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Displaying Vehicle Driving Mode – Effects on Pedestrian Behavior and Perceived Safety
Abstract
The type and amount of information pedestrians should receive while interacting with an autonomous vehicle (AV) remains an unsolved challenge. The information about the vehicle driving mode could help pedestrians to develop the right expectations regarding further actions. The aim of this study is to investigate how the information about the vehicle driving mode affects pedestrian crossing behavior and perceived safety. A controlled field experiment using a Wizard-of-Oz approach to simulate a driverless vehicle was conducted. 28 participants experienced a driverless and a human-operated vehicle from the perspective of a pedestrian. The vehicle was equipped with an external human machine interface (eHMI) that displayed the driving mode of the vehicle (driverless vs. human-operated). The results show that the crossing behavior, measured by critical gap acceptance, and the subjective reporting of perceived safety did not differ statistically significantly between the driverless and the human-operated driving condition.
... Examples include a tinted windshield (Bindschädel et al., 2023;Chen et al., 2020;Zadeh Darrehshourian, 2021), a dummy steering wheel (Habibovic et al., 2018), a mirror film (Liu & Hirayama, 2023;Liu et al., 2021), or reading a newspaper with the car being controlled from the passenger seat (Rodríguez Palmeiro et al., 2018b). However, the most commonly used option is that of a seat suit, imitating an empty driver's seat Fuest et al., 2018;Hensch et al., 2019;Joisten et al., 2020;Karlsson & Löfvenberg, 2019;Large et al., 2023;Li et al., 2020;Liang et al., 2016;Loew et al., 2022;Rothenbücher et al., 2016;Shutko et al., 2018;Taima & Daimon, 2023;Wang et al., 2021). In the present study, a seat suit was also used in the 'no driver' trials. ...
... Our research may have implications for the development of technological solutions such as eHMIs that inform road users about an AV's actions (e.g., Bindschädel et al., 2023;Colley et al., 2021;Forke et al., 2021), its awareness of them (e.g., Block et al., 2023;Eisele & Petzoldt, 2022;Epke et al., 2021), or its automated driving mode (e.g., Daimon et al., 2021;Joisten et al., 2020). Results from the interviews following Session 2 indicated that most participants would like to receive such information, especially through visual eHMIs on the AV. ...
... It is unknown how many participants believed the vehicle was driving automatically. Previous Wizard-of-Oz research showed believability percentages ranging from 97 to 100% Habibovic et al., 2018;Joisten et al., 2020), but also 60% to 88% (Currano et al., 2018;Faas & Baumann, 2019;Hensch et al., 2019;Large et al., 2023;Li et al., 2020;Moore et al., 2019;Rothenbücher et al., 2016;Shutko et al., 2018), and 40% (Rodríguez Palmeiro et al., 2018b) or even 0% (Karlsson & Löfvenberg, 2019). These percentages appear to be contextdependent, for example, whether a modern-looking vehicle was used. ...
As automated vehicles (AVs) gain traction, questions arise about the interaction between AVs and cyclists. Amidst conflicting views on the necessity of substituting drivers’ social cues with external human-machine interfaces (eHMIs) on AVs, our research investigated whether cyclists could detect the absence of a driver in an AV and how this influences their perceptions. Using a Wizard-of-Oz method with a concealed driver, 37 participants cycled a designated route and encountered the AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were explicitly instructed to detect the presence or absence of a driver. We recorded the participants’ gaze behaviour with eye-tracking and their responses in interviews. The results showed that thirty percent of the cyclists spontaneously noticed the absence of a driver, and when prompted, they detected a driver with 93% accuracy. The eye-tracking data revealed that cyclists looked more frequently and longer at the vehicle in Session 2 compared to Session 1. Furthermore, cyclists exhibited intermittent sampling of the vehicle, and they looked in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews indicated that participants were curious but felt safe and acknowledged the need to receive information about the AV’s driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perceptions of safety. Further research is needed to explore these findings in real-world traffic conditions.
... Furthermore, AVs have the possibility to communicate via eHMIs. Such eHMIs may consist of LED strips (e.g., Fuest, Feierle et al., 2020;Hensch et al., 2020b;Weber et al., 2019;Zhang et al., 2018), displays (e.g., Clercq et al., 2019;Joisten et al., 2020;Rettenmaier et al., 2019;Rettenmaier, Albers, & Bengler, 2020;Song et al., 2018), or projections (e.g., Dietrich et al., 2018;Kühn et al., 2019). In addition, acoustic signals can be used to communicate with HRUs (e.g., Deb, Strawderman, & Carruth, 2018). ...
... In recent years, the method has gained more popularity and is used to assess driving behavior (e.g., Currano et al., 2018;Fuest, Michalowski et al., 2018;Fuest, Michalowski et al., 2019;Moore et al., 2019;Rothenbücher et al., 2016) and eHMIs (Clamann et al., 2017;Habibovic et al., 2018;Hensch et al., 2020b;Joisten et al., 2020;Mahadevan et al., 2018;Matthews et al., 2017). ...
