Aging and the use of an in-vehicle intersection crossing assist system: An on-road study

One of the principal facets of age-related decline–diminished perceptual ability, can also be viewed as a prominent factor when crossing intersections, particularly rural intersections that have disproportionately high fatality rate and where vehicles travel at higher velocities. Providing information through in-vehicle technology may aid drivers in improving crossing of such intersections. The current study examines the efficacy of an in-vehicle intersection crossing assist system in a real-world rural setting across age groups. Thirty-two, older and younger drivers completed several crossings of a busy rural intersection. Drivers completed two blocks of trials in which the presence/absence of the in-vehicle system was counterbalanced. The results showed a limited impact of the system on driving performance, exhibited in longer wait time before crossing and rising trend towards reduced probability of accepting small crossing gaps. Older drivers performed similarly to younger, although they showed a greater tendency towards conservative driving behaviour. The current study represents an initial effort to examine an in-vehicle intersection crossing assist system in a real-world rural environment, generating results that reveal a potential for these types of systems to be assistive to drivers across age groups and increase the safety at rural intersections.     

Young male drivers’ risky driving 15 months after licensure – The role of intervention,attitudes towards accompanied driving,and parents’ risk

An indispensable issue in contemporary research on risk-taking by young drivers is parents’ influence on their offspring’s driving behavior. The current study measures this influence by using a risk index of parents’ driving behavior measured via in-vehicle data recorders together with young drivers’ self-reported answers to a set of questionnaires. Both parents and young drivers participated in one of three intervention program groups to enhance safe driving within a longitudinal study, and the outcome of this intervention was measured 15 months following licensure. The aim of the current study was thus to assess the contribution of parents’ actual driving behavior, participation in the intervention, and teen drivers’ attitudes towards accompanied driving (measured six months after licensure), to the reported risky driving of the young drivers fifteen months after receiving their driving license. The data consist of a sample of 78 parent-young driver dyads who were originally randomly assigned to one of three intervention groups (receiving different forms of feedback) or a control group (with no feedback). Findings indicate that the feedback and training to parents intervention group, as well as parents’ risky driving events rate, were positively associated with the reported proneness to reckless driving and the reckless driving habits of the young drivers. In addition, lower perception of accompanied driving as enabling a sense of relatedness with parents, and higher negative perceptions of this period, were related to higher reported risky driving among young drivers fifteen months after licensure. The results highlight the importance of parents’ behavior and relationships with their offspring as key concepts in moderating risky driving among young drivers. Practical implications for road safety are discussed.     

How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors

Autonomous vehicles and advanced driver assistance technology are growing exponentially, and vehicles equipped with conditional automation, which has features like Traffic Jam Pilot and Highway Assist, are already available in the market. And this could expose the driver to a stressful driving condition during the takeover mission. To identify stressful takeover situations and better interact with automated systems, the relationship and effect between drivers’ physiological responses, situational factors (e.g., takeover request [TOR] lead time, takeover frequencies, and scenario types), and takeover criticality were investigated.34 participants were involved in a series of takeover events in a simulated driving environment, which are varied by different TOR lead time conditions and driving scenes. The situational factors, drivers’ skin conductance (SC), heart rate (HR), gaze behaviors, and takeover criticality ratings were collected and analyzed. The results indicated that drivers had a higher takeover criticality rating when they experienced a shorter TOR lead time level or at first to fourth take-overs. Besides, drivers who encountered a dynamic obstacle reported higher takeover criticality ratings when they were at the same Time to collision (TTC). We also observed that the takeover situations of higher criticality have larger driver’s maximum HR, mean pupil size, and maximum change in the SC (relative to the initial value of a takeover stage). Those findings of situational factors and physiological responses can provide additional support for the designing of adaptive alert systems and environmental soothing technology in conditionally automated driving, which will improve the takeover performances and drivers’ experience.     

