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Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained. [ABSTRACT FROM AUTHOR]

With the transformation of media in the context of digitization, reading is once again becoming a highly debated topic. Not only are fears being expressed that the end of the 'Gutenberg Galaxy' will set back the spread of reading as a cultural technique, but more recent debates discuss the consequences of 'digital reading' from pedagogical and neurophysiological perspectives. The aim of this paper is to analyze the media and cultural debates about the consequences of digital reading. The different varieties and reference discourses of the discussion will be distinguished and related to the history of discourses critical of reading. The pedagogical critique of 'digital reading' is based on the thesis that it can lead to a diminution of cognitive abilities. The materiality of reading media and the physicality of the reading process are attributed with far-reaching mental and psychological effects. Furthermore, from a culturally pessimistic perspective, the spread of digital reading is seen as a cultural caesura that threatens the ability to think critically and empathize. The central reference discourse for the plausibility of the alleged dangers of digital reading is neuroscientific studies. [ABSTRACT FROM AUTHOR]

The collection of think aloud data on critical thinking tasks requires participants, many of whom are postsecondary students, to engage with real-life and potentially controversial topics. Accuracy of verbal reports can be enhanced with clear instructions and by minimizing distracting events. For example, interviewers can minimize external distractions such as ambient noise by holding think aloud sessions in a quiet room. However, internal distractions such as participants' fears about freely expressing their thoughts about controversial topics may be more difficult for interviewers to address. Although the fear of freely expressing thoughts during think aloud interviews has not been empirically investigated, this needs to change. Large-scale surveys indicate that a sizable portion of postsecondary students report discomfort with expressing their thoughts on some topics. This paper offers a theoretical case for why participants' fears about voicing thoughts freely and without reprisal during think aloud sessions may not only potentially undermine the truthfulness of verbal reports and validity of inferences, but also the very study of critical thinking. Thus, an empirical case for the freedom to think aloud must be considered. [ABSTRACT FROM AUTHOR]

Driver distraction detection not only effectively prevents traffic accidents but also promotes the development of intelligent transportation systems. In recent years, thanks to the powerful feature learning capabilities of deep learning algorithms, driver distraction detection methods based on deep learning have increased significantly. However, for resource-constrained onboard devices, real-time lightweight models are crucial. Most existing methods tend to focus solely on lightweight model design, neglecting the loss in detection performance for small targets. To achieve a balance between detection accuracy and network lightweighting, this paper proposes a driver distraction detection method that combines enhancement and global saliency optimization. The method mainly consists of three modules: context fusion enhancement module (CFEM), channel optimization feedback module (COFM), and channel saliency distillation module (CSDM). In the CFEM module, one-dimensional convolution is used to capture information between distant pixels, and an injection mechanism is adopted to further integrate high-level semantic information with low-level detail information, enhancing feature fusion capabilities. The COFM module incorporates a feedback mechanism to consider the impact of inter-layer and intra-layer channel relationships on model compression performance, achieving joint pruning of global channels. The CSDM module guides the student network to learn the salient feature information from the teacher network, effectively balancing the model's real-time performance and accuracy. Experimental results show that this method outperforms the state-of-the-art methods in driver distraction detection tasks, demonstrating good performance and potential application prospects. [ABSTRACT FROM AUTHOR]

This article investigates the use of in-real-time Study with Me streaming sessions as an out-ofclass tool for English-language learning among Austrian secondary school students. Taking the form of a multiple-case study, this paper examines how three students utilize Study with Me to support their English-language development. Through semi-structured interviews and member checking, the study explores participants' motivations, interaction patterns, and learning strategies during Study with Me sessions. Findings reveal a nuanced approach to Study with Me use, emphasizing its role in minimizing distractions, fostering a sense of community, and enhancing self-regulation skills. While participants perceive Study with Me as beneficial primarily for subjects requiring process internalization, they acknowledge only limited utility for English-language learning. The study underscores the need for further research on the efficacy of Study with Me in non-language learning contexts and highlights students' adaptive learning strategies within an exams-oriented education system. [ABSTRACT FROM AUTHOR]

