What and how to tell beforehand: The effect of user education on understanding,interaction and satisfaction with driving automation

The success of introducing automated driving systems to consumers will depend on an appropriate understanding and human-automation interaction with this technology. Educating users on driving automation technology bears the potential to attain these two requirements. In a driving simulator study, we investigated the effects of user education on mental models, human-automation interaction performance (i.e., time on task, error rate, experimenter rating) and satisfaction with a Human-Machine Interface (HMI) for automated driving. N = 80 participants were randomly assigned to one of three different user education conditions or to a baseline. Subsequently, they completed several driver-initiated control transitions between manual, Level 2 (L2), and Level 3 (L3) automated driving. The results revealed that user education promoted an accurate evolution of mental models for driving automation. These, in turn, facilitated interaction performance in transitions from manual to both L2 and L3 automated driving. There was no comparable influence of prior education on performance in transitions between the automation levels. Due to the performance enhancing effects of user education, no further improvements of interaction performance were observed for educated users in comparison to uneducated users. There was no effect of user education on satisfaction. The current findings emphasize the necessity to provide information about automated vehicle HMIs to first-time users to support accurate understanding and behavior. Based on the current findings, we propose conceptual approaches to teach users and derive implications for user studies on automated vehicle HMIs.     

Effects of environmental,vehicle and human factors on comfort in partially automated driving: A scenario-based study

Although it is key to improving acceptability, there is sparse scientific literature on the experience of humans as passengers in partially automated cars. The present study therefore investigated the influence of road type, weather conditions, traffic congestion level, vehicle speed, and human factors (e.g., trust in automated cars) on passenger comfort in an automated car classified as Level 3 according to the Society of Automotive Engineers (SAE). Participants were exposed to scenarios in which a character is driven by an SAE Level 3 automated car in different combinations of conditions (e.g., highway × heavy rain × very congested traffic × vehicle following prescribed speed). They were asked to rate their perceived comfort as if they were the protagonist. Results showed that comfort was negatively affected by driving in downtown (vs. highway), heavy rain, and congested traffic. Interaction analyses showed that reducing the speed of the vehicle improved comfort in these two last conditions, considered either individually or in combination. Cluster analysis revealed four profiles: trusting in automation, averse to speed reduction, risk averse, and mistrusting automation. These profiles were all influenced differently by the driving conditions, and corresponded to varying levels of trust in automated cars. This study suggests that optimizing comfort in automated cars should take account of both driving conditions and human profiles.     

Predicting Driver's Intention Based on Own Vulnerability: A Social Interaction Process between Road Users at an Intersection in Japan

When road users predict the future movement of an approaching vehicle at an intersection, they need to consider not only the physical environment but also the predicted behavioral intention of the approaching driver. In the present experiments, we asked participants to imagine how a vehicle would approach in a natural traffic environment. In Experiment 1, participants estimated the time‐to‐contact with an approaching vehicle as longer when they were physically vulnerable. In Experiment 2, we confirmed that differences in participants’ eye height did not explain the findings of Experiment 1. In Experiment 3, we asked participants to indicate the last possible moment at which they could cross an intersection in front of an approaching vehicle. Participants left a shorter safety margin when they were more physically vulnerable than the approaching vehicle. The results indicate that road users’ perceived vulnerability is a cue for them to trust the approaching driver's intention to decelerate. This viewpoint is important for interventions to prevent unrealistic trust that an approaching vehicle will avoid an accident and subsequent trust‐based behaviors.     

Effects of visual factors during automated driving of mobility scooters on user comfort: An exploratory simulator study

An automated mobility scooter is expected to provide convenient and safe transportation for users in their living area. However, there is limited research on user comfort compared to that on user safety for the automated driving of mobility scooters. Because the user does not perform driving tasks in automated driving, the visual information from the peripheral environment and visual behavior is expected to closely affect the psychological comfort of the user. This study clarifies the effects of factors related to the automated driving of mobility scooters and the peripheral environment on the visual behavior and psychological comfort of the user. Effects of driving velocity and pedestrian density on the visual behavior and psychophysiological responses of users were investigated via a driving simulator. The results showed that automated driving in an environment with a high pedestrian density can result in a decrease in fixation duration, deactivation of visual processing, sympathetic activation, and feeling of negative emotion. This implies that the assessment of visual behaviors of users is important for the design of automated mobility scooters to improve user comfort.     

