Longitudinal relations among perceived parental warmth,self-esteem and social behaviours from middle childhood to early adolescence in China: Disentangling between- and within-person associations

Although several studies have addressed the relations between perceived parental warmth and social behaviours, few have distinguished their between- and within-person effects or explored their within-person mediating mechanisms. This study examined the transactional relations among perceived parental warmth (i.e. maternal warmth and paternal warmth), children's self-esteem and children's positive/negative social behaviours (i.e. prosocial behaviour and delinquent behaviour) along with the mediating role of self-esteem after disentangling between- and within-person effects. A total of 4315 Chinese elementary children (44.9% girls; M_age = 9.93 years, SD = 0.73) completed relevant measures on four occasions employing 6-month intervals. Results of random-intercept cross-lagged panel models showed that (a) perceived parental warmth reciprocally and positively predicted prosocial behaviour and self-esteem; (b) perceived paternal warmth reciprocally and negatively predicted delinquent behaviour; (c) self-esteem reciprocally predicted prosocial and delinquent behaviour; (d) perceived maternal warmth reciprocally and positively predicted prosocial behaviour through self-esteem; (e) perceived parental warmth reciprocally and negatively predicted delinquent behaviour through self-esteem; and (f) perceived maternal and paternal warmth differed in their relations with prosocial and delinquent behaviours through self-esteem. These findings illuminated the complicated longitudinal within-person interactions among perceived parental warmth, self-esteem, and social behaviours, the specific mediating mechanism of self-esteem, and the differing results associated with perceived maternal and paternal warmth, all of which yield significant implications for assessments and early interventions aimed to promote positive social behaviour.     

Intraindividual variability in driving simulator parameters of healthy drivers of different ages

Intraindividual variability is a fundamental behavioural characteristic of aging but has been examined to a very limited extent in driving. This study investigated intraindividual variability in driving simulator measures in healthy drivers of different ages using the coefficient of variation (COV) as a variability measure. Participants were healthy volunteers who were regular drivers, who were divided into a “young” group, a “middle-aged” group, and an “old” group. They drove in two environments (rural, 72 drivers; urban, 60 drivers), under conditions of moderate and high traffic load, without and with distraction (conversation). Significant differences in COV were observed in the rural condition for headway distance and lateral position as a function of traffic load, with high traffic (without and with distraction) resulting in increased COV of headway and decreased COV of lateral position. Significant differences in COV were observed in the urban condition for headway distance only, with high traffic (without and with distraction) resulting in increased COV of headway. No age effects were found for any of the driving conditions. The results indicate that traffic load affected headway distance and lateral position in opposite directions in all three age groups: high traffic resulted in increased variability of headway in both rural and urban conditions but in decreased variability of lateral position in the rural conditions compared to moderate traffic irrespective of distraction. The study indicates that driving conditions affect the intraindividual variability of driving measures in selective ways, which may be linked to the extent of automatization of the driving variables and to adaptive changes to traffic condition challenges.     

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.     

Exploration of the effects of task-related fatigue on eye-motion features and its value in improving driver fatigue-related technology

Mental fatigue has been lacked attention in developing eye-tracking fatigue detection system for drivers. However, it has great influence on eye movement which could account for the poor validity of current fatigue detectors only focusing on sleep-related fatigue. This work sought to investigate the influence of two types of task-related mental fatigue on eye movement by examining 8 saccade-based, 3 blink-based, and 1 pupil-based metrics. We propose that two types of task-related fatigue caused by cognitive overload and prolonged underload will induce different physiological responses to eye-motion features. Twenty participants completed a vigilance task before and after a 1-h driving with a secondary task in a virtual simulation environment, while forty participants, divided equally into two groups, finished the same task before and after a 1-h and 1.5-h monotonous driving. T-test was applied to analyse the eye-motion, subjective and vigilance data during vigilance task. We found that overload driving made drivers vigilance ability decrease. The eye metrics showed different changes in underload and overload scenario. The blink duration, the mean velocity of saccade and saccade duration increased after 1-h overload driving, while the pupil diameter decreased. However, none of those changes were observed in 1.5-h underload driving, but saccade duration had a significant increase. The fatigue response to heavy demands over short periods of driving is different from the lighter demands over long periods in terms of eye-motion metrics. Considering mental fatigue in designing an eye-tracking fatigue detection system could possibly improve its accuracy.     