For years Christian institutions in the West have observably supported and maintained kataphatic approaches to faith formation. Respectively, many schools have neglected apophatic considerations when teaching principles and approaches within emerging adult faith formation. Layered into this predicament is the strong inclination of predominantly kataphatic learning contexts to use increasing digital forms of instruction. Since the onset of the COVID-19 pandemic, escalating reliance on digital technology in college classrooms has only exacerbated this dilemma. As a result, apophatic attentions, which tend to move away from trending pedagogical approaches and subsequent outcomes, remain formidable. The following paper employs a 14-year case review of students' spiritual type similarities and differences at a private Christian university in the U.S. The review's theoretical and phenomenological framework utilizes spiritual typologies associated with apophatic and kataphatic nomenclature found in the "Circle of Sensibility" and espoused by spiritual type theorists. The data supports the theory of a general dividing line between approaches to Christian spirituality between apophatic and kataphatic taxonomical constructs. Additionally, perhaps most notably, this paper looks at digital distraction in current academic, spiritual formation contexts as a critical explanation for the lack of apophatic emphases. Digital distraction, in other words, undercuts the power of apophatic teaching. Based on the findings, recommendations for incorporating complementary kataphatic and apophatic approaches in spiritual formation coursework are offered. [ABSTRACT FROM AUTHOR]

Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies. Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents, thereby providing a guarantee for the safety of drivers. However, detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds, varying target scales, and different resolutions. Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios, this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5. The algorithm integrates Attention-based Intra-scale Feature Interaction (AIFI) into the backbone network, enabling it to focus on enhancing feature interactions within the same scale through the attention mechanism. By emphasizing important features, this approach improves detection accuracy, thereby enhancing performance in complex backgrounds. Additionally, a Triple Feature Encoding (TFE) module has been added to the neck network. This module utilizes multi-scale features, encoding and fusing them to create a more detailed and comprehensive feature representation, enhancing object detection and localization, and enabling the algorithm to fully understand the image. Finally, the shape-IoU (Intersection over Union) loss function is adopted to replace the original IoU for more precise bounding box regression. Comparative evaluation of the improved YOLOv5 distraction detection algorithm against the original YOLOv5 algorithm shows an average accuracy improvement of 1.8%, indicating significant advantages in solving distracted driving problems. [ABSTRACT FROM AUTHOR]

Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on. [ABSTRACT FROM AUTHOR]

Previous studies have focused on the impact of individual factors on lane departure warning (LDW) utility during driving. However, comprehensive analysis has not been considered based on multiple variables, such as driver characteristics. This paper aims to propose a methodology in exploring the utility of LDW under varied warning timing situations, focusing on changes in driving style and distraction level to obtain the optimal warning timing matching relationship. A driving simulator experiment with a mixed 4 × 3 factor design was conducted. The design matrix includes four level of secondary task (ST) conditions and three warning timings situations for drivers with various driving styles. To estimate the utility of the LDW system, lane departure duration (LDD) was selected as a time-based measure of utility. Both the Kaplan-Meier method and COX model were applied and compared. Combined with questionnaire results, the results indicate that both driving style and distraction state are significant influence factors. Generally, the results suggest that the more aggressive drivers lead to the more severe lane departure behavior and they preferred late warning. In terms of distraction state, the LDD increases with the level of ST remarkably. This implies that the earlier warning timing should be given for the higher-level distraction state condition. It was also observed that adaptive warning timing is needed based on the analysis of the interactive effect among multiple variables. The results provide empirical data for the optimization of LDW system design. [ABSTRACT FROM AUTHOR]

This paper argues that we can get a better grip on the divergences and convergences between Paul and Seneca on the ethics of love than those on offer in the large and growing literature comparing the two by distinguishing their attitudes to two broad and conceptually distinct families of love, which Tyler VanderWeele has described as "contributory love" (which desires that the good of the beloved be promoted for its own sake) and "unitive love" (which regards the beloved as a good to be enjoyed in one's own life). I argue that debates over whether Seneca and Paul had the more universal ethic of neighbor love are largely a distraction; the two are fundamentally in agreement about the scope of love, although Paul's thinking in this area is shaped in fundamental ways by his conviction that the whole cosmos is defined to be eventually and finally united and so conformed to the resurrected and glorified Christ. Rather, Paul's true difference from Seneca on the ethics of love lies instead in the importance he affords to unitive love; for Paul, the flourishing life does not consist simply in virtuous activity but also requires appropriate union with one's beloveds, paradigmatically with God in Christ and with Christ's body, the church. Seneca and Paul would each have accepted Sigmund Freud's observation that "we are never so defenseless against suffering as when we love". Seneca, however, would have taken it as a caution against attachments to anything whose loss might cause us to suffer, while Paul, by contrast, would have taken it as a statement of our condemnation to suffering "in this present evil age" (Gal. 1:4), in which we must love what we will inevitably lose. [ABSTRACT FROM AUTHOR]

