Driver Trust Research Articles

Willingness to Financially Support the Healthcare System: Evidence from Two Experimental Studies during COVID-19 in Italy

ABSTRACT A key challenge for public institutions is to ensure adequate funding for health, especially in times of crisis, and motivating citizens’ prosocial financial behavior is critical to ensuring the efficiency of healthcare. This article investigates citizens’ willingness to support healthcare with data from two cross-sectional Italian studies conducted during the peak of the pandemic. This article presents the results of two cross-sectional studies, conducted during the pandemic peaks in Italy, investigating citizens’ willingness to support the healthcare. Findings show different reactions towards voluntary contributions and mandatory subsidies. Furthermore, trust in money management and altruistic drivers predicted citizens’ willingness, which declined over time. Results offer guidance as to when and how be more effective in designing communications aimed at raising funds for healthcare.

A Nationwide Impact Assessment of Automated Driving Systems on Traffic Safety Using Multiagent Traffic Simulations

The objective of this paper is to propose a methodology to estimate nationwide traffic safety impacts of automated vehicle technologies using multi-agent traffic simulations. The influence of three levels of driver trust in the automation system (appropriate, over trust, distrust) is considered in the simulation and takes different transition modes of control between the driver and the system into account. The nationwide estimation of crashes is obtained by projecting results of the simulations using traffic data for three different and representative municipalities. Results indicated that Automated Driving Systems and Advanced Driver Assistance Systems significantly reduced the number of casualties and fatalities compared to manual driving. Simulation results in consideration of the influence of driver trust also found that this reduction may be negatively affected by over- and under-trust parameters. However, even with the introduction of these parameters, the reduction rate was still significant compared to manual driving. The proposed methodology using multi-agent traffic simulations may thus address concerns surrounding the deployment of automated driving systems which is a feature not found in conventional simulations, provide useful insight for interested parties to develop research and policy making strategies that accelerate traffic safety improvements, and to support social acceptance efforts.

Effects of automation trust in drivers' visual distraction during automation.

With ongoing improvements in vehicle automation, research on automation trust has attracted considerable attention. In order to explore effects of automation trust on drivers’ visual distraction, we designed a three-factor 2 (trust type: high trust group, low trust group) × 2 (video entertainment: variety-show videos, news videos) × 3 (measurement stage: 1–3) experiment. 48 drivers were recruited in Dalian, China for the experiment. With a driving simulator, we used detection-response tasks (DRT) to measure each driver’s performance. Their eye movements were recorded, and automation-trust scale was used to divide participants into high trust group and low trust group. The results show that: (1) drivers in the high trust group has lower mental workload and paid more attention to visual non-driving-related tasks; (2) video entertainment also has an impact on distraction behavior, variety-show videos catch more attention than news videos. The findings of the present study indicate that drivers with high automation trust are more likely to be involved in non-driving-related visual tasks.

Driver trust in and training for advanced driver assistance systems in Real-World driving

Two-hundred and twenty-three participants completed an online survey regarding their experiences with advanced driver assistance systems (ADAS) on their personal vehicles, with focus on 1) drivers’ trust in 13 ADAS technologies, and 2) perceived effectiveness of currently used methods of training. Eighteen drivers participated in focus groups designed to probe more deeply into survey responses. Results of the survey showed that participant ratings of trust increased significantly with longer vehicle ownership, but participants who experienced unexpected ADAS technology behavior rated their trust over time significantly lower on ADAS technologies with the exception of rear collision avoidance. The majority (75.8%) of participants reported receiving some ADAS instruction at their vehicle dealership, but only 16.6% indicated it was formal. Participants who received formalized training reported it to be significantly more effective than those who received informal overviews of their systems. Use of trial and error and the owner’s manual were the most frequently reported methods of learning outside of dealership training. Responses indicated that the lack of content tailored to trim-specific vehicle features in owner’s manuals was a barrier to effective use.

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.

Tools for Transport: Driven to Learn With Connected Vehicles.

Vehicle automation and assistance technologies have been touted as a means to reduce traffic collisions by minimizing or eliminating "error-prone" and inefficient human operators. In concept, automation exists on a continuum that includes engaged driving by a human operator augmented by automated support features, vigilant driver monitoring of vehicle behavior with the possibility of driver take-over, to full automation with no active monitoring by a human operator. Moreover, the degree of automation varies by vehicle features (e.g., lane centering, emergency braking, adaptive cruise control, parking), by setting, meaning that automated features may or may not be available depending on specific attributes of the traffic environment (e.g., traffic volume, road geometry, etc), and by implementation (e.g., haptic vs. auditory warnings). Thus, these automotive "transportation tools" are highly heterogeneous and pose unique challenges and opportunities for driver training. In this paper, we report the results of an experimental study (n = 36) to determine if enhanced vehicle feedback influences driver trust, effort, frustration, and performance (indexed by reaction time) in a virtual driving environment. Results are contextualized in the extant literature on learning to operate motor vehicles and outline key research questions essential for understanding the processes by which skilled performance develops with respect to a real-world practical tool: the increasingly automated automobile.

Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: Effects of automated driving styles and driver’s driving styles

Automated Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, drivers could be reluctant to ride with AVs due to their lack of trust and acceptance of AVs’ driving styles. The present study investigated the effects of the designed driving style of AV (aggressive/defensive) and driver’s driving style (aggressive/defensive) on driver's trust, acceptance, and take-over behavior in a fully AV. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve driving scenarios in either an aggressive AV or a defensive AV. Results revealed that driver’s trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. General estimating equations were conducted to analyze the relationships between driver’s trust, acceptance, and take over frequency. The results showed that the effect of driver’s trust in AVs on takeover frequency was mediated by driver’s acceptance of AVs. These findings implied that driver’s trust and acceptance of AVs could be enhanced when the designed AV’s driving style aligned with driver’s own driving style, which in turn, reduce undesired take over behavior. However, the “aggressive” AV driving style should be designed carefully considering driver safety.

Roles of consumer trust and risks on continuance intention in the sharing economy: An empirical investigation

• An online consumer survey on the Chinese ridesharing market was conducted. • Structure assurance trust affects continuance intention (CI) and platform trust. • Consumer satisfaction (CS) reduces the attractiveness of alternatives for CI. • Driver and platform trust positively mediate the CS-CI relationship. • Physical and psychological risks positively moderate driver trust in CI. This empirical study explores the roles of trust, risks and attractiveness of alternatives on customer confirmation, satisfaction and continuance intention in the sharing economy. The interacting effects of physical and psychological risks on them are also examined. Data from an online consumer survey of 458 valid responses were analyzed by structural equation modeling. The results show that the relationship between satisfaction and continuance intention is partially mediated by platform-based trust, driver-based trust and the attractiveness of alternatives and inconsistently mediated by structural assurance. Physical and psychological risks can positively moderate the relationship between driver-based trust and continuance intention. This study provides new insights on specifying which types of trust and risks can affect consumers’ intention to reuse in the sharing economy. The study also suggests new ideas for service providers to retain their consumers in this emerging sharing economy.

The relationship between anti‐consumption lifestyle and the trust triangle in a ride‐sharing platform: A cross‐cultural study of U.S. and Indian consumers

AbstractThe aim of this study is to examine three types of trust in services provided in the sharing economy toward the goal of increasing consumers’ intention to use a ride‐sharing service. The three trust factors for sharing services are trust in drivers, trust in users, and trust in platforms. It is hypothesized that related to consumers’ consumption lifestyles would influence their trust in a sharing service; in particular, previous researchers found that anti‐consumption influenced consumers to participate in the sharing economy. Based on previous findings, the attempt with this study is to determine whether an anti‐consumption would affect formation of the three types of trust with regards to the ride‐sharing service. Specifically, the differing effects of anti‐consumption on trust formation in a ride‐sharing service are compared between the United States and India. Data are collected from 253 online surveys from the United States and 266 from India, and data analysis reveals that the anti‐consumption affects the formation of all three types of trust in the ride‐sharing service. In the United States in particular, non‐voluntary anti‐consumption has a greater effect on trust formation than it did in India.

“They have to be better than human drivers!” Motorcyclists’ and cyclists’ perceptions of autonomous vehicles

Road users and the general population by and large recognise the value of vehicles with automated driving systems and features (otherwise typically known as Autonomous Vehicles (AVs)) in terms of road safety, reduced emissions and convenience, but are still wary of their capability, preferring the ‘comfort zone’ of human operator intervention. Motorcyclists and cyclists conversely, are vulnerable to human fallibility in driving, with the majority of crashes occurring as a consequence of other drivers’ inattention. The transition period associated with the introduction of AVs will require AVs and motorcyclists/cyclists sharing the road for a number of years yet, so we need to understand motorcyclists’/cyclists’ perception of AVs. The question of interest here is whether motorcyclists/cyclists reflect the historical literature in this area by having higher levels of trust for human drivers over AVs, or whether they have higher levels of trust in AVs because it removes the ‘human element’ that has been proven to be particularly dangerous for them. Here we surveyed motorcyclists and cyclists about their trust in human drivers and AVs, and developed a novel suite of questions designed to interrogate the difference between trust in general versus trust as a concept of their own personal safety. Some of the salient outcomes suggest that motorcyclists have medium to low levels of trust for both human drivers and AVs, but are significantly more likely to believe that AVs are safer in terms of their own personal safety, such as prioritising or detecting the rider, compared to human drivers. This relationship varies with age and crash experience. The results here are consistent with the logic that motorcyclists/cyclists have a heightened sense of vulnerability on the road and welcome the introduction of AVs as a way of mitigating personal risk when riding. This insight will be crucial to the subsequent roll-out of AVs in the future.

