What are the factors determining user intentions to use AV while impaired?

In this study, we employ the Integrated Choice Latent Variable (ICLV) framework to model the public’s intention of using Autonomous Vehicles (AVs) while impaired under the influence of alcohol, medicine, or fatigue. We identify five latent constructs from psychometric indicators that define respondent’s perception and attitudes towards AVs which are i) perceived benefits, ii) perceived risks, iii) enjoy driving, iv) wheels public transport attitude, and v) rails public transport attitude. We use these latent variables along with explanatory variables to study user intentions regarding delegation of vehicle control from human driving to autonomous driving. The study uses survey data collected from 1,065 Czech residents between 2017 and 2018. Our findings indicate that user intentions are primarily defined by attitudes rather than socio-demographic attributes. However, the inclusion of both types of variables is crucial in evaluating user intentions. Despite a positive outlook towards AVs, people were found to be reluctant in using AVs while impaired which can be attributed to distrust towards the technology. Our analysis shows that with appropriate efforts from policymakers, the public’s attitude can be changed to promote adoption. The efforts will have to be emphasized towards building positive attitudes (such as perceived benefits) and diminishing existing negative attitudes (such as perceived risks).     

Differential effects of driver sleepiness and sleep inertia on driving behavior

ObjectivesDriver sleepiness is one of the major safety issues in conventional driving and sleep inertia emerges as a driver state in automated driving. The aim of the present study was to assess the differential impacts of sleepiness and sleep inertia on driving behavior.Method61 participants completed a 10-min manual driving task during an otherwise automated drive. They completed the task (a) under an alert state, (b) under a sleepy state, and (c) after EEG-confirmed sleep. Driving performance was assessed with the parameters lane-keeping, speed choice, and speed-keeping. The eye-blink-based sleepiness measure PERCLOS (the proportion of time with eyes closed) was compared for the three driver states.ResultsLane- and speed-keeping performance were impaired under the sleepy state and after sleep, relative to the alert state. After sleep, lane-keeping behavior recovered rapidly and speed-keeping recovered by trend. Under the sleepy state, performance deteriorated. After sleep, the mean speed was lower than in the sleepy state and in the alert state. PERCLOS was increased after sleep and under the sleepy state, relative to the alert state.ConclusionsAlthough sleep inertia had detrimental effects on driving parameters similar to sleepiness, this effect rapidly vanished. Hence, while brief naps might be suitable to restore alertness in general, the minimal time needed to regain full capacity after napping should be a focus of future research.     

Effects of a driver assistance system with foresighted deceleration control on the driving performance of elderly and younger drivers

It is imperative to enhance the safety of elderly individuals on the roads to ensure the quality of their daily life. Near-miss incidents or accidents at blind intersections often result from a conflict between the behaviors of the driver and of other road users (pedestrians and cyclists). The failure to search for potential conflict in the context of blind intersections is a concern pertaining to road safety. The proposed assistance system performs a proactive braking intervention to achieve a referenced velocity in uncertain situations, such as one in which an unobserved pedestrian might initiate a road crossing. The proactive braking intervention attempts to manage the potential risk of crashing with respect to covert hazards. Because an automated system may impair a human’s ability to perceive and respond to hazardous situations while driving, this study was designed to examine the effects of proactive braking intervention and visual support cues on elderly and younger drivers’ ability to respond to information about potentially hazardous situations. We conducted a public-road driving experiment involving 108 elderly and younger drivers from two non-overlapping age groups. It was observed that the vehicle slowdown realized through the proactive braking intervention enabled the drivers to perform safety confirmation near blind spots and caused them to be more sensitive to and wary of potential hazards. This approach could be effective not only for elderly drivers, but also for young or inexperienced ones.     

