Anticipatory information makes the difference: Behavioral effects and user assessments of a cyclist warning system to enhance cyclists' situation awareness.

PROBLEM: Increasing numbers of crashes involving pedelecs, and particularly older pedelec users, induce a need to enhance cycling safety. We evaluated a prototype cyclist warning system (CWS) that aims to increase situation awareness (SA) by alerting to safety critical events (SCE) with trimodal (auditory, visual, tactile). METHOD: To investigate the effects of CWS usage, we conducted a 2x2 mixed design bicycle simulator study with factors (1) CWS usage (within: rides WITH vs. WITHOUT CWS) and (2) age group (between: younger vs. older cyclists) on braking reaction time, gaze behavior, mental workload, and perceived safety. In sum, N = 64 participants (n = 32 younger, 18-40 years; n = 32 older, ≥ 55 years) took part in the study and experienced two balanced blocks of short rides including SCE of particular relevance for cycling safety. RESULTS: CWS usage resulted in earlier braking reactions to all investigated SCE and partly earlier fixation on the critical interaction partners (CIP) indicating increased cyclists' SA. Consistently to behavioral measures, participants' assessments regarding perceived safety further supported the safety improvements derived from CWS independently of age group. Moreover, CWS usage did not add to mental workload ratings. Age effects were selectively found for gaze data showing that across all SCE, older adults fixated longer and more frequently on street alignment, and less frequently on other road users. DISCUSSION: Taken together, the CWS evaluation showed promising results indicating the potential of the tested CWS to increase SA and enhance cyclists' safety both on a behavioral level and regarding subjective assessments. Further research should address the systems' safety potential under real-world conditions and for situations of higher complexity. PRACTICAL APPLICATIONS: Understanding the potential impact of road safety measures such as CWS is important to contribute effectively to reducing SCE.

Using event-related brain potentials to evaluate motor-auditory latencies in virtual reality.

Actions in the real world have immediate sensory consequences. Mimicking these in digital environments is within reach, but technical constraints usually impose a certain latency (delay) between user actions and system responses. It is important to assess the impact of this latency on the users, ideally with measurement techniques that do not interfere with their digital experience. One such unobtrusive technique is electroencephalography (EEG), which can capture the users' brain activity associated with motor responses and sensory events by extracting event-related potentials (ERPs) from the continuous EEG recording. Here we exploit the fact that the amplitude of sensory ERP components (specifically, N1 and P2) reflects the degree to which the sensory event was perceived as an expected consequence of an own action (self-generation effect). Participants (N = 24) elicit auditory events in a virtual-reality (VR) setting by entering codes on virtual keypads to open doors. In a within-participant design, the delay between user input and sound presentation is manipulated across blocks. Occasionally, the virtual keypad is operated by a simulated robot instead, yielding a control condition with externally generated sounds. Results show that N1 (but not P2) amplitude is reduced for self-generated relative to externally generated sounds, and P2 (but not N1) amplitude is modulated by delay of sound presentation in a graded manner. This dissociation between N1 and P2 effects maps back to basic research on self-generation of sounds. We suggest P2 amplitude as a candidate read-out to assess the quality and immersiveness of digital environments with respect to system latency.

Assessing mental workload with wearable devices - Reliability and applicability of heart rate and motion measurements.

Wearable devices are increasingly used for assessing physiological data. Industry 4.0 aims to achieve the real-time assessment of the workers' condition to adapt processes including the current mental workload. Mental workload can be assessed via physiological data. This paper researches the potential of wearable devices for mental workload assessment by utilizing heart rate and motion data collected with a smartwatch. A laboratory study was conducted with four levels of mental workload, ranging from none to high and during sitting and stepping activities. When sitting, a difference in the heart rate and motion data from the smartwatch was only found between no mental workload and any mental workload task. For the stepping condition, differences were found for the movement data. Based on these results, wearable devices could be useful in the future for detecting whether a mental demanding task is currently performed during low levels of physical activity.

The Effect of eHMI Malfunctions on Younger and Elderly Pedestrians' Trust and Acceptance of Automated Vehicle Communication Signals.

