Evidence for the role of personality in the cognitive performance of older male drivers

ObjectivesThis study aimed to examine whether in older male drivers the level of information processing under increasing cognitive workload varies with the level of personality traits.MethodThe study involved 60 male, active drivers aged between 65 and 81 (M = 71, SD = 4.4). During passive driving they performed central (a test in the n-back scheme, in three cognitive workload levels) and peripheral (memorizing objects characteristic for road traffic) tasks presented simultaneously in the central and peripheral visual fields. A wide-angle high-fidelity collimated visual system was used to display visual stimuli. In the central task, the reaction time and its correctness were measured, while the peripheral task measured the number of memorized objects. The EPQ-R questionnaire was used to assess personality traits, and the memory functions were assessed using the MMSE test.ResultsAs the central task-induced cognitive workload increases, the performance of both central and peripheral tasks deteriorates. These tasks were performed less accurately by drivers with a higher level of psychoticism than those with a lower level of this trait. This dependency related to all levels of cognitive workload. In contrast, the drivers with a higher level of neuroticism showed a lower level of performance of the peripheral task than those with a low level of this trait. This dependency concerned only the central task with a medium cognitive workload.ConclusionThe level of personality traits, especially the ones related to the emotional sphere, contributed to the decrease in the effectiveness of action (longer reaction time, increase in the number of errors, decrease in the number of memorized objects) in the conditions of increasing cognitive workload. A higher level of neuroticism and psychoticism can be conducive to making errors on the road. This applies especially to situations that pose high demands on the driver’s cognitive system.     

Prediction of effort and eye movement measures from driving scene components

For transitions of control in automated vehicles, driver monitoring systems (DMS) may need to discern task difficulty and driver preparedness. Such DMS require models that relate driving scene components, driver effort, and eye measurements. Across two sessions, 15 participants enacted receiving control within 60 randomly ordered dashcam videos (3-second duration) with variations in visible scene components: road curve angle, road surface area, road users, symbols, infrastructure, and vegetation/trees while their eyes were measured for pupil diameter, fixation duration, and saccade amplitude. The subjective measure of effort and the objective measure of saccade amplitude evidenced the highest correlations (r = 0.34 and r = 0.42, respectively) with the scene component of road curve angle. In person-specific regression analyses combining all visual scene components as predictors, average predictive correlations ranged between 0.49 and 0.58 for subjective effort and between 0.36 and 0.49 for saccade amplitude, depending on cross-validation techniques of generalization and repetition. In conclusion, the present regression equations establish quantifiable relations between visible driving scene components with both subjective effort and objective eye movement measures. In future DMS, such knowledge can help inform road-facing and driver-facing cameras to jointly establish the readiness of would-be drivers ahead of receiving control.     

How drivers adapt their behaviour to changes in task complexity: The role of secondary task demands and road environment factors

The present study investigates the impact of different sources of task complexity such as driving demands and secondary task demands on driver behaviour. Although much research has been dedicated to understanding the impact of secondary task demands or specific road traffic environments on driving performance, there is little information on how drivers adapt their behaviour to their combined presence. This paper aims to describe driver behaviour while negotiating different sources of task complexity, including mobile phone use while driving (i.e., calling and texting) and different road environments (i.e., straight segments, curves, hills, tunnels, and curves on hills). A driving simulator experiment was conducted to explore the effects of different road scenarios and different types of distraction while driving. The collected data was used to estimate driving behaviour through a Generalized Linear Mixed Model (GLMM) with repeated measures. The analysis was divided into two phases. Phase one aimed to evaluate driver performance under the presence and absence of pedestrians and oncoming traffic, different lanes width and different types of distraction. The second phase analysed driver behaviour when driving through different road geometries and lane widths and under different types of distraction. The results of the experiment indicated that drivers are likely to overcorrect position in the vehicle lane in the presence of pedestrians and oncoming traffic. The effect of road geometry on driver behaviour was found to be greater than the effect of mobile phone distraction. Curved roads and hills were found to influence preferred speeds and lateral position the most. The results of this investigation also show that drivers under visual-manual distraction had a higher standard deviation of speed and lateral position compared to the cognitive distraction and the non-distraction condition.     

Safety effects of parking maneuvers

Parking maneuvers, particularly a vehicle’s maneuver for entering and leaving a parking space, have varying rates of use and safety impacts. In this effort, crash data were collected for parking lots in the vicinity of a university campus and compared to observational parking position data. The campus was selected for this study because a change in the parking enforcement process was expected to (and did) change parking maneuver choices. When entering and leaving a parking space, three maneuver options exist for drivers: (1) forward, (2) reverse, and (3) pulling through an adjacent parking space. When specifically entering a parking space, the maneuver options become: (1) pull-in, (2) back-in, and (3) pull-through. When leaving the parking space, the maneuver options become: (1) pull-out, (2) back-out, and (3) pull-through. This study found that the pull-in/back-out vehicle maneuver’s percentage of total crashes was greater than the percentage of vehicles that were actually observed to use the same maneuver. The analysis from this study implies that the pull-in/back-out parking maneuver is more likely to result in a collision and therefore, is associated with a higher crash risk. Further analysis of North Carolina’s parking related fatal and serious injury crashes found that vehicles backing out of parking spaces was overwhelmingly the main cause for these serious injuries. 90% of North Carolina’s parking related fatal and serious injuries occurred during a back-out maneuver. Overall, this study concludes that the back-in/pull-out parking maneuver is safer than the pull-in/back-out maneuver and is the recommended approach to 90° parking.     