Mild functional decline and driving performance of older drivers without a diagnosed dementia: Study of leukoaraiosis and cognitive function

Motor vehicle collisions involving older drivers have increased and become an important social issue. It is known that the decline of cognitive function, including dementia, affects driving performance. A series of studies using the Mini-Mental State Examination (MMSE) and other tests of dementia have attempted to prevent motor vehicle collisions by identifying as early as possible older drivers who may be unable to maintain their driving performance. Further, the performance of older drivers may deteriorate even if they do not have a diagnosis of dementia. Therefore we focused on the relationship between cognitive functioning assessed by the MMSE and diagnosis of leukoaraiosis (LA), or changes in the cerebral white matter, with different aspects of driving behavior resulting from aging. Qualified driving instructors evaluated participants’ driving behaviors on an outdoor driving course at a driving school. Visual search duration and angle at intersections were obtained by wearable wireless sensors. Vehicle speed and minimum vehicle speed were recorded from vehicle speed pulse signals. Duration of signaling and visual searches at unsignalized intersections were recorded using an in-vehicle camera. We assessed instructors’ evaluations and the scores on two instruments to evaluate the effects of MMSE scores and the grade of LA on driving performance were verified. The results suggest that lower MMSE scores and higher LA grade can predict some aspects of poor driving performance in older drivers before they experience dementia or an evident decline in cognitive functioning. Based on these results, we discuss countermeasures that may prevent motor vehicle collisions involving older drivers.     

Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes

Supplying training to drivers that teaches them about automated driving and requests to intervene may help them to build and maintain a mental representation of how automation works and thereby improve takeover performance. We aimed to investigate the effect of different types of training programmes about the functioning of automated driving on drivers’ takeover performance during real driving. Fifty-two participants were split into three groups for training sessions: paper (short notice), video (3-minute tutorial) and practice (short drive). After the training, participants experienced automated driving and both urgent and non-urgent requests to intervene in a Wizard-of-Oz vehicle on public roads. Participants’ takeover time, visual behaviour, mental workload, and flow levels during the requests to intervene were assessed. Our results indicated that in urgent circumstances, participants’ takeover response times were faster in the practice training condition compared to the other training conditions. Nevertheless, the practice training session did not present any other positive effect on drivers’ visual behaviour. This could indicate that prior training, particularly when reinforcing drivers' motor skills, improved their takeover response time at the latest motor stages rather than in the early sensory states. In addition, the analysis of in-vehicle videos revealed that participants’ attention was captured in the first place by the in-vehicle human-machine interface during the urgent request to intervene. This highlights the importance for designers to display information on the HMI in an appropriate way to optimise drivers’ situation awareness in critical situations.     

Imperfect in-vehicle collision avoidance warning systems can aid distracted drivers

The purpose of this study was to evaluate the efficacy of a type of in-vehicle collision avoidance warning system (IVCAWS) under conditions of driver distraction. Forty-three participants responded to an imperfect warning system while simultaneously driving a simulator and performing a visual/cognitive task. The major concerns were whether drivers would be more inclined to rely on such a system when they are distracted by subsidiary tasks, and if this reliance would be counterproductive. We found that distracted drivers responded, by increasing their temporal headway, to the less reliable system’s alarms, but the warning system at the higher reliability levels led to over reliance and ultimately to maintaining shorter headways. This study has practical implications for the use of warning systems as driving aids for drivers. Although aids may be helpful and, in many cases, the more reliable aid is preferable, in the case of distraction, drivers may misuse the aid.     