... The crossing time and error rate can be used to compare different eHMIs (Chang et al., 2017). Another possibility is the critical gap acceptance, where participants are asked to press a button (Beggiato et al., 2017;Joisten et al., 2020), raise their hand, or take a step forward or backward (Rodríguez Palmeiro, 2017) "at the last moment they are willing to cross the road in front of the vehicle" (Joisten et al., 2020, p. 253). ...
Different study setups—e.g., videos, virtual reality, or Wizard of Oz—are used to evaluate automated vehicle’s driving profiles. It seems that the method has an impact on the outcome of the study results. The exact extent of transferability of the results between these studies is still unknown. The aim of the thesis is to give recommendations which method should be used to evaluate an automated vehicle’s driving behavior to communicate a yielding or non-yielding intention to a pedestrian.
... To meet these new communication requirements, it has been proposed to equip SDVs with an eHMI. The potential information content of an eHMI can be broken down (Liu et al., 2020; into advising pedestrians to cross (e.g., Ackermann, Beggiato, Schubert & Krems, 2019;Hudson et al., 2019;Song et al., 2018;Stadler et al., 2019), displaying the SDV's perception of the pedestrian (e.g., Chang et al., 2017;Mahadevan et al., 2018), displaying the SDV's intent to yield (e.g., Böckle et al., 2017;Habibovic et al., 2019;Mahadevan et al., 2018) and displaying the SDV's automated mode (e.g., Ackermann et al., 2019;Habibovic et al., 2018;Joisten et al., 2019). The SDV's ability to display its automated driving mode can be seen as the most general information content (Bengler et al., 2020) and indeed was shown to be the most influential information content to pedestrians (Faas, Kao et al., 2020;Faas, Mathis et al., 2020). ...
... Few eHMI studies have compared pedestrian encounters of a driverless SDV to an encounter of a conventional vehicle steered by an attentive driver. In a Wizard-of-Oz field study, Joisten et al. (2019) evaluated an eHMI communicating automated driving mode using an SDV symbol positioned in front of the radiator grille. Pedestrians did not differ in perceived safety when encountering a driverless vehicle with engaged eHMI (automated driving mode) in relation to encountering the same vehicle with an attentive driver and disengaged eHMI (conventional driving mode). ...
... Without an eHMI, pedestrians felt less safe encountering the driverless vehicle as compared to carrying an attentive driver. However, if the driverless vehicle indicated its automated driving mode via an eHMI, pedestrians did not differ in perceived safety when compared to the conventional vehicle steered by an attentive driver, which is in line with the results of Joisten et al. (2019). While pedestrians' perceived safety remained stable for conventional vehicle encounters and driverless vehicle encounters without eHMI, with time, pedestrians felt safer encountering a driverless vehicle with an eHMI. ...
With self-driving vehicles (SDVs), pedestrians lose the possibility of making eye contact with an attentive driver. This study investigated whether an external human-machine interface (eHMI) displaying the automated driving mode (a. without eHMI vs. b. with eHMI) affects how pedestrians respond to different driver's states: (1) attentive driver, (2) tinted windshield, (3) distracted driver (within-subject design). At a test site, N = 65 pedestrians crossed a pedestrian crossing while a Wizard-of-Oz SDV approached. We assessed perceived safety and crossing onset times after each trial. Findings reveal that without an eHMI, pedestrians felt significantly less safe if the windshield was tinted or the driver was distracted as compared to an attentive driver. With an eHMI, pedestrians did not differ in perceived safety with regard to the driver's state. We observed no significant differences in pedestrians' crossing onset times. We conclude that an eHMI helps pedestrians to not consider the driver's state.
... In this approach, an investigator-who is hidden from the user-simulates the system [23]. In most WoZ studies that examine the interaction between AVs and pedestrians, seat covers are used to hide the driver from the pedestrians' view, so as to simulate an AV [24][25][26][27][28]. The results of WoZ studies demonstrated that being able to see the driver is not very important for pedestrians [12,25,28]. ...
... In most WoZ studies that examine the interaction between AVs and pedestrians, seat covers are used to hide the driver from the pedestrians' view, so as to simulate an AV [24][25][26][27][28]. The results of WoZ studies demonstrated that being able to see the driver is not very important for pedestrians [12,25,28]. In the study by [12], only half of the sample recognized the driver; however, when asked directly, they expressed that they felt safer when a driver is present. ...
... In the study by [12], only half of the sample recognized the driver; however, when asked directly, they expressed that they felt safer when a driver is present. This result stands in contrast to the results of [28], where the perceived safety was not influenced by being able to see the driver. As a reason for their increased feeling of safety in the study of [12], some participants did not mention the eHMI, but instead mentioned the driving strategy of the AV [12]. ...