临床医生要理解与实践医学的目的

针对医疗进入市场后,一些临床医生对医学的特征与属性认识不足,医学的行为与目的产生矛盾,提出了医生的人文精神就体现在医疗过程的言行之中。在中国目前的卫生状况下,实现医学的目的,医生们可以有所作为,有所贡献。     

Temporal fluctuations in driving demand: The effect of traffic complexity on subjective measures of workload and driving performance

Traffic density has been shown to be a factor of traffic complexity which influences driver workload. However, little research has systematically varied and examined how traffic density affects workload in dynamic traffic conditions. In this driving simulator study, the effects of two dynamically changing traffic complexity factors (Traffic Flow and Lane Change Presence) on workload were examined. These fluctuations in driving demand were then captured using a continuous subjective rating method and driving performance measures. The results indicate a linear upward trend in driver workload with increasing traffic flow, up to moderate traffic flow levels. The analysis also showed that driver workload increased when a lane change occurred in the drivers’ forward field of view, with further increases in workload when that lane change occurred in close proximity. Both of these main effects were captured via subjective assessment and with driving performance parameters such as speed variation, mean time headway and variation in lateral position. Understanding how these traffic behaviours dynamically influence driver workload is beneficial in estimating and managing driver workload. The present study suggests possible ways of defining the level of workload associated with surrounding traffic complexity, which could help contribute to the design of an adaptive workload estimator.     

Measuring drivers’ visual information needs during braking: A simulator study using a screen-occlusion method

It is commonly accepted that vision plays an important role in car braking, but it is unknown how people brake in the absence of visual information. In this simulator study, we measured drivers’ braking behaviour while they had to stop their car at designated positions on the road. The access to visual information was manipulated by occluding the screen at the start of half of the braking trials, while the temporal demand was manipulated by varying the time-to-arrival (TTA). Results showed that for the longer TTA values (⩾6 s), participants in the occlusion condition stopped too early and at variable positions on the road as compared to the control condition. In the occlusion condition, participants were likely to apply an intermediate brake pedal depression, whereas in the control condition participants more often applied low or high pedal depressions. The results are interpreted in light of a distance estimation test, in which we found that participants underestimated the actual distance by 70%.     

The quiet passenger: Cognitions,avoidance, and onset among people with driving anxiety

BackgroundAnxiety over driving can have consequences for road safety and individual well-being. This area is under-researched in Australia, despite international research suggesting that most drivers experience some level of anxiety over driving.ObjectivesThis study aimed to contribute to the understanding of driving anxiety by 1) confirming the factor structure of two questionnaires designed to understand the concerns (Driving Cognitions Questionnaire: DCQ) and avoidance behaviours (Driving and Riding Avoidance Scale: DRAS); 2) providing evidence of anxiety manifestations in Australian drivers, and 3) understanding whether these differ according to the initial onset of this anxiety.MethodsA total of 1,600 people (77% females; age ranging from 18 to 89 [M = 27.92; ± 13.49], 75% licenced, 20% learning, 5% unlicenced) in Australia who identified as having “some” level of anxiety over driving, completed an online questionnaire regarding their anxiety.ResultsConfirmatory Factor Analyses showed the two-factor structure of the DRAS (general and traffic avoidance; and weather and riding avoidance) and the two-factor structure of the DCQ (crash-related concerns and social and panic related concerns) best fit the data. The most common anxiety onsets were crash involvement (14%), knowing someone who had been in a crash (9%) and due to criticism from others (9%). While no differences emerged between these groups on avoidance behaviour nor on crash concerns, social and panic concerns were higher for the criticism onset group.ConclusionsThe results demonstrate difference sources of anxiety and provide evidence of the importance of interactions with passengers in determining how a driver feels about the driving task.Practical implicationsAvenues for the reduction or avoidance of anxiety are proposed. These include better awareness and education for drivers regarding the importance of positive interactions and/or well as better journey management to avoid triggers of anxiety.     

A field investigation of red-light-running in Shanghai,China

Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0–0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed.     

Decomposing filial piety into filial attitudes and filial enactments

In an attempt to explore the attitude-behaviour relationship, the present study examined the mixed findings from research on filial piety by differentiating filial attitudes from filial behaviours. The Filial Behaviour Scale was developed to tap the behavioural manifestations of filial piety, and its nomological network was established in two Chinese contexts, Hong Kong and Beijing. In addition to filial attitudes, we used values, social beliefs, and self-construals to explain filial behaviours. The results of hierarchical regression analysis showed that the value factor of conservation and the social axiom of reward for application predicted filial behaviour above and beyond filial attitudes; an interdependent self-construal moderated the effect of cultural group on filial behaviour. Gender and regional differences in filial piety were also examined. Males were found to score higher on filial attitudes, but not significantly higher on filial behaviours. Regional differences existed in filial behaviours, but not in filial attitudes, with Mainland Chinese displaying a higher level of filial behaviours than their Hong Kong counterparts. These identified differences suggest the importance of differentiating filial attitudes from filial behaviours in future attempts to understand Chinese filiality.