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.     

Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

In the near future, conditionally automated vehicles (CAVs; SAE Level 3) will travel alongside manual drivers (≤ SAE level 2) in mixed traffic on the highway. It is yet unclear how manual drivers will react to these vehicles beyond first contact when they interact repeatedly with multiple CAVs on longer highway sections or even during entire highway trips. In a driving simulator study, we investigated the subjective experience and behavioral reactions of N = 51 manual drivers aged 22 to 74 years (M = 41.5 years, SD = 18.1, 22 female) to driving in mixed traffic in repeated interactions with first-generation Level 3 vehicles on four highway sections (each 35 km long), each of which included three typical speed limits (80 km/h, 100 km/h, 130 km/h) on German highways. Moreover, the highway sections differed regarding the penetration rate of CAVs in mixed traffic (within-subjects factor; 0%, 25%, 50%, 75%). The drivers were assigned to one of three experimental groups, in which the CAVs differed regarding their external marking, (1) status eHMI, (2) no eHMI, and (3) a control group without information about the mixed traffic. After each highway section, drivers rated perceived safety, comfort, and perceived efficiency. Drivers were also asked to estimate the penetration rate of CAVs on the previous highway section. In addition, we analyzed drivers’ average speed and their minimum time headways to lead vehicles for each speed zone (80 km/h, 100 km/h, 130 km/h) as well as the percentage of safety critical interactions with lead vehicles (< 1 s time headway). Results showed that manual drivers experienced driving in mixed traffic, on average, as more uncomfortable, less safe and less efficient than driving in manual traffic, but not as dangerous. A status eHMI helps manual drivers identify CAVs in mixed traffic, but the eHMI had no effect on manual drivers’ subjective ratings or driving behavior. Starting at a level of 25% Level 3 vehicles in mixed traffic, participants' average speed decreased significantly. At the same time, the percentage of safety critical interactions with lead vehicles increased with an increasing penetration rate of CAVs. Accordingly, additional measures may be necessary in order to at least keep the existing safety level of driving on the highway.     

Impact of the driver’s visual engagement on situation awareness and takeover quality

During automated driving (SAE Level 3), drivers can delegate control of the vehicle and monitoring of the road to an automated system. They may then devote themselves to tasks other than driving and gradually lose situational awareness (SA). This could result in difficulty in regaining control of the vehicle when the automated system requires it. In this simulator study, the level of SA was manipulated through the time spent performing a non-driving task (NDRT), which alternated with phases where the driver could monitor the driving scene, prior to a critical takeover request (TOR). The SA at the time of TOR, the visual behaviour after TOR, and the takeover quality were analysed. The results showed that monitoring the road just before the TOR allowed the development of limited perception of the driving situation, which only partially compensated for the lack of a consolidated mental model of the situation. The quality of the recovery, assessed through the number of collisions, was consistent with the level of development of SA. The analysis of visual behaviour showed that engagement in the non-driving task at the time of TOR induced a form of perseverance in consulting the interface where the task was displayed, to the detriment of checking the mirrors. These results underline the importance of helping the driver to restore good SA well in advance of a TOR.     

Can we adapt to highly automated vehicles as passengers? The mediating effect of trust and situational awareness on role adaption moderated by automated driving style

The emergence of highly automated driving technology provides safe and convenient travel while also causing user inadaptation. Therefore, based on human factors engineering, it is necessary to study highly automated vehicles (HAVs) that meet different user needs. Thus, this study aims to investigate the relationships between state anxiety, situational awareness, trust, and role adaptation. The adaptation model is constructed to conduct a study on the adaptation of HAVs with different automated styles when user roles change from driver to passenger. Simulated riding was conducted in the HAV experiment (N = 117), collecting scale data after each participant had experienced each automated driving style. A structural equation modeling approach was applied to analyze the adaptation model based on scale data. The results showed that there was a significant correlation between state anxiety, situational awareness, trust, and role adaptation. State anxiety has a significant negative predictive effect on trust, situational awareness, and role adaptation. In addition to its direct impact on role adaptation, state anxiety also has an indirect effect on role adaptation through situational awareness and trust. Furthermore, the automated driving style has been confirmed to have a moderating role in the relationship between the direct and indirect effects of state anxiety and role adaptation. Our findings contribute to multiple streams of the literature and have important implications for designing personalized automated driving to improve user acceptance.     

Occupational Therapy in the Rehabilitation of the Patient with Anorexia Nervosa

The first part of this paper develops an ecological and rehabilitative stance for the occupational therapist in relation to the anorexic patient. Maximizing the individual's level of psychosocial functioning is regarded as the primary aim of treatment. A model for approaching the anorexic patient is described which examines environmental factors, and the importance of assessment and follow up. The cognitive behavioral approach is discussed and suggestions are made for adapting it to the occupational therapist's orientation towards practical activity. In the second part of the paper concrete suggestions for therapy are made.     

驾驶中移动电话的使用：基于自我调整行为的研究视角

因使用移动电话而导致的“驾驶分心”已成为一种严重的道路安全隐患。驾驶者总体上能意识到其危害, 但在驾驶中使用移动电话的现象很普遍。以往研究不能全面地对这一矛盾予以解释, 长期以来更是忽略了对“驾驶分心中自我调整行为”进行研究, 而这一问题能够直接解释驾驶中的移动电话使用行为。从以下三个方面可以对与驾驶分心中自我调整行为有关的研究进行分析和综述：(1)驾驶中移动电话使用行为的一般规律(类型、频率和危险感知等), 驾驶者对移动电话使用行为的理解是诱发自我调整行为的基础; (2)驾驶情境下移动电话使用中可能触发的自我调整行为, 该部分将主要分析自我调整行为的类型及相关属性; (3)如何对移动电话使用行为和自我调整行为进行预测。总体上, 基于对补偿式安全信念进行理解和度量、并以此为切入点对驾驶中移动电话使用及与其有关的自我调整行为展开研究, 这将有助于更好地理解驾驶分心行为。     

Factors influencing the decision to engage in alcohol-impaired driving among Arab-Israeli youths

Alcohol-Impaired Driving (AID) is rarely studied among Arab communities. As contacts between Arab and Western cultures grow, alcohol consumption and safety-related issues are emerging as a major public health concern. This paper examines factors influencing the decision to engage in AID in a sample of young Arab-Israeli bar patrons (n = 300, age 17–34), including both Muslims (n = 77) and Christians (n = 176); alcohol is prohibited for the former, but is normative for the latter. Studies of AID often use agent-oriented models, most notably the theory of planned behavior (TPB). The current study complements this tradition using the structure-oriented Social-Cultural (SC) model. Over 70% of participants reported engaging in AID after consuming 3–4 portions of alcohol. The TPB model proved to have significant explanatory power while most cultural factors, including religion, failed to gain statistical significance. The latter finding is counter-intuitive given the different status of alcohol in the cultures investigated. The TPB variable Personal Behavioral Control (PBC) and a specific sub-component of the subjective norms variable emerged as providing the greatest contribution to the model. The results support the robustness of TPB and demonstrate that beyond identifying idiosyncratic patterns structure-based models can be useful in refuting pre-conceived conceptions. Thus, both approaches should be utilized to inform policymaking. Governmental authorities, particularly in Israel, must consider the neglected issue of AID among Arab communities, including both Christians and Muslims.