Effectiveness of the compensatory strategy adopted by older drivers: Difference between professional and non-professional drivers

It has been a controversial issue for the effect of ageing population on driving safety. Apparently, drivers’ physiological and cognitive performances deteriorate with age. However, older drivers may compensate for the elevated risk by adjusting their behaviors, known as compensatory strategy. Despite the extensive research on this topic, the compensatory strategy of older professional drivers is not well understood since many studies focused on the differences in compensatory behavior between older and young drivers. Professional drivers tend to be more skillful and able to cope with the unfavorable driving environments, thus presenting a higher capability to mitigate the risk. This study attempts to examine the compensatory behavior and its safety effect amongst older professional drivers, as compared to those of older non-professional drivers, using the driving simulator approach. In the driving simulator experiment, participants were asked to follow a leading vehicle for one hour, and two sudden brake events were presented. 41 (mid-aged and older) drivers completed the driving tests. Each participant was required to complete a car-following test, either under high or low traffic flow conditions. Performance indicators include driving capability (i.e. lateral control, longitudinal control, and brake reaction time) and compensatory behavior (i.e. average speed, and time headway). Additionally, two modified traffic conflict measures: time exposed time-to-collision (TET) and time integrated time-to-collision (TIT) are applied to indicate the traffic conflict risk. The random parameter Tobit models were estimated to measure the association between conflict risk and driver attributes, and random intercept models were used to assess other driving performance indicators. Results show that despite the impaired lateral control performance and longer brake reaction time of older drivers, the likelihood of severe traffic conflict of older drivers is lower than that of mid-aged drivers. Furthermore, though both older professional and older non-professional drivers adopted longer time headway, the reduction in the risk of severe traffic conflict is more profound among the older professional drivers. Such findings suggest that older professional drivers are more capable of mitigating the possible collision risk by adopting the compensatory strategy, as compared to older non-professional drivers. This justifies the existence of compound effect by the compensatory strategy of older driver and better driving skills of professional driver. This research provides useful insights into driver training and management strategies for employers, as older drivers would become a major cohort in the transportation industry.     

On-road study investigating the mode of feedback delivery on young drivers’ speed management

Feedback has been shown to be instrumental in learning. Refining how it is delivered in driver training has the potential to reduce crash risk. Therefore, the aim of the present study was to examine the effect of the medium in which feedback is delivered (verbal, written, graphical) on young drivers’ speed management behaviour. Sixty young drivers, randomly allocated to one of four feedback groups (i.e., Control, Verbal feedback, Written feedback, and Graphical feedback) completed one training and two test drives using an instrumented vehicle on-road. After the first (baseline) drive, participants were provided feedback about the number of times they exceeded the speed limit, their maximum speed, as well as the financial penalties associated with such driving behaviour, and safety implications. The content of feedback remained the same for all training groups, except for the manner in which the feedback was presented. Verbal feedback was provided verbally by the researcher; written feedback on paper; and graphical feedback using a series of graphs on a computer screen. Control received no such intervention. Two test drives followed, one immediate post-training, and the second one week post-training. Feedback, irrespective of medium improved young drivers’ speed management. Verbal and graphical feedback yielded the greatest reduction in speeding behaviour, with graphical feedback having the most impact in the two speed zones under examination (50 km/h & 80 km/h). Control showed the poorest speed management behaviour in all drives. These findings have important implications for the development of a new approach to improve young drivers’ speed management behaviour.     

Influence of following vehicle’s tailway and classification on subject driver’s response to the circular yellow indication

Traffic crashes at signalized intersections are frequently linked to driver behavior at the onset of the circular yellow (CY) indication. To better understand behavioral factors that influence a driver’s decision to stop or go at an intersection, this study analyzed the behavior of the driver of a subject vehicle at the onset of the CY indication. Driver performance data from 53 participants were collected in the Oregon State University Driving Simulator, simulating scenarios of driving through high-speed intersections under various conditions. Data included interactions where the driver stopped at the stop line (n = 644) or proceeded through the intersection (n = 628) in response to a CY indication. Data were analyzed as panel data while considering 12 indicator variables related to the driver’s stop/go decision. These indicator variables included time to stop line (TTSL), tailway time, following vehicle type, vehicle speed at the onset of the CY indication, and demographics (age, gender, driving experience, level of education, personal vehicle type, number of times driving per week, number of miles driving last year, participation in previous simulation studies. A random-parameter binary logit model was used to determine contributing factors for driver decision making at the onset of CY indication while accounting for unobserved heterogeneity. Four indicator variables were significantly related to the driver’s stop/go decision, but three factors varied across observations. Findings showed that a driver’s stop/go decision in response to a CY indication was associated with the time to the stop line (TTSL), tailway time to the following vehicle, subject vehicle speed at the onset of the CY indication, and driver’s age (20–36 years), but was not significantly associated with classification of the following vehicle. Also, the findings indicated that a shorter tailway increased a subject driver’s red-light running frequency. These findings provide insights into variables that affect driver decisions in a vehicle-following situation at the onset of the CY indication. This information can help make better decisions in smart traffic control systems such as to extend/decrease the green interval slightly to avoid decisions that are more difficult.     