Adaptation of the safe driving climate among friends scale in Chinese drivers and its associations with risky driving behaviours

The present study aimed to adapt the Safe Driving Climate among Friends Scale (SDCaF) to Chinese drivers and to examine its reliability and validity. Three hundred and sixty drivers aged from 18 to 24 years old were asked to complete the SDCaF and the Risky Driving Behaviour Scale. A confirmatory factor analysis (n = 360) was conducted to examine the factorial structure of the SDCaF. The validity of the scale was then evaluated by examining the associations between the SDCaF factors, risky driving behaviours and traffic violations. The CFA results showed that the model fit of the Chinese version of the scale (SDCaF-C) was acceptable. Second, the SDCaF-C factors were weakly or moderately correlated with speeding, self-assertiveness and rule violations. Third, significant gender differences were found for the variables of friend pressure and communication, with male drivers scoring higher than female drivers. Moreover, drivers who had traffic violations in the past year scored higher on friend pressure and lower on both communication and shared commitment to safe driving compared to those who had not had traffic violations. The findings supported the psychological properties of the SDCaF-C and highlighted the importance of concerning the effects of safe diving climate among friends on young drivers’ risky driving behaviours.     

A novel conceptual framework investigating the relationship between roadside advertising and road safety: The driver behaviour and roadside advertising conceptual framework

This paper proposes a conceptual framework to understand the relationship between roadside advertising signs, driver behaviour, and road safety outcomes. Roadside advertising signs are external distractions that may take a driver's attention away from safety-critical driving tasks, potentially increasing crash risk through driver distraction and inattention. Although studies report safety concerns, as a whole, the body of research in the field is inconclusive with inconsistent quality, making it difficult to draw firm conclusions. Definitive links between roadside advertising and road trauma are not yet evident, which has major consequences for road regulators' capacity to develop evidence-based policy to safely administer public roads. However, a lack of consistent evidence does not indicate an absence of risk but underscores its complexity. To address this problem, the Driver Behaviour and Roadside Advertising Conceptual Framework (DBRA framework) was developed to strategically investigate and conceptualise the phenomena of roadside advertising. A new term – “extended engagement” – has also been proposed to account for situations of prolonged attentional engagement with a roadside advertising sign. Further, it is posited that important variations in driving performance may be associated with a driver's extended engagement with a roadside advertising sign. Built on extant theories of driver behaviour and empirical research, the DBRA framework is designed to be a robust tool that encourages a common agenda for future roadside advertising research.     

Takeover performance of older drivers in automated driving: A review

IntroductionThe introduction of automated vehicles to the road environment brings new challenges for older drivers. Level 3 of conditional automation requires drivers to take over control of their vehicle whenever the automated system reaches its limits. Even though autonomous vehicles may be of great benefit to older drivers in terms of safely maintaining their mobility, a better understanding of their takeover performance remains crucial. The objective of this review of the literature is to shed more light on the effects that aging has on takeover performance during automated driving.MethodsThree database searches were conducted: PsychINFO, Web Of Sciences, and TRID. Studies from the last decade which included groups of older drivers were reviewed.ResultsAfter checking through abstracts and texts of articles, 9 articles, 4 proceedings papers, and 1 technical report were included in this review. All studies included a driving simulator that refers to level 3 of automation (which requires supervision by the driver). Five out of fourteen studies showed that older adults had poorer takeover performance (in terms of takeover time and takeover quality) than younger adults. However, several factors, such as the type of non-driving related task (NDRT), were seen to influence takeover performance in older adults. Speed, type and duration of notification interval, distribution and duration of driving modes, and number of takeovers were all also factors of influence.ConclusionThis review synthesizes the results of 14 articles which investigate the effects of age-related changes on takeover performance. Various external factors as NDRTs, speed, type and duration of notification to take over, duration of the automated phase, distribution of the automated/manual phases may affect takeover performance in older adults. Even if the majority of articles showed that older adults are globally slower at taking over a vehicle than younger adults, findings concerning take over quality yield divergent results. It's probably due to age related cognitive changes, particularly in executive functions or to a great heterogeneity in this population. This literature review highlights the need to develop new research on the impact of aging on takeover performance.     