To ensure traffic flow and road safety in automated driving, external human-machine interfaces (eHMIs) could prospectively support the interaction between automated vehicles (AVs; SAE Level 3 or higher) and pedestrians if implicit communication is insufficient. Particularly elderly pedestrians (≥65 years) who are notably vulnerable in terms of traffic safety might benefit of the advantages of additional signals provided by eHMIs. Previous research showed that eHMIs were assessed as useful means of communication in AVs and were preferred over exclusively implicit communication signals. However, the attitudes of elderly users regarding technology usage and acceptance are ambiguous (i.e., less intention to use technology vs. a tendency toward overreliance on technology compared to younger users). Considering potential eHMI malfunctions, an appropriate level of trust in eHMIs is required to ensure traffic safety. So far, little research respected the impact of multiple eHMI malfunctions on participants' assessment of the system. Moreover, age effects were rarely investigated in eHMIs. In the current monitor-based study, N = 36 participants (19 younger, 17 elderly) repeatedly assessed an eHMI: During an initial measurement, when encountering a valid system and after experiencing eHMI malfunctions. Participants indicated their trust and acceptance in the eHMI, feeling of safety during the interaction and vigilance toward the eHMI. The results showed a positive effect of interacting with a valid system that acted consistently to the vehicle's movements compared to an initial assessment of the system. After experiencing eHMI malfunctions, participants' assessment of the system declined significantly. Moreover, elderly participants assessed the eHMI more positive across all conditions than younger participants did. The findings imply that participants considered the vehicle's movements as implicit communication cues in addition to the provided eHMI signals during the encounters. To support traffic safety and smooth interactions, eHMI signals are required to be in line with vehicle's movements as implicit communication cues. Moreover, the results underline the importance of calibrating an appropriate level of trust in eHMI signals. An adequate understanding of eHMI signals needs to be developed. Thereby, the requirements of different user groups should be specifically considered.

Effects of Gesture-Based Interaction on Driving Behavior: A Driving Simulator Study Using the Projection-Based Vehicle-in-the-Loop.

OBJECTIVE: We observe the driving performance effects of gesture-based interaction (GBI) versus touch-based interaction (TBI) for in-vehicle information systems (IVISs). BACKGROUND: As a contributing factor to a number of traffic accidents, driver distraction is a significant problem for traffic safety. More specifically, visual distraction has a strong negative impact on driving performance and risk perception. Thus, the implementation of new interaction systems that use midair gestures to encourage glance-free interactions could reduce visual distraction among drivers. METHODS: In this experiment, participants drove a projection-based Vehicle-in-the-Loop. The projection-based technology combines a visual simulation with kinesthetic, vestibular, and auditory feedback from a car on a test track. While driving, participants used GBI or TBI to perform IVIS tasks. To investigate driving behavior related to critical driving situations and car-following maneuvers, vehicle data based upon longitudinal and lateral driving were collected. RESULTS: Participants reacted faster to critical driving situations when using GBI compared to TBI. For drivers using TBI, steering performance decreased and time headway to a preceding vehicle was higher. CONCLUSION: Gestures provide a safe alternative to in-vehicle interactions. Moreover, GBI has fewer effects on driver distraction than TBI. APPLICATION: Potential applications of this research include all in-vehicle interaction systems used by drivers.

The box task - a method for assessing in-vehicle system demand.

The use of advanced in-vehicle information systems (IVIS) and other complex devices such as smartphones while driving can lead to driver distraction, which, in turn, increases safety-critical event risk. Therefore, using methods for measuring driver distraction caused by IVIS is crucial when developing new in-vehicle systems. In this paper, we present the setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) as a tool to assess visual-manual and cognitive distraction effects. The BT+DRT represents a low-cost and easy-to-use method which can be easily implemented by researchers in laboratory settings and which was validated in previous research. Moreover, at the end of this paper we describe the experimental procedure, the data analysis and discuss potential modifications of the method.•The setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) is described.•The method allows for measuring visual-manual and cognitive distraction of drivers.•The BT+DRT is a cost-effective and easy-to-use method that can be implemented in laboratory settings or driving simulators.

Information stored in memory affects abductive reasoning.