Scene depth super-resolution (DSR) poses an inherently ill-posed problem due to the extremely large space of one-to-many mapping functions from a given low-resolution (LR) depth map, which possesses limited depth information, to multiple plausible high-resolution (HR) depth maps. This characteristic renders the task highly challenging, as identifying an optimal solution becomes significantly intricate amidst this multitude of potential mappings. While simplistic constraints have been proposed to address the DSR task, the relationship between LR and HR depth maps and the color image has not been thoroughly investigated. In this paper, we introduce a novel mapping constraint network (MCNet) that incorporates additional constraints derived from both LR depth maps and color images. This integration aims to optimize the space of mapping functions and enhance the performance of DSR. Specifically, alongside the primary DSR network (DSRNet) dedicated to learning LR-to-HR mapping, we have developed an auxiliary degradation network (ADNet) that operates in reverse, generating the LR depth map from the reconstructed HR depth map to obtain depth features in LR space. To enhance the learning process of DSRNet in LR-to-HR mapping, we introduce two mapping constraints in LR space: (1) the cycle-consistent constraint, which offers additional supervision by establishing a closed loop between LR-to-HR and HR-to-LR mappings, and (2) the region-level contrastive constraint, aimed at reinforcing region-specific HR representations by explicitly modeling the consistency between LR and HR spaces. To leverage the color image effectively, we introduce a feature screening module to adaptively fuse color features at different layers, which can simultaneously maintain strong structural context and suppress texture distraction through subspace generation and image projection. Comprehensive experimental results across synthetic and real-world benchmark datasets unequivocally demonstrate the superiority of our proposed method over state-of-the-art DSR methods. Our MCNet achieves an average MAD reduction of 3.7% and 7.5% over state-of-the-art DSR method for ×8 and ×16 cases on Milddleburry dataset, respectively, without incurring additional costs during inference. [ABSTRACT FROM AUTHOR]

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With the rapid increase in the number of drivers, traffic accidents due to driver distraction is a major threat around the world. In this paper, we present a novel long-term recurrent convolutional network (LRCN) model for real-time driver activity recognition during both day- and nighttime conditions. Unlike existing works that use static input images and rely on major pre-processing measures, we employ a TimeDistributed convolutional neural network (TimeDis-CNN) layer to process a sequential input to learn the spatial and temporal information of the driver activity without requiring any major pre-processing effort. A pre-trained (CNN) layer is applied for robust initialization and extraction of the primary spatial features of the sequential image inputs. Then, a long short-term memory (LSTM) network is employed to recognize and synthesize the dynamical long-range temporal information of the driver's activity. The proposed system is capable of detecting nine types of driver activities: driving, drinking, texting, smoking, talking, controlling, looking outside, head nodding, and fainting. For evaluation, we utilized a real vehicle environment and collected data from 35 participants, where 14 of the drivers were in real driving scenarios and the remaining in non-driving conditions. The proposed model achieved accuracies of 88.7% and 92.4% for the daytime and nighttime datasets, respectively. Moreover, the binary classifier's accuracy in determining whether the driver is non-distracted or in a distracted state was 93.9% and 99.2% for the daytime and nighttime datasets, respectively. In addition, we deployed the proposed model on a Jetson Xavier embedded board and verified its effectiveness by conducting real-time predictions. [ABSTRACT FROM AUTHOR]

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver's distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers' cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers' vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction. [ABSTRACT FROM AUTHOR]

