scholarly journals Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Frederik Naujoks ◽  
Yannick Forster ◽  
Katharina Wiedemann ◽  
Alexandra Neukum
Keyword(s):  
Driving Simulator ◽  
Primary Task ◽  
Automated Driving ◽  
Task Interference ◽  
Driving System ◽  
Lane Changes ◽  
Speech Output ◽  
Driving Time ◽  
Standard Information ◽  
Machine Interface

During conditionally automated driving (CAD), driving time can be used for non-driving-related tasks (NDRTs). To increase safety and comfort of an automated ride, upcoming automated manoeuvres such as lane changes or speed adaptations may be communicated to the driver. However, as the driver’s primary task consists of performing NDRTs, they might prefer to be informed in a nondistracting way. In this paper, the potential of using speech output to improve human-automation interaction is explored. A sample of 17 participants completed different situations which involved communication between the automation and the driver in a motion-based driving simulator. The Human-Machine Interface (HMI) of the automated driving system consisted of a visual-auditory HMI with either generic auditory feedback (i.e., standard information tones) or additional speech output. The drivers were asked to perform a common NDRT during the drive. Compared to generic auditory output, communicating upcoming automated manoeuvres additionally by speech led to a decrease in self-reported visual workload and decreased monitoring of the visual HMI. However, interruptions of the NDRT were not affected by additional speech output. Participants clearly favoured the HMI with additional speech-based output, demonstrating the potential of speech to enhance usefulness and acceptance of automated vehicles.

scholarly journals Does a Confidence Level for Automated Driving Time Estimations Improve the Subjective Evaluation of an Automation HMI?

2020 ◽  
Vol 4 (3) ◽  
pp. 36
Author(s):  
Tobias Hecht ◽  
Simon Danner ◽  
Alexander Feierle ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Subjective Evaluation ◽  
Mixed Design ◽  
Automated Driving ◽  
Traffic Conditions ◽  
Driving Time ◽  
Level 3 ◽  
Machine Interface ◽  
The Impact ◽  
The City

Current research in human factors and automated driving is increasingly focusing on predictable transitions instead of urgent and critical take-overs. Predictive human–machine interface (HMI) elements displaying the remaining time until the next request to intervene were identified as a user need, especially when the user is engaging in non-driving related activities (NDRA). However, these estimations are prone to errors due to changing traffic conditions and updated map-based information. Thus, we investigated a confidence display for Level 3 automated driving time estimations. Based on a preliminary study, a confidence display resembling a mobile phone connectivity symbol was developed. In a mixed-design driving simulator study with 32 participants, we assessed the impact of the confidence display concept (within factor) on usability, frustration, trust and acceptance during city and highway automated driving (between factor). During automated driving sections, participants engaged in a naturalistic visual NDRA to create a realistic scenario. Significant effects were found for the scenario: participants in the city experienced higher levels of frustration. However, the confidence display has no significant impact on the subjective evaluation and most participants preferred the baseline HMI without a confidence symbol.

scholarly journals Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 62 ◽  
Author(s):  
Alexander Feierle ◽  
Simon Danner ◽  
Sarah Steininger ◽  
Klaus Bengler
Keyword(s):  
Visual Attention ◽  
Information Needs ◽  
Driving Simulator ◽  
Automation System ◽  
Automated Driving ◽  
Navigation Data ◽  
Levels Of Automation ◽  
Machine Interface ◽  
The Individual ◽  
Highly Automated Driving

During highly automated driving, the passenger is allowed to conduct non-driving related activities (NDRA) and no longer has to act as a fallback at the functional limits of the driving automation system. Previous research has shown that at lower levels of automation, passengers still wish to be informed about automated vehicle behavior to a certain extent. Due to the aim of the introduction of urban automated driving, which is characterized by high complexity, we investigated the information needs and visual attention of the passenger during urban, highly automated driving. Additionally, there was an investigation into the influence of the experience of automated driving and of NDRAs on these results. Forty participants took part in a driving simulator study. As well as the information presented on the human–machine interface (system status, navigation information, speed and speed limit), participants requested information about maneuvers, reasons for maneuvers, environmental settings and additional navigation data. Visual attention was significantly affected by the NDRA, while the experience of automated driving had no effect. Experience and NDRA showed no significant effect on the need for information. Differences in information needs seem to be due to the requirements of the individual passenger, rather than the investigated factors.