Effects of forward collision warning technology in different pre-crash scenarios

The forward collision warning (FCW) system is expected to reduce rear-end crashes; however, its effects on driving behavior and safety have not been thoroughly investigated, specifically the effect variations between different pre-crash scenarios. To identify these variations, this study conducted a driving simulator experiment and compared the FCW’s effects between three pre-crash scenarios: the freeway scenario, the arterial scenario and the intersection dilemma zone scenario. Thirty-nine participants were involved in the experiment. The results showed that the adaptation of driver behavior in impending rear-end collision events resulted from both the FCW and the scenario. The intersection dilemma zone scenario has indications of slowing down, which encouraged drivers to take a more aggressive response strategy under the FCW; the arterial scenario might be regarded as an “easy-to-handle” situation in which a significant portion of drivers adopted moderate level of response strategy under the FCW; both the intersection dilemma zone scenario and freeway scenario have burdened driving tasks, and this might deteriorate a driver’s ability to adapt to the FCW. In addition, different types of drivers experienced varied benefits from the FCW in each scenario. The FCW would be particularly recommended for non-experienced drivers in the freeway scenario and for female drivers in the arterial scenario; moreover, in the scenario of the intersection dilemma zone, the FCW would be particularly recommended for drivers who have a crash/citation before. The results also support specific FCW designs which are able to highlight the collision risk. This study demonstrated that it would be better to indicate the effects of the FCW under the restriction of specific scenario features and develop the FCW based on that.     

Effects of speed feedback signs and law enforcement on driver speed

This study analyzes the effects of implementing three speed management strategies, namely speed feedback signs, periodic law enforcement, and speed feedback sign supported with periodic law enforcement on driver speed behavior and compliance. To analyze the effectiveness of each strategy, nine locations in Pima County, Arizona, were studied in a cross-sectional framework. For each study site, the driver’s speed, date, time, and vehicle’s length were collected at a location prior to the speed management zone as the baseline, at the speed management zone, and downstream of the speed management zone. The general effect showed that all the strategies were effective in reducing average speed and the proportion of drivers exceeding the speed limit. In addition, the results of the robust heteroscedastic ANOVA test showed that among all the strategies, the speed feedback sign supported with periodic law enforcement was the most effective one. Moreover, it was shown that by supporting the speed feedback sign with periodic law enforcement, the reduction in average speed and proportion of drivers exceeding the speed limit would last, even after passing the speed management strategy. In other words, the existence of periodic law enforcement could potentially modify drivers’ behaviors and increase the spatial effectiveness of speed feedback signs. Comparing the behavior of truck and passenger car drivers also revealed similar results. That is, both truck and passenger car drivers tend to slow down after observing the speed management strategy. The experimental evidence indicates positive benefits for reducing excessive speeding behaviors at the sites.     

Driver conflict response during supervised automation: Do hands on wheel matter?

Securing appropriate driver responses to conflicts is essential in automation that is not perfect (because the driver is needed as a fall-back for system limitations and failures). However, this is recognized as a major challenge in the human factors literature. Moreover, in-depth knowledge is lacking regarding mechanisms affecting the driver response process. The first aim of this study was to investigate how driver conflict response while using highly reliable (but not perfect) supervised automation differ for drivers that (a) crash or avoid a conflict object and (b) report high trust or low trust in automation to avoid the conflict object. The second aim was to understand the influence on the driver conflict response of two specific factors: a hands-on-wheel requirement (with vs. without), and the conflict object type (garbage bag vs. stationary vehicle). Seventy-six participants drove with highly reliable but supervised automation for 30 min on a test track. Thereafter they needed to avoid a static object that was revealed by a lead-vehicle cut-out. The driver conflict response was assessed through the response process: timepoints for driver surprise reaction, hands-on-wheel, driver steering, and driver braking. Crashers generally responded later in all actions of the response process compared to non-crashers. In fact, some crashers collided with the conflict object without even putting their hands on the wheel. Driver conflict response was independent of the hands-on-wheel requirement. High-trust drivers generally responded later than the low-trust drivers or not at all, and only high trust drivers crashed. The larger stationary vehicle triggered an earlier surprise reaction compared to the garbage bag, while hands-on-wheel and steering response were similar for the two conflict object types. To conclude, crashing is associated with a delay in all actions of the response process. In addition, driver conflict response does not change with a hands-on-wheel requirement but changes with trust-level and conflict object type. Simply holding the hands on the wheel is not sufficient to prevent collisions or elicit earlier responses. High trust in automation is associated with late response and crashing, whereas low trust is associated with appropriate driver response. A larger conflict object trigger earlier surprise reactions.