Integrating automated vehicles into mixed traffic entails several challenges. Their driving behavior must be designed such that is understandable for all human road users, and that it ensures an efficient and safe traffic system. Previous studies investigated these issues, especially regarding the communication between automated vehicles and pedestrians. These studies used different methods, e.g., videos, virtual reality, or Wizard of Oz vehicles. However, the extent of transferability between these studies is still unknown. Therefore, we replicated the same study design in four different settings: two video, one virtual reality, and one Wizard of Oz setup. In the first video setup, videos from the virtual reality setup were used, while in the second setup, we filmed the Wizard of Oz vehicle. In all studies, participants stood at the roadside in a shared space. An automated vehicle approached from the left, using different driving profiles characterized by changing speed to communicate its intention to let the pedestrians cross the road. Participants were asked to recognize the intention of the automated vehicle and to press a button as soon as they realized this intention. Results revealed differences in the intention recognition time between the four study setups, as well as in the correct intention rate. The results from vehicle–pedestrian interaction studies published in recent years that used different study settings can therefore only be compared to each other to a limited extent.
... References: [13][14][15][16][17][18][19][20][21][22][23][24]. ...
... References: [21,34,35,46,[61][62][63][64][65][66][67][68][69][70]. ...
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.
... Some studies have suggested using either advise information or information about the vehicle's behavioral intention rather than information about the vehicle status (Ackermann et al., 2019b;Mahadevan et al., 2018;Schieben et al., 2019). Indeed, a simple informative message about the nature of the vehicle (i.e., the eHMI just communicates that the vehicle is autonomous) has no impact about making the street-crossing decision or the felt trust during the street-crossing (Joisten et al., 2020). By using focus groups and questionnaires, Schieben et al. (2019) highlighted that information on the vehicle speed was considered less relevant than the vehicle intentions. ...
... However, all vehicles are equipped with indicators to indicate a change of direction. These messages are more than information on the vehicle's status, which are considered to be not useful by pedestrians (e.g., Joisten et al., 2020). The three designed eHMIs and the four delivered messages are based on previous work analysing pedestrian needs and a co-design session. ...
The number of studies on autonomous vehicles has increased over recent years. Many of these studies have indicated the importance of an external Human-Machine Interface of communication (eHMI) on autonomous vehicles to indicate their intentions to other road users. Using an experimental design, we compared three eHMIs coupled to three road infrastructures to observe pedestrians' crossing behavior and collect their feelings about different vehicle types. Our results showed that the eHMIs influence the pedestrians' decision to cross the street, confirming the importance of setting up eHMIs. The proportion of pedestrians who crossed in front of the autonomous vehicles was more significant for vehicles equipped with an eHMI than vehicles without an eHMI. In 10% of cases, pedestrians used circumvention strategies rather than crossing in front of a vehicle without an eHMI. This behavior was more often observed when there was no protected infrastructure. Finally, while our objective data failed to indicate whether a specific eHMI is better accepted than another, the subjective data on the participants' preferences provided some promising ideas for further studies and the eHMI final implementation.
... Communication of the automated driving mode explains the absence of an attentive driver and, hence, pedestrians no longer miss seeing one (Faas et al., 2020a) and no longer differ in perceived safety with respect to the driver's state (Faas et al., 2021a). Indeed, pedestrians feel as safe to encounter a driverless SDV with an eHMI as they feel to encounter a conventional vehicle steered by a visible human driver (Faas et al., 2020a;Joisten et al., 2019). ...
... y = .38). This design reflects a good learnability (Faas et al., 2020a) and was shown to be effective in prior studies (Faas et al., 2020b;Joisten et al., 2019) if explained previously to participants (Habibovic et al., 2018;Hensch et al., 2019). Blue-green light represents a novel color in traffic which is not yet associated with a specific meaning (Dietrich et al., 2018;Faas and Baumann, 2019;Werner, 2018). ...
Pedestrians rely on vehicle dynamics, engine sound, and driver cues. The lack of engine sound now constitutes an addressed pedestrian safety issue for (hybrid) electric vehicles ((H)EVs). Analogously, lacking driver cues may constitute a pedestrian safety issue for self-driving vehicles (SDVs). The purpose of this study was to systematically compare the relevance of substituting driver cues with an external human-machine interface among SDVs (no eHMI vs. eHMI) with the relevance of substituting engine sound with artificial sound among (H)EVs (no engine sound vs. engine sound). In a within-subject design, twenty-nine participants acting as pedestrians encountered a simulated SDV in a parking lot. The results revealed that both informational cues have equally large effects on subjective measures such as perceived safety. In semi-structured interviews, participants stated that it is equally crucial to equip SDVs with an eHMI as equipping (H)EVs with an artificial sound generator. We conclude that an eHMI for SDVs seems to be as relevant as an artificial sound for (H)EVs.
... The usual approach has been to create an encounter situation consisting of an AV and other road users, usually pedestrians. This can be achieved by setting up real physical prototypes with artificially created real-world scenarios [10]- [16] or by creating virtual encounter situations through simulations [17]- [28]. ...
