the level of full vehicle automation is reached, users of vehicle automation systems will be required to take over manual control of the vehicle occasionally and stay fallback-ready to some extent during the drive. Both, drowsiness caused by inactivity and the engagement in distracting non-driving related tasks (NDRTs) such as entertainment or office work have been suggested to impair the driver's ability to safely handle these transitions of control. Thus, it is an open question whether engagement in NDRTs will impair or improve take-over performance.
Method: In a motion-based driving simulator, 64 participants completed an automated drive that lasted either one or two hours using either a partially or highly automated driving system. In the partially automated driving condition, a warning was issued after several seconds when drivers took both hands off the steering wheel, while the highly automated driving system allowed hands-off driving permanently. Drivers were allowed to bring along their smartphones and to use them during the drive. They engaged in a wide variety of NDRTs such as reading or using social media. At the end of the session, drivers had to react to a sudden lead vehicle braking event. In the partial automation condition, there was no take-over request (TOR) to notify the drivers of the braking vehicle, while in the highly automated condition, the situation happened right after the drivers had deactivated the automation in response to a TOR. The lead time of the TOR was set at 8 s. Driver's level of drowsiness, workload (visual, mental and motoric) from carrying out the NDRT and motivational appeal of the NDRT right before the control transition were video-coded and used to predict the outcome of the braking event (i.e., reaction and system deactivation times, minimal Time-to-collision (TTC) and self-reported criticality) with a multiple regression approach.
Results: In the partial automation condition, reaction times to the braking vehicle and situation criticality as measured by the minimum TTC could be well predicted. Main predictors for increased reaction time were drowsiness and motivational appeal of the NDRT. However, visual and mental demand associated with NDRTs did decrease reaction time, suggesting that the NDRT helped the drivers to maintain alertness during the partially automated drive. Accordingly, drowsiness and motivational appeal of the NDRT increased situation criticality, while cognitive load due to the NDRT decreased it. In the highly automated condition, however, it was not possible to predict system deactivation time (in reaction to the TOR), brake reaction time to the braking vehicle and situation criticality by observed drowsiness and NDRT engagement.
Discussion: The results suggest a relationship between the driver's drowsiness and NDRT engagement in partial automation but not in highly automated driving. Several explanations for this finding are discussed. It could be possible that the lead time of 8 s might have given the drivers enough time to complete the driver state transition process from executing NDRTs to manual driving, putting them in a position to be able to cope with the driving event, while this was not possible in the partial automation condition. Methodological issues that might have led to a non-detection of an effect of drowsiness or NDRT engagement in the highly automated driving condition, such as the sample size and sensitivity of the observer ratings, are also discussed.
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http://dx.doi.org/10.1016/j.aap.2018.08.018 | DOI Listing |
Commun Biol
January 2025
Department of Infectious Diseases, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
Critical to the success of CRISPR-based diagnostic assays is the selection of a diagnostic target highly specific to the organism of interest, a process often requiring iterative cycles of manual selection, optimisation, and redesign. Here we present PathoGD, a bioinformatic pipeline for rapid and high-throughput design of RPA primers and gRNAs for CRISPR-Cas12a-based pathogen detection. PathoGD is fully automated, leverages publicly available sequences and is scalable to large datasets, allowing rapid continuous monitoring and validation of primer/gRNA sets to ensure ongoing assay relevance.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, Serbia.
The expansion of LEAN and small batch manufacturing demands flexible automated workstations capable of switching between sorting various wastes over time. To address this challenge, our study is focused on assessing the ability of the Segment Anything Model (SAM) family of deep learning architectures to separate highly variable objects during robotic waste sorting. The proposed two-step procedure for generic versatile visual waste sorting is based on the SAM architectures (original SAM, FastSAM, MobileSAMv2, and EfficientSAM) for waste object extraction from raw images, and the use of classification architecture (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, ResNet, and Inception-v3) for accurate waste sorting.
View Article and Find Full Text PDFJ Chromatogr A
January 2025
Environmental Health Laboratory Branch, Center for Laboratory Sciences, California Department of Public Health, Richmond, CA 94804, USA.
A solvent-free, thermal extraction method for analysis of polycyclic aromatic hydrocarbons (PAHs) in gas phase airborne samples was developed. A fully automated thermal desorber (TD) coupled with highly selective and sensitive gas chromatography-tandem mass spectrometry (GC-MS/MS) was used to determine the concentration of trace level PAHs. Air sampling was conducted to tune the sampling and analytical conditions.
View Article and Find Full Text PDFJ Orthop Surg Res
January 2025
Department of Mechanical Engineering, Centre for Mechanical Technology & Automation (TEMA), University of Aveiro, Aveiro, 3810-193, Portugal.
Background: Bone fractures represent a global public health issue. Over the past few decades, a sustained increase in the number of incidents and prevalent cases have been reported, as well as in the years lived with disability. Current monitoring techniques predominantly rely on imaging methods, which can result in subjective assessments, and expose patients to unnecessary cumulative doses of radiation.
View Article and Find Full Text PDFAnal Chim Acta
March 2025
Holosensor Medical Technology Ltd, Room 12, No. 1798, Zhonghuayuan West Road, Yushan Town, Suzhou, 215000, China; Department of Veterinary Medicine, University of Cambridge, Cambridge, UK. Electronic address:
Rapid and sensitive protein detection methods are of benefit to clinical diagnosis, pathological mechanism research, and infection prevention. However, routine protein detection technologies, such as enzyme-linked immunosorbent assay and Western blot, suffer from low sensitivity, poor quantification and labourious operation. Herein, we developed a fully automated protein analysis system to conduct fast protein quantification at the single molecular level.
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