The utility of psychological measures in evaluating perceived usability of automated vehicle interfaces – A study with older adults

The design of the traditional vehicle human-machine interfaces (HMIs) is undergoing major change as we move towards fully connected and automated vehicles (CAVs). Given the diversity of user requirements and preferences, it is vital for designers to gain a deeper understanding of any underlying factors that could impact usability. The current study employs a range of carefully selected psychological measures to investigate the relationship with self-report usability of an in-CAV HMI integrated into a fully automated Level 5 simulator, during simulated journeys. Twenty-five older adults (65-years+) participated and were exposed to four journeys in a virtual reality fully automated CAV simulator (with video recorded journeys) into which our HMI was integrated. Participants completed a range of scales and questionnaires, as well as computerized cognitive tests. Key measures were: perceived usability of the HMI, cognitive performance, personality, attitudes towards computers, trust in technology, simulator sickness, presence and emotion. HMI perceived usability correlated positively with cognitive performance (e.g., working memory) and some individual characteristics such as trust in technology and negatively with neuroticism anxiety. Simulator sickness was associated negatively with CAV HMI perceived usability. Positive emotions correlated positively with reported usability across all four journeys, while negative emotions were negatively associated with usability only in the case of the last two journeys. Increased sense of presence in the virtual CAV simulator was not associated with usability. Implications for design are critically discussed. Our research is highly relevant in the design of high-fully automated vehicle HMIs, particularly for older adults, and in informing policy-makers and automated mobility providers of how to improve older people’s uptake of this technology.     

General and Familiar Trust in Websites

When people rely on the web to gather and distribute information, they can build a sense of trust in the websites with which they interact. Understanding the correlates of trust in most websites (general website trust) and trust in websites that one frequently visits (familiar website trust) is crucial for constructing better models of risk perception and online behavior. We conducted an online survey of active Internet users and examined the associations between the two types of web trust and several independent factors: information technology competence, adverse online events, and general dispositions to be trusting or cautious of others. Using a series of nested ordered logistic regression models, we find positive associations between general trust, general caution, and the two types of web trust. The positive effect of information technology competence erases the effect of general caution for general website trust but not for familiar website trust, providing evidence that general trust and self-reported competence are stronger associates of general website trust than broad attitudes about prudence. Finally, the experience of an adverse online event has a strong, negative association with general website trust, but not with familiar website trust. We discuss several implications for online behavior and suggest website policies that can help users make informed decisions about interacting with potentially risky websites.     

External HMI for self-driving vehicles: Which information shall be displayed?

The objective of this study was to investigate pedestrians’ informational needs towards self-driving vehicles (SDVs). Previous research has shown that external human-machine interfaces (eHMIs) compensate for pedestrian-driver communication when SDVs are integrated into traffic. However, detailed insights on which information the eHMI shall provide lack so far. In a mixed design study, N = 59 participants encountered a simulated driverless vehicle in different traffic scenarios (a. unsignalized intersection vs. b. parking lot; between-subject factor). We investigated the effect of no eHMI (baseline) vs. eHMIs displaying the automated driving system (ADS) status, and informing subsequently about its perception of the pedestrian and/or its intent for the next maneuver ((1) no eHMI, (2) status eHMI, (3) status + perception eHMI, (4) status + intent eHMI, (5) status + perception + intent eHMI; within-subject factor). A mixed-methods design was used to explore participants’ subjective feelings, traffic behavior, and underlying attitudes. The findings reveal that any eHMI contributes to a more positive feeling towards SDVs compared to the baseline condition without eHMI, consistent among traffic scenarios: participants felt significantly safer, reflected greater trust and user experience ratings, and perceived the SDV as more intelligent and transparent. The status indicator mainly drives these beneficial effects on subjective measures. Participants reported that the status information explains the absence of a driver steering the vehicle. Compared to the status eHMI, the status + perception eHMI reflects no further benefit regarding subjective feelings and even has a negative impact on traffic flow. Moreover, participants regarded the additional information on the vehicle’s perception as an obvious gimmick. On the contrary, the status + intent eHMI increases user experience, perceived intelligence, and transparency for pedestrians more than the mere status eHMI. Participants reported that additionally informing about the vehicle’s intent adds a further sense of safety. The present study failed to show any improvements in traffic flow but found evidence for individual crossing and clearing strategies among pedestrians. This work can inform the future design of eHMIs.     