This article presents the Motor Transport Institute's research to set out road safety indicators of behaviors and opinions on one of the increasingly important road traffic problems. Two types of studies on the unsafe use of mobile phones and others on board devices (distraction) by road users in Poland between 2014 and 2023 are presented. Safety Performance Indicators (SPI) on handheld mobile phone use from roadside observations and declared opinions on talking, reading messages, checking social media/news, and listening to headphones while driving, cycling, and walking are presented. Overall, 306,240 passenger car drivers observed on all road types all week long in Poland showed a dropping trend (1.8%) between before the study in 2014 SPI 4.1% distracted drivers to SPI 2.3% after the study in 2022. More drivers used mobile phones on single carriageways both in built-up and outside built-up areas. On motorways outside built-up areas, where vehicles are speeding, drivers of passenger cars rarely talk on their handheld mobiles. Results from the first European Union (EU) Baseline project to collect road indicators produced a Key Performance Indicator of 3.8% distracted passenger car (all types of roads, all weekdays and weekend) from 44,011 observed in Poland in 2021. Results from the EU project E-Survey on Road Users' Attitude, third edition (ESRA3), showed declared opinions of mobile use while driving, cycling, and walking down the street in Poland in 2023 compared to 2018 (ESRA2). A positive trend is seen among car drivers who declared lower use of handheld mobile phones than in 2018 (a drop of 11.2%). The survey showed Polish cyclist used more often headphones to listen to music, checked news and messages than cyclists in Europe in 2023. More by 10.1% pedestrians in Europe than in Poland declared checking social media/news, reading messages/emailing while walking down the street in 2023. This lower rate in Poland can be explained by pedestrian's legal prohibition of mobile phone use while crossing the street since 2021. There is strong support for policy measures forbidding all drivers of motorized vehicles to use handheld mobile phone use while driving in all of Europe. Enforcement measures are twice as strong in Europe than in Poland. As the number of accidents and fatalities on distraction is unknown, calculating SPI during roadside observations and questionnaire surveys gives in-depth knowledge of the problem. The authors of the paper represent the Motor Transport Institute's (MTI's) research institution, which systematically analyzes trends in road safety in Poland during its own, national, and international projects. [ABSTRACT FROM AUTHOR]

This paper deals with legal regulation in the field of the selfdriving car (SDC), an autonomous vehicle without a human driver. A survey carried out in Serbia on the acceptability of this vehicle in everyday life indicated the distrust of the population in driving with SDCs due to concerns of violation the privacy, distrust in the protection of personal data and insufficient security and cyber security. For proper functioning and especially for artificial intelligence (AI) decision-making the SDC needs a large set of data, including personal information. In this paper the legal framework for collecting, storing, processing and erasing of personal data for SDCs is developed. Most of regulation are new, due to the technical novelty of the SDC, which uncouples the driver and the vehicle are in the driving process. In the paper, documents and acts which protect privacy according to the EUs General Data Protection Regulation (GDPR) are suggested. Principles for data security and, especially, for cybersecurity of data are legally regulated and proposed to be incorporated into the future law of transportation. The suggested legal regulation would increase the trust of population in the SDC. [ABSTRACT FROM AUTHOR]

Copyright of Urban Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we decompose selective sustained attending behavior into components of continuous attention maintenance and attentional transitions and study how each of these components develops in young children. Our results in two experiments suggest that changes in children's ability to return attention to a target locus after distraction ("Returning") play a crucial role in the development of selective sustained attention between the ages of 3.5–6 years, perhaps to a greater extent than changes in the ability to continuously maintain attention on the target ("Staying"). We further distinguish Returning from the behavior of transitioning attention away from task (i.e., becoming distracted) and investigate the relative contributions of bottom‐up and top‐down factors on these different types of attentional transitions. Overall, these results (a) suggest the importance of understanding the cognitive process of transitioning attention for understanding selective sustained attention and its development, (b) provide an empirical paradigm within which to study this process, and (c) begin to characterize basic features of this process, namely its development and its relative dependence on top‐down and bottom‐up influences on attention. Research Highlights: Young children exhibited an endogenously ability, Returning, to preferentially transition attention to task‐relevant information over task‐irrelevant information.Selective sustained attention and its development were decomposed into Returning and Staying, or task‐selective attention maintenance, using novel eye‐tracking‐based measures.Returning improved between the ages of 3.5–6 years, to a greater extent than Staying.Improvements in Returning supported improvements in selective sustained attention between these ages. [ABSTRACT FROM AUTHOR]

