Transparent Passenger Communication during Minimal Risk Maneuvers in Highly Automated Vehicles (L4)

Highly automated vehicles (HAVs, SAE 4) promise efficient, safe and inclusive transportation at an affordable price. Advanced concepts were developed without any fallback driver inside the vehicle. Though this is necessary to improve efficiency and affordability, it creates an unknown situation for future passengers. They no longer have a human driver present to reassure in uncertain situations, for example in minimal risk maneuvers (MRMs). MRMs are triggered when the vehicle automation encounters situations it cannot handle. In these situations, the HAV needs further assistance, which can be realized by the use of remote operation (RO). RO incorporates a human operator, who supports the HAV remotely (e.g. from distance) during MRMs and gives instructions to resolve these unknown situations. The combination of new situations, and the lack of a driver who could interact with passengers results in the need for new informational concepts inside the HAVs. The design and information richness of these concepts most likely influence passenger’s experience and acceptance of HAVs. Additionally, a basic and more intuitive understanding of the automation and its functionalities might be beneficial for passengers’ experience. Previous studies indicate that providing transparency by design might improve passengers understanding of the automation systems and increase trust. In theory, transparency improves when information about the actions and reasoning of a systems behavior is presented alongside its behavior. Yet, the specific design of informational interfaces in order to be transparent about the reasoning, especially in MRMs, is still unclear. Therefore, we investigated the impact of promising factors in an online study. Participants of the study evaluated different interfaces in multiple scenarios, where the HAV has to perform an MRM. The presented interfaces varied in displayed information richness to systematically manipulate transparency in the vehicle automation. The information varied concerning the vehicle´s behavior and reasoning. The design with the highest information richness also added expected consequences, like delay time, to the interface. The results of this study indicate that improvements of passenger’s understanding in MRMs are linked with increased transparency of provided information. Understanding regarding those MRMs scored significantly higher in the information richest design compared to the design without MRM specific information

What´s happening right now? Passenger Understanding of Highly Automated Shuttle’s Minimal Risk Maneuvers by Internal Human-Machine Interfaces

Remote Operation (RO) is a promising technology, that could close the gap between current vehicle automation functionalities and their expected capabilities especially when focusing on the „Unknowable Unknowns” problem of hidden operational design domain (ODD) borders within AI-based highly automated vehicles (HAVs) (Koopman & Wagner, 2017). It is unlikely that vehicle automation systems will be created in the foreseeable future, that are capable of solving every possible situation they are confronted with (Schneider et al., 2023). A possible way to overcome these technological limitations is to incorporate a human operator and benefiting from his problem-solving skills in novel situations (Cummings et al., 2020). New communication technologies allow for this human support by remotely interacting with vehicles and therefore supporting numerous vehicles at once (Zhang et al., 2021). On the other hand, this would lead to the inability of HAV passengers to interact with a human driver, unlike in manually driven vehicles, where no human driver will be inside the shuttle to support and inform passengers, if necessary (Meurer et al., 2020). The absence of a human diver, who can reassure and comfort passengers in these automated systems, could lead to novel passenger insecurity patterns (Meurer et al., 2020). This is particularly relevant in unfamiliar situations with a high level of uncertainty like minimal risk maneuvers (MRMs). MRMs are maneuvers in which the vehicle tries to minimize risk, for example by stopping in a safe manner. These controlled stopping maneuvers can be triggered by “Unknowable Unknowns” outside the vehicles ODD and are likely to lead to passengers´ confusion and uncertainty (Koo et al., 2015). The passengers confusion may further increase due to the opaque nature of AI-based automation systems, where it is not always clear, what the vehicle´s/AI´s reasoning behind its action is (Cysneiros et al., 2018). The issues in understanding complicated AI-based systems, are in part the result of increasingly complex algorithms (Eschenbach, 2021). One possible scenario is, that passengers in automated Shuttles, will be confronted with an HMI, which doesn´t depict the systems reasoning behind its actions well enough. As a result, passengers will not be able to explain HAV behavior, especially during MRMs, which will result in lower trust and acceptance towards HAVs in general. In order to better understand a HAV´s behavior, the reasoning of its algorithms need to be more explainable to the vehicle´s users (Schmidt et al., 2021). In part, this may be achieved by giving certain information about the AI´s decision making (Guidotti et al., 2018) or by giving examples as an explanation to certain behavioral patterns (Cai et al., 2019). In order to deal with this confusion and uncertainty, Human-machine interfaces (HMIs) could be utilized, by giving information regarding these MRMs and reduce these insecurities by increasing the passengers understanding of the HAV-system. (Koo et al., 2015; L.F. Penin et al., 2000). Though for the individual user this information about specific algorithms and their existence is not central, the existence of an AI-system and relevant key information might be sufficient for an informed user and should be incorporated in the HMI information (Dahl, 2018). The present study investigates how systemic explanatory transparency via different approaches of onboard HMI of an automated shuttle bus (ASB) is able to reduce this uncertainty and may lead to a better understanding of the vehicle´s AI´s reasoning and its behavior during an MRM that might result in higher trust, understanding and subjective safety (Oliveira et al., 2020). For this purpose, we designed several interfaces for the communication between the HAV and the passengers with varying degrees of (exemplary) information, concerning the situation that led to the MRM and the vehicle´s interpretation of that situation. The MRM information consisted of vehicle status, delay times, specific MRM information and the involvement of a teleoperator. The involvement of a teleoperator was explained as a process consisting of multiple steps for supporting the vehicle´s automation. The MRM information was incorporated in a basic mapbased interface and gave information about the ASB´s route, passengers destinations, time and the vehicle´s operational status. The resulting HMI variants were presented via pictures in an online questionnaire study and evaluated in regards to understandability and usability. In addition to the varying amounts of given information, different design choices were evaluated as well. Results of the study aim to provide insights in the informative needs of SAV passengers during the performance of MRMs. This research aims to improve future designs of HAV HMIs and to support passenger experiences while using highly automated shuttle busses

