Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 15428

Special Issue Editors


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Guest Editor
BMW Group, 80809 Munich, Germany
Interests: usability; human–machine interaction; human factors; automated driving
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
BMW Group, 80809 Munich, Germany
Interests: usability; human–machine interaction; human factors; automated driving

E-Mail Website
Guest Editor
BMW Group, 80809 Munich, Germany
Interests: usability; human–machine interaction; human factors; automated driving
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Wuerzburg Institute for Traffic Sciences, 97209 Veitshöchheim, Germany
Interests: usability; human–machine interaction; human factors; automated driving
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
BMW Group, 80809 Munich, Germany
Interests: human-machine interaction; safety in use; distracted driving; usability; human factors; assisted and automated driving
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Wuerzburg Institute for Traffic Sciences, 97209 Veitshöchheim, Germany
Interests: usability; human–machine interaction; human factors; automated driving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Standardized test and evaluating methods for in-vehicle Human–Machine Interfaces (HMIs) have traditionally focused on minimizing distraction effects and enhancing usability (e.g., AAM guidelines or NHTSA visual-distraction guidelines). With the introduction of automated driving systems (ADS) and advanced driver assistance systems, the communication of the driver’s responsibilities and the AD’s capabilities has, however, become an important topic in recent years. For example, partially automated driving (SAE L2) systems need to be able to communicate that the driver is still fully responsible for the driving safety, whereas higher levels of vehicle automation need to be able to communicate that the driver has to act as a fallback ready user in case of system limits and malfunctions (SAE L3). During the same trip, different levels of automation might be available to the driver (e.g., L2 in urban environments, L3 on highways), making it even more crucial that the driving mode is efficiently displayed. These developments require new test and evaluation methods for ADS, as available test methods cannot be easily transferred and adapted.

For example, ADS might allow new and more comfortable seating positions and engagement in non-driving related tasks that were not allowed in manual driving, which might generate motion sickness or lower the user’s availability for a transfer of control. At the same time, the ADS HMI should be capable of informing the user about the current mode and minimizing confusion about the status of the ADS and the user's current responsibilities. During partially automated driving, the user interface might remind the user that she/he is still fully responsible for driving safety by means of driver monitoring systems. Regarding interaction with other road users, ADS might behave differently than manually driven vehicles, which might generate a need for external HMIs or standardized motion patterns to adequately interact with non-equipped traffic participants.

This Special Issue welcomes theoretical papers as well as empirical studies that deal with these new challenges by proposing new and innovative test methods in the evaluation of ADS HMIs in areas such as (but not limited to the topics below):

  • Mode awareness and mode indicators;
  • Testing of minimum HMI requirements;
  • Distraction and driver monitoring systems in the context of ADS;
  • Trust in ADS;
  • External HMIs for ADS;
  • Guidelines for HMIs for ADS;
  • Motion sickness in ADS;
  • Validity of test settings (on-road, driving simulators, etc.);
  • Learnability and usability of ADS;                        
  • Comfortable, usable and pleasurable User Experience of ADS;
  • Cultural differences in Usability and User Experience of ADS.

Dr. Frederik Naujoks
Dr. Yannick Forster
Dr. Andreas Keinath
Dr. Nadja Schömig
Dr. Sebastian Hergeth
Dr. Katharina Wiedemann
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • automated driving
  • human–machine interface
  • test methods
  • user studies
  • evaluation

Published Papers (8 papers)

