Traffic crashes remain a leading cause of accidental human death where aggressive driving is a significant contributing factor. To review the driver's performance presented in aggressive driving, this systematic review screens 2412 pieces of relevant literature, selects and synthesizes 31 reports with 34 primary studies that investigated the driver's control performance among the general driver population in four-wheeled passenger vehicles and published with full text in English. These 34 selected studies involved 1731 participants in total. By examining the selected 34 studies, the measures relating to vehicle speed (e.g., mean speed, n = 22), lateral control (e.g., lane deviation, n = 17) and driving errors (e.g., violation of traffic rules, n = 12) were reported most frequently with a significant difference observed between aggressive driving and driving in the control group. The result of the meta-analysis indicates that the aggressive driving behaviour would have 1) a significantly faster speed than the behaviour in the control group with an increase of 5.32 km/h (95% confidence interval, [3.27, 7.37] km/h) based on 8 studies with 639 participants in total; 2) 2.51 times more driving errors (95% confidence interval, [1.32, 3.71] times) than the behaviour in the control group, based on 5 studies with 136 participants in total. This finding can be used to support the identification and quantification of aggressive driving behaviour, which could form the basis of an in-vehicle aggressive driving monitoring system.

Highly automated driving will likely result in drivers being out-of-the-loop during specific scenarios and engaging in a wide range of non-driving related tasks. Manifesting in lower levels of risk perception to emerging events, and thus affect drivers’ availability to take-over manual control in safety-critical scenarios. In this empirical research, we measured drivers’ (N = 20) risk perception with cardiac and skin conductance indicators through a series of high-fidelity, simulated highly automated driving scenarios. By manipulating the presence of surrounding traffic and changing driving conditions as long-term risk modulators, and including a driving hazard event as a short-term risk modulator, we hypothesised that an increase in risk perception would induce greater physiological arousal. Our results demonstrate that heart rate variability features are superior at capturing arousal variations from these long-term, low to moderate risk scenarios. In contrast, skin conductance responses are more sensitive to rapidly evolving situations associated with moderate to high risk. Based on this research, future driver state monitoring systems should adopt multiple physiological measures to capture changes in the long and short term, modulation of risk perception. This will enable enhanced perception of driver readiness and improved availability to safely deal with take-over events when requested by an automated vehicle.

Highly Automated Driving technology will be facing major challenges before being pervasively integrated across production vehicles. One of them will be monitoring drivers’ state and determining whether they are ready to take over control under certain circumstances. Thus, we have explored their physiological responses and the effects on trust of different scenarios with varying traffic complexity in a driving simulator. Using a mixed repeated measures design, twenty-seven participants were divided in two reliability groups with opposite induced automation reliability expectations -low and high-. We hypothesized that expectations would modulate participants’ trust in automation, and consequently, their physiological responses across different scenarios. That is, increasing traffic complexity would also increase participants’ arousal, and this would be accentuated or mitigated by automation reliability expectations. Although reliability group differences could not be observed, our results show an increase of physiological activation within high complexity driving conditions (i.e., a mentally demanding non-driving related task and urban scenarios). In addition, we observed a modulation of trust in automation according to the group expectations delivered. These findings provide a background methodology from which further research in driver monitoring systems can benefit and be used to train machine learning methods to classify drivers’ state in changing scenarios. This would potentially help mitigate inappropriate take-overs, calibrate trust and increase users’ comfort and safety in future Highly Automated Vehicles.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Now the fourth leading cause of death, physical inactivity is a primary element of non-communicable diseases. Despite a great number of attempts, there is still a lack of effective approaches that can motivate sedentary populations to increase their levels of physical activity over a sustained period. Financial and non-financial incentives for exercise can provide an immediate reward for increasing activity levels, but due to limited funding to provide rewards, previous programmes using this approach have only shown short-term changes in behaviour. Sweatcoin, a UK-based technology company, has developed a digital platform that tracks and verifies physical movement to convert it into virtual currency. The currency can subsequently be exchanged for goods and services on their marketplace, providing a continuous incentive to be active. This study investigates the physical activity behaviour change observed in Sweatcoin users over a 6-month period of app usage. Objective: To investigate the change in physical activity (measured using daily step-count) of Sweatcoin users following registration with the app, the longevity of the change and whether this change can be predicted through and driven by demographic and other lifestyle variables. Methods: Activity data from a sample of 5,406 Sweatcoin users was used to analyse daily step count. Activity change was measured in terms of the percentage change in average daily step count for each month after registration, relative to that in the 3 months prior to the app being used. A subset of over 800 users completed a questionnaire capturing demographic and activity information. Change in activity behaviour following app download was calculated and users were grouped according to having no/negative, moderate or high activity change. Differences between groups in terms of activity and demographic status were investigated using regression analyses. Seasonal effects were also investigated and accounted for. Results: Analyses show an overall average increase in daily step count of 19.5% over the 6-months following registration, in comparison to 3-months prior to registration (P<.001). Of the questionnaire respondents, 678 were valid responses. Of those, 34% were identified to have shown high levels of behaviour change following app registration. The logistic regression identified the key drivers of high activity behaviour change. There was an obvious impact of seasonality, with those registering the app in winter (OR, 6.89, p<.001) or spring (OR, 5.87, p=.002) were more likely to show high activity behaviour change than those registering in summer. More striking were the results identifying those classified as overweight (measured through body mass index (BMI); OR, 2.22, p=.014) and less active (based on self-report scale of physical activity; OR, 0.85, p=.037), being most likely to show high levels of physical activity change following registration with app. Conclusions: The results highlight that an incentives-based app can induce significant sustainable physical activity behaviour change. Importantly, the results suggest that those typically lacking motivation to exercise (sedentary and high BMI) are most likely to be incentivised to increase their activity levels.

Trust in automation is crucial for the safe and appropriate adoption of automated driving technology. Current research methods to measure trust mainly rely on subjective scales, with several intrinsic limitations. This empirical experiment proposes a novel method to measure trust objectively, using functional near-infrared spectroscopy (fNIRS). Through manipulating participants’ expectations regarding driving automation credibility, we have induced and successfully measured opposing levels of trust in automation. Most notably, our results evidence two separate yet interrelated cortical mechanisms for trust and distrust. Trust is demonstrably linked to decreased monitoring and working memory, whereas distrust is event-related and strongly tied to affective (or emotional) mechanisms. This paper evidence that trust in automation and situation awareness are strongly interrelated during driving automation usage. Our findings are crucial for developing future driver state monitoring technology that mitigates the impact of inappropriate reliance, or over trust, in automated driving systems.

Objective: Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving. Background: Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers’ states posits it could become a novel method for measuring risk perception. Methods: Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports. Results: Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario. Conclusion: This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario. Application: Little is known regarding the effects of drivers’ risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.