Modelling the influence of time pressure on reaction time of drivers

Speeding is a major traffic violation and time pressure is one of the leading contributors to speeding. High-speed driving requires an immediate response to perilous events from the driver to avoid a crash. Reaction time is one of the important driving performance measures to assess the driver’s response to the event. Therefore, the current study examined the influence of time pressure on reaction times of the drivers measured for two different perilous events (pedestrians crossing and obstacle overtaking). Eighty-five Indian licensed drivers participated in a driving simulation study designed for three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP). The survival analysis technique was used to model the effect of time pressure and driver characteristics with reaction times of the drivers. It was observed that drivers’ reaction times decreased by 18% and 9% in LTP and 28% and 16% in HTP during the pedestrians crossing and obstacle overtaking events, respectively. Further, 1 m/second increase in approach speed resulted in 2% and 4% reduction in reaction times of the drivers in pedestrians crossing and obstacle overtaking events, respectively. Young drivers responded 21% faster than mature drivers during the pedestrians crossing event. Interestingly, sleeping hours and physical fitness played an important role in driver’s reaction to the events. The drivers performing regular physical exercise and having minimum eight-hours of overnight sleep reacted 16% and 17% earlier in pedestrians crossing and obstacle overtaking events, respectively. The overall findings from this study showed enhanced stimulus-response behaviour of the drivers under time pressure driving conditions. The results obtained from the study can give new insight into various safety-related ITS applications.     

Evaluating the impacts of situational awareness and mental stress on takeover performance under conditional automation

Several safety concerns emerge for the transition of control from the automated driving system to a human driver after the vehicle issues a takeover warning under conditional vehicle automation (SAE Level 3). In this context, recent advances in in-vehicle driver monitoring systems enable tracking drivers’ physiological indicators (e.g., eye-tracking and heart rate (HR) measures) to assess their real-time situational awareness (SA) and mental stress. This study seeks to analyze differences in driver’s SA and mental stress over time (i.e., successive experiment runs) using these physiological indicators to assess their impacts on takeover performance. We use eye-tracking measures (i.e., on-road glance rate and road attention ratio) as indicators of driver’s SA during automated driving. Further, we use the pre-warning normalized HR (NHR) and HR variability (HRV) as well as the change in NHR and HRV after the takeover warning as indicators of mental stress immediately before and the change in mental stress after the takeover warning, respectively. To analyze the effects of driver state (in terms of SA and mental stress) on the overall takeover performance, this study uses a comprehensive metric, Takeover Performance Index (TOPI), proposed in our previous work (Agrawal & Peeta, 2021). The TOPI combines multiple driving performance indicators while partly accounting for their interdependencies. Results from statistical analyses of data from 134 participants using driving simulator experiments illustrate significant differences in driver state over successive experiment runs, except for the change in mental stress after the takeover warning. Some significant correlations were found between the physiological indicators of SA and mental stress used in this study. Takeover performance model results illustrate a significant negative effect of change in NHR after the takeover warning on the TOPI. However, none of the other physiological indicators show significant impacts on takeover performance. The study findings provide valuable insights to auto manufacturers for designing integrated in-vehicle driver monitoring and warning systems that enhance road safety and user experience.     

The impact of an in-vehicle display on glance distribution in partially automated driving in an on-road experiment

One of the major challenges of designing an HMI for partially automated vehicles is the trade-off between a sufficient level of system information and avoidance of distracting the driver. This study aimed to investigate drivers’ glance behavior as an indicator of distraction when vehicle guidance is partially automated. Therefore, an on-road experiment was conducted comparing two versions of an in-vehicle display (during partially automated driving) and no display (during manual driving) on a heavy congested highway segment. The distribution of drivers’ total glance durations on the HMI showed that visual attention was shifted away from monitoring the central road scene towards looking at the in-vehicle display to a considerable extent. However, an analysis of the distribution of single glance durations supports the view that using partial automation and a respective HMI does not lead to a critical increase in distraction. Driving with a simplified version of the HMI had the potential to reduce glance duration on and thus potential distraction of the in-vehicle display.     

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.     

Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks

Globally, motor vehicle crashes account for over 1.2 million fatalities per year and are the leading cause of death for people aged 15–29 years. The majority of road crashes are caused by human error, with risk heightened among young and novice drivers learning to negotiate the complexities of the road environment. Direct feedback has been shown to have a positive impact on driving behaviour. Methods that could detect behavioural changes and therefore, positively reinforce safer driving during the early stages of driver licensing could have considerable road safety benefit. A new methodology is presented combining in-vehicle telematics technology, providing measurements forming a personalised driver profile, with neural networks to identify changes in driving behaviour. Using Long Short-Term Memory (LSTM) recurrent neural networks, individual drivers are identified based on their pattern of acceleration, deceleration and exceeding the speed limit. After model calibration, new, real-time data of the driver is supplied to the LSTM and, by monitoring prediction performance, one can assess whether a (positive or negative) change in driving behaviour is occurring over time. The paper highlights that the approach is robust to different neural network structures, data selections, calibration settings, and methodologies to select benchmarks for safe and unsafe driving. Presented case studies show additional model applications for investigating changes in driving behaviour among individuals following or during specific events (e.g., receipt of insurance renewal letters) and time periods (e.g., driving during holiday periods). The application of the presented methodology shows potential to form the basis of timely provision of direct feedback to drivers by telematics-based insurers. Such feedback may prevent internalisation of new, risky driving habits contributing to crash risk, potentially reducing deaths and injuries among young drivers as a result.     

Cell-Phone–Induced Driver Distraction

ABSTRACT— Our research examined the effects of hands-free cell-phone conversations on simulated driving. We found that even when participants looked directly at objects in the driving environment, they were less likely to create a durable memory of those objects if they were conversing on a cell phone. This pattern was obtained for objects of both high and low relevance, suggesting that very little semantic analysis of the objects occurs outside the restricted focus of attention. Moreover, in-vehicle conversations do not interfere with driving as much as cell-phone conversations do, because drivers are better able to synchronize the processing demands of driving with in-vehicle conversations than with cell-phone conversations. Together, these data support an inattention-blindness interpretation wherein the disruptive effects of cell-phone conversations on driving are due in large part to the diversion of attention from driving to the phone conversation.     

The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow

Models for describing the microscopic driving behavior rarely consider the “social effects” on drivers’ driving decisions. However, social effect can be generated due to interactions with surrounding vehicles and affect drivers’ driving behavior, e.g., the interactions result in imitating the behavior of peer drivers. Therefore, social environment and peer influence can impact the drivers’ instantaneous behavior and shift the individuals’ driving state. This study aims to explore empirical evidence for existence of a social effect, i.e., when a fast-moving vehicle passes a subject vehicle, does the driver mimic the behavior of passing vehicle? High-resolution Basic Safety Message data set (N = 151,380,578) from the Safety Pilot Model Deployment program in Ann Arbor, Michigan, is used to explore the issue. The data relates to positions, speeds, and accelerations of 63 host vehicles traveling in connected vehicles with detailed information on surrounding environment at a frequency of 10 Hz. Rigorous random parameter logit models are estimated to capture the heterogeneity among the observations and to explore if the correlates of social effect can vary both positively and negatively. Results show that subject drivers do mimic the behavior of passing vehicles –in 16 percent of passing events (N = 18,099 total passings occurred in freeways), subject vehicle drivers are observed to follow the passing vehicles accelerating. We found that only 1.2 percent of drivers normally sped up (10 km/hr in 10 s) during their trips, when they were not passed by other vehicles. However, if passed by a high speed vehicle the percentage of drivers who sped up is 16.0 percent. The speed change of at least 10 km/hr within 10 s duration is considered as accelerating threshold. Furthermore, the acceleration of subject vehicle is more likely if the speed of subject driver is higher and more surrounding vehicles are present. Interestingly, if the difference with passing vehicle speed is high, the likelihood of subject driver’s acceleration is lower, consistent with expectation that if such differences are too high, the subject driver may be minimally affected. The study provides new evidence that drivers’ social interactions can change traffic flow and implications of the study results are discussed.     