... As also stated in the review by Rouchitas et al. [29], these results cannot be determined in real field tests with the Wizard of Oz approach. Here the critical gap is measured via button presses, which is the gap below the subjects will not begin to cross the road [13]- [16]. Besides, field tests with one pedestrian and one AV in test facilities were rated as not realistic, and due to weather conditions, messages, or symbols on the eHMI can not be recognized [15]. ...
Additional signaling devices for highly automated vehicles (AVs) that can communicate their driving state to other road users can simplify the integration process in existing road traffic. This paper presents the results of an international, virtual reality-based study conducted in China, South Korea and the USA in which subjects assume the role of a pedestrian and are placed in direct encounter situations with an AV in a parking lot. A novel communication interface consisting of three displays is attached to the AV's front and used to show additional information about its driving state. In total, three encounter scenarios are investigated: the AV approaches from the left, front and right outside of the pedestrian's line of sight. The influence of different symbol types on the subject's moving behavior, recognition of intention and perceived safety is investigated. The results show that additional signals ensure a better perception of the AV's intention and increase the perceived safety. The moving behavior of subjects is significantly changed when additional signals are used during driving tasks compared to the same tasks without such signals. The change of moving behaviour is similar in encounter situations where the AV approaches from the left and front but differs in encounter situations from the right. These results could equally be proven for all nationalities, which shows that a uniform, international solution for additional signaling devices of highly automated vehicles is possible.
... Moreover, the critical gap acceptance and the perceived safety of participants crossing a road in front of an AV is not affected by the vehicle's driving mode (manual vs. automated) [9]. A comparable result was found in the study by Rodríguez Palmeiro [10]: even if participants noticed that the vehicle had an automated-driving sign, and they were subjectively influenced by feeling less safe and more doubtful, the objective behavior of participants did not change [10]. ...
... Even though the focus of eHMIs is on other communication content, they result in additional marking of the AV. Light strips (e.g., [11,13,14]), displays (e.g., [9,[15][16][17]) and projections (e.g., [18]) have primarily been used to communicate intentions to pedestrians (e.g., [13][14][15]) or human drivers (e.g., [16,19]). Cyan is recommended for eHMIs as it is a highly visible color and has no specific association in road traffic contexts [11,18,20,21]. ...
Due to the short range of the sensor technology used in automated vehicles, we assume that the implemented driving strategies may initially differ from those of human drivers. Nevertheless, automated vehicles must be able to move safely through manual road traffic. Initially, they will behave as carefully as human learners do. In the same way that driving-school vehicles tend to be marked in Germany, markings for automated vehicles could also prove advantageous. To this end, a simulation study with 40 participants was conducted. All participants experienced three different highway scenarios, each with and without a marked automated vehicle. One scenario was based around some roadworks, the next scenario was a traffic jam, and the last scenario involved a lane change. Common to all scenarios was that the automated vehicles strictly adhered to German highway regulations, and therefore moved in road traffic somewhat differently to human drivers. After each trial, we asked participants to rate how appropriate and disturbing the automated vehicle's driving behavior was. We also measured objective data, such as the time of a lane change and the time headway. The results show no differences for the subjective and objective data regarding the marking of an automated vehicle. Reasons for this might be that the driving behavior itself is sufficiently informative for humans to recognize an automated vehicle. In addition, participants experienced the automated vehicle's driving behavior for the first time, and it is reasonable to assume that an adjustment of the humans' driving behavior would take place in the event of repeated encounters.
... We explored some of the most recent practices and how the lack of physical crossing was addressed: To start with, Joisten et al. (2020) measured participants' crossing behavior in terms of critical gap acceptance and perceived safety with a simulated WoZ AV. However, the participants were not permitted to step in front of the vehicle for safety reasons. ...
With the development of autonomous vehicle (AV) technology, understanding how pedestrians interact with AVs is of increasing importance. In most field studies on pedestrian crossing behavior when encountering AVs, pedestrians were not permitted to physically cross the street due to safety restrictions. Instead, the physical crossing experience was replaced with indirect methods (e.g., by signalizing with gestures). We hypothesized that this lack of a physical crossing experience could influence the participants’ crossing behavior. To test this hypothesis, we adapted a reference study and constructed a crossing facility using a virtual reality (VR) simulation. In a controlled experiment, the participants encountered iterations of oncoming AVs. For each interaction, they were asked to either cross the street or signify their crossing decisions by taking steps at the edge of the street without crossing.
Our study reveals that the lack of a physical crossing can lead to a significantly lower measured critical gap and perceived stress levels, thus indicating the need for detailed analysis when indirect methods are applied for future field studies.
Practical Relevance: Due to safety requirements, experiments will continue to measure participants’ crossing behavior without permitting them to physically walk in front of an oncoming vehicle. Our study was the first attempt to reveal how this lack of crossing could potentially affect pedestrians’ behavior, and we obtained empirical evidence in support of our hypothesis, thus providing insights for future studies.