Abductive reasoning describes the process of deriving an explanation from given observations. The theory of abductive reasoning (TAR; Johnson and Krems, Cognitive Science 25:903-939, 2001) assumes that when information is presented sequentially, new information is integrated into a mental representation, a situation model, the central data structure on which all reasoning processes are based. Because working memory capacity is limited, the question arises how reasoning might change with the amount of information that has to be processed in memory. Thus, we conducted an experiment (N = 34) in which we manipulated whether previous observation information and previously found explanations had to be retrieved from memory or were still visually present. Our results provide evidence that people experience differences in task difficulty when more information has to be retrieved from memory. This is also evident in changes in the mental representation as reflected by eye tracking measures. However, no differences are found between groups in the reasoning outcome. These findings suggest that individuals construct their situation model from both information in memory as well as external memory stores. The complexity of the model depends on the task: when memory demands are high, only relevant information is included. With this compensation strategy, people are able to achieve similar reasoning outcomes even when faced with tasks that are more difficult. This implies that people are able to adapt their strategy to the task in order to keep their reasoning successful.

Memory-related cognitive load effects in an interrupted learning task: A model-based explanation.

BACKGROUND: The Cognitive Load Theory provides a well-established framework for investigating aspects of learning situations that demand learners' working memory resources. However, the interplay of these aspects at the cognitive and neural level is still not fully understood. METHOD: We developed four computational models in the cognitive architecture ACT-R to clarify underlying memory-related strategies and mechanisms. Our models account for human data of an experiment that required participants to perform a symbol sequence learning task with embedded interruptions. We explored the inclusion of subsymbolic mechanisms to explain these data and used our final model to generate fMRI predictions. RESULTS: The final model indicates a reasonable fit for reaction times and accuracy and links the fMRI predictions to the Cognitive Load Theory. CONCLUSIONS: Our work emphasizes the influence of task characteristics and supports a process-related view on cognitive load in instructional scenarios. It further contributes to the discussion of underlying mechanisms at a neural level.

Measuring driver distraction - Evaluation of the box task method as a tool for assessing in-vehicle system demand.

Several tools have been developed over the past twenty years to assess the degree of driver distraction caused by secondary task engagement. A relatively new and promising method in this area is the box task combined with a detection response task (BT + DRT). However, no evaluation regarding the BT's sensitivity currently exists. Thus, the aim of the present study was to evaluate the BT + DRT by comparing its sensitivity to the sensitivity of already established methods. Twenty-nine participants engaged in several artificial and realistic secondary tasks while either performing the BT + DRT, the Lane Change Test (LCT), or driving through a simple course in a simulator. The results showed that the BT parameters (especially the standard deviation of box position and size) were sensitive to differences in demand across the visual-manual secondary tasks. This was comparable to what was found with the LCT. Surprisingly, the BT performance measures were more sensitive than those of the driving simulation task. The BT + DRT also captured cognitive distraction effects with the integration of the DRT. Hence, the BT + DRT could be a cost-effective method to assess in-vehicle system demand. However, further investigations are necessary to better understand the potential of the BT method.

Using European naturalistic driving data to assess secondary task engagement when stopped at a red light.

PROBLEM: Some evidence exists that drivers choose to engage in secondary tasks when the driving demand is low (e.g., when the car is stopped). While such a behavior might generally be considered as rather safe, it could be argued that the associated diversion of attention away from the road still leads to a reduction of situational awareness, which might increase collision risk once the car regains motion. This is especially relevant for texting, which is associated with considerable eyes-off-the-road-time. Nonetheless, it seems that previous research has barely addressed the actual engagement in secondary tasks while waiting at a red light (as compared to just addressing the tasks' mere prevalence). OBJECTIVE: The present study investigated secondary task engagement while stopped at a red light using European naturalistic driving data collected through the UDRIVE project. Attention was given to the whole engagement process, including simple prevalence and the tasks' relation (in terms of start/end) to the red light period. Moreover, given that texting is one of the most problematic forms of distraction, it was characterized in more detail regarding glance behavior. METHOD: Videos of 804 red light episodes from 159 drivers were annotated. Glance behavior was also coded for a sub-set of 75 texting events and their matched baselines. Results, conclusions and practical applications: Drivers engaged in at least one secondary task across almost half of the annotated red light episodes. Drivers who texted while stopped spent most of the time looking at their cell phone. Consequently, drivers might not have been prepared for potentially unexpected events once the light turned green. Further, drivers concluded texting a considerable number of times well after the red light period, which has potential implications for traffic safety.