scholarly journals Theoretical considerations and development of a questionnaire to measure trust in automation

2018 ◽  
Author(s):  
Moritz Körber
Keyword(s):  
Driving Simulator ◽  
Item Difficulty ◽  
Preliminary Evidence ◽  
Gaze Behavior ◽  
Automated Driving ◽  
Driving System ◽  
Online Study ◽  
Trust In Automation ◽  
Trust Intention ◽  
Theoretical Considerations

The increasing number of interactions with automated systems has sparked the interest of researchers in trust in automation because it predicts not only whether but also how an operator interacts with an automation. In this work, a theoretical model of trust in automation is established and the development and evaluation of a corresponding questionnaire (Trust in Automation, TiA) are described. Building on the model of organizational trust by Mayer, Davis, and Schoorman (1995) and the theoretical account by Lee and See (2004), a model for trust in automation containing six underlying dimensions was established. Following a deductive approach, an initial set of 57 items was generated. In a first online study, these items were analyzed and based on the criteria item difficulty, standard deviation, item-total correlation, internal consistency, overlap with other items in content, and response quote, 40 items were eliminated and two scales were merged, leaving six scales (Reliability/Competence, Understandability/Predictability, Propensity to Trust, Intention of Developers, Familiarity, and Trust in Automation) containing a total of 19 items. The internal structure of the resulting questionnaire was analyzed in a subsequent second online study by means of an exploratory factor analysis. The results show sufficient preliminary evidence for the proposed factor structure and demonstrate that further pursuit of the model is reasonable but certain revisions may be necessary. The calculated omega coefficients indicated good to excellent reliability for all scales. The results also provide evidence for the questionnaire’s criterion validity: Consistent with the expectations, an unreliable automated driving system received lower trust ratings as a reliably functioning system. In a subsequent empirical driving simulator study, trust ratings could predict reliance on an automated driving system and monitoring in form of gaze behavior. Possible steps for revisions are discussed and recommendations for the application of the questionnaire are given.

Building a Simulation Environment to Test the Human-Machine Interface of an Automated Driving System

2017 ◽  
Vol 2017.15 (0) ◽  
pp. A26
Author(s):  
Masaatsu KUSUNOKI ◽  
Sunkil YUN ◽  
Hidekazu NISHIMURA ◽  
Takaaki TESHIMA ◽  
Mitsuo NISHIMURA
Keyword(s):  
Human Machine Interface ◽  
Simulation Environment ◽  
Automated Driving ◽  
Driving System ◽  
Machine Interface

scholarly journals The Effects of a Predictive HMI and Different Transition Frequencies on Acceptance, Workload, Usability, and Gaze Behavior during Urban Automated Driving

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 73 ◽  
Author(s):  
Tobias Hecht ◽  
Stefan Kratzert ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Transition Frequency ◽  
Gaze Behavior ◽  
Automated Driving ◽  
Negative Effects ◽  
Level 3 ◽  
Machine Interface ◽  
Time Critical ◽  
New Applications ◽  
Study Questionnaire

Automated driving research as a key topic in the automotive industry is currently undergoing change. Research is shifting from unexpected and time-critical take-over situations to human machine interface (HMI) design for predictable transitions. Furthermore, new applications like automated city driving are getting more attention and the ability to engage in non-driving related activities (NDRA) starting from SAE Level 3 automation poses new questions to HMI design. Moreover, future introduction scenarios and automated capabilities are still unclear. Thus, we designed, executed, and assessed a driving simulator study focusing on the effect of different transition frequencies and a predictive HMI while freely engaging in naturalistic NDRA. In the study with 33 participants, we found transition frequency to have effects on workload and acceptance, as well as a small impact on the usability evaluation of the system. Trust, however, was not affected. The predictive HMI was used and accepted, as can be seen by eye-tracking data and the post-study questionnaire, but could not mitigate the above-mentioned negative effects induced by transition frequency. Most attractive activities were window gazing, chatting, phone use, and reading magazines. Descriptively, window gazing and chatting gained attractiveness when interrupted more often, while reading magazines and playing games were negatively affected by transition rate.