The unexplored potential of trust to boost customer loyalty for transport platforms

Ride-sharing and services associated with mobility are developing a growing presence in society, especially those that are focused on mobility in cities. These services are provided to a great extent through a digital platform. Measure the aspects of quality, trust and loyalty are relevant to understand the rationale of this kind of business models. The purpose of this paper is to test empirically the relationship between quality, trust (digital and driver trust), satisfaction (platform and driver satisfaction) and loyalty. Specially, we examine the mediating role of trust among these relationships in on-demand ridesharing services. A sample of 429 respondents completed an on-line questionnaire in four European countries (France, Spain, Portugal, and Italy). We propose a model for investigating this relationship and assess its constructs using exploratory factor analysis, and the whole research model by Structural Equating Modelling, using EQS. 6.4 software. In addition, a focus group with users from the four countries was conducted to confirm the results. The present study contributes theoretically to the field of examining the antecedents and consequences of trust in the sharing economy. In addition, this research proposes several trust-building actions that managers can take to increase both business and the level of loyalty to these digital platforms.

Measurement and prediction of driver trust in automated vehicle technologies: An application of hand position transition probability matrix

Driver trust has a great impact on the intention to accept, use, and adapt to automated vehicles. To date, driver trust in automated vehicle technologies has mostly been estimated by subjective data. Currently available objective measures of driver trust primarily rely on self-reported ratings as the ground truth, while the largely reported inconsistency between drivers’ self-reported trust levels and observed actual behavior suggests that drivers’ trust measures shall not solely rely on subjective reporting values. To address the issue, this study proposes an objective method to assess and predict driver trust in automated vehicle technologies, by using the transition probability matrix of drivers’ hand positions during automated vehicle system engagement. An on-road experiment was conducted on public roadways in real traffic. Data on use frequencies of advanced driver assistance systems (ADAS) and self-reported trust ratings were collected and combined in classifying driver trust levels: lower, medium, and higher based on the K-means clustering results. Drivers’ hand positions during ADAS engagement (i.e., during lane-departure warning and lane-keeping assist system uses) were then found closely associated with their trust levels. Differences of frequencies and transition probabilities for hand positions were further compared within and across the three trust groups. Results showed that drivers from the lower, medium, and higher trust groups were more likely to keep hands on the top, mid, and low positions of the wheel, respectively. Factors affecting driver trust in automated vehicle technologies were also explored through mixed model analyses. Middle-aged drivers placed more trust in ADAS than younger drivers, while female drivers exhibited greater trust than male drivers. The Random Forests algorithm was applied to build a prediction model for driver trust in automated vehicle technologies, by inputting the hand position transition probability matrix, age, gender, and ADAS types as independent variables. The overall prediction accuracy was approximately 80%. Findings in this study could contribute to the objective and real-time estimations of driver trust in automated vehicle technologies.

Developing Human-Machine Trust: Impact of Driver Trust in Automobile Brand and Cues from Drivers' Cognitive Structures

Developing Human-Machine Trust: Impact of Driver Trust in Automobile Brand and Cues from Drivers' Cognitive Structures

Trust transfer and the intention to use app-enabled carpooling service

PurposeIn China, with the rapid dissemination of mobile communications technology along with congested traffic and increasingly expensive transportation costs, consumers are turning to smartphone-enabled, ride-sharing services. Sharing economy requires trust in strangers. Based on trust transfer theory and a dyadic conceptualization of trust from cognitive to affective, the purpose of this study is to examine trust building through the use of Didi, a third-party, ride-sharing platform that mediates exchanges among strangers.Design/methodology/approachStructural equation modeling (SEM) results based on 242 observations indicate that the platform functions as an important enabler of trust, which influences a consumer's behavioral intention.FindingsSpecifically, Didi's reputation and security assurance have a positive influence on passengers' cognitive trust in drivers. There is also evidence that the interaction as mediated by the app between passengers and drivers helps the formation of affective trust, while the results do not support a relationship between cognitive and affective trust.Originality/valueThe research findings address trust transference between participants in the sharing economy and its effects, which have significant theoretical and practical implications and offer opportunities for future research in other sectors of the sharing economy.

Preliminary Investigation of System Transparency and System Failure on Driver Trust in Partial Vehicle Automation

Preliminary Investigation of System Transparency and System Failure on Driver Trust in Partial Vehicle Automation

Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors

Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors.