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25 pages, 2800 KiB  
Article
Driving across Markets: An Analysis of a Human–Machine Interface in Different International Contexts
by Denise Sogemeier, Yannick Forster, Frederik Naujoks, Josef F. Krems and Andreas Keinath
Information 2024, 15(6), 349; https://doi.org/10.3390/info15060349 - 12 Jun 2024
Viewed by 354
Abstract
The design of automotive human–machine interfaces (HMIs) for global consumers’ needs to cater to a broad spectrum of drivers. This paper comprises benchmark studies and explores how users from international markets—Germany, China, and the United States—engage with the same automotive HMI. In real [...] Read more.
The design of automotive human–machine interfaces (HMIs) for global consumers’ needs to cater to a broad spectrum of drivers. This paper comprises benchmark studies and explores how users from international markets—Germany, China, and the United States—engage with the same automotive HMI. In real driving scenarios, N = 301 participants (premium vehicle owners) completed several tasks using different interaction modalities. The multi-method approach included both self-report measures to assess preference and satisfaction through well-established questionnaires and observational measures, namely experimenter ratings, to capture interaction performance. We observed a trend towards lower preference ratings in the Chinese sample. Further, interaction performance differed across the user groups, with self-reported preference not consistently aligning with observed performance. This dissociation accentuates the importance of integrating both measures in user studies. By employing benchmark data, we provide insights into varied market-based perspectives on automotive HMIs. The findings highlight the necessity for a nuanced approach to HMI design that considers diverse user preferences and interaction patterns. Full article
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28 pages, 6205 KiB  
Article
Learnability in Automated Driving (LiAD): Concepts for Applying Learnability Engineering (CALE) Based on Long-Term Learning Effects
by Naomi Y. Mbelekani and Klaus Bengler
Information 2023, 14(10), 519; https://doi.org/10.3390/info14100519 - 22 Sep 2023
Cited by 3 | Viewed by 1371
Abstract
Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD [...] Read more.
Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD (specifically extended learnability methods) to guide the scientific paradigm. As a result, this generates scientific discord and, thus, leaves many facets of long-term learning effects associated with automated driving in dire need of significant research courtesy. This, we believe, is a constraint to knowledge discovery on quality interaction design phenomena. In a sense, it is imperative to abstract knowledge on how long-term effects and learning effects may affect (negatively and positively) users’ learning and mental models. As well as induce changeable behavioural configurations and performances. In view of that, it may be imperative to examine operational concepts that may help researchers envision future scenarios with automation by assessing users’ learning ability, how they learn and what they learn over the sequence of time. As well as constructing a theory of effects (from micro, meso and macro perspectives), which may help profile ergonomic quality design aspects that stand the test of time. As a result, we reviewed the literature on learnability, which we mined for LiAD knowledge discovery from the experience perspective of long-term learning effects. Therefore, the paper offers the reader the resulting discussion points formulated under the Learnability Engineering Life Cycle. For instance, firstly, contextualisation of LiAD with emphasis on extended LiAD. Secondly, conceptualisation and operationalisation of the operational mechanics of LiAD as a concept in ergonomic quality engineering (with an introduction of Concepts for Applying Learnability Engineering (CALE) research based on LiAD knowledge discovery). Thirdly, the systemisation of implementable long-term research strategies towards comprehending behaviour modification associated with extended LiAD. As the vehicle industry revolutionises at a rapid pace towards automation and artificially intelligent (AI) systems, this knowledge is useful for illuminating and instructing quality interaction strategies and Quality Automated Driving (QAD). Full article
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17 pages, 298 KiB  
Article
Principles for External Human–Machine Interfaces
by Marc Wilbrink, Stephan Cieler, Sebastian L. Weiß, Matthias Beggiato, Philip Joisten, Alexander Feierle and Michael Oehl
Information 2023, 14(8), 463; https://doi.org/10.3390/info14080463 - 17 Aug 2023
Cited by 1 | Viewed by 1690
Abstract
Automated vehicles will soon be integrated into our current traffic system. This development will lead to a novel mixed-traffic environment where connected and automated vehicles (CAVs) will have to interact with other road users (ORU). To enable this interaction, external human–machine interfaces (eHMIs) [...] Read more.
