From manual to automated driving: how does trust evolve?

Fostering Drivers' Trust in Automated Driving Styles: The Role of Driver Perception of Automated Driving Maneuvers

OBJECTIVE

This study investigated the impact of driving styles of drivers and automated vehicles (AVs) on drivers' perception of automated driving maneuvers and quantified the relationships among drivers' perception of AV maneuvers, driver trust, and acceptance of AVs.

BACKGROUND

Previous studies on automated driving styles focused on the impact of AV's global driving style on driver's attitude and driving performance. However, research on drivers' perception of automated driving maneuvers at the specific driving style level is still lacking.

METHOD

Sixteen aggressive drivers and sixteen defensive drivers were recruited to experience twelve driving scenarios in either an aggressive AV or a defensive AV on the driving simulator. Their perception of AV maneuvers, trust, and acceptance was measured via questionnaires, and driving performance was collected via the driving simulator.

RESULTS

Results revealed that drivers' trust and acceptance of AVs would decrease significantly if they perceived AVs to have a higher speed, larger deceleration, smaller deceleration, or shorter stopping distance than expected. Moreover, defensive drivers perceived significantly greater inappropriateness of these maneuvers from aggressive AVs than defensive AVs, whereas aggressive drivers didn't differ significantly in their perceived inappropriateness of these maneuvers with different driving styles.

CONCLUSION

The driving styles of automated vehicles and drivers influenced drivers' perception of automated driving maneuvers, which influence their trust and acceptance of AVs.

APPLICATION

This study suggested that the design of AVs should consider drivers' perceptions of automated driving maneuvers to avoid undermining drivers' trust and acceptance of AVs.

Understanding trust calibration in automated driving: the effect of time, personality, and system warning design

Under the human-automation codriving future, dynamic trust should be considered. This paper explored how trust changes over time and how multiple factors (time, trust propensity, neuroticism, and takeover warning design) calibrate trust together. We launched two driving simulator experiments to measure drivers' trust before, during, and after the experiment under takeover scenarios. The results showed that trust in automation increased during short-term interactions and dropped after four months, which is still higher than pre-experiment trust. Initial trust and trust propensity had a stable impact on trust. Drivers trusted the system more with the two-stage (MR + TOR) warning design than the one-stage (TOR). Neuroticism had a significant effect on the countdown compared with the content warning.Practitioner summary: The results provide new data and knowledge for trust calibration in the takeover scenario. The findings can help design a more reasonable automated driving system in long-term human-automation interactions.

Providing different levels of accuracy about the reliability of automation to a human operator: impact on human performance

Previous research has suggested that supervising automation can lead to a decrease in human performance, especially when automation is not totally reliable. Providing context-related information about reliability can help operators to better adjust their behaviour in a human-automation interaction context. However, previous studies have not specified the level of accuracy that this information should provide to the human operator. The objective of this study was to investigate the effects of different levels of information accuracy about an automation's reliability on human performance. Results showed that accuracy of information about reliability improves performance when specific percentages of reliability were given to the participants. Participants had a better performance in the condition of high accuracy of information. A link between perceived reliability and trust was found: the more the trust in automation increased, the more the perceived reliability increased.Practitioner summary: The experiment dealt with how accurate information about automation's reliability influences people's performance when supervising an automated task. Overall, this research suggests that designing systems that provide accurate, useful information can reduce the frequency of automation bias. Trust and perceived reliability of automation are related.Abbreviations: MATB: multi attribute task battery.

Why Does the Automation Say One Thing but Does Something Else? Effect of the Feedback Consistency and the Timing of Error on Trust in Automated Driving

