New parents and driver safety: What’s sleep got to do with it? A systematic review

Fatigue-related motor vehicle crashes are common worldwide and have been addressed by a range of road safety campaigns. These campaigns are typically directed towards at-risk groups (e.g., heavy vehicle drivers), who may be likely to experience fatigue resulting from reduced or disrupted sleep opportunities. Another population likely to experience sleep loss and disruption is new parents. The sleep of new parents is likely to be significantly disrupted by childcare responsibilities. As such, new parents may also be likely to experience fatigue while driving. A systematic review of five databases (PubMed, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, EMBASE, and the Cochrane Central Register of Controlled Trials) was performed to identify what research is currently available on sleep, fatigue, and driving in new parents. A total of twelve documents were included in this review. A synthesis of findings suggests new parents are at risk of fatigued driving – though the amount and quality of evidence available is limited. A research agenda is proposed to address the limitations of this field of research.     

The relationship between simulator sickness and driving performance in a high-fidelity simulator

Driving simulators are highly valuable tools for various applications such as research, training, and rehabilitation. However, they are also known to cause simulator sickness, a special form of traditional motion sickness. Common side effects of simulator sickness include nausea, headache, dizziness, eye-strain, and/or disorientation, all symptoms which may negatively impact driving performance. The goal of the present study was to investigate the relationship between simulator sickness and driving performance obtained in a high-fidelity driving simulator. Twenty-one healthy participants were engaged in a simulated driving task containing rural, city, and highway sections for approx. 25 min. Participants were asked to drive naturally while obeying traffic rules and completing common driving maneuvers (including reactions to sudden events). Driving performance was evaluated based on various driving measures, such as lane positioning, speed measures, following distance, or the number of steering reversals. Simulator sickness was measured before, during, and after the simulated drive using a combination of the Simulator Sickness Questionnaire and the Fast Motion Sickness scale. Overall, correlations between the level of simulator sickness and driving performance measures were low to moderate (r’s from -0.37 to 0.40) and were not significant. Additionally, participants who reported higher levels of simulator sickness did not differ with regards to their driving performance from those who reported lower simulator sickness scores. Our results suggest that the presence of simulator sickness is not strongly related to performance in a driving simulator.     

Refreshing Older Adults’ Driving Skills (ROADSkills): A randomized controlled trial examining the effect of video feedback

ObjectivesThis study evaluated the effectiveness of a personalized video-based driver training program on the behind-the-wheel skills of community-dwelling older adults.MethodIn this randomized controlled trial (RCT), 80 older drivers [mean age (SD) = 71.0 (3.9)] were randomly assigned to view one of two educational videos: 1) personalized video feedback on their driving (n = 40) or 2) a generic video on aging-in-place (n = 40). The primary outcome, the total number of errors accrued in a 30-minute standardized on-road evaluation, was analyzed at baseline and 4 weeks after watching the assigned video. On-road evaluations were video-recorded and scored by a blinded rater. Self-report measures of driving abilities, behaviors, and comfort were also compared.ResultsAt the 4-week follow-up, the mean difference in the number of on-road performance errors [mean (95% CI) = -12.0 (-16.6, −7.4), p < 0.001] favored the intervention group compared to controls, with significant improvements across multiple domains: vehicle control [mean (95% CI) = -4.8 (-8.1, −1.5), p < 0.01], observing the roadway [mean (95% CI) = -5.5 (-8.4, −2.6), p < 0.001], and compliance with traffic rules [mean (95% CI) = -1.3 (-2.3, −0.2), p < 0.05]. Within-group change on behind-the-wheel errors was significant for the intervention group [mean (95% CI) = -10.3 (-13.5, −7.1), p < 0.001], but not for the control group [mean (95% CI) = 1.7 (-1.6, 4.9), p > 0.05]. No significant differences were found on the self-report measures.DiscussionA novel, video-based approach that provided personalized feedback to older drivers significantly reduced behind-the-wheel errors 4-weeks post-intervention. Changes in self-reported driving behaviors and abilities were not significant. Future research will examine the long-term effects of this approach on older adults’ actual and perceived driving abilities.     

An evaluation of the effectiveness of countermeasures for improving the safety of dilemma zones: A driving simulator study

The main objective of this driving simulator study is to analyze the behavior of the driver at the start of the yellow signal of a signalized rural intersection and identify the most effective countermeasures for tackling the dilemma zone, namely an area on the intersection approach where vehicles at the start of the yellow phase can neither safely stop before the stop line nor cross the intersection. The following countermeasures were tested in the study on a sample of 48 drivers: green signal countdown timers, GSCT (C₁); a new pattern of vertical and horizontal warning signs (C₂); and an advanced on-board driver assistance system based on augmented reality (AR) and connected vehicle technologies (C₃). These countermeasures were tested and compared to a baseline condition (B) where no countermeasures were applied. Based on the results of this study, the C₂ and C₃ countermeasures have proven to be valid tools for reducing driver indecision when approaching signalized intersections at the start of the yellow signal. In fact, using C₂ and C₃, the length of the dilemma zone was equal to 30 m and 36 m, respectively, with a reduction of about 50%, as compared to the baseline condition (B). Moreover, a reduced number of false behaviors was recorded, as well as a greater consistency in driver decision-making behaviors. Conversely, the C₁ countermeasure did not lead to a significant improvement in the dilemma zone: an unnecessary increase in early stop rates was recorded, resulting in reduced intersection efficiency and operations.     