... [24,27,28]), or the automation mode (e.g. [29,30]). Studies have shown that compared to AVs without eHMI, pedestrians feel safe and better informed when an eHMI is present [27,29,31] and are more inclined to cross when an eHMI indicates so (e.g. ...
Automated vehicles (AVs) may feature blinded (i.e. blacked-out) windows and external human-machine interfaces (eHMIs), and the driver may be inattentive or absent, but how these features affect cyclists is unknown. In a crowdsourcing study, participants viewed images of approaching vehicles from a cyclist's perspective and decided whether to brake. The images depicted different combinations of traditional vehicles versus AVs, eHMI presence , vehicle approach direction, driver visibility/window-blinding, visual complexity of the surroundings, and distance to the cyclist (urgency). The results showed that the eHMI and urgency level had a strong impact on crossing decisions, whereas visual complexity had no significant influence. Blinded windows caused participants to brake for the traditional vehicle. A second crowdsourcing experiment aimed to clarify the findings of Experiment 1 by also requiring participants to detect the vehicle features. It was found that the eHMI 'GO' and blinded windows yielded high detection rates and that driver eye contact caused participants to continue pedalling. To conclude, blinded windows increase the probability that cyclists brake, and driver eye contact stimulates cyclists to continue cycling. Our findings , which were obtained with large international samples, may help elucidate how AVs (in which the driver may not be visible) affect cyclists' behaviour.
... However, this may change as AVs become more commonplace and trust in AVs grow. Interestingly, some people mentioned that their preference for the ENY eHMI stems from wanting an assurance that the Automated Driving (AD) system is working properly, which was addressed in prior work [23,40]. However, this can be addressed with an Automated-Driving Mode pilot lamp on the AV which is divorced from an eHMI that communicates vehicle intent. ...
... Several studies directly compared behavior and evaluations towards AVs and MVs, with mixed results. Some report no differences in the feeling of safety and comfort when encountering and interacting with an AV or MV [14,24,44,58]. Also, similar cooperative tendencies were found towards AVs and MVs [25]. ...
... Most of the eHMI research thus far has been conducted online or in virtual-reality environments, with little opportunity for visual or cognitive distraction. There is a limited but growing number of eHMI studies conducted with real vehicles, but typically in simple settings such as parking lots (Ahn et al., 2021;Chen et al., 2020;Hensch et al., 2020;Liu et al., 2021), indoor environments (Burns et al., 2019;Reschke et al., 2018), test tracks (Faas et al., 2021;Fuest et al., 2020;Horn et al., 2021), or roads with otherwise restricted access (Barendse, 2019;Dey et al., 2021a;Habibovic et al., 2018;Joisten et al., 2019;Morales Alvarez et al., 2019;Mührmann, 2019;Papakostopoulos et al., 2021;Zadeh Darrehshourian, 2021). Research in real traffic is still relatively rare (Cefkin et al., 2019;Forke et al., 2021;Merat et al., 2018;Mirnig et al., 2021;Monzel et al., 2021), and some evidence concurs that eHMIs will have to compete with other visual cues in the environment. ...
The last few years have seen a wealth of research on external human-machine interfaces (eHMIs). It has been argued that eHMIs are vital because they fill the social interaction void that arises with the introduction of automated vehicles (AVs). However, there is still much discussion about whether eHMIs are needed. The present article surveys arguments for and against eHMIs. We list three arguments against eHMIs: (1) Implicit communication dominates pedestrian-AV interaction, and there is no social interaction void to be filled, (2) There is a large variety of eHMI concepts and a lack of standardization and consensus, and (3) eHMIs may elicit various negative effects such as distraction, confusion, and overreliance. Next, we present five reasons why eHMIs may be useful or required: (1) eHMIs can make planned actions of the AV visible, thereby increasing the efficiency of pedestrian-AV interaction, (2) Participants value an eHMI compared to no eHMI, (3) eHMIs do not have to be limited to showing instructions or the AV's planned actions; showing the AV mode or the AV's cooperative or detection capabilities are other uses of eHMIs, (4) Recent research shows that driver eye contact is important in traffic, and a social interaction void thus exists, and (5) A large portion of pedestrian-vehicle accidents in current traffic is caused by unclear implicit communication, suggesting that pedestrians may benefit from explicit eHMIs. It is hoped that this article contributes to the critical discussion of whether eHMIs are needed and how they should be designed.
... Most of the eHMI research thus far has been conducted online or in virtual-reality environments, with little opportunity for visual or cognitive distraction. There is a limited but growing number of eHMI studies conducted with real vehicles, but typically in simple settings such as parking lots (Ahn et al., 2021;Chen et al., 2020;Hensch et al., 2020;Liu et al., 2021), indoor environments (Burns et al., 2019;Reschke et al., 2018), test tracks (Faas et al., 2021;Fuest et al., 2020;Horn et al., 2021), or roads with otherwise restricted access (Barendse, 2019; Dey et al., 2021a;Habibovic et al., 2018;Joisten et al., 2019;Morales Alvarez et al., 2019;Mührmann, 2019;Papakostopoulos et al., 2021;Zadeh Darrehshourian, 2021). Research in real traffic is still relatively rare (Cefkin et al., 2019;Forke et al., 2021;Merat et al., 2018;Mirnig et al., 2021;Monzel et al., 2021), and some evidence concurs that eHMIs will have to compete with other visual cues in the environment. ...