Purpose: Digital distraction is a common phenomenon in e-reading contexts, and it is worth exploring in depth from the perspectives of information (digital content), users (readers) and technology (digital reading medium). Since screen reading has close links with multitasking and potential distraction, any investigation of reading in the digital environment must factor in this reality. This paper aims to investigate the extent and effects of digital distraction while reading on screens. Special emphases go to exploring multitasking while reading. Design/methodology/approach: Survey and analysis methods are employed. Findings: The extent of digital distraction among college students it found is alarming. All the top four sources of distraction are communication-based activities. Female students tend to concentrate more than males when they read on screens. An overwhelming majority of participants choose to read in print to reduce distractions and to concentrate effectively. Screen reading is inherently distracting, primarily due to multitasking. It appears that repeated multitasking during academic endeavors carries substantial costs. Originality/value: Implications of digital distraction are discussed, and directions of future research are suggested. [ABSTRACT FROM AUTHOR]

Rear-end collisions between autonomous vehicles (AVs) and human-driven vehicles (HVs) represent critical scenarios in road networks. Few studies have focused on the scenarios where a HV hits an AV from behind (called a HV-AV collision). This paper aims to investigate the occurrence of HV-AV collisions in the stop-in-lane (SiL) scenario where a HV follows an AV. A humanlike brake control (HLBC) model is firstly proposed to simulate the driver brake control. The HLBC model considers human driving intention, vision-based expectancy, and certain inherent characteristics of human driving to achieve dynamic humanlike braking. Additionally, the joint distribution of off-road-glance and time-headway is originally introduced to simulate the glance distraction of drivers during their dynamic vehicle control. Sequentially, we apply the HLBC model to the SiL scenario to investigate how the HV-AV collision probability changes with respect to various dynamic driving parameters. The results of the case study provide a thorough understanding of the dynamic driving conditions that lead to HV-AV collisions and pave the way for identifying practical countermeasures to improve the road safety involving AVs. [ABSTRACT FROM AUTHOR]

Hypothesis-driven research rests on clearly articulated scientific theories. The building blocks for communicating these theories are scientific terms. Obviously, communication – and thus, scientific progress – is hampered if the meaning of these terms varies idiosyncratically across (sub)fields and even across individual researchers within the same subfield. We have formed an international group of experts representing various theoretical stances with the goal to homogenize the use of the terms that are most relevant to fundamental research on visual distraction in visual search. Our discussions revealed striking heterogeneity and we had to invest much time and effort to increase our mutual understanding of each other's use of central terms, which turned out to be strongly related to our respective theoretical positions. We present the outcomes of these discussions in a glossary and provide some context in several essays. Specifically, we explicate how central terms are used in the distraction literature and consensually sharpen their definitions in order to enable communication across theoretical standpoints. Where applicable, we also explain how the respective constructs can be measured. We believe that this novel type of adversarial collaboration can serve as a model for other fields of psychological research that strive to build a solid groundwork for theorizing and communicating by establishing a common language. For the field of visual distraction, the present paper should facilitate communication across theoretical standpoints and may serve as an introduction and reference text for newcomers. [ABSTRACT FROM AUTHOR]

Background: Despite a sufficient number of papers on the technique of transtibial amputations, the technique of Ertl-type reamputation in short tibial stump remains unreported. Aim: To propose a modification of the Ertl operation in the proximal tibia. Case Presentation: The technique of bone bridge creation in a patient with a malformed stump in the upper third of the tibia at the expense of the regenerate formed after corticotomy of the tibial remnant and dosed distraction of the graft by the Ilizarov apparatus is described. Radiological, ultrasound and MRI methods were used to control the regenerate. The follow-up period was 36 months. At 3.5 months, a synostosis was formed, which allowed primary and then permanent prosthesis with a full-contact prosthesis. At 36 months, the organotypic remodelling of the regenerate was completed. The patient works, uses the prosthesis for 15– 16 hours a day, and walks on average 8– 10 km. Conclusion: The use of the proposed method makes it possible to obtain tibial synostosis without their shortening with elimination of valgus deviation of the fibula stump and the possibility of early functional loading. Synostosis formation occurs within 3.5 months after surgery. Organotypic bone remodelling occurs during primary and then permanent prosthetics. The formed bone bridge has a large support area, which is maintained during the whole follow-up period of 36 months and allows to perform full-contact prosthetics with maximum load on the residual limb end. [ABSTRACT FROM AUTHOR]