Instrument for the assessment of road user automated vehicle acceptance: A pyramid of user needs of automated vehicles

This study proposed a new methodological approach for the assessment of automated vehicle acceptance (AVA) from the perspective of road users inside and outside of AVs pre- and post- AV experience. Users can be drivers and passengers, but also external road users, such as pedestrians, (motor-)cyclists, and other car drivers, interacting with AVs. A pyramid was developed, which provides a hierarchical representation of user needs. Fundamental user needs are organized at the bottom of the pyramid, while higher-level user needs are at the top of the pyramid. The pyramid distinguishes between six levels of needs, which are safety trust, efficiency, comfort and pleasure, social influence, and well-being. Some user needs universally exist across users, while some are user-specific needs. These needs are translated into operationalizable indicators representing items of a questionnaire for the assessment of AVA of users inside and outside AVs. The formulation of the questionnaire items was derived from established technology acceptance models. As the instrument was based on the same model for all road users, the comparison of AVA between different road users is now possible. We recommend future research to validate this questionnaire, administering it in studies to contribute to the development of a short, efficient, and standardized metric for the assessment of AVA.Comment: 17 pages, 1 figur

Principles for External Human-Machine Interfaces

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

What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of Automated Road Transport Systems

The main aim of this study was to use an adapted version of the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the factors that influence users’ acceptance of automated road transport systems (ARTS). A questionnaire survey was administered to 315 users of a CityMobil2 ARTS demonstration in the city of Trikala, Greece. Results provide evidence of the usefulness of the UTAUT framework for increasing our understanding of how public acceptance of these automated vehicles might be maximised. Hedonic Motivation, or users’ enjoyment of the system, had a strong impact on Behavioural Intentions to use ARTS in the future, with Performance Expectancy, Social Influence and Facilitating Conditions also having significant effects. The anticipated effect of Effort Expectancy did not emerge from this study, suggesting that the level of effort required is unlikely to be a critical factor in consumers’ decisions about using ARTS. Based on these results, a number of modifications to UTAUT are suggested for future applications in the context of automated transport. It is recommended that designers and developers should consider the above issues when implementing more permanent versions of automated public transport

Entwicklung einer lichtbasierten Anzeige- und Interaktionsstrategie als MenschMaschine-Schnittstelle zur Fahrerunterstützung in unterschiedlichen Level des automatisierten Fahrens