Pedestrian interaction with automated vehicles at uncontrolled intersections

Automated Vehicles (AVs) are being developed rapidly and tested on public roads, but pedestrians’ interaction with AV is not comprehensively understood or thoroughly investigated to ensure safe operations and the public’s trust of AVs. In this study, we aimed to provide another research evidence to enhance such understanding with the use of external interfaces for facilitating the interaction between pedestrians and AVs. We developed five external interfaces, including text, symbol, animated-eye, a combination of text and symbol, and speed. These interfaces communicated five types of information, including (1) intent of AV; 2) advice to pedestrians of what to do, (3) AV’s awareness of pedestrians, (4) combination of intent and advice, and (5) vehicle movement (i.e., speed). We tested the interfaces through two field studies at uncontrolled intersections with crosswalks. The Wizard of Oz method was used, in which an experimenter worked as a driver in an instrumented vehicle and wore an outfit to be invisible to the pedestrians, thus rendering the set-up to simulate an AV interacting with a pedestrian. The interfaces were displayed on an LED panel mounted on the AV. Results showed that the AV’s external interface did not change pedestrians’ response time in comparison with the baseline without any interface. There was no statistically significant difference in response time among the external interfaces either. According to the post-experimental interview, vehicle movement pattern (e.g., vehicle speed) continued to be a significant cue for pedestrians to decide when to cross the intersections. Participants perceived the communication of the AV’s intent and vehicle speed as more beneficial than the communication of AV’s awareness. The subjective ratings showed positive effects of those interfaces that were easy to understand (e.g., text interface and speed interface), which also helped pedestrians feel safer when interacting with the AV.     

External communication of automated vehicles in mixed traffic: Addressing the right human interaction partner in multi-agent simulation

The urban traffic system is most likely to change in the next years to a mixed traffic with human drivers, vulnerable road users, and automated vehicles. In the past, the development of external communication approaches for automated vehicles focused on scenarios where an automated vehicle communicates with either a pedestrian or a human driver. However, interactions with more than one traffic partner are more realistic. Therefore, a study with 42 participants was conducted with a multi-agent simulation in which an automated vehicle interacted simultaneously with two participants, a pedestrian and a driver of a manual vehicle. In this study, two main scenarios were investigated in order to evaluate the safety and efficiency of the interactions and to determine whether the human road users feel correctly addressed. In one scenario, the pedestrian had to cross the road in front of the automated and the manual vehicle, which were approaching from different sides. In the other, the manual vehicle had to drive through a bottleneck in front of the oncoming automated vehicle, while the pedestrian had to cross the road after both vehicles passed. The communication approach of the automated vehicle consisted of implicit signals using a speed profile and lateral offset within its lane, and explicit signals using an external human–machine interface. The results of the study show that no collisions were observed in terms of safety and no significant negative effects on efficiency were measured. However, in contrast to single agent interactions, a majority of participants felt wrongly addressed in situations where the automated vehicle signals the right-of-way to the other human road user. It can be concluded that the communication approach of the automated vehicle needs to be modified in order to address certain road users more clearly.     

