Frequency and impact of hands-free telephoning while driving – Results from naturalistic driving data

The frequency and impact of hands-free telephoning while driving was analyzed based on naturalistic driving data from 106 drivers. The results from naturalistic driving data were compared with the results from experimental approaches. The implication of the overall results and the differences across drivers are discussed. Continuous information on the usage of the hands-free phone equipment was available which made it possible to include the entire database (∼1 000 000 km) in a completely automatized analysis. Results show that drivers talked on a hands-free phone about 11% of driving time. There were large differences across drivers in the frequency and usage of a hands-free phone. While telephoning, an adaptation of driving behavior could be found. Drivers slowed down and increased their distance to the lead vehicle. Furthermore, during telephoning, an overall reduction of potentially critical driving situations was found. Overall, the results indicate that compensation for telephoning was carried out with a long-term change of driving behavior, rather than with a short term adaption to the situation.     

A matter of style? Driver attributional ‘style’ in accounting for the driving of others as protective or as predisposing drivers towards retaliatory aggressive driving

Driver cognitions about aggressive driving of others are potentially important to the development of evidence-based interventions. Previous research has suggested that perceptions that other drivers are intentionally aggressive may influence recipient driver anger and subsequent aggressive responses. Accordingly, recent research on aggressive driving has attempted to distinguish between intentional and unintentional motives in relation to problem driving behaviours. This study assessed driver cognitive responses to common potentially provocative hypothetical driving scenarios to explore the role of attributions in driver aggression. A convenience sample of 315 general drivers 16–64 yrs (M = 34) completed a survey measuring trait aggression (Aggression Questionnaire AQ), driving anger (Driving Anger Scale, DAS), and a proxy measure of aggressive driving behaviour (Australian Propensity for Angry Driving AusPADS). Purpose designed items asked for drivers’ ‘most likely’ thought in response to AusPADS scenarios. Response options were equivalent to causal attributions about the other driver. Patterns in endorsements of attribution responses to the scenarios suggested that drivers tended to adopt a particular perception of the driving of others regardless of the depicted circumstances: a driving attributional style. No gender or age differences were found for attributional style. Significant differences were detected between attributional styles for driving anger and endorsement of aggressive responses to driving situations. Drivers who attributed the on-road event to the other being an incompetent or dangerous driver had significantly higher driving anger scores and endorsed significantly more aggressive driving responses than those drivers who attributed other driver’s behaviour to mistakes. In contrast, drivers who gave others the ‘benefit of the doubt’ endorsed significantly less aggressive driving responses than either of these other two groups, suggesting that this style is protective.     

The Force/Force-Variability Relationship Under Controlled Temporal Conditions

Previously, an inverted U relationship between force and force variability was demonstrated in both static and dynamic responses. Recent research suggests that the inverted U function may be due to a lack of control of the temporal aspects of the response. To investigate this hypothesis, we examined the relationship between force and force variability in rapid movements under controlled temporal conditions. Subjects (N = 4) made rapid reversal responses with a horizontal lever (using elbow flexion and extension) such that the time to reversal (160 ms) and the distance to reversal (45°) were held constant in each of six load conditions (either 0, .260, .520, .780,1.040, or 1.560 kg added to the lever). When time to reversal and time to peak acceleration were held constant, a curvilinear relationship between force and force variability resulted, suggesting that the inverted U function is related to control of the temporal aspects of the response.     

Drivers’ performances and their subjective feelings about their driving during a 40-min test on a circuit versus a dynamic simulator

Car manufacturers expect driving simulators to be reliable research and development tools. Questions arise, however, as to whether drivers’ behavior on simulators exactly matches that observed when they are driving real cars. Drivers’ performances and their subjective feelings about their driving were compared between two groups during a 40-min driving test on the same circuit in a real car (n = 20) and a high-fidelity dynamic simulator (n = 27). Their speed and its variability, the braking force and the engine revolutions per minute (rpm) were recorded five times on a straight line and three times on a curve. The differences observed in these measurements between circuit driving (CD) and simulator driving (SD) from the 6th to 40th minute showed no significant changes during the drive. The drivers also completed the NASA Raw Task Load Index (NASA RTLX) questionnaire and the Simulator Sickness Questionnaire (SSQ) and estimated the ease and standard of their own driving performances. These subjective feelings differed significantly between the two groups throughout the experiment. The SD group’s scores on the NASA RTLX and SSQ questionnaires increased with time and the CD group’s perceived driving quality and ease increased with time, reaching non-significantly different levels from their usual car driving standards by the end of the drive. These findings show the existence of a fairly good match between real-life and simulated driving, which stabilized six minutes after the start of the test, regardless of whether the road was straight or curved. These objective findings and subjective assessments suggest possible ways of improving the match between drivers’ performances on simulators and their real-life driving behavior.     

Traffic Offences: Planned or Habitual? Using the Theory of Planned Behaviour and habit strength to explain frequency and magnitude of speeding and driving under the influence of alcohol

This study addresses the socio‐cognitive determinants of traffic offences, in particular of speeding and drinking and driving. It has two aims: (1) to test the hypothesis of a direct effect of habits on offences (i.e., independent of intentions) by employing a specific measure of habits (i.e., the SRIH) and (2) to analyse the offences by taking account of three distinct parameters: Frequency, usual magnitude (i.e., the most frequent deviation from the law) and maximal magnitude (i.e., the greatest deviation occasionally adopted) in order to represent more accurately the variability of the offending behaviours. A total of 642 drivers replied to a questionnaire. The results corroborate the idea that intention and habit are distinct and direct determinants of offences. The use of the SRIH dismisses the criticisms made with regard to the measure of past behaviour. The distinction between the three behavioural parameters proves to be relevant, as their determinants are not exactly similar. Finally, attitude and subjective norm had direct effects on the maximal magnitude and/or on the frequency of the offence. The discussion concerns the contribution of this study to the analysis of offences as well as its limitations and addresses the theoretical plausibility of the direct effects of attitude and the subjective norm.     