Tracing current explanations in memory: A process analysis based on eye-tracking.

Sequential abductive reasoning is the process of finding the best explanation for a set of observations. Explanations can be multicausal and require the retrieval of previously found ones from memory. The theory of abductive reasoning (TAR) allows detailed predictions on what information is stored and retrieved from memory during reasoning. In the research to date, however, these predictions have never been directly tested. In this study, we tested process assumptions such as the construction of a mental representation from TAR using memory indexing, an eye-tracking method that makes it possible to trace the retrieval of explanations currently held in working memory. Gaze analysis revealed that participants encode the presented evidence (i.e., observations) together with possible explanations into memory. When new observations are presented, the previously presented evidence and explanations are retrieved. Observations that are not explained immediately are encoded as abstractly explained. Abstract explanations enter a refinement process in which they become concrete before they enter the situation model. With the memory indexing method, we were able to assess the process of information retrieval in abductive reasoning, which was previously believed to be unobservable. We discuss the results in the light of TAR and other current theories on the diagnostic reasoning process.

Evaluation of Gesture-Based In-Vehicle Interaction: User Experience and the Potential to Reduce Driver Distraction.

OBJECTIVE: We observe the effects of in-vehicle system gesture-based interaction versus touch-based interaction on driver distraction and user experience. BACKGROUND: Driver distraction is a major problem for traffic safety, as it is a contributing factor to a number of accidents. Visual distraction in particular has a highly negative impact on the driver. One possibility for reducing visual driver distraction is to use new forms of interaction in the vehicle, such as gesture-based interaction. METHOD: In this experiment, participants drove on a motorway or in a city scenario while using touch-based interaction or gesture-based interaction. Subjective data, such as acceptance and workload, and objective data, including glance behavior, were gathered. RESULTS: As a result, participants rated their subjective impressions of safe driving as higher when using gesture-based interaction. More specifically, acceptance and attractiveness were higher, and workload was lower. The participants performed significantly fewer glances to the display and the glances were much shorter. CONCLUSION: Gestures are a positive alternative for in-vehicle interaction since effects on driver distraction are less significant when compared to touch-based interaction. APPLICATION: Potential application of this research includes interaction design of typical in-vehicle information and entertainment functions.

An experimental study to investigate design and assessment criteria: What is important for communication between pedestrians and automated vehicles?

In the near future, more vehicles will have automated functions. The traffic system will be a shared space of automated and manually driven vehicles. In our study we focused on the perspective of vulnerable road users, namely pedestrians, in cooperative situations with automated vehicles. Established communication methods, such as eye-contact between pedestrians and drivers, may no longer work when automated vehicles represent the interaction partner. Therefore, we evaluated several human-machine-interfaces (HMI) in order to implement smooth and comfortable communication. We conducted a two-stage study consisting of an explorative focus group discussion with naïve pedestrians (n = 6), followed by an experimental video simulation study (n = 25) based on the results of the focus group discussion. From the focus group we sought member opinion about various HMI, upon presentation of acoustic and visual communication systems such as projections, displays and LED light strips, in addition to portable communication systems, specifically smart watches. On the basis of the focus group discussion, an evaluation criteria was derived. For the video simulation study, HMI designs were created with variations in position, type and coding of the message, and technology. These were assessed by 25 subjects according to the focus discussion derived evaluation criteria: recognizability, unambiguousness, interaction comfort and intuitive comprehensibility. The results show that direct instructions to cross the street are preferred over status information of the vehicle and that large-scale text-based messages from the vehicle to the pedestrian, deliver better results. Design recommendations for HMIs for communication between automated vehicles are derived, and the extent external HMIs may supplement informal communication strategies such as vehicle movement or braking maneuvers, is discussed.

Regenerative braking failures in battery electric vehicles and their impact on the driver.