The last few years have seen a wealth of research on external human-machine interfaces (eHMIs). It has been argued that eHMIs are vital because they fill the social interaction void that arises with the introduction of automated vehicles (AVs). However, there is still much discussion about whether eHMIs are needed. The present article surveys arguments for and against eHMIs. We list three arguments against eHMIs: (1) Implicit communication dominates pedestrian-AV interaction, and there is no social interaction void to be filled, (2) There is a large variety of eHMI concepts and a lack of standardization and consensus, and (3) eHMIs may elicit various negative effects such as distraction, confusion, and overreliance. Next, we present five reasons why eHMIs may be useful or required: (1) eHMIs can make planned actions of the AV visible, thereby increasing the efficiency of pedestrian-AV interaction, (2) Participants value an eHMI compared to no eHMI, (3) eHMIs do not have to be limited to showing instructions or the AV’s planned actions; showing the AV mode or the AV’s cooperative or detection capabilities are other uses of eHMIs, (4) Recent research shows that driver eye contact is important in traffic, and a social interaction void thus exists, and (5) A large portion of pedestrian-vehicle accidents in current traffic is caused by unclear implicit communication, suggesting that pedestrians may benefit from explicit eHMIs. It is hoped that this article contributes to the critical discussion of whether eHMIs are needed and how they should be designed.
... They represent the messages "You can Cross", "Do not Cross", and "Pedestrian Detected". In order to identify the best available representations of these messages, we reviewed current academic literature, in which comprehension scores of different icons are provided (Ackermann et al., 2019;Chang et al., 2017Chang et al., , 2018Clamann et al., 2017;Deb et al., 2018;Fridman et al., 2019;Hamm et al., 2018;Joisten et al., 2019;Othersen et al., 2019;Reschke et al., 2018Reschke et al., , 2019Stadler et al., 2019). The reportedly best understood icons were identified in Reschke (2018) and (2019). ...
External human–machine-interfaces (eHMIs) might support the interaction between automated vehicles and pedestrians. The messages conveyed by eHMIs need to be understood quickly and correctly by their addressees. If implemented in the future, pedestrians will repeatedly encounter eHMIs in situations that feature different traffic context. So far, little is known about the influence of contextual cues like regulatory elements or (presumed) model behavior of fellow road users on the comprehension of eHMIs. In order to investigate possible effects of such contextual cues on comprehension, we conducted a picture-based online study among German residents (N = 175). Participants repeatedly interpreted three eHMI icons (“you can cross”, “do not cross”, and “pedestrian detected”) either without any context (control group) or within varying degrees of relevant context (experimental group). Context facilitated comprehensibility in terms of accuracy and subjective certainty. Relevant context was especially beneficial at first encounter. As soon as an icon’s meaning was internalized, the necessity of relevant context decreased. The effect of context should therefore be considered in future eHMI research as real-world comprehension might be underestimated otherwise.
... • Allocentric symbolic eHMIs, such as eyes on the car (Chang, Toda, Sakamoto, & Igarashi, 2017), a car with a giving way icon (Weber et al., 2019), or a car depicting it is in automated mode (Joisten et al., 2019). ...
... Knowing that in general there is a hesitation and mistrust towards automated driving [2,33], it is possible that the knowledge that the vehicle was automated stimulated participants to err on the side of caution compared to a manually driven vehicle. This may be the reason why they wanted more information earlier, although this theory needs to be tested because other research also suggests that pedestrians' willingness to cross in front of automated vehicles does not differ significantly from ordinary, manually driven vehicles [9,24]. ...
External human-machine interfaces (eHMIs) support automated vehicles (AVs) in interacting with vulnerable road users such as pedestrians. While related work investigated various eHMIs concepts, these concepts communicate their message in one go at a single point in time. There are no empirical insights yet whether distance-dependent multi-step information that provides additional context as the vehicle approaches a pedestrian can increase the user experience. We conducted a video-based study (N=24) with an eHMI concept that offers pedestrians information about the vehicle's intent without providing any further context information, and compared it with two novel eHMI concepts that provide additional information when approaching the pedestrian. Results show that additional distance-based information on eHMIs for yielding vehicles enhances pedestrians' comprehension of the vehicle's intention and increases their willingness to cross. This insight posits the importance of distance-dependent information in the development of eHMIs to enhance the usability, acceptance, and safety of AVs.
