European NCAP Driver State Monitoring Protocols: Prevalence of Distraction in Naturalistic Driving.

OBJECTIVE: examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours. BACKGROUND: The 2023 introduction of Occupant Status monitoring (OSM) into Euro NCAP will accelerate uptake of Driver State Monitoring (DSM). Euro NCAP outlines distraction behaviours that DSM must detect to earn maximum safety points. Distraction behaviour prevalence and driver alerting and intervention frequency have yet to be examined in naturalistic driving. METHOD: Twenty healthcare workers were provided with an instrumented vehicle for approximately two weeks. Data were continuously monitored with automotive grade DSM during daily work commutes, resulting in 168.8 hours of driver head, eye and gaze tracking. RESULTS: Single long distraction events were the most prevalent, with .89 events/hour. Implementing different thresholds for driving-related and driving-unrelated glance regions impacts alerting rates. Lizard glances (primarily gaze movement) occurred more frequently than owl glances (primarily head movement). Visual time-sharing events occurred at a rate of .21 events/hour. CONCLUSION: Euro NCAP-described driver distraction occurs naturalistically. Lizard glances, requiring gaze tracking, occurred in high frequency relative to owl glances, which only require head tracking, indicating that less sophisticated DSM will miss a substantial amount of distraction events. APPLICATION: This work informs OEMs, DSM manufacturers and regulators of the expected alerting rate of Euro NCAP defined distraction behaviours. Alerting rates will vary with protocol implementation, technology capability, and HMI strategies adopted by the OEMs, in turn impacting safety outcomes, user experience and acceptance of DSM technology.

Organizing for Mars: A Task Management Perspective on Work within Spaceflight Multiteam Systems.

OBJECTIVE: The aim of this study was to examine how task, social, and situational factors shape work patterns, information networks, and performance in spaceflight multiteam systems (MTSs). BACKGROUND: Human factors research has explored the task and individual characteristics that affect decisions regarding when and in what order people complete tasks. We extend this work to understand how the social and situational factors that arise when working in MTSs affect individual work patterns. METHODS: We conducted a complex multi-site space analog simulation with NASA over the course of 3 years. The MTS task required participants from four teams (Geology, Robotics, Engineering, and Human Factors) to collaborate to design a well on Mars. We manipulated the one-way communication delay between the crew and mission support: no time lag, 60-second lag, and 180-second lag. RESULTS: The study revealed that team and situational factors exert strong effects: members whose teams have less similar mental models, those whose teams prioritize their team goal over the MTS goal, and those working in social isolation and/or under communication delay engage longer on tasks. Time-on-task positively predicts MTS information networks, which in turn positively predict MTS performance when communication occurs with a delay, but not when it occurs in real-time. CONCLUSION: Our findings contribute to research on task management in the context of working in teams and multiteam systems. Team and situational factors, along with task factors, shape task management behavior. APPLICATION: Social and situational factors are important predictors of task management in team contexts such as spaceflight MTSs.

A Fast Storage Method for Drone-Borne Passive Microwave Radiation Measurement.

A drone-borne microwave radiometer requires a high sampling frequency and a continuous acquisition capability to detect and mitigate radio frequency interference (RFI), but existing methods cannot store such a large amount of data. In this paper, the dual polling write method (DPSM) for secure digital cards triggered by a timer under a multitask framework based on STM32 MCU is proposed to meet the requirements of continuous data storage. The card programming step was changed from a query waiting structure to a polling query flag bit structure, and time-sharing processing and parallel processing were used to simulate multithreading. The experimental results were as follows: (1) the time consumption of the whole storage procedure was reduced from 4000 microseconds to 200-400 microseconds; (2) the time consumption of the card programming step was reduced from 3000 microseconds in the first block and 1000 microseconds in the second and subsequent blocks to 17-174 microseconds and 18-71 microseconds, respectively, compared with the existing method; (3) the delay in the whole sampling cycle was reduced from 3942 microseconds to 0 microseconds. The results of this paper can meet the data storage requirements of a drone-borne microwave radiometer and be applied to the high-speed storage of other devices.

LR-selHSQMBC: Simultaneous Detection and Quantification of Very Weak Long-Range Heteronuclear NMR Correlations.

