Testing Scenarios for Human Factors Research in Level 3 Automated Vehicles

How Can the Trust-Change Direction be Measured and Identified During Takeover Transitions in Conditionally Automated Driving? Using Physiological Responses and Takeover-Related Factors

OBJECTIVE

This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs).

BACKGROUND

Takeover requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse.

METHOD

34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator. The relationships and effects between drivers' physiological responses, takeover-related factors, and trust-change directions during TTs were explored by the combination of an unsupervised learning algorithm and statistical analyses. Furthermore, different typical machine learning methods were applied to establish recognition models of trust-change directions during TTs based on takeover-related factors and physiological parameters.

RESULT

Combining the change values in the subjective trust rating and monitoring behavior before and after takeover can reliably measure trust-change directions during TTs. The statistical analysis results showed that physiological parameters (i.e., skin conductance and heart rate) during TTs are negatively linked with the trust-change directions. And drivers were more likely to increase trust during TTs when they were in longer TOR lead time, with more takeover frequencies, and dealing with the stationary vehicle scenario. More importantly, the F1-score of the random forest (RF) model is nearly 77.3%.

CONCLUSION

The features investigated and the RF model developed can identify trust-change directions during TTs accurately.

APPLICATION

Those findings can provide additional support for developing trust monitoring systems to mitigate both drivers' overtrust and undertrust in conditionally AVs.

Usability Evaluation—Advances in Experimental Design in the Context of Automated Driving Human–Machine Interfaces

The projected introduction of conditional automated driving systems to the market has sparked multifaceted research on human–machine interfaces (HMIs) for such systems. By moderating the roles of the human driver and the driving automation system, the HMI is indispensable in avoiding side effects of automation such as mode confusion, misuse, and disuse. In addition to safety aspects, the usability of HMIs plays a vital role in improving the trust and acceptance of the automated driving system. This paper aggregates common research methods and findings based on an extensive literature review. Empirical studies, frameworks, and review articles are included. Findings and conclusions are presented with a focus on study characteristics such as test cases, dependent variables, testing environments, or participant samples. These methods and findings are discussed critically, taking into consideration requirements for usability assessments of HMIs in the context of conditional automated driving. The paper concludes with a derivation of recommended study characteristics framing best practice advice for the design of experiments. The advised selection of scenarios and metrics will be applied in a future validation study series comprising a driving simulator experiment and three real driving experiments on test tracks in Germany, the USA, and Japan.

Standardized Test Procedure for External Human–Machine Interfaces of Automated Vehicles

Research on external human–machine interfaces (eHMIs) has recently become a major area of interest in the field of human factors research on automated driving. The broad variety of methodological approaches renders the current state of research inconclusive and comparisons between interface designs impossible. To date, there are no standardized test procedures to evaluate and compare different design variants of eHMIs with each other and with interactions without eHMIs. This article presents a standardized test procedure that enables the effective usability evaluation of eHMI design solutions. First, the test procedure provides a methodological approach to deduce relevant use cases for the evaluation of an eHMI. In addition, we define specific usability requirements that must be fulfilled by an eHMI to be effective, efficient, and satisfying. To prove whether an eHMI meets the defined requirements, we have developed a test protocol for the empirical evaluation of an eHMI with a participant study. The article elucidates underlying considerations and details of the test protocol that serves as framework to measure the behavior and subjective evaluations of non-automated road users when interacting with automated vehicles in an experimental setting. The standardized test procedure provides a useful framework for researchers and practitioners.

Effects of Task-Induced Fatigue in Prolonged Conditional Automated Driving

OBJECTIVE

The aim of this study was to investigate the effects of task-induced fatigue in prolonged conditional automated driving on takeover performance.

BACKGROUND

In conditional automated driving, the driver can engage in non-driving related tasks (NDRTs) and does not have to monitor the system and the driving environment. In the event that the system hits its limits, the human driver must regain control of the car. To ensure safety, adequate driver fallback performance is necessary. Effects of the drivers' state and the engagement in NDRTs need to be investigated.

METHOD

Seventy-three participants experienced prolonged conditional automated rides and simultaneously had to engage in either an activating quiz or a fatiguing monitoring task (between subjects). After 50 minutes, a takeover situation occurred, and participants had to regain control of the car.

RESULTS

Prolonged conditional automated driving and simultaneously engaging in NDRTs affected the driver's state and the takeover performance of the participants. Takeover performance was impaired when participants had to deal with monotonous NDRTs.

CONCLUSION

An engagement in monotonous monitoring tasks in conditional automated driving affects the driver's state and takeover performance when it comes to takeover situations. Especially in prolonged automated driving, an adequate driver state seems to be necessary for safety reasons.

APPLICATION

The results of this study demonstrate that engagement in monotonous NDRTs while driving conditionally automated may negatively affect takeover performance. A monitoring of the driver state and adapted assistance in a takeover situation seems to be a good opportunity to ensure safety.