Human machine interface design for continuous support of mode awareness during automated driving: An online simulation

In the transition towards higher levels of vehicle automation, one of the key concerns with regards to human factors is to avoid mode confusion, when drivers misinterpret the driving mode and therewith misjudge their own tasks and responsibility. To enhance mode awareness, a clear human centered Human Machine Interface (HMI) is essential. The HMI should support the driver tasks of both supervising the driving environment when needed and self-regulating their non-driving related activities (NDRAs). Such support may be provided by either presenting continuous information on automation reliability, from which the driver needs to infer what task is required, or by presenting continuous information on the currently required driving task and allowed NDRA directly. Additionally, it can be valuable to provide continuous information to support anticipation of upcoming changes in the automation mode and its associated reliability or required and allowed driver task(s). Information that could support anticipation includes the available time until a change in mode (i.e. time budget), information on the upcoming mode, and reasons for changing to the upcoming mode. The current work investigates the effects of communicating this potentially valuable information through HMI design. Participants received information from an HMI during simulated drives in a simulated car presented online (using Microsoft Teams) with an experimenter virtually accompanying and guiding each session. The HMI either communicated on automation reliability or on the driver task, and either included information supporting anticipation or did not include such information. Participants were thinking aloud during the simulated drives and reported on their experience and preferences afterwards. Anticipatory information supported understanding about upcoming changes without causing information overload or overreliance. Moreover, anticipatory information and information on automation reliability, and especially a combination of the two, best supported understandability and usability. Recommendations are provided for future work on facilitating supervision and NDRA self-regulation during automated driving through HMI design.     

Modelling perceived risk and trust in driving automation reacting to merging and braking vehicles

Perceived risk and trust are crucial for user acceptance of driving automation. In this study, we identify important predictors of perceived risk and trust in a driving simulator experiment and develop models through stepwise regression to predict event-based changes in perceived risk and trust. 25 participants were tasked to monitor SAE Level 2 driving automation (ACC + LC) while experiencing merging and hard braking events with varying criticality on a motorway. Perceived risk and trust were rated verbally after each event, and continuous perceived risk, pupil diameter and ECG signals were explored as possible indictors for perceived risk and trust.The regression models show that relative motion with neighbouring road users accounts for most perceived risk and trust variations, and no difference was found between hard braking with merging and hard braking without merging. Drivers trust the automation more in the second exposure to events. Our models show modest effects of personal characteristics: experienced drivers are less sensitive to risk and trust the automation more, while female participants perceive more risk than males. Perceived risk and trust highly correlate and have similar determinants. Continuous perceived risk accurately reflects participants’ verbal post-event rating of perceived risk; the use of brakes is an effective indicator of high perceived risk and low trust, and pupil diameter correlates to perceived risk in the most critical events. The events increased heart rate, but we found no correlation with event criticality. The prediction models and the findings on physiological measures shed light on the event-based dynamics of perceived risk and trust and can guide human-centred automation design to reduce perceived risk and enhance trust.     

Supporting cooperative driving behaviour by technology – HMI solution,acceptance by drivers and effects on workload and driving behaviour

The paper compares and evaluates three different HMIs (Human Machine Interface) for an ADAS (Advanced Driver Assistance System) supporting cooperative interactions between drivers while merging and turning left. In road traffic, cooperation means that drivers (cooperation partners) coordinate their driving behaviour in a way that they facilitate each other’s intended driving manoeuvres. An experimental study was conducted with 30 participants in a static high-end simulator. The test scenarios included merging onto a motorway and turning left at a rural intersection. As independent variables, the HMI (Baseline vs. Sensor vs. C2X (Car-to-everything)) was varied in addition to the cooperation situation (merging vs. turning left). All HMI variants were based on a HUD (Head-Up Display). In the Baseline condition, the HMI only showed information about speed and navigation. The Sensor HMI visualised additionally the driving situation as it can be detected by the vehicle's own sensors. The C2X HMI was based on C2X communication and also represented the different phases of manoeuvre coordination with the cooperation partner. The traffic flow and the behaviour of the surrounding traffic did not differ between the different HMI variants, in order to ensure that the traffic situation did not influence the participants’ evaluation of the HMI variants. The dependent variables included subjective (e.g. acceptance, usability) and objective measures (e.g. driving and gaze behaviour). The results showed that a system supporting cooperative interactions is generally accepted by drivers. The most preferred system was the C2X HMI. The advantages of a C2X based HMI were an improved user experience leading to a greater intent to use the ADAS for cooperative driving interactions, increased system trust, and an easier handling of the system. The workload of the C2X HMI did not exceed the level reported for the Baseline or the Sensor HMI – although the C2X HMI presented more information. The results are used to derive indications for the design of assistance systems supporting cooperative driving behaviour.