The optimum detection and accurate measurement of longer-range (4 J and higher) heteronuclear NMR correlations is described. The magnitude and/or the sign of a wide range of large and small long-range couplings can be simultaneously determined for protonated and non-protonated 13 C and 15 N nuclei using the LR-selHSQMBC experiment.

Does Reducing Sedentarity With Standing Desks Hinder Cognitive Performance?

OBJECTIVE: The goal of this study was to determine if using a standing desk would affect the productivity of workers, based on the type of work they perform. BACKGROUND: Standing desks are a promising new health intervention in the workplace, but users and employers often require more specific recommendations related to productivity, such as the type of work that is more suited for the standing desk. METHOD: Thirty-seven young and healthy adults performed eight cognitive tasks in a 2 × 2 × 2 within-subject design of the following independent variables: posture (sitting/standing), task difficulty (easy/hard), and input device (computer mouse/tactile screen) in a counterbalanced order. RESULTS: Our results revealed that using a standing desk had no negative effect on performance or perception, but it did lead to increased brain activity in the alpha band for the parietal region (ß = 0.186, p = .001). CONCLUSION: We conclude that users of standing desks can freely stand for any level of task difficulty for work that involves working memory. However, more research is needed to generalize these results to other types of cognitive abilities and prolonged use of standing desks. APPLICATION: Our results simplify recommendations for workers as they do not need to worry about the type of work they are performing when using a standing desk.

Performance evaluation of time-sharing utilization of multi-function sponge space to reduce waterlogging in a highly urbanizing area.

Urban waterlogging is a dilemma faced by many highly urbanizing areas. To solve the contradiction between the space requirement for waterlogging control and the scarcity of urban space, time-sharing utilization of the multi-function sponge space (MFSS) is promoted in some urbanizing areas. The MFSS is designed to have certain social or economic functions during dry or light rain events and detains stormwater on heavy rain events. However, there is lack of understanding on how to achieve the maximum benefit of multi-function. In this study, three time-sharing utilization modes are proposed to use MFSS to detain runoff: when the rain event begins (Mode A), when cumulative rainfall is greater than a specific threshold (Mode B), or when rainfall intensity is higher than a specific threshold (Mode C). A methodological framework based on the Storm Water Management Model (SWMM) is proposed to evaluate the waterlogging reduction performance of the three modes under different rainfall conditions and thresholds for enabling MFSS in an urbanizing catchment in Shenzhen, China. The performance is measured by comparing the total volume of overflow from manholes of the drainage system with and without MFSS during a storm event. The results indicate that: (1) Under Mode A, the performance is more effective under a light storm event with an early peak; (2) Under Mode B, as the cumulative rainfall threshold for enabling MFSS increases, the overflow first decreases and then increases. Different threshold values have to be set for different types of rainfall events to achieve the best performance; (3) Under Mode C, as the rainfall intensity threshold for enabling MFSS increases, the overflow also first decreases and then rapidly increases at a high threshold value. The mode has an identical range of optimal thresholds under different types of rainfall events. Furthermore, Mode C has higher efficiency in overflow reduction than the other two modes, no matter whether under design storms or historical storms. Therefore, Mode C is recommended as an efficient and stable utilization mode for MFSS.

The Effect of Walking on Auditory Localization, Visual Discrimination, and Aurally Aided Visual Search.

OBJECTIVE: The present study was designed to examine the impact that walking has on performance in auditory localization, visual discrimination, and aurally aided visual search tasks. BACKGROUND: Auditory localization and visual search are critical skills that are frequently conducted by moving observers, but most laboratory studies of these tasks have been conducted on stationary listeners who were either seated or standing during stimulus presentation. METHOD: Thirty participants completed three different tasks while either standing still or while walking at a comfortable self-selected pace on a treadmill: (1) an auditory localization task, where they identified the perceived location of a target sound; (2) a visual discrimination task, where they identified a visual target presented at a known location directly in front of the listener; and (3) an aurally aided visual search task, where they identified a visual target that was presented in the presence of multiple visual distracters either in isolation or in conjunction with a spatially colocated auditory cue. RESULTS: Participants who were walking performed auditory localization and aurally aided visual search tasks significantly faster than those who were standing, with no loss in accuracy. CONCLUSION: The improved aurally aided visual search performance found in this experiment may be related to enhanced overall activation caused by walking. It is also possible that the slight head movements required may have provided auditory cues that enhanced localization accuracy. APPLICATION: The results have potential applications in virtual and augmented reality displays where audio cues might be presented to listeners while walking.

