Sebastian Hergeth

Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving

Objective: The feasibility of measuring drivers’ automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study. Background: Earlier research from other domains indicates that drivers’ automation trust might be inferred from gaze behavior, such as monitoring frequency. Method: The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared. Results: Overall, there was a consistent relationship between drivers’ automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency. Conclusion: We suggest that (a) the current results indicate a negative relationship between drivers’ self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers’ automation trust during highly automated driving might be inferred from gaze behavior. Application: Potential applications of this research include the estimation of drivers’ automation trust and reliance during highly automated driving.

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.

Cross-Country Validation of a Cultural Scale in Measuring Trust in Automation

Human automation interaction is a complex process. How autonomous assistance impacts trust in automation as well as how trust affects human calibration and use of automation has been investigated for both dynamic contexts, including the internal variables (e.g., cultural characteristics) and external factors (e.g., system settings). Having standardized measures to capture trust and its antecedents is particularly critical to understanding how factors associated with the human operators and autonomous applications affects the way they are used. This paper reports the development of a trust instrument and several rounds of cross-country validation, including U.S., German, Taiwanese, and Turkish populations. The results confirm that the instrument which was developed reliably measured human trust in automation across cultures.

Take-over performance in evasive manoeuvres

We investigated after effects of automation in take-over scenarios in a high-end moving-base driving simulator. Drivers performed evasive manoeuvres encountering a blocked lane in highway driving. We compared the performance of drivers 1) during manual driving, 2) after automated driving with eyes on the road while performing the cognitively demanding n-back task, and 3) after automated driving with eyes off the road performing the visually demanding SuRT task. Both minimum time to collision (TTC) and minimum clearance towards the obstacle disclosed a substantial number of near miss events and are regarded as valuable surrogate safety metrics in evasive manoeuvres. TTC proved highly sensitive to the applied definition of colliding paths, and we prefer robust solutions using lane position while disregarding heading. The extended time to collision (ETTC) which takes into account acceleration was close to the more robust conventional TTC. In line with other publications, the initial steering or braking intervention was delayed after using automation compared to manual driving. This resulted in lower TTC values and stronger steering and braking actions. Using automation, effects of cognitive distraction were similar to visual distraction for the intervention time with effects on the surrogate safety metric TTC being larger with visual distraction. However the precision of the evasive manoeuvres was hardly affected with a similar clearance towards the obstacle, similar overshoots and similar excursions to the hard shoulder. Further research is needed to validate and complement the current simulator based results with human behaviour in real world driving conditions. Experiments with real vehicles can disclose possible systematic differences in behaviour, and naturalistic data can serve to validate surrogate safety measures like TTC and obstacle clearance in evasive manoeuvres.

Use Cases for Assessing, Testing, and Validating the Human Machine Interface of Automated Driving Systems

Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be generalized to different systems or combinations of systems. In particular, there is no exhaustive set of use cases that allows entities to assess and validate the HMI of a given ADS that takes into account all possible system modes and transitions. The present paper describes a newly developed framework based on combinatorics of SAE (Society of Automotive Engineers) automation levels that incorporates a comprehensive taxonomy of use cases required for the assessment and validation of ADS HMIs. This forms a much-needed basis for test methods required to verify whether an HMI meets minimum requirements such as those outlined in the National Highway Traffic Safety Administration’s Federal Automated Vehicles policy.

How Usability Can Save the Day - Methodological Considerations for Making Automated Driving a Success Story

It will not be long until Level 3 Automated Driving Systems (L3 ADS) enter the consumer market. An important role corresponds to methodology development. The present paper gives impetus to the process of developing robust methods for evaluating Human-Machine Interfaces (HMI) for L3 ADS. First, a literature review on automotive interfaces concerning methodology application is outlined showing that studies often lack to provide both self-report and observational data. To derive a comprehensive image of HMI quality, we recommend multi-method approach in user research. Subsequently, we provide an overview of state-of-the-art self-report and observational measures. From the availability of measures and the necessity to include both in user studies, the issue of the performance-preference dissociation arises. It threatens study designs and interpretation of results. Following methodological recommendations from the present work supports researchers and practitioners in the area of automated driving for proper study design and interpretation of study results.

Designing the human-machine interface for highly automated cars — Challenges, exemplary concepts and studies

During the last years, intensive research has been conducted to make high degrees of automation available in cars. However, driver assistance systems today still need the driver to monitor the system. This will most probably change in near future, as highly automated driving becomes available. With the driver out of the control loop, this driving mode has beneficial aspects for the driver and could improve traffic safety as a high portion of traffic accidents are due to human error. On the other hand, high degrees of automation can have detrimental effects which are well known from other domains like aviation. These effects have led to fatal accidents in the past. The authors investigate various aspects of this topic and the corresponding challenges for the human-machine interface in future cars. In this paper, we present three of our research areas: the take-over process, trust in automation and utilization of drivetime. For each area, we explain theoretical background, current challenges and studies we conducted.