Automated vehicles will soon be integrated into our current traffic system. This development will lead to a novel mixed-traffic environment where connected and automated vehicles (CAVs) will have to interact with other road users (ORU). To enable this interaction, external human–machine interfaces (eHMIs) have been shown to have major benefits regarding the trust and acceptance of CAVs in multiple studies. However, a harmonization of eHMI signals seems to be necessary since the developed signals are extremely varied and sometimes even contradict each other. Therefore, the present paper proposes guidelines for designing eHMI signals, taking into account important factors such as how and in which situations a CAV needs to communicate with ORU. The authors propose 17 heuristics, the so-called eHMI-principles, as requirements for the safe and efficient use of eHMIs in a systematic and application-oriented manner. Full article
17 pages, 4537 KiB  
Article
Is Users’ Trust during Automated Driving Different When Using an Ambient Light HMI, Compared to an Auditory HMI?
by Rafael Cirino Gonçalves, Tyron Louw, Yee Mun Lee, Ruth Madigan, Jonny Kuo, Mike Lenné and Natasha Merat
Information 2023, 14(5), 260; https://doi.org/10.3390/info14050260 - 27 Apr 2023
Cited by 2 | Viewed by 1953
Abstract
The aim of this study was to compare the success of two different Human Machine Interfaces (HMIs) in attracting drivers’ attention when they were engaged in a Non-Driving-Related Task (NDRT) during SAE Level 3 driving. We also assessed the value of each on [...] Read more.
The aim of this study was to compare the success of two different Human Machine Interfaces (HMIs) in attracting drivers’ attention when they were engaged in a Non-Driving-Related Task (NDRT) during SAE Level 3 driving. We also assessed the value of each on drivers’ perceived safety and trust. A driving simulator experiment was used to investigate drivers’ response to a non-safety-critical transition of control and five cut-in events (one hard; deceleration of 2.4 m/s2, and 4 subtle; deceleration of ~1.16 m/s2) over the course of the automated drive. The experiment used two types of HMI to trigger a takeover request (TOR): one Light-band display that flashed whenever the drivers needed to takeover control; and one auditory warning. Results showed that drivers’ levels of trust in automation were similar for both HMI conditions, in all scenarios, except during a hard cut-in event. Regarding the HMI’s capabilities to support a takeover process, the study found no differences in drivers’ takeover performance or overall gaze distribution. However, with the Light-band HMI, drivers were more likely to focus their attention to the road centre first after a takeover request. Although a high proportion of glances towards the dashboard of the vehicle was seen for both HMIs during the takeover process, the value of these ambient lighting signals for conveying automation status and takeover messages may be useful to help drivers direct their visual attention to the most suitable area after a takeover, such as the forward roadway. Full article
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18 pages, 8222 KiB  
Article
Transparency Assessment on Level 2 Automated Vehicle HMIs
by Yuan-Cheng Liu, Nikol Figalová and Klaus Bengler
Information 2022, 13(10), 489; https://doi.org/10.3390/info13100489 - 10 Oct 2022
Cited by 1 | Viewed by 2118
Abstract
The responsibility and role of human drivers during automated driving might change dynamically. In such cases, human-machine interface (HMI) transparency becomes crucial to facilitate driving safety, as the states of the automated vehicle have to be communicated correctly and efficiently. However, there is [...] Read more.
The responsibility and role of human drivers during automated driving might change dynamically. In such cases, human-machine interface (HMI) transparency becomes crucial to facilitate driving safety, as the states of the automated vehicle have to be communicated correctly and efficiently. However, there is no standardized transparency assessment method to evaluate the understanding of human drivers toward the HMI. In this study, we defined functional transparency (FT) and, based on this definition, proposed a transparency assessment method as a preliminary step toward the objective measurement for HMI understanding. The proposed method was verified in an online survey where HMIs of different vehicle manufacturers were adopted and their transparencies assessed. Even though no significant result was found among HMI designs, FT was found to be significantly higher for participants more experienced with SAE Level 2 automated vehicles, suggesting that more experienced users understand the HMIs better. Further identification tests revealed that more icons in BMW’s and VW’s HMI designs were correctly used to evaluate the state of longitudinal and lateral control. This study provides a novel method for assessing transparency and minimizing confusion during automated driving, which could greatly assist the HMI design process in the future. Full article
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19 pages, 2999 KiB  
Article
A Cognitive Model to Anticipate Variations of Situation Awareness and Attention for the Takeover in Highly Automated Driving
by Marlene Susanne Lisa Scharfe-Scherf, Sebastian Wiese and Nele Russwinkel
Information 2022, 13(9), 418; https://doi.org/10.3390/info13090418 - 6 Sep 2022
Cited by 7 | Viewed by 2112
Abstract