Multiscale Time-Sharing Elastography Algorithms and Transfer Learning of Clinicopathological Features of Uterine Cervical Cancer for Medical Intelligent Computing System.

Intelligent medical diagnosis and computing system faces many challenges in complex object recognition, large-scale data imaging and real-time diagnosis, such as poor real-time computing, low efficiency of data storage and low recognition rate of lesions. In order to solve the above problems, this paper proposes a medical intelligent computing system and a series of algorithms for the clinical pathology of cervical cancer based on the multi-scale imaging and transfer learning framework. Firstly, based on data dimensions, imaging errors and other factors, this paper designs a multi-scale time-sharing elastic imaging algorithm based on image reconstruction time and data sample characteristics. Then, taking the burst imaging cohort and the calculation data set of new cervical cancer cases as the objects, based on the difficulties of cervical cancer feature modeling, this paper proposes the transfer learning algorithm of clinical and pathological features of cervical cancer. Finally, a medical intelligent computing system for cervical cancer pathology analysis and calculation with high efficiency and reliability is established. A series of proposed algorithms are compared with single-scale Retinex (SSR), which is based on single-scale Retinex migration learning (SSR-TL). The experimental results show that the proposed algorithm in cervical cancer pathological imaging and scoring, as well as the feature extraction and recognition of lesions, especially the efficiency of system execution, is obviously due to the comparison algorithm.

GWS-A Collaborative Load-Balancing Algorithm for Internet-of-Things.

This paper firstly replaces the first-come-first-service (FCFS) mechanism with the time-sharing (TS) mechanism in fog computing nodes (FCNs). Then a collaborative load-balancing algorithm for the TS mechanism is proposed for FCNs. The algorithm is a variant of a work-stealing scheduling algorithm, and is based on the Nash bargaining solution (NBS) for a cooperative game between FCNs. Pareto optimality is achieved through the collaborative working of FCNs to improve the performance of every FCN. Lastly the simulation results demonstrate that the game-theory based work-stealing algorithm (GWS) outperforms the classical work-stealing algorithm (CWS).

Older Adult Multitasking Performance Using a Gaze-Contingent Useful Field of View.

Objective We implemented a gaze-contingent useful field of view paradigm to examine older adult multitasking performance in a simulated driving environment. Background Multitasking refers to the ability to manage multiple simultaneous streams of information. Recent work suggests that multitasking declines with age, yet the mechanisms supporting these declines are still debated. One possible framework to better understand this phenomenon is the useful field of view, or the area in the visual field where information can be attended and processed. In particular, the useful field of view allows for the discrimination of two competing theories of real-time multitasking, a general interference account and a tunneling account. Methods Twenty-five older adult subjects completed a useful field of view task that involved discriminating the orientation of lines in gaze-contingent Gabor patches appearing at varying eccentricities (based on distance from the fovea) as they operated a vehicle in a driving simulator. In half of the driving scenarios, subjects also completed an auditory two-back task to manipulate cognitive workload, and during some trials, wind was introduced as a means to alter general driving difficulty. Results Consistent with prior work, indices of driving performance were sensitive to both wind and workload. Interestingly, we also observed a decline in Gabor patch discrimination accuracy under high cognitive workload regardless of eccentricity, which provides support for a general interference account of multitasking. Conclusion The results showed that our gaze-contingent useful field of view paradigm was able to successfully examine older adult multitasking performance in a simulated driving environment. Application This study represents the first attempt to successfully measure dynamic changes in the useful field of view for older adults completing a multitasking scenario involving driving.

Driving While Interacting With Google Glass: Investigating the Combined Effect of Head-Up Display and Hands-Free Input on Driving Safety and Multitask Performance.