A unique feature of battery electric vehicles (BEV) is their regenerative braking system (RBS) to recapture kinetic energy in deceleration maneuvers. If such a system is triggered via gas pedal, most deceleration maneuvers can be executed by just using this pedal. This impacts the driving task as different deceleration strategies can be applied. Previous research has indicated that a RBS failure leading to a sudden reduced deceleration represents an adverse event for BEV drivers. In the present study, we investigated such a failure's impact on the driver's evaluation and behavior. We conducted an experiment on a closed-off test track using a modified BEV that could temporarily switch off the RBS. One half of the 44 participants in the study received information about an upcoming RBS failure whereas the other half did not. While 91% of the drivers receiving prior information noticed the RBS failure, only 48% recognized it in the "uniformed" group. In general, the failure and the perception of its occurrence influenced the driver's evaluation and behavior more than receiving prior information. Nevertheless, under the tested conditions, drivers kept control and were able to compensate for the RBS failure. As the participants drove quite simple maneuvers in our experiment, further studies are needed to validate our findings using more complex driving settings. Given that RBS failures could have severe consequences, appropriate information and warning strategies for drivers are necessary.

Driving comfort, enjoyment and acceptance of automated driving - effects of drivers' age and driving style familiarity.

Automated driving has the potential to improve the safety and efficiency of future traffic and to extend elderly peoples' driving life, provided it is perceived as comfortable and joyful and is accepted by drivers. Driving comfort could be enhanced by familiar automated driving styles based on drivers' manual driving styles. In a two-stage driving simulator study, effects of driving automation and driving style familiarity on driving comfort, enjoyment and system acceptance were examined. Twenty younger and 20 older drivers performed a manual and four automated drives of different driving style familiarity. Acceptance, comfort and enjoyment were assessed after driving with standardised questionnaires, discomfort during driving via handset control. Automation increased both age groups' comfort, but decreased younger drivers' enjoyment. Younger drivers showed higher comfort, enjoyment and acceptance with familiar automated driving styles, whereas older drivers preferred unfamiliar, automated driving styles tending to be faster than their age-affected manual driving styles. Practitioner Summary: Automated driving needs to be comfortable and enjoyable to be accepted by drivers, which could be enhanced by driving style individualisation. This approach was evaluated in a two-stage driving simulator study for different age groups. Younger drivers preferred familiar driving styles, whereas older drivers preferred driving styles unaffected by age.

Covert shifts of attention can account for the functional role of "eye movements to nothing".

When trying to remember verbal information from memory, people look at spatial locations that have been associated with visual stimuli during encoding, even when the visual stimuli are no longer present. It has been shown that such "eye movements to nothing" can influence retrieval performance for verbal information, but the mechanism underlying this functional relationship is unclear. More precisely, covert in comparison to overt shifts of attention could be sufficient to elicit the observed differences in retrieval performance. To test if covert shifts of attention explain the functional role of the looking-at-nothing phenomenon, we asked participants to remember verbal information that had been associated with a spatial location during an encoding phase. Additionally, during the retrieval phase, all participants solved an unrelated visual tracking task that appeared in either an associated (congruent) or an incongruent spatial location. Half the participants were instructed to look at the tracking task, half to shift their attention covertly (while keeping the eyes fixed). In two experiments, we found that memory retrieval depended on the location to which participants shifted their attention covertly. Thus, covert shifts of attention seem to be sufficient to cause differences in retrieval performance. The results extend the literature on the relationship between visuospatial attention, eye movements, and verbal memory retrieval and provide deep insights into the nature of the looking-at-nothing phenomenon.

Using SHRP 2 naturalistic driving data to assess drivers' speed choice while being engaged in different secondary tasks.