scholarly journals Context-Adaptive Availability Notifications for an SAE Level 3 Automation

2021 ◽  
Vol 5 (4) ◽  
pp. 16
Author(s):  
Simon Danner ◽  
Alexander Feierle ◽  
Carina Manger ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Human Machine Interface ◽  
Gaze Behavior ◽  
Activation Time ◽  
Automated Driving ◽  
Driving Function ◽  
The Road ◽  
Level 3 ◽  
Machine Interface ◽  
Simulator Study

Context-adaptive functions are not new in the driving context, but even so, investigations into these functions concerning the automation human–machine interface (aHMI) have yet to be carried out. This study presents research into context-adaptive availability notifications for an SAE Level 3 automation in scenarios where participants were surprised by either availability or non-availability. For this purpose, participants (N = 30) took part in a driving simulator study, experiencing a baseline HMI concept as a comparison, and a context-adaptive HMI concept that provided context-adaptive availability notifications with the aim of improving acceptance and usability, while decreasing frustration (due to unexpected non-availability) and gaze deviation from the road when driving manually. Furthermore, it was hypothesized that participants, when experiencing the context-adaptive HMI, would activate the automated driving function more quickly when facing unexpected availability. None of the hypotheses could be statistically confirmed; indeed, where gaze behavior was concerned, the opposite effects were found, indicating increased distraction induced by the context-adaptive HMI. However, the trend in respect to the activation time was towards shorter times with the context-adaptive notifications. These results led to the conclusion that context-adaptive availability notifications might not always be beneficial for users, while more salient availability notifications in the case of an unexpected availability could be advantageous.

scholarly journals Engagement in Non-Driving Related Tasks as a Non-Intrusive Measure for Mode Awareness: A Simulator Study

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 239 ◽  
Author(s):  
Yannick Forster ◽  
Viktoria Geisel ◽  
Sebastian Hergeth ◽  
Frederik Naujoks ◽  
Andreas Keinath
Keyword(s):  
Driving Simulator ◽  
Minimum Degree ◽  
Block Design ◽  
Automated System ◽  
Automated Driving ◽  
Repeated Interaction ◽  
Wide Range ◽  
Machine Interface ◽  
Simulator Study ◽  
Design And Practice

Research on the role of non-driving related tasks (NDRT) in the area of automated driving is indispensable. At the same time, the construct mode awareness has received considerable interest in regard to human–machine interface (HMI) evaluation. Based on the expectation that HMI design and practice with different levels of driving automation influence NDRT engagement, a driving simulator study was conducted. In a 2 × 5 (automation level x block) design, N = 49 participants completed several transitions of control. They were told that they could engage in an NDRT if they felt safe and comfortable to do so. The NDRT was the Surrogate Reference Task (SuRT) as a representative of a wide range of visual–manual NDRTs. Engagement (i.e., number of inputs on the NDRT interface) was assessed at the onset of a respective episode of automated driving (i.e., after transition) and during ongoing automation (i.e., before subsequent transition). Results revealed that over time, NDRT engagement increased during both L2 and L3 automation until stable engagement at the third block. This trend was observed for both onset and ongoing NDRT engagement. The overall engagement level and the increase in engagement are significantly stronger for L3 automation compared to L2 automation. These results outline the potential of NDRT engagement as an online non-intrusive measure for mode awareness. Moreover, repeated interaction is necessary until users are familiar with the automated system and its HMI to engage in NDRTs. These results provide researchers and practitioners with indications about users’ minimum degree of familiarity with driving automation and HMIs for mode awareness testing.

scholarly journals Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

2018 ◽  
Vol 2 (4) ◽  
pp. 68 ◽  
Author(s):  
Natalie T. Richardson ◽  
Lukas Flohr ◽  
Britta Michel
Keyword(s):  
Driving Simulator ◽  
Situational Awareness ◽  
Automated Driving ◽  
Human Machine Interaction ◽  
Transport Efficiency ◽  
Additional Information ◽  
Level 3 ◽  
Driving Task ◽  
Machine Interface ◽  
Vehicle Automation

Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

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