OBJECTIVE: This study evaluated the individual and combined effects of voice (vs. manual) input and head-up (vs. head-down) display in a driving and device interaction task. BACKGROUND: Advances in wearable technology offer new possibilities for in-vehicle interaction but also present new challenges for managing driver attention and regulating device usage in vehicles. This research investigated how driving performance is affected by interface characteristics of devices used for concurrent secondary tasks. A positive impact on driving performance was expected when devices included voice-to-text functionality (reducing demand for visual and manual resources) and a head-up display (HUD) (supporting greater visibility of the driving environment). METHOD: Driver behavior and performance was compared in a texting-while-driving task set during a driving simulation. The texting task was completed with and without voice-to-text using a smartphone and with voice-to-text using Google Glass's HUD. RESULTS: Driving task performance degraded with the addition of the secondary texting task. However, voice-to-text input supported relatively better performance in both driving and texting tasks compared to using manual entry. HUD functionality further improved driving performance compared to conditions using a smartphone and often was not significantly worse than performance without the texting task. CONCLUSION: This study suggests that despite the performance costs of texting-while-driving, voice input methods improve performance over manual entry, and head-up displays may further extend those performance benefits. APPLICATION: This study can inform designers and potential users of wearable technologies as well as policymakers tasked with regulating the use of these technologies while driving.

Trust and the Compliance-Reliance Paradigm: The Effects of Risk, Error Bias, and Reliability on Trust and Dependence.

OBJECTIVE: This study provides a theoretical link between trust and the compliance-reliance paradigm. We propose that for trust mediation to occur, the operator must be presented with a salient choice, and there must be an element of risk for dependence. BACKGROUND: Research suggests that false alarms and misses affect dependence via two independent processes, hypothesized as trust in signals and trust in nonsignals. These two trust types manifest in categorically different behaviors: compliance and reliance. METHOD: Eighty-eight participants completed a primary flight task and a secondary signaling system task. Participants evaluated their trust according to the informational bases of trust: performance, process, and purpose. Participants were in a high- or low-risk group. Signaling systems varied by reliability (90%, 60%) within subjects and error bias (false alarm prone, miss prone) between subjects. RESULTS: False-alarm rate affected compliance but not reliance. Miss rate affected reliance but not compliance. Mediation analyses indicated that trust mediated the relationship between false-alarm rate and compliance. Bayesian mediation analyses favored evidence indicating trust did not mediate miss rate and reliance. Conditional indirect effects indicated that factors of trust mediated the relationship between false-alarm rate and compliance (i.e., purpose) and reliance (i.e., process) but only in the high-risk group. CONCLUSION: The compliance-reliance paradigm is not the reflection of two types of trust. APPLICATION: This research could be used to update training and design recommendations that are based upon the assumption that trust causes operator responses regardless of error bias.

Differences in Multitask Resource Reallocation After Change in Task Values.

OBJECTIVE: The objective was to characterize multitask resource reallocation strategies when managing subtasks with various assigned values. BACKGROUND: When solving a resource conflict in multitasking, Salvucci and Taatgen predict a globally rational strategy will be followed that favors the most urgent subtask and optimizes global performance. However, Katidioti and Taatgen identified a locally rational strategy that optimizes only a subcomponent of the whole task, leading to detrimental consequences on global performance. Moreover, the question remains open whether expertise would have an impact on the choice of the strategy. METHOD: We adopted a multitask environment used for pilot selection with a change in emphasis on two out of four subtasks while all subtasks had to be maintained over a minimum performance. A laboratory eye-tracking study contrasted 20 recently selected pilot students considered as experienced with this task and 15 university students considered as novices. RESULTS: When two subtasks were emphasized, novices focused their resources particularly on one high-value subtask and failed to prevent both low-value subtasks falling below minimum performance. On the contrary, experienced people delayed the processing of one low-value subtask but managed to optimize global performance. CONCLUSION: In a multitasking environment where some subtasks are emphasized, novices follow a locally rational strategy whereas experienced participants follow a globally rational strategy. APPLICATION: During complex training, trainees are only able to adjust their resource allocation strategy to subtask emphasis changes once they are familiar with the multitasking environment.

Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching.

OBJECTIVE: The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible. BACKGROUND: The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed. METHOD: In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker. RESULTS: Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation. CONCLUSIONS: The multiattribute decision model provided a good fit to the data. APPLICATIONS: The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive.

Cognitive Workload ≠ Crash Risk: Rejoinder to Study by Strayer et al. (2015).