Mit aufsteigendem Automationslevel übernimmt die Fahrzeugautomation zunehmend Aufgaben der dynamischen Fahraufgabe. In Abhängigkeit des aktuellen Automationslevels unterscheiden sich die Aufgaben des Fahrers und damit verbunden auch die Anforderungen an ihn. Um diesen neuen Anforderungen gerecht zu werden, benötigt er andere oder anders aufbereitete Informationen. Die Forschungsfrage der vorliegenden Arbeit beschäftigt daher mit der Entwicklung einer lichtbasierten Anzeigeund Interaktionsstrategie als Mensch-Maschine-Schnittstelle zwischen Fahrer und Fahrzeug über unterschiedliche Level des automatisierten Fahrens hinweg. Ziel des gestalteten Anzeige- und Interaktionskonzept ist es, den Nutzer in unterschiedlichen Automationsleveln durch aufgabenspezifische Informationen in unterschiedlichen Ebenen des Situationsbewusstseins optimal zu unterstützen. Im Fokus der Arbeit stehen hierbei die Automationslevel manuelles Fahren (SAE0), teilautomatisiertes Fahren (SAE2) und bedingt-automatisiertes Fahren (SAE3). Besonders bei Wechseln zwischen diesen Automationsleveln (Transitionen) muss die neue Rolle des Fahrers möglichst transparent und verständlich kommuniziert werden. Um dies zu gewährleisten, ist die erarbeitete Anzeige- und Interaktionsstrategie als ein holistisches und automationslevel-übergreifendes Interaktionskonzept designt. Abgeleitet aus bestehenden Prototypen und Erkenntnissen der Wahrnehmungstheorien wurde hierfür in einem iterativen Prozess eine lichtbasierte Anzeige- und Interaktionsstrategie entwickelt. Im Verlauf der Arbeit werden drei Nutzerstudien zur Evaluation des lichtbasierten Anzeige- und Interaktionskonzepts im Detail vorgestellt. Die erste Studie verfolgt das Ziel der Fahrerunterstützung und der Verbesserung der Verkehrssicherheit während des manuellen Fahrens (SAE0) im statischen Fahrsimulator. Die zweite Nutzerstudie beschäftigt sich mit der Fahrerunterstützung und der Unfallvermeidung bei Transitionen zwischen dem bedingt-automatisierten (SAE3) und dem teilautomatisierten Fahren (SAE2). Um den Realitätsgrad von Studie zu Studie zu steigern, wird diese Untersuchung im dynamischen Fahrsimulator durchgeführt. In der dritten Studie wird das Anzeige- und Interaktionskonzept während einer realen, bedingt-automatisierten Fahrt (SAE3) in einem Forschungsfahrzeug des DLR auf einem Testgelände untersucht. Die Ergebnisse zeigen, dass Fahrer während der manuellen Fahrzeugführung (SAE0) durch das entwickelte Anzeige- und Interaktionskonzept bei der Wahrnehmung wichtiger Informationen unterstützt werden können. Hieraus resultieren signifikant häufiger situationsangepasste Fahrerreaktionen, verglichen mit einer Kontrollgruppe. Während der teilautomatisierten Fahrt (SAE2) profitieren Fahrer in unterschiedlichen Verkehrssituationen von den zusätzlichen Informationen der Fahrzeugautomation hinsichtlich wahrgenommener Objekte mittels lichtbasiertem Anzeige- und Interaktionskonzeptes. Die direkte Adressierung der zweiten Ebene (Verständnis) des Situationsbewusstseins durch das Anzeige- und Interaktionskonzept scheint zur Förderung des System-Verständnisses und der Fahrerunterstützung im teilautomatisierten Fahren (SAE2) hervorragend geeignet. Durch die erhöhte Transparenz des Systemverhaltens kann ein verbessertes Verständnis der Fahrzeugautomation und damit auch eine bessere Antizipation ihres zukünftigen Verhaltens erzielt werden. Als Resultat zeigten Fahrer eine beschleunigte Reaktion im Falle von stillen Automationsfehlern und berichteten von höherem subjektivem Vertrauen und einer gesteigerten Akzeptanz in das Gesamtsystem. Im bedingtautomatisierten Automationslevel (SAE3) erhalten Nutzer der lichtbasierten Anzeigeund Interaktionsstrategie zusätzliche Informationen bezüglich des zukünftigen Systemverhaltens. Hieraus resultierten überdurchschnittlich hohe subjektive Bewertungen hinsichtlich des Verständnisses der Informationen für das aktuelle Verhalten als auch der Antizipation für eine zukünftige Entwicklung der Situation bzw. des zukünftigen Verhaltens der Fahrzeugautomation. Die Arbeit zeigt über alle drei Nutzerstudien hinweg eine erfolgreiche Fahrerunterstützung durch das lichtbasierte Anzeige- und Interaktionskonzeptes als Mensch-Maschine-Schnittstelle im manuellen als auch im automatisierten Fahren