Driver stress as influenced by driving maneuvers and roadway conditions

Excess stress can influence driving performance and increase crash likelihood. The level of stress can also vary based on different driving conditions. Past research has not differentiated among these conditions, but rather has focused on individual driver differences. The goal of this study is to understand how different driving tasks and roadway conditions may influence the stress perceived by drivers. This was accomplished using data from a survey that assessed drivers’ stress under various road, traffic and weather-related scenarios. Factor analytic techniques were used to find groups of driving scenarios that generate similar stress levels in drivers. The results revealed four scenarios that were categorized in terms of (1) weather, (2) visibility, (3) interactions with other drivers, and (4) driving tasks. Ordered logistic regression models were then used to determine the effect of socioeconomic characteristics, trip behavior, and crash history for different stressful driving scenarios. Increases in stress with these four factors were influenced by age and gender, with females being more likely to report higher levels of stress than males. The effect of age varied in that older drivers generally reported higher stress levels, except when interacting with other drivers. Drivers with a history of crashes reported significantly higher stress levels when there was limited visibility, in adverse weather, and while performing common driving tasks. The results revealed that stress depends not only on driver characteristics, but also on the specific driving environment.     

Individual differences in stress and fatigue in two field studies of driving

This paper reports the findings of two field studies of Australian drivers in which individual differences in stress and fatigue were investigated. In the first study, 58 professional drivers completed measures of mood, fatigue and other subjective stress state measures, before and after performing a prolonged driving trip. The results indicated that the scales were sensitive to increased fatigue following the driving trip, and correlated appropriately with Fatigue Proneness, a driver stress trait. In the second study, 104 non-professional drivers completed identical subjective stress state measures as the professional drivers, before and after performing a driving trip. Drivers completed a measure of driving-related stress traits, the Driver Stress Inventory (DSI), and a measure of coping, the Driving Coping Questionnaire (DCQ). Both measures were predictive of state response to driving, and the association between Fatigue Proneness and post-drive fatigue found in the first study was replicated. Findings from these studies suggest that fatigue and stress reactions to driving are psychometrically distinct, but may have some common antecedents, such as use of emotion-focused coping. The studies confirm the importance of fatigue and stress as potential safety problems, but also highlight the role of individual differences in response to the demands of driving.     

Drivers’ attitudes toward imperfect distraction mitigation strategies

Studies were conducted to assess driver acceptance of and trust in distraction mitigation strategies. Previous studies have shown that in-vehicle tasks undermine driver safety, and that there is a need for strategies to reduce the effects of in-vehicle distractions. Trust and acceptance of such strategies strongly influence their effectiveness. Different strategies intended to reduce distraction were categorized in a taxonomy. Focus groups were conducted to help refine this taxonomy and explore driver acceptance issues related to these strategies. A driving simulator experiment was then conducted using two of the strategies: an advising strategy that warns drivers of potential dangers and a locking strategy that prevents the driver from continuing a distracting task. These strategies were presented to 16 middle-aged and 12 older drivers in two modes (auditory, visual) with two levels of adaptation (true, false). Older drivers accepted and trusted the strategies more than middle-aged drivers. Regardless of age, all drivers preferred strategies that provided alerts in a visual mode rather than an auditory mode. When the system falsely adapted to the road situation, trust in the strategies declined. The findings show that display modality has a strong effect on driver acceptance and trust, and that older drivers are more trusting and accepting of distraction mitigation technology even when it operates imperfectly.     

Internal causes of return trip effect based on eye movement and EEG indices

When traveling by car, the driver normally feels that the return trip is shorter than the outbound trip. The reason for this feeling, called return trip effect, is not clear. To explore the internal causes of this return trip effect, an indoor driving simulation experiment and a real car driving experiment were performed in this study. Questionnaires were used to obtain the estimated time of the outbound and return trips of the drivers. An eye tracker and an electroencephalograph equipment were used to record driver's eye movement and EEG data in the real-car driving experiment. The physiological indices and questionnaire results showed that the difference in the driver's cognitive loads of the outbound and return trips was the internal cause of the return trip effect. Drivers who were unfamiliar with the road had a different cognitive load between the two trips, which resulted in the return trip effect. However, drivers who were familiar with the road did not experience a return trip effect due to the close cognitive loads of the two trips.     

How does emotional intelligence predict driving behaviors among non-commercial drivers?