On-Road vehicle study of the experience of automated driving

Prior studies of automated driving have focused on drivers’ evaluations of advanced driving assistance systems and their knowledge of the technology. An on-road experiment with novice drivers who had never used automated systems was conducted to examine the effects of the automation on the driving experience. Participants drove a Tesla Model 3 sedan with level 2 automation engaged or not engaged on a 4-lane interstate freeway. They reported that driving was more enjoyable and less stressful during automated driving than manual driving. They also indicated that they were less anxious and nervous, and able to relax more with the automation. Their intentions to use and purchase automated systems in the future were correlated with the favorableness of their automated driving experiences. The positive experiences of the first-time users suggest that consumers may not need a great deal of persuading to develop an appreciation for partially automated vehicles.     

Behavioral adaptations of human drivers interacting with automated vehicles

Advancements in technology are bringing automated vehicles (AVs) closer to wider deployment. However, in the early phases of their deployment, AVs will coexist and frequently interact with human-driven vehicles (HDVs). These interactions might lead to changes in the driving behavior of HDVs. A field test was conducted in the Netherlands with 18 participants focusing on gap acceptance, car-following, and overtaking behaviors to understand such behavioral adaptations. The participants were asked to drive their vehicles in a controlled environment, interacting with an HDV and a Wizard of Oz AV. The effects of positive and negative information regarding AV behavior on the participants’ driving behavior and their trust in AVs were also studied. The results show that human drivers adopted significantly smaller critical gaps when interacting with the approaching AV as compared to when interacting with the approaching HDV. Drivers also maintained a significantly shorter headway after overtaking the AV in comparison to overtaking the HDV. Positive information about the behavior of the AV led to closer interactions in comparison to HDVs. Additionally, drivers showed higher trust in the interacting AV when they were provided with positive information regarding the AV in comparison to scenarios where no information was provided. These findings suggest the potential exploitation of AV technology by HDV drivers.     

The effects of adaptive cruise control (ACC) headway time on young-experienced drivers' overtaking tendency in a driving simulator

The preference to maintain a certain desired speed is perhaps the most prevalent explanation for why a driver of a manually driven car decides to overtake a lead vehicle. Still, the motivation for overtaking is also affected by other factors such as aggressiveness, competitiveness, or sensation-seeking caused by following another vehicle. Whether such motivational factors for overtaking play a role in partially automated driving is yet to be determined. This study had three goals: (i) to investigate whether and how a driver's tendency to overtake a lead vehicle changes when driving a vehicle equipped with an adaptive cruise control (ACC) system. (ii) To study how such tendencies change when the headway time configuration of the ACC system varies. (iii) To examine how the manipulation of the speed and speed variance of the lead vehicle affect drivers' tendencies to overtake a lead vehicle. We conducted two different experiments, where the second experiment followed the first experiment's results. In each experiment, participants drove three 10–12 min simulated drives under light traffic conditions in a driving simulator under manual and level one (L1) automation driving conditions. The automation condition included an ACC with two headway time configurations. In the first experiment, it was 1 sec and 3 secs, and in the second, it was 1 sec and 2 secs. Each drive included six passing opportunities representing three different speeds of the lead vehicle (−3 km/h, +3 km/h, +6 km/h relative to the participant), with or without speed variance. Results show that drivers tended to overtake a lead vehicle more often in manual mode than in automated driving modes. In the first experiment, ACC with a headway time of 1 sec led to more overtaking events than ACC with 3 secs headway time. In addition, the relative speed of the lead vehicle and its speed variability affected overtaking tendencies. In the second experiment, the relative speed of the lead vehicle and its speed variability affected overtaking tendencies only when interacting with each other and with driving configuration. When the speed of the lead vehicle was +3 km/h and included variability, more overtaking events occurred in manual mode than both automation modes. This work has shown that driving with ACC might help reduce overtaking frequencies and more considerable when the headway time is set to 3 secs.     

The prevalence of in-vehicle driving distractions in road traffic collisions as a function of road type

While some previous research suggests that conversing with passengers is the most prevalent in-vehicle distraction while driving, others have concluded instead that it is mobile phone use. One possible explanation for these differences is that distraction prevalence varies with road type. To test this proposal the current study investigated the prevalence of in-vehicle driving distraction in road traffic collisions (RTCs) as recorded in national records from the US and New Zealand. Analysis using odds ratios suggested conversing with passengers to be a more prevalent distraction in RTCs on minor roads than on major roads, and mobile phone use to be a more prevalent distraction on major roads than on minor roads. These results show the importance of considering the type of road when investigating the prevalence of driving distractions in RTCs in future research.     

Development and validation of the traffic displaced aggression questionnaire

Displaced aggression is defined as the aggression conducted against someone or something that is not considered to be the trigger of the emotional state of the aggressor. Whereas it has been deeply studied in a general context, to date, very few studies have analysed it in the specific context of driving. Considering the inexistence of instruments to assess it, the aim of the current research was to develop and validate a self-report in order to assess displaced aggression in the driving context. A sample of 467 participants (66.8% female, M_age = 34.74) filled in a set of questionnaires including the 29 items that were designed to assess traffic displaced aggression, as well as other instruments measuring different ways of expressing both general and driving aggression. The results of both Parallel Analysis (PA, sub-sample 1) and Confirmatory Factor Analysis (CFA, sub-sample 2) showed a good fit of the two-factor model, retaining 20 out of the initial 29 items. The first factor was labelled as Displaced aggression due to the anger generated outside the vehicle (6 items, α = 0.83), whereas the second factor was labelled as Displaced aggression due to the anger generated inside the vehicle (14 items, α = 0.91). Both factors, as well as the total score, showed good results regarding convergent and divergent validity. Practical implications of the results, future research lines and limitations of the current study are discussed.