Get Ready for Take-Overs: Using Head-Up Display for Drivers to Engage in Non-Driving-Related Tasks in Automated Vehicles.

OBJECTIVE: The study aims to investigate the potential of using HUD (head-up display) as an approach for drivers to engage in non-driving-related tasks (NDRTs) during automated driving, and examine the impacts on driver state and take-over performance in comparison to the traditional mobile phone. BACKGROUND: Advances in automated vehicle technology have the potential to relieve drivers from driving tasks so that they can engage in NDRTs freely. However, drivers will still need to take-over control under certain circumstances. METHOD: A driving simulation experiment was conducted using an Advanced Driving Simulator and real-world driving videos. Forty-six participants completed three drives in three display conditions, respectively (HUD, mobile phone and baseline without NDRT). The HUD was integrated with the vehicle in displaying NDRTs while the mobile phone was not. Drivers' visual (e.g. gaze, blink) and physiological (e.g. ECG, EDA) data were collected to measure driver state. Two take-over reaction times (hand and foot) were used to measure take-over performance. RESULTS: The HUD significantly shortened the take-over reaction times compared to the mobile phone condition. Compared to the baseline condition, drivers in the HUD condition also experienced lower cognitive workload and physiological arousal. Drivers' take-over reaction times were significantly correlated with their visual and electrodermal activities during automated driving prior to the take-over request. CONCLUSION: HUDs can improve driver performance and lower workload when used as an NDRT interface. APPLICATION: The study sheds light on a promising approach for drivers to engage in NDRTs in future AVs.

Can't simply roll it out: Evaluating a real-world virtual reality intervention to reduce driving under the influence.

Driving under the influence (DUI) increases the risk of crashes. Emerging technologies, such as virtual reality (VR), represent potentially powerful and attractive tools for the prevention of risky behaviours, such as DUI. Therefore, they are embraced in prevention efforts with VR interventions primed to grow in popularity in near future. However, little is known about the actual effectiveness of such DUI-targeting VR interventions. To help fill the knowledge gap, this study explored the effects of one VR intervention as delivered in the real world. Using pre and post test design, including an intervention group (n = 98) and a control group (n = 39), the intervention evaluation examined young drivers' (aged 18 to 25, no known history of DUI) intention and self-reported behaviour three months after the intervention as compared to the baseline. The results did not provide evidence for statistically significant effects of the VR intervention on self-reported DUI behaviour during the three months post intervention and DUI intention at three months post intervention. Such results might be due to the fact that the recruited participants generally self-reported little DUI behaviour, i.e. positively changing behaviour that is already positive is inherently challenging. Nevertheless, the results question the utility of funding the roll-out of arguably attractive technologies without a thorough understanding of their effectiveness in particular settings. To improve the potential for future positive outcomes of such interventions, we provide suggestions on how VR software might be further developed and, subsequently, leveraged in future research to improve the likelihood for behavioural change, e.g. by collecting, analysing and presenting objective driving performance data. Alternatively, future endeavours might focus on participants with known DUI history and examine the effects of the VR intervention for this particular higher-risk group.

Driving under the influence of drugs or alcohol: Predicting the intentions of young drivers.

Objectives: Driving under the influence (DUI) of drugs or alcohol impairs driving performance and, as a result, increases the risk of crashes. The risk of DUI is five-fold higher for young drivers (aged 18-25 years), but little is known about what determines their DUI intentions. This study applied an extended model of the Theory of Planned Behavior (TPB) to address the research question of what factors might influence young drivers' future intentions to DUI. Methods: This study used a survey obtaining data from 329 young drivers (Mage = 20.92 years, SD = 2.16) in Australia. Beyond the standard TPB measures of attitudes, subjective norms and perceived behavioral control (PBC), the current study included demographic variables and additional predictors (i.e., moral norm, peers' norm, perceived risk, impulsivity and past DUI behavior). Results: A vast majority of the participants (85.1%) selected the maximum (9, never), meaning that they had no intention to DUI in the future. Overall, a stepwise multilevel logistic regression analysis (Step 1: demographics, Step 2: TPB measures, and Step 3: additional predictors) explained between 30.1% and 52.9% of the variance in DUI intentions. It showed past DUI behavior as the strongest predictor of DUI intention, followed by instrumental attitude and descriptive norms. Conclusions: This study explored an extended TPB model to understand young drivers' DUI intentions better. With this new knowledge of understanding the factors that influence DUI, researchers and practitioners can create interventions and strategies that are better tailored to the population of young drivers at higher risk.

Understanding the predictors of young drivers' speeding intention and behaviour in a three-month longitudinal study.

This study aimed to examine to what extent an Adolescent Speeding Specific Model (ASSM), extending the theory of planned behaviour (TPB), predicts young drivers' (aged 18-25) future and past speeding (n = 126). The ASSM tested the contribution of demographics, split TPB, additional predictors and past behaviour to young drivers' speeding at two moments of time, over three months. Hierarchical multiple regression revealed that participants most likely to speed in the future were those who have done so in the past (independent predictor (ip): past compliance with the speed limit), and who were not certain in their ability to control their speeding (ip: self-efficacy). ASSM also indicated that people who reported speeding at T1 did so at T2 as well (ip: past compliance with the speed limit). The results also show that sensitive to rewards people would speed more (ip: sensitivity to reward), similar to ones with less control over their behaviour (ip: perceived controllability) or with more driving experience (ip: GDL phase). Overall, the ASSM explained 73% of the intention to comply with speed limits variation and 62% of the present compliance with the speed limit variation. Compared to models, similar in structure to ASSM, our model explained variance in intention, equal to the previously maximum observed, and 22% more variance in behaviour. As a result, our findings may help design better targeted educational campaigns to prevent young drivers' speeding.

The antecedents, experience, and coping strategies of driver boredom in young adult males.

INTRODUCTION: Road crash statistics are evidence of the severe consequences resulting from human error, especially among young adult males. Drivers perform best and safest when they are adequately engaged in the driving task. Boredom and a lack of engagement in the driving task may cause risk taking and phone use. However, the antecedents to driver boredom, the subjective experience itself, as well as the coping strategies to combat boredom are not well understood. The aim of this study was to investigate these aspects. METHOD: We carried out a qualitative study in a simulated, safe, yet highly immersive driving environment. The 24 participants included male drivers aged 18 to 25 susceptible to risky driving and phone use. A phenomenological framework was used to analyze their accounts of the experience of boredom while driving. RESULTS: Results indicate that situations giving rise to driver boredom include low traffic, slow or constant speed, and routine drives. Feelings comprising the experience were frustration, vigilance, relaxing, autopilot, mind wandering, and discomfort. Coping mechanisms manifest themselves in approach strategies related to the driving task such as speeding, which are often dangerous, and avoidance strategies, which include phone use. CONCLUSIONS: We conclude that driver boredom bears similarities to the experience of boredom at work (unlike boredom at home) due to the situational constraints, where people feel stuck, trapped, or obliged to remain vigilant. PRACTICAL APPLICATIONS: The findings present an opportunity for the road safety and automotive technology community to address the issue of under-stimulation through safety interventions aimed at increased task engagement. Our work can also aid in investigating driver experiences in partially automated driving, which is likely to induce boredom as well.