Dangerous driving behaviors have been found to be a leading contributor to vehicle crashes and fatalities, with more than 2.7 million people injured and 36,560 people killed in the United States in 2018 (NHTSA, 2020). Drivers’ emotions have been found to be among the leading contributors to dangerous driving behaviors. Emotions can be measured and understood through one’s emotional intelligence (EI). Previous research has confirmed the relationship between EI and dangerous driving behaviors among general driving populations in limited scope. This study analyzed dangerous driving behaviors (e.g., aggressive driving) among non-commercial US drivers. 615 US drivers ages 18 to 65 (M = 31.14, SD = 11.15) with valid US driver’s licenses (non-commercial) participated in this study. Participants completed an online survey through Qualtrics that included the Trait Emotional Intelligence Questionnaire (TEIQue-SF) to measure different dimensions of EI and the Dula Dangerous Driving Index (DDDI) and the Driving Behavior Questionnaire (DBQ) to measure dangerous driving behaviors. Furthermore, participants reported their demographic information, including age, sex, and location. Correlation analysis revealed that significant associations exist between dangerous driving behaviors and EI. The emotionality component of EI was found to be the strongest predictor of dangerous driving behaviors. The findings concluded that participants with higher EI scores engaged in less dangerous driving behavior, resulting in fewer crashes and fatalities. Thus, promoting and improving EI may be useful in preventing risky driving among non-commercial drivers. Incorporating emotional intelligence education in driver’s education, workplace training, and licensing procedures can be helpful to develop safer drivers. Further research is needed to investigate commercial drivers’ behaviors in relation to EI.     

Do sport modes cause behavioral adaptation?

A key question in transportation research is whether drivers show behavioral adaptation, that is, slower or faster driving, when new technology is introduced into the vehicle. This study investigates behavioral adaptation in response to the sport mode, a technology that alters the vehicle’s auditory, throttle-mapping, power-steering, and chassis settings. Based on the literature, it can be hypothesized that the sport mode increases perceived sportiness and encourages faster driving. Oppositely, the sport mode may increase drivers’ perceived danger, homeostatically causing them to drive more slowly. These hypotheses were tested using an instrumented vehicle on a test track. Thirty-one drivers were asked to drive as they normally would with different sport mode settings: Baseline, Modified Throttle Mapping (MTM), Artificial Engine Sound enhancement (AESe), MTM and AESe combined (MTM-AESe), and MTM, AESe combined with four-wheel steering, increased damping, and decreased power steering (MTM-AESe-4WS). Post-trial questionnaires showed increased perceived sportiness but no differences in perceived danger for the three MTM conditions compared to Baseline. Furthermore, compared to Baseline, MTM led to higher vehicle accelerations and, with a smaller effect size, a higher time-percentage of driving above the 110 km/h speed limit, but not higher cornering speeds. The AESe condition did not significantly affect perceived sportiness, perceived danger, and driving speed compared to Baseline. These findings suggest that behavioral adaptation is a functional and opportunistic phenomenon rather than mediated by perceived sportiness or perceived danger.     

Identifying effects of driving and secondary task demands,passenger presence,and driver characteristics on driving errors and traffic violations – Using naturalistic driving data segments preceding both safety critical events and matched baselines

Researchers have identified various factors that likely affect aberrant driving behaviors and therefore crash risk. However, it remains unclear which of these factors poses the greatest risk for committing either errors or violations under naturalistic driving conditions. This study investigated important variables contributing to driving errors and traffic violations based on naturalistic driving data from the second Strategic Highway Research Program (SHRP 2). The analyzed driving segments preceded both safety critical events and matched baselines. Results showed that intersection influence, high-risk visually distracting secondary tasks, and the severities of the safety critical events were the main factors associated with driving errors. The primary factors linked to violations were intersection influence, persistent individual differences in driver behavior, and the severities of the safety critical events. Furthermore, the number of aberrant driving behaviors in trip segments preceding crashes was higher than in the matched segments unrelated to safety critical events. However, the most common aberrant driving behavior types in the respective segment groups appeared to resemble each other. This suggests that crashes became more likely due to drivers committing more violations and errors overall as opposed to drivers making one certain type of error or violation.