The development of highly automated driving requires dynamic approaches that anticipate the cognitive state of the driver. In this paper, a cognitive model is developed that simulates a spectrum of cognitive processing and the development of situation awareness and attention guidance in different [...] Read more.
The development of highly automated driving requires dynamic approaches that anticipate the cognitive state of the driver. In this paper, a cognitive model is developed that simulates a spectrum of cognitive processing and the development of situation awareness and attention guidance in different takeover situations. In order to adapt cognitive assistance systems according to individuals in different situations, it is necessary to understand and simulate dynamic processes that are performed during a takeover. To validly represent cognitive processing in a dynamic environment, the model covers different strategies of cognitive and visual processes during the takeover. To simulate the visual processing in detail, a new module for the visual attention within different traffic environments is used. The model starts with a non-driving-related task, attends the takeover request, makes an action decision and executes the corresponding action. It is evaluated against empirical data in six different driving scenarios, including three maneuvers. The interaction with different dynamic traffic scenarios that vary in their complexity is additionally represented within the model. Predictions show variances in reaction times. Furthermore, a spectrum of driving behavior in certain situations is represented and how situation awareness is gained during the takeover process. Based on such a cognitive model, an automated system could classify the driver’s takeover readiness, derive the expected takeover quality and adapt the cognitive assistance for takeovers accordingly to increase safety. Full article
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15 pages, 7410 KiB  
Article
What Attracts the Driver’s Eye? Attention as a Function of Task and Events
by Yke Bauke Eisma, Dirk J. Eijssen and Joost C. F. de Winter
Information 2022, 13(7), 333; https://doi.org/10.3390/info13070333 - 11 Jul 2022
Cited by 4 | Viewed by 2032
Abstract
This study explores how drivers of an automated vehicle distribute their attention as a function of environmental events and driving task instructions. Twenty participants were asked to monitor pre-recorded videos of a simulated driving trip while their eye movements were recorded using an [...] Read more.
This study explores how drivers of an automated vehicle distribute their attention as a function of environmental events and driving task instructions. Twenty participants were asked to monitor pre-recorded videos of a simulated driving trip while their eye movements were recorded using an eye-tracker. The results showed that eye movements are strongly situation-dependent, with areas of interest (windshield, mirrors, and dashboard) attracting attention when events (e.g., passing vehicles) occurred in those areas. Furthermore, the task instructions provided to participants (i.e., speed monitoring or hazard monitoring) affected their attention distribution in an interpretable manner. It is concluded that eye movements while supervising an automated vehicle are strongly ‘top-down’, i.e., based on an expected value. The results are discussed in the context of the development of driver availability monitoring systems. Full article
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17 pages, 1870 KiB  
Technical Note
S.A.D.E.—A Standardized, Scenario-Based Method for the Real-Time Assessment of Driver Interaction with Partially Automated Driving Systems
by Nadja Schömig, Katharina Wiedemann, André Wiggerich and Alexandra Neukum
Information 2022, 13(11), 538; https://doi.org/10.3390/info13110538 - 14 Nov 2022
Cited by 2 | Viewed by 1629
Abstract
Vehicles equipped with so-called partially automated driving functions are becoming more and more common nowadays. The special feature of this automation level is that the driver is relieved of the execution of the lateral and longitudinal driving task, although they must still monitor [...] Read more.
Vehicles equipped with so-called partially automated driving functions are becoming more and more common nowadays. The special feature of this automation level is that the driver is relieved of the execution of the lateral and longitudinal driving task, although they must still monitor the driving environment and the automated system. The method presented in this paper should enable the assessment of the usability and safety of such systems in a standardized manner. It is designed to capture a driver’s interaction with a system via the human–machine interface in specific scenarios in user studies. It evaluates several observable aspects of this interaction in real time and codes inadequate behavior in the categories “system operation”, “driving behavior” and “monitoring behavior”. A generic rating regarding the overall handling of the scenario is derived from these criteria. The method can be used with the assistance of a tablet application called the S.A.D.E. app (Standardized Application for Automated Driving Evaluation). Initial studies using driving simulators show promising results regarding its ability to detect problems related to a system or HMI, with some future challenges remaining open. Full article
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