Driving automation deeply modifies the role of the human operator behind the steering wheel. Trust is required for drivers to engage in such automation, and this trust also seems to be a determinant of drivers’ behaviors during automated drives. On the one hand, first experiences with automation, either positive or not, are essential for drivers to calibrate their level of trust. On the other hand, an automation that provides feedback about its own level of capability to handle a specific driving situation may also help drivers to calibrate their level of trust. The reported experiment was undertaken to examine how the combination of these two effects will impact the driver trust calibration process. Four groups of drivers were randomly created. Each experienced either an early (i.e., directly after the beginning of the drive) or a late (i.e., directly before the end of it) critical situation that was poorly handled by the automation. In addition, they experienced either a consistent continuous feedback (i.e., that always correctly informed them about the situation), or an inconsistent one (i.e., that sometimes indicated dangers when there were none) during an automated drive in a driving simulator. Results showed the early- and poorly-handled critical situation had an enduring negative effect on drivers’ trust development compared to drivers who did not experience it. While being correctly understood, inconsistent feedback did not have an effect on trust during properly managed situations. These results suggest that the performance of the automation has the most severe influence on trust, and the automation’s feedback does not necessarily have the ability to influence drivers’ trust calibration during automated driving.

Normal blindness: when we Look But Fail To See

Humans routinely miss important information that is 'right in front of our eyes', from overlooking typos in a paper to failing to see a cyclist in an intersection. Recent studies on these 'Looked But Failed To See' (LBFTS) errors point to a common mechanism underlying these failures, whether the missed item was an unexpected gorilla, the clearly defined target of a visual search, or that simple typo. We argue that normal blindness is the by-product of the limited-capacity prediction engine that is our visual system. The processes that evolved to allow us to move through the world with ease are virtually guaranteed to cause us to miss some significant stimuli, especially in important tasks like driving and medical image perception.

Psychometric properties of the Chinese version of the trust between People and Automation Scale (TPAS) in Chinese adults

Trust in automation plays a leading role in human-automation interaction. As there lack of scales measuring trust in automation in China, the purpose of this study was to adapt the trust between People and Automation Scale (TPAS) into Chinese and to demonstrate its psychometric properties among Chinese adults. A total of 310 Chinese adults were randomly selected as sample 1, and 508 Chinese adults as sample 2. Results of the item analysis revealed that each item had a good quality, and the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) suggested that the two-factor model with 12 items was the best fitting model. In addition, the TPAS was positively correlated with Interpersonal Trust Scale (ITS), proving good evidence based on relations to other variables to support the TPAS. In sum, the study suggested that the Chinese version of the TPAS could be used as an effective tool to assess trust in automation in the Chinese context.

Sustainable Public Safety and the Case of Two Epidemics: COVID-19 and Traffic Crashes. Can We Extrapolate from One to the Other?

COVID-19 and motor vehicle crashes (MVC) are both considered epidemics by the U.S. Centers for Disease Control (CDC) and the World Health Organization (WHO), yet their progression, treatment and success in treatment have been very different. In this paper, we propose that the well-established sustainable safety approach to road safety can be applied to the management of COVID-19. We compare COVID-19 and MVC in terms of several defining characteristics, including evolvement and history, definitions and measures of evaluation, main attributes and characteristics, countermeasures, management and coping strategies, and key success factors. Despite stark differences, there are also some similarities between the two epidemics, and these enable insights into how the principles of sustainable road safety can be utilized to cope with and guide the treatment of COVID-19. Major guidelines that can be adopted include an aggressive policy set at the highest national level. The policy should be data- and science-based and would be most effective when relying on a systems approach (such as Sweden’s Vision Zero, the Netherlands’ Sustainable Safety, and the recommended EU Safe System). The policy should be enforceable and supplemented with positive public information and education campaigns (rather than scare tactics). Progression of mortality and morbidity should be tracked continuously to enable adjustments. Ethical issues (such as invasion of privacy) should be addressed to maximize public acceptance. Interestingly, the well-established domain of MVC can also benefit from the knowledge, experience, and strategies used in addressing COVID-19 by raising the urgency of detection and recognition of new risk factors (e.g., cell phone distractions), developing and implementing appropriate policy and countermeasures, and emphasizing the saliency of the impact of MVC on our daily lives.

Drivers’ Age and Automated Vehicle Explanations

Automated vehicles (AV) have the potential to benefit our society. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about automated decision-making. However, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-age, and older). Participants were presented with: (1) no explanation, or (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results highlight both commonalities and differences between age groups. These results have important implications in designing AV explanations and promoting trust.