... Several studies suggest that AVs should communicate using external Human-Machine Interfaces (eHMIs). eHMIs can take various forms, including screens and light bars that depict instructions (e.g., Deb et al., 2016;Fridman et al., 2017;Hudson et al., 2019), intentions (e.g., Deb et al., 2016;Habibovic et al., 2018;Kaß et al., 2020), or the automation mode (e.g., Faas et al., 2020;Joisten et al., 2019). Studies have shown that compared to AVs without eHMI, pedestrians feel safe and better informed when an eHMI is present (Ackermans et al., 2020;Faas et al., 2020;Habibovic et al., 2018) and are more inclined to cross when an eHMI indicates so (e.g., Ackermans et al., 2020;Dietrich et al., 2020;Hudson et al., 2019). ...
Automated vehicles (AVs) may feature blinded (i.e., blacked-out) windows and external Human-Machine Interfaces (eHMIs), and the driver may be inattentive or absent, but how these features affect cyclists is unknown. In a crowdsourcing study, participants viewed images of approaching vehicles from a cyclist’s perspective and decided whether to brake. The images depicted different combinations of traditional versus automated vehicles, eHMI presence, vehicle approach direction, driver visibility/window-blinding, visual complexity of the surroundings, and distance to the cyclist (urgency). The results showed that the eHMI and urgency level had a strong impact on crossing decisions, whereas visual complexity had no significant influence. Blinded windows caused participants to brake for the traditional vehicle. A second crowdsourcing experiment aimed to clarify the findings of Experiment 1 by also requiring participants to detect the vehicle features. It was found that the eHMI ‘GO’ and blinded windows yielded high detection rates and that driver eye contact caused participants to continue pedalling. To conclude, blinded windows increase the probability that cyclists brake, and driver eye contact stimulates cyclists to continue cycling. Our findings, which were obtained with large international samples, may help elucidate how AVs (in which the driver may not be visible) affect cyclists’ behaviour.
... To counteract this fact, auditory eHMIs could have the potential to support communication between electric AVs and VRU. At present, visual eHMIs are almost exclusively investigated in research for AVs. Figure 5 structures visual eHMIs in four different categories, marking a vehicle as "driving automated" [50,51], light-strips [52][53][54] communicating via light patterns, displays [55][56][57][58][59] showing text or symbols, and laser projections [60][61][62][63] projecting the message of the eHMI on the street. Figure 5. Categorization of different visual eHMI types clustered in the groups labelled as "automated" [51], light-strips [54], display [59], and projection [63]. ...
During automated driving, there is a need for interaction between the automated vehicle (AV) and the passengers inside the vehicle and between the AV and the surrounding road users outside of the car. For this purpose, different types of human machine interfaces (HMIs) are implemented. This paper introduces an HMI framework and describes the different HMI types and the factors influencing their selection and content. The relationship between these HMI types and their influencing factors is also presented in the framework. Moreover, the interrelations of the HMI types are analyzed. Furthermore, we describe how the framework can be used in academia and industry to coordinate research and development activities. With the help of the HMI framework, we identify research gaps in the field of HMI for automated driving to be explored in the future.
... Also, we cannot conclude yet that an egocentric perspective should be adopted in real traffic. As mentioned in the introduction, a number of recommendations in the literature state that an allocentric perspective should be used and that an AV should not instruct others what to do (Cefkin, 2018;Volvo Cars, 2018a) but only display its own current state (Joisten et al., 2019) or target state (e.g., Deb et al., 2016). The use of egocentric eHMIs may be confusing or even dangerous in real traffic if multiple pedestrians are present: in such cases, it might be unclear to which pedestrian(s) the message refers, and directional communication (Dietrich et al., 2018) or allocentric messages may be a suitable alternative. ...
Bazilinskyy, P., Dodou, D., & De Winter, J. C. F. (2019). Survey on eHMI concepts: The effect of text, color, and perspective. Transportation Research Part F: Traffic Psychology and Behaviour, 67, 175–194.
https://doi.org/10.1016/j.trf.2019.10.013 ----
The automotive industry has presented a variety of external human-machine interfaces (eHMIs) for automated vehicles (AVs). However, there appears to be no consensus on which types of eHMIs are clear to vulnerable road users. Here, we present the results of two large crowdsourcing surveys on this topic. In the first survey, we asked respondents about the clarity of 28 images, videos, and patent drawings of eHMI concepts presented by the automotive industry. Results showed that textual eHMIs were generally regarded as the clearest. Among the non-textual eHMIs, a projected zebra crossing was regarded as clear, whereas light-based eHMIs were seen as relatively unclear. A considerable proportion of the respondents mistook non-textual eHMIs for a sensor. In the second survey, we examined the effect of perspective of the textual message (egocentric from the pedestrian's point of view: 'Walk', 'Don't walk' vs. allocentric: 'Will stop', 'Won't stop') and color (green, red, white) on whether respondents felt safe to cross in front of the AV. The results showed that textual eHMIs were more persuasive than color-only eHMIs, which is in line with the results from the first survey. The eHMI that received the highest percentage of 'Yes' responses was the message 'Walk' in green font, which points towards an egocentric perspective taken by the pedestrian. We conclude that textual egocentric eHMIs are regarded as clearest, which poses a dilemma because textual instructions are associated with practical issues of liability, legibility, and technical feasibility.