User gesticulation inside an automated vehicle with external communication can cause confusion in pedestrians and a lower willingness to cross

Automated vehicles are expected to require some form of communication (e.g., via LED strip or display) with vulnerable road users such as pedestrians. However, the passenger inside the automated vehicle could perform gestures or motions which could potentially be interpreted by the pedestrian as contradictory to the outside communication of the car. To explore this conflict, we conducted an online experiment (N = 59) with different message types (no message, intention, command), gestures (no gesture, wave, stop), and user positions (driver, co-driver) and measured the pedestrian’s confidence in crossing. Our results show that certain combinations (e.g., car indicates cross while the user in the driver seat gestures stop) confused the pedestrian, resulting in significantly lower confidence to cross. We further show that designing intention-based external communication led to less confusion and a significantly higher intention to cross.     

Canadian Immigrants’ Access to a First Job in Their Intended Occupation

Using detailed information on employment trajectory provided by the Longitudinal Survey of Immigrants to Canada, this study examines the labor market outcomes of recent immigrants in terms of the duration until access to a first job in their intended occupation, as determined by a question in the first wave interview on labor market intentions. The matching between actual and intended occupations is obtained from the first two digits of the National Occupational Classification codes, which consider successively occupation type and skill level. Using a Cox proportional hazards model, the study investigates the roles of factors related to human and social capital in speeding up the matching process between actual and intended occupations. It is found that the initial year in Canada is critical for an immigrant to land a job in the intended field; after that period, the hazards of finding employment in the intended occupation flatten down. In general, those with intention to work in nonprofessional jobs, such as sales and services, trades, transport and equipment operators, primary industry, and processing and manufacturing occupations, enter the first job in the intended occupation more quickly. The results also show that education, English language ability, Canadian work experience, and friend networks facilitate access to the intended occupation.     

Brain-computer interface research comes of age: traditional assumptions meet emerging realities

Brain-computer interfaces (BCIs) could provide important new communication and control options for people with severe motor disabilities. Most BCI research to date has been based on 4 assumptions that: (a) intended actions are fully represented in the cerebral cortex; (b) neuronal action potentials can provide the best picture of an intended action; (c) the best BCI is one that records action potentials and decodes them; and (d) ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible. Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually as the CNS adapts to optimize performance. BCIs must track and guide these adaptations if they are to achieve and maintain good performance. Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users' intentions.     

Emotional expressions facilitate human–human trust when using automation in high-risk situations

This study investigated the utility of emotional expression for human decision aids, when human aids conflict with an automated decision support system (DSS). The increasing presence of automation in society has resulted in critical, and often life threatening, situations when information from human and automated sources disagree. It has been known that reliance on human aids decrease during high-risk situations, while reliance on automated aids increase. However, it is also possible that human decision aids gain credibility from users when they embody the charismatic and emotionally expressive gesticulations seen in successful organizational leaders. The present study tested how a human agent's expressiveness when providing information would influence participants' behavioral reliance. Using the program Convoy Leader, participants (n=56) engaged in three decision-making scenarios where risk was manipulated as a within-subject factor and emotional expression as a between-subject factor. Emotional susceptibility, perceived risk, and trust for human as well as automated aids were measured. Overall trust was higher for the automated tool than human decision aid, and that pattern was amplified in conditions without an emotionally expressive human aid. Reliance was greater for emotionally expressive human aids, than stoic human aids, particularly during high risk conditions. The findings suggest that emotional expression of a human aid significantly impacts both reliance and trust of a decision aid, especially at higher risk levels. Emotionally expressive human agents should be utilized in decision conflicts where the automated system has certainly failed.     

Developing human-machine trust: Impacts of prior instruction and automation failure on driver trust in partially automated vehicles

To prompt the use of driving automation in an appropriate and safe manner, system designers require knowledge about the dynamics of driver trust. To enhance this knowledge, this study manipulated prior information of a partial driving automation into two types (detailed and less) and investigated the effects of the information on the development of trust with respect to three trust attributions proposed by Muir (1994): predictability, dependability, and faith. Furthermore, a driving simulator generated two types of automation failures (limitation and malfunction), and at six instances during the study, 56 drivers completed questionnaires about their levels of trust in the automation. Statistical analysis found that trust ratings of automation steadily increased with the experience of simulation regardless of the drivers’ levels of knowledge. Automation failure led to a temporary decrease in trust ratings; however, the trust was rebuilt by a subsequent experience of flawless automation. Results showed that dependability was the most dominant belief of drivers’ trust throughout the whole experiment, regardless of their knowledge level. Interestingly, detailed analysis indicated that trust can be accounted by different attributions depending on the drivers’ circumstances: the subsequent experience of error-free automation after the exposure to automation failure led predictability to be a secondary predictive attribution of drivers’ trust in the detailed group whilst faith was consistently the secondary contributor to shaping trust in the less group throughout the experiment. These findings have implications for system design regarding transparency and for training methods and instruction aimed at improving driving safety in traffic environments with automated vehicles.     