Triangulated investigation of trust in automated driving: Challenges and solution approaches for data integration

In automated driving, an appropriate level of driver trust is essential to improve safety and ensure zero fatalities. Drivers must have a sufficient level of trust to intervene correctly in safety-critical situations: very low levels may lead to either continuous and excessive monitoring of the functions, reducing the attention paid to the environment or switching off these functions, whereas extreme trust in automated driving functions can result in dangerous driving situations because the environment is either insufficiently monitored or not monitored at all. A deeper understanding of trust in automated driving is challenging and requires a triangulated study in which the type of driver, vehicle usage, and environmental data are varied. However, many previous studies were based on a rather limited set of data sources, often relying on qualitative means such as pre-and-post interviews or trust questionnaires to evaluate trust in autonomous driving functions. Although data gathered through empirical research, such as conducting quantitative surveys or qualitative interviews, are simple to store and analyze, the collection and integration of vehicle and sensor data from different data sources usually pose important technical challenges in practice. Hence, a suitable data collection and integration strategy is required to address these challenges. In this context, we propose a general framework for collecting and integrating data from different sources with diverse capabilities and requirements to determine a driver’s trust in automated driving. Our proposed framework facilitates the integration of empirical and measurement data, allowing a triangulated investigation to provide a road map for the automotive industry.

Do engineer perceptions about automated vehicles match user trust? Consequences for design

To maximize road safety, driver trust in an automated vehicle should be aligned with the vehicle’s technical reliability, avoiding under- and over-estimation of its capabilities. This is known as trust calibration. In the study reported here, we asked how far participant assessments of vehicle capabilities aligned with those of the engineers. This was done by asking the engineers to rate the reliability of the vehicle in a specific set of scenarios. We then carried out a driving simulator study using the same scenarios, and measured participant trust. The results suggest that user trust and engineer perceptions of vehicle reliability are often misaligned, with users sometimes under-trusting and sometimes over-trusting vehicle capabilities. On these bases, we formulated recommendations to mitigate under- and over-trust. Specific recommendations to improve trust calibration include the adoption of a more defensive driving style for first-time users, the visual representation of the objects detected by the automated driving system in its surroundings in the Human Machine Interface, and real-time feedback on the performance of the technology.

Investigating what level of visual information inspires trust in a user of a highly automated vehicle

The aim of this research is to investigate whether visual feedback alone can affect a driver's trust in an autonomous vehicle, and in particular, what level of feedback (no feedback vs. moderate feedback vs. high feedback) will evoke the appropriate level of trust. Before conducting the experiment, the Human Machine Interfaces (HMI) were piloted with two sets of six participants (before and after iterations), to ensure the meaning of the displays can be understood by all. A static driving simulator experiment was conducted with a sample of 30 participants (between 18 and 55). Participants completed two pre-study questionnaires to evaluate previous driving experience, and attitude to trust in automation. During the study, participants completed a trust questionnaire after each simulated scenario to assess their trust level in the autonomous vehicle and HMI displays, and on intention to use and acceptance. The participants were shown 10 different driving scenarios that lasted approximately 2 minutes each. Results indicated that the ‘high visual feedback’ group recorded the highest trust ratings, with this difference significantly higher than for the ‘no visual feedback’ group (U = .000; p = <0.001 < α) and the ‘moderate visual feedback’ group (U = .000; p = <0.001 < α). There is an upward inclination of trust in all groups due to familiarity to both the interfaces and driving simulator over time. Participants' trust level was also influenced by the driving scenario, with trust reducing in all displays during safety verses non-safety-critical situations.

‘Do not talk to strangers’: A study on trust in Brazilian ridesharing apps

The emergence of digital platforms based on network algorithms can mediate relationships of trust, boosting sharing economy business models. This study aims to identify the motivations and trust elements of ridesharing apps and analyze the weight of motivation variables in the explanation of trust in the platform service. The survey method was applied to a sample of 485 Brazilian users of a ridesharing app were addressed. Five factors were analyzed to users’ motivation: Perceived sustainability, Security risks, Enjoyment, Platform quality, and Economic reward. Results show that 50.6% of respondents use the service until three times per month (n = 246), while 12,6% use more than four times per week. This study contributes by showing that: (i) five key user motives and two types of trusts are empirically measured and examined for the Brazilian ridesharing market; (ii) the most significant variable that explains trust in platform is “Information providing”, following by platform safety and platform security; (iii) pleasant participation, enjoyment, and cheaper alternative were also significant for the trust in the platform; and (iv) trust in platform is higher than trust in drivers. These contributions could enhance applied solutions for sharing economy platforms and guide academic research on the topic.