Objective: This study aimed at investigating the driver's takeover performance when switching from working on different non-driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions.
Background: Conditionally automated driving systems, which are currently close to market introduction, require the user to stay fallback ready. As users will be allowed to engage in more complex NDRTs during the automated drive than when driving manually, the time needed to regain full manual control could likely be increased.
Method: Thirty-four users engaged in different everyday NDRTs while driving automatically with a Wizard of Oz vehicle. After approximately either 5 min or 15 min of automated driving, users were requested to take back vehicle control in noncritical situations. The test drive took place in everyday traffic on German freeways in the metropolitan area of Stuttgart.
Results: Particularly tasks that required users to turn away from the central road scene or hold an object in their hands led to increased takeover times. Accordingly, increased variance in the driver's lane position was found shortly after the switch to manual control. However, the drivers rated the takeover situations to be mostly "harmless."
Conclusion: Drivers managed to regain control over the vehicle safely, but they needed more time to prepare for the manual takeover when the NDRTs caused motoric workload.
Application: The timings found in the study can be used to design comfortable and safe takeover concepts for automated vehicles.
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http://dx.doi.org/10.1177/0018720818824002 | DOI Listing |
J Hazard Mater
January 2025
Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
This study investigates brake wear particle (BWP) emissions from light-duty electric vehicles (EVs) and heavy-duty vehicles (HDVs) using a self-developed whole-vehicle testing system and a modified brake dynamometer. The results show that regenerative braking significantly reduces emissions: weak and strong regenerative braking modes reduce brake wear PM by 75 % and 87 %, and brake wear PM by 90 % and 95 %, respectively. HDVs with drum brakes produce lower emissions and higher PM/PM ratios than those with disc brakes.
View Article and Find Full Text PDFChaos
January 2025
College of Computer Science and Software Engineering, Shenzhen University, Guangdong 518060, China.
This paper considers the selection and optimization of drive nodes based on the controllability of multilayer networks. The intra-layer network topologies are arbitrary, and the node dynamics are linear time-invariant dynamical systems. The study focuses on the number and selection of drive nodes in a special class of drive-response networks.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Faculty of Natural and Applied Sciences, Department of Computer Science and Information Technology, Sol Plaatje University, Kimberley, South Africa.
The rapid adoption and evolving nature of artificial intelligence (AI) is playing a significant role in shaping the music streaming industry. AI has become a key player in transforming the digital music streaming industry, particularly in enhancing user experiences and driving subscription growth. Through AI automation, platforms personalize music recommendations, optimize subscription offerings, and improve customer support services.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Traffic Management School, People's Public Security University of China, Beijing, 100038, China.
The takeover issue, especially the setting of the takeover time budget, is a critical factor restricting the implementation and development of conditionally automated vehicles. The general fixed takeover time budget has certain limitations, as it does not take into account the driver's non-driving behaviors. Here, we propose an intelligent takeover assistance system consisting of all-round sensing gloves, a non-driving behavior identification module, and a takeover time budget determination module.
View Article and Find Full Text PDFComput Biol Med
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, China. Electronic address:
Breast cancer poses a significant health threat worldwide. Contrastive learning has emerged as an effective method to extract critical lesion features from mammograms, thereby offering a potent tool for breast cancer screening and analysis. A crucial aspect of contrastive learning is negative sampling, where the selection of hard negative samples is essential for driving representations to retain detailed lesion information.
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