INTRODUCTION: The engagement in secondary tasks while driving has been found to result in considerable impairments of driving performance. Texting has especially been suspected to be associated with an increased crash risk. At the same time, there is evidence that drivers use various self-regulating strategies to compensate for the increased demands caused by secondary task engagement. One of the findings reported from multiple studies is a reduction in driving speed. However, most of these studies are of experimental nature and do not let the drivers decide for themselves to (not) engage in the secondary task, and therefore, eliminate other strategies of self-regulation (e.g., postponing the task). The goal of the present analysis was to investigate if secondary task engagement results in speed adjustment also under naturalistic conditions. METHOD: Our analysis relied on data of the SHRP 2 naturalistic driving study. To minimize the influence of potentially confounding factors on drivers' speed choice, we focused on episodes of free flow driving on interstates/highways. Driving speed was analyzed before, during, and after texting, smoking, eating, and adjusting/monitoring radio or climate control; in a total of 403 episodes. RESULTS: Data show some indication for speed adjustment for texting, especially when driving with high speed. However, the effect sizes were small and behavioral patterns varied considerably between drivers. The engagement in the other tasks did not influence drivers' speed behavior significantly. CONCLUSIONS AND PRACTICAL APPLICATIONS: While drivers might indeed reduce speed slightly to accommodate for secondary task engagement, other forms of adaptation (e.g., strategic decisions) might play a more important role in a natural driving environment. The use of naturalistic driving data to study drivers' self-regulatory behavior at an operational level has proven to be promising. Still, in order to obtain a comprehensive understanding about drivers' self-regulatory behavior, a mixed-method approach is required.

Does this range suit me? Range satisfaction of battery electric vehicle users.

User satisfaction is a vital design criterion for sustainable systems. The present research aimed to understand factors relating to individually perceived range satisfaction of battery electric vehicle (BEV) users. Data from a large-scale BEV field trial (N = 72) were analyzed. Apart from an initial drop in range satisfaction, increasing practical experience was related to increased range satisfaction. Classical indicators of users' mobility profiles (daily travel distances) were only weakly related to lower range satisfaction (not significant), after controlling for practical experience and preferred coverage of mobility needs. The regularity/predictability of users' mobility patterns, the percentage of journeys not coverable because of range issues, and users' individual comfortable range accounted for variance in range satisfaction. Finally, range satisfaction was related to key indicators of general BEV acceptance (e.g., purchase intentions). These results underline the complex dynamics involved in individual range satisfaction, as well as its central role for BEV acceptance.

Watching diagnoses develop: Eye movements reveal symptom processing during diagnostic reasoning.

Finding a probable explanation for observed symptoms is a highly complex task that draws on information retrieval from memory. Recent research suggests that observed symptoms are interpreted in a way that maximizes coherence for a single likely explanation. This becomes particularly clear if symptom sequences support more than one explanation. However, there are no existing process data available that allow coherence maximization to be traced in ambiguous diagnostic situations, where critical information has to be retrieved from memory. In this experiment, we applied memory indexing, an eye-tracking method that affords rich time-course information concerning memory-based cognitive processing during higher order thinking, to reveal symptom processing and the preferred interpretation of symptom sequences. Participants first learned information about causes and symptoms presented in spatial frames. Gaze allocation to emptied spatial frames during symptom processing and during the diagnostic response reflected the subjective status of hypotheses held in memory and the preferred interpretation of ambiguous symptoms. Memory indexing traced how the diagnostic decision developed and revealed instances of hypothesis change and biases in symptom processing. Memory indexing thus provided direct online evidence for coherence maximization in processing ambiguous information.

Prior Familiarization With Takeover Requests Affects Drivers' Takeover Performance and Automation Trust.

OBJECTIVE: The objective for this study was to investigate the effects of prior familiarization with takeover requests (TORs) during conditional automated driving on drivers' initial takeover performance and automation trust. BACKGROUND: System-initiated TORs are one of the biggest concerns for conditional automated driving and have been studied extensively in the past. Most, but not all, of these studies have included training sessions to familiarize participants with TORs. This makes them hard to compare and might obscure first-failure-like effects on takeover performance and automation trust formation. METHOD: A driving simulator study compared drivers' takeover performance in two takeover situations across four prior familiarization groups (no familiarization, description, experience, description and experience) and automation trust before and after experiencing the system. RESULTS: As hypothesized, prior familiarization with TORs had a more positive effect on takeover performance in the first than in a subsequent takeover situation. In all groups, automation trust increased after participants experienced the system. Participants who were given no prior familiarization with TORs reported highest automation trust both before and after experiencing the system. CONCLUSION: The current results extend earlier findings suggesting that prior familiarization with TORs during conditional automated driving will be most relevant for takeover performance in the first takeover situation and that it lowers drivers' automation trust. APPLICATION: Potential applications of this research include different approaches to familiarize users with automated driving systems, better integration of earlier findings, and sophistication of experimental designs.