Strayer et al.'s article is a significant attempt to scale the cognitive workload of different potentially distracting tasks. It is tempting but not warranted to equate the workload with the relative risk of crash involvement. In this article, I list the reasons why the scaling should not be generalized to safety implications in real driving and argue for the combination of studies of maximal performance assessment (e.g., simulation) with behavioral assessment (e.g., naturalistic driving).

Driver Compensation: Impairment or Improvement?

Strayer et al.'s conclusion that their "cognitive distraction scale" for auditory-vocal tasks indicates "significant impairments to driving" is not supported by their data. Additional analysis demonstrates that slower brake reaction times during auditory-vocal tasks were fully compensated for by longer following distances to the lead car. Naturalistic driving data demonstrate that cellular conversation decreases crash risk, the opposite of the article's assumption. Hence, the scale's internal and external validities for indicating driving impairment are highly questionable.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Priority influences task selection decisions in multi-task management.

The role of task priority on task selection in multi-task management is unclear based on prior work, leading to a common finding of 'priority neglect'. However, properties such as urgency and conflict may influence whether operators weigh priority in their decision. We examined the role of instructed task prioritization, bolstered by more urgent and conflicting conditions, on how operators select among emergent, concurrent tasks when multitasking. Using the Multi-Attribute Task Battery (MATB) multitasking platform we tested both an auditory communications task and a manual tracking task as the priority tasks. Results showed that instructed priority significantly increased target task selection under the conflicting task conditions for both tasks. Urgency itself may modulate whether instructions to prioritize affect task selection choices when multitasking, and therefore counter to prior results instructions may yet be useful for helping operators select a higher priority task under conflict, a generalizable effect to be further explored.

Attention control in a demanding dynamic time-sharing environment: An eye-tracking study.

In this study, we examined different models of cognitive control in dynamic time-sharing situations. We investigated attentional allocation by registering participants' eye movements while they performed a new time-sharing task that forced them to solve resource conflicts between subtasks through prioritization. Participants were monitoring four subtasks each requiring different amounts of visual attention and response frequencies. Participants' attention allocation was operationalized in terms of the time spent dwelling on subtasks, the rate they visually sampled the tasks, and the duration of dwells. Additionally, the accuracy of responses and efficiency of time-sharing were estimated. In Experiment 1, we studied adaptation to a time-sharing environment in which priority order of the subtasks was kept constant from trial to trial. We found that the participants sampled the most important subtasks more frequently, spent more time on them, and shifted their gaze earlier to them than to less important subtasks. That is, they allocated their attention according to the subtask priorities. In Experiment 2, subtask priorities changed from trial to trial. Despite the higher demands of the constantly changing situation, participants again adapted to the varying priorities of the subtasks almost instantly. Our results suggest that performance in complex and dynamic time-sharing situations is not managed by a system relying on liberal resource allocation policies and gradual learning. Instead, the participants' rapid adaptation is more consistent with tighter executive and authoritative control and intelligent use of prioritization information.

Research on the supervision mechanism of new energy time-sharing rental vehicles in the background of carbon peak.

At present, new energy time-sharing rental vehicles have become a new generation of travel modes, playing a positive role in environmental protection and being regarded as one of the most promising travel modes in the future. However, due to the rapid pace of market expansion, the problems in their development have come one after another, such as the leakage of personal information, difficulty refunding the deposit and other phenomena frequently occurring, causing problems for consumer user experience. The reasons are as follows: government policies and regulations need to be perfected; the level of enterprise operation needs to be observed, financing is difficult, and the level of consumer participation in supervision is low. Based on the traits of bounded rationality in the internet plus the periodic rental market, this paper introduces the government into the market subject and constructs a three-party dynamic evolution game model between the government, enterprises, and consumers to reveal the control strategy. This paper conducts a concrete analysis to verify the model using a case study by revealing the game process between the regulation strategy and behavior decisions of enterprises and consumers and provides a theoretical basis and reference for policy-making and decision-making. The results showed that when the key parameters are in different numerical ranges, the system has four evolutionary stability results. By appropriately increasing the number of subsidies and penalties, increasing the proportion of the compensation coefficient to consumers, and urging enterprises to reduce operating costs, government participation is conducive to the healthy development of the new energy time-sharing automobile industry.