Motorcycle riders’ self-reported aggression when riding compared with car driving

Aggressive driving has been shown to be related to increased crash risk for car driving. However, less is known about aggressive behaviour and motorcycle riding and whether there are differences in on-road aggression as a function of vehicle type. If such differences exist, these could relate to differences in perceptions of relative vulnerability associated with characteristics of the type of vehicle such as level of protection and performance. Specifically, the relative lack of protection offered by motorcycles may cause riders to feel more vulnerable and therefore to be less aggressive when they are riding compared to when they are driving. This study examined differences in self-reported aggression as a function of two vehicle types: passenger cars and motorcycles. Respondents (n = 247) were all motorcyclists who also drove a car. Results were that scores for the composite driving aggression scale were significantly higher than on the composite riding aggression scale. Regression analyses identified different patterns of predictors for driving aggression from those for riding aggression. Safety attitudes followed by thrill seeking tendencies were the strongest predictors for driving aggression, with more positive safety attitudes being protective whilst greater thrill seeking was associated with greater self-reported aggressive driving behaviour. For riding aggression, thrill seeking was the strongest predictor (positive relationship), followed by self-rated skill, such that higher self-rated skill was protective against riding aggression. Participants who scored at the 85th percentile or above for the aggressive driving and aggressive riding indices had significantly higher scores on thrill seeking, greater intentions to engage in future risk taking, and lower safety attitude scores than other participants. In addition participants with the highest aggressive driving scores also had higher levels of self-reported past traffic offences than other participants. Collectively, these findings suggest that people are less likely to act aggressively when riding a motorcycle than when driving a car, and that those who are the most aggressive drivers are different from those who are the most aggressive riders. However, aggressive riders and drivers appear to present a risk to themselves and others on road. Importantly, the underlying influences for aggressive riding or driving that were identified in this study may be amenable to education and training interventions.     

Human factors of transitions in automated driving: A general framework and literature survey

The topic of transitions in automated driving is becoming important now that cars are automated to ever greater extents. This paper proposes a theoretical framework to support and align human factors research on transitions in automated driving. Driving states are defined based on the allocation of primary driving tasks (i.e., lateral control, longitudinal control, and monitoring) between the driver and the automation. A transition in automated driving is defined as the process during which the human-automation system changes from one driving state to another, with transitions of monitoring activity and transitions of control being among the possibilities. Based on ‘Is the transition required?’, ‘Who initiates the transition?’, and ‘Who is in control after the transition?’, we define six types of control transitions between the driver and automation: (1) Optional Driver-Initiated Driver-in-Control, (2) Mandatory Driver-Initiated Driver-in-Control, (3) Optional Driver-Initiated Automation-in-Control, (4) Mandatory Driver-Initiated Automation-in-Control, (5) Automation-Initiated Driver-in-Control, and (6) Automation-Initiated Automation-in-Control. Use cases per transition type are introduced. Finally, we interpret previous experimental studies on transitions using our framework and identify areas for future research. We conclude that our framework of driving states and transitions is an important complement to the levels of automation proposed by transportation agencies, because it describes what the driver and automation are doing, rather than should be doing, at a moment of time.     

Characteristics of college students who text while driving: Do their perceptions of a significant other influence their decisions?

IntroductionTexting while driving is a significant risk factor for automobile collisions. The use of cell phones is prevalent among young people and commonly reported when they drive.MethodA web-based survey of 861 college student drivers determined how texting was associated with other forms of risky driving, perceptions of risk, and their driving and texting interactions with a significant other.ResultsTexting drivers were more likely to engage in other risky driving behaviors, perceived less risk in texting and driving, felt more immune to traffic risks, and had friends who text and drive. Logistic regression analyses showed that even after adjusting for risky driving behaviors and perceived risk, texting drivers were significantly more likely to do so if they saw their significant other text and drive.ConclusionsTraffic safety campaigns need to address important social influences on this behavior.     

Traffic congestion,driver stress,and driver aggression

Drivers were interviewed over cellular telephones in high‐ and low‐congestion conditions during a single commute. During each interview, state measures of driver stress and driver behaviors were obtained. Behavior responses were subdivided into six categories: aggressive, information seeking, planning, minor self‐destructive, distraction, and relaxation techniques. Both state driver stress and aggression were greater in high‐ than in low‐congestion conditions. No other behavior category differed between low and high congestion. Multiple regressions were calculated to determine the predictors of state driver stress. In low congestion, time urgency predicted state driver stress, while aggression predicted driver stress in high congestion. In both conditions, a trait susceptibility toward viewing driving as generally stressful was predictive of state driver stress levels, which further strengthens the use of the Driving Behaviour Inventory—General as a predictor of “trait” driver stress. Females and males did not differentiate on state stress or any behavior category. Aggr. Behav. 25:409–423, 1999. © 1999 Wiley‐Liss, Inc.