• The impact of NDRTs on TOT in the automated driving system has been examined. • Four dimensions of NDRTs were categorized to consolidate their effects on TOT. • Both Vi-Me-Mo and Vi-Me tasks could increase TOT. • Subgroup analysis and meta-regressions were employed to explore moderators. In the pre-era of fully automated vehicles, humans occupy a pivotal role within the driving system. Extensive research has been conducted to explore how drivers interact with automated vehicles across diverse scenarios. This article presents a meta-analysis of 42 papers to examine the influence of non-driving-related tasks (NDRTs) on takeover time (TOT). To consolidate the effect of NDRTs on TOT, this paper classified the NDRTs into four dimensions (visual, auditory, motoric, and mental), which aimed to provide a unified understanding of this impact. This paper employed the following three analyses to understand this impact. Firstly, a synthetical analysis was conducted to compare the effect sizes across primary studies. Secondly, a two-group analysis was performed on studies that included eligible control and experiment groups. Lastly, a moderator analysis, incorporating seven potential moderators, was conducted to further explore the underlying mechanism. The results from the synthetical and two-group analyses revealed that both visual-mental-motoric (Vi-Me-Mo) and visual-mental (Vi-Me) tasks could increase TOT, with the former having a greater effect than the latter. The moderator analysis, including subgroup analysis and meta-regression, further confirmed the significance of moderators within their respective subgroups. However, most moderators exhibited non-significant effects across different scenarios. The findings of this study underscore the crucial importance of attending to TOT and tailoring automated driving systems based on individual driver characteristics. Furthermore, this paper contributes significantly to the advancement of scientific research and engineering design by providing valuable insights into the automotive industry. [ABSTRACT FROM AUTHOR]

• A framework for studying the intersection-approaching behavior is proposed and the human-machine interaction is visualized. • A signal energy detection method based on the wavelet transform is then developed to monitor the driver's response behavior. • Finally, a Bayesian non-parametric method is used to quantify the distribution of driving patterns. The in-vehicle communication provides promising opportunities to improve the road safety and traffic efficiency. Previous studies demonstrated that the professional drivers have better driving skills than the non-professional drivers who allocate more attention to secondary tasks. However, they may not be sensitive to the new in-vehicle technology. In addition, these qualitative studies failed to elaborate on the visual and response behavior differences among different driver groups (professional drivers such as taxi, bus, motorcoach, and non-professional drivers), and lacked the quantitative analysis of driving patterns in a new environment. This paper explores the differences in visual interaction, response characteristics, driving performance, and behavior patterns between the professional and non-professional drivers in the connected environment through a case study of intersection-approaching behavior using a driving simulator. More precisely, two driving scenarios (baseline and human–machine interface (HMI)) were designed in the driving simulator, and 65 participants, including 34 professional drivers and 31 non-professional drivers, completed the experiment. In the HMI scenario, the driver was provided with the signal light phase and phase transition remaining time of the current intersection. This paper also proposes a driving pattern extraction model based on the Bayesian non-parametric method combined with a text clustering algorithm to perform a quantitative description of the driving patterns. The results show that the professional drivers tend to interact less with the HMI compared with the non-professional drivers. Moreover, the professional drivers' first gaze at the HMI occurs and responds earlier. The proposed driving model can effectively describe 7 patterns of intersection-approaching behavior. The connected information can significantly improve the efficiency of the intersection traffic and the driving behavior. However, the professional drivers are more responsive and behave more consistently. This study can provide insights into the development of personalized assisted driving systems, as the two driving populations differ in their interactions, responses, and behavioral patterns. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the evolution and impact of property development activities on REIT performance. The paper provides insights on whether REITs should venture into property development in addition to their core-business of holding income producing properties. Design/methodology/approach: This paper charts and highlights the evolution of development activities of US REITs from 1992 to 2020. The Tobin's Q of property developing REITs and non-property developing REITs are compared using univariate analysis. Findings: Development activities of US REITs grew dramatically during the run up to global financial crisis (GFC) in 2008. The level of development activities has dropped since the GFC and it has not return to its pre-crisis peak. In comparison, development activities of listed property investment companies and homebuilders are less volatile over the same period. The data reveals that property developing REITs enjoy significantly higher Tobin's Q as compared to their non-developing counterparts. Practical implications: Our graphical evidence from a market without development restriction suggests that development restriction in other REIT regimes has it value in limit REITs' excessive risk-taking tendency during a booming property market. The positive relationship between Tobin's Q and the existence of property development activity support the value creation of this business activity to REITs. Originality/value: This paper raises overbuilding as a potential cause of the underperformance of the REIT sector during the GFC. [ABSTRACT FROM AUTHOR]