Learning and development of trust,acceptance and the mental model of ACC. A longitudinal on-road study

To harness the potential of advanced driver assistance systems, drivers must learn how to use them in a safe and appropriate manner. The present study investigates the learning process, as well as the development of trust, acceptance and the mental model for interacting with adaptive cruise control (ACC). Research questions aim to model the learning process in mathematical/statistical terms, examine moments and conditions when these processes stabilize, and assess how experience changes the mental model of the system. A sample of fifteen drivers without ACC experience drove a test vehicle with ACC ten consecutive times on the same route within a 2-month period. All participants were fully trained in ACC functionality by reading the owner’s manual in the beginning. Results show that learning, as well as the development of acceptance and trust in ACC follows the power law of learning. All processes stabilize at a relatively high level after the fifth session, which corresponds to 185 km or 3.5 h of driving. No decline is observable with ongoing system experience. However, limitations that are not experienced tend to disappear from the mental model if they are not activated by experience. Therefore, it is recommended that users be periodically reminded of system limitations (e.g. by intelligent tutoring systems) to make sure that corresponding knowledge nodes are activated.     

Using agent transparency to support situation awareness of the Autonomous Squad Member

Agent transparency has been proposed as a solution to the problem of facilitating operators’ situation awareness in human-robot teams. Sixty participants performed a dual monitoring task, monitoring both an intelligent, autonomous robot teammate and performing threat detection in a virtual environment. The robot displayed four different interfaces, corresponding to information from the Situation awareness-based Agent Transparency (SAT) model. Participants’ situation awareness of the robot, confidence in their situation awareness, trust in the robot, workload, cognitive processing, and perceived usability of the robot displays were assessed. Results indicate that participants using interfaces corresponding to higher SAT level had greater situation awareness, cognitive processing, and trust in the robot than when they viewed lower level SAT interfaces. No differences in workload or perceived usability of the display were detected. Based on these findings, we observed that transparency has a significant effect on situation awareness, trust, and cognitive processing.     

Key factors associated with Australian parents’ willingness to use an automated vehicle to transport their unaccompanied children

This study aimed to identify the key factors associated with Australian parents' willingness to use an automated vehicle to transport their unaccompanied child(ren). Seven hundred and seventy-five participants completed the online survey (M = 40.7 years, SD = 8.9 years, Range = 18.0–65.0 years; Female: 56.4%). Most participants reported that they would 'never' use an automated vehicle to transport their unaccompanied child(ren) (43.5%). The results of a logistic regression model showed that participants' age, gender, level of education, propensity for technology adoption, aberrant driving behaviours, awareness of advanced driver assistance safety (ADAS) technologies, perceived knowledge regarding automated vehicles, as well their requirements for assurance-related vehicle features were significantly associated with their willingness to use an automated vehicle to transport their unaccompanied child(ren), χ2(12) = 137.41, p < 0.001). Overall, the findings suggest that Australian parents are mostly unwilling to use an automated vehicle to transport their unaccompanied child(ren) unless various reassurance features and technologies (i.e., microphones, camera, ability to summon assistance if the vehicle breaks down) are embedded in the vehicle. Therefore, automated vehicle manufacturers are encouraged to consider these requirements when prototyping their designs so that all user groups, including child occupants, can benefit from the impending arrival of automated technologies.     

A conceptual framework for establishing trust in real world intelligent systems

Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the system so that they can explore results and find patterns that can be compared with their expected solution. Reflecting features and patterns of human understanding of a domain against algorithmic results can create awareness of such patterns and may increase the trust that a user has in the solution. If expectations are not met, close inspection can be used to decide whether a solution conforms to the expectations or whether it goes beyond the expected. By either accepting or rejecting a solution, the user’s set of expectations evolves and a learning process for the users is established. In this paper we present a conceptual framework that reflects and supports this process. The framework is the result of an analysis of two exemplary case studies from two different disciplines with information systems that assist experts in their complex tasks.     

Cyclists’ intentions to yield for automated cars at intersections when they have right of way: Results of an experiment using high-quality video animations

What will cyclists do in future conflict situations with automated cars at intersections when the cyclist has the right of way? In order to explore this, short high-quality animation videos of conflicts between a car and a cyclist at five different intersections were developed. These videos were ‘shot’ from the perspective of the cyclist and ended when a collision was imminent should the car or the bicyclist not slow down. After each video participants indicated whether they would slow down or continue cycling, how confident they were about this decision, what they thought the car would do, and how confident they were about what the car would do. The appearance of the approaching car was varied as within-subjects variable with 3 levels (Car type): automated car, automated car displaying its intentions to the cyclists, and traditional car. In all situations the cyclist had right of way. Of each conflict, three versions were made that differed in the moment that the video ended by cutting off fractions from the longest version, thus creating videos with an early, mid, and late moment for the cyclist to decide to continue cycling or to slow down (Decision moment). Before the video experiment started the participants watched an introductory video about automated vehicles that served as prime. This video was either positive, negative, or neutral about automated vehicles (Prime type). Both Decision moment and Prime type were between subject variables. After the experiment participants completed a short questionnaire about trust in technology and trust in automated vehicles. 1009 participants divided in nine groups (one per Decision moment and Prime) completed the online experiment in which they watched fifteen videos (5 conflicts × 3 car types). The results show that participants more often yielded when the approaching car was an automated car than when it was a traditional car. However, when the approaching car was an automated car that could communicate its intentions, they yielded less often than for a traditional car. The earlier the Decision moment, the more often participants yielded but this increase in yielding did not differ between the three car types. Participants yielded more often for automated cars (both types) after they watched the negative prime video before the experiment than when they watched the positive video. The less participants trusted technology, and the capabilities of automated vehicles in particular, the more they were inclined to slow down in the conflict situations with automated cars. The association between trust and yielding was stronger for trust in the capabilities of automated vehicles than for trust in technology in general.     

A structural equation modeling approach for the acceptance of driverless automated shuttles based on constructs from the Unified Theory of Acceptance and Use of Technology and the Diffusion of Innovation Theory

The present study investigated the attitudes and acceptance of automated shuttles in public transport among 340 individuals physically experiencing the automated shuttle ‘Emily’ from Easymile in a mixed traffic environment on the semi-public EUREF (Europäisches Energieforum) campus in Berlin. Automated vehicle acceptance was modelled as a function of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs performance expectancy, effort expectancy, social influence, and facilitating conditions, the Diffusion of Innovation Theory (DIT) constructs compatibility and trialability, as well as trust and automated shuttle sharing. The results show that after adding the DIT constructs, automated shuttle sharing, and trust to the model, the effect of performance expectancy on behavioural intention was no longer significant. Instead, compatibility with current travel was the strongest predictor of behavioural intention to use automated shuttles. It was further found that individuals who are willing to share rides in an automated shuttle with fellow travelers (i.e., automated shuttle sharing) and who trust automated shuttles (i.e., trust) are more likely to intend to use automated shuttles (i.e., behavioural intention). The highest mean rating was obtained for believing that automated shuttles are easy to use, while the lowest mean rating was obtained for feeling safe inside the automated shuttle without any type of supervision. The analysis revealed a preference for the supervision of the automated shuttle via an external control room to the supervision by a human steward onboard. We recommend future research to investigate the hypothesis that compatibility could serve as an even stronger predictor of the behavioural intention to use automated shuttles in public transport than performance expectancy.