... A variety of locations for eHMIs have been proposed, including: 1. The windscreen ( Ackermann et al., 2019;Nissan, 2015;Sweeney et al., 2018;Technologies, 2018;Weber et al., 2019) 2. The front/grille of the car ( Chang et al., 2018;Clamann et al., 2017;Daimler, 2017;De Clercq et al., 2019;Joisten et al., 2019;Otherson et al., 2018;Semcon, 2016;Song et al., 2018;Nuñez Velasco et al., 2019;Stadler et al., 2019;Toyota, 2018;Weber et al. 2019) 3. The roof of the car (Deb et al., 2019;Hensch et al., 2019;Mahadevan et al., 2018;Vlakveld et al., 2019) 4. Near the wheels (drive.ai, 2018; also proposed by Colley et al., 2017) 5. ...
In the future, automated cars may feature external human–machine interfaces (eHMIs) to communicate relevant information to other road users. However, it is currently unknown where on the car the eHMI should be placed. In this study, 61 participants each viewed 36 animations of cars with eHMIs on either the roof, windscreen, grill, above the wheels, or a projection on the road. The eHMI showed ‘Waiting’ combined with a walking symbol 1.2 s before the car started to slow down, or ‘Driving’ while the car continued driving. Participants had to press and hold the spacebar when they felt it safe to cross. Results showed that, averaged over the period when the car approached and slowed down, the roof, windscreen, and grill eHMIs yielded the best performance (i.e., the highest spacebar press time). The projection and wheels eHMIs scored relatively poorly, yet still better than no eHMI. The wheels eHMI received a relatively high percentage of spacebar presses when the car appeared from a corner, a situation in which the roof, windscreen, and grill eHMIs were out of view. Eye-tracking analyses showed that the projection yielded dispersed eye movements, as participants scanned back and forth between the projection and the car. It is concluded that eHMIs should be presented on multiple sides of the car. A projection on the road is visually effortful for pedestrians, as it causes them to divide their attention between the projection and the car itself.
In traditional traffic environments, human drivers can communicate with other drivers, cyclists, and pedestrians through gestures, facial expressions, etc., to convey their intentions. However, most of the current autonomous vehicles cannot effectively communicate with other road users with an autonomous driving model. The critical problem is that other road users need help understanding the intention of the autonomous vehicle. Thus, autonomous vehicles need to communicate with the outside world in addition to the ability to detect other road users and make relevant maneuvers to avoid potential conflicts. The external human-machine interface (eHMI) communicates with other road users outside the vehicle. It is believed to effectively resolve the conflict between fully autonomous vehicles and other road users. As pedestrians need to understand the intentions of the vehicles quickly and accurately, usability is of great importance for an eHMI. The current study sought to thoroughly analyze the existing literature on evaluation methods and establish a theory-driven evaluation system. We developed our evaluative framework based on the Situational Awareness model of pedestrians. In making a safe crossing decision, the pedestrians need to form a correct situation awareness in which they must form accurate perception, proper understanding, and timely prediction of vehicle behaviors [1–3]. Based on this model, a well-designed eHMI must facilitate the perception, understanding, and prediction processes. In this way, safety, perceptibility, intelligibility, and adaptability are required, and the four major usability domains have been theoretically established.KeywordseHMIAutonomous VehiclePedestrianEvaluation
Modelling the behaviour of automated vehicles requires an understanding of the acceptance towards certain behaviours by the human cooperation partners. This work addresses the evaluation of two communication means on the motorway slip road from the perspective of drivers in the target lane. In a video study ( N = 68) two implicit communication means (position and duration of lane change) were investigated. The cooperation partner is either a manual vehicle or a car labelled as automated by a status eHMI. The results show no significant differences in the cooperation and criticality ratings between non-automated or automated cooperation partners. A slow lane change is rated as less critical and more cooperative. A non-linear relationship emerges for the position of the change. A change in the middle of the slip road is rated most cooperative and least critical.
Automated vehicles are expected to require some form of communication (e.g., via LED strip or display) with vulnerable road users such as pedestrians. However, the passenger inside the automated vehicle could perform gestures or motions which could potentially be interpreted by the pedestrian as contradictory to the outside communication of the car. To explore this conflict, we conducted an online experiment (N = 59) with different message types (no message, intention, command), gestures (no gesture, wave, stop), and user positions (driver, co-driver) and measured the pedestrian’s confidence in crossing. Our results show that certain combinations (e.g., car indicates cross while the user in the driver seat gestures stop) confused the pedestrian, resulting in significantly lower confidence to cross. We further show that designing intention-based external communication led to less confusion and a significantly higher intention to cross.
Future automated vehicles may be equipped with external Human-Machine Interfaces (eHMIs). Currently, little is known about the effect of the perspective of the eHMI message on crossing decisions of pedestrians. We performed an experiment to examine the effects of images depicting eHMI messages of different perspectives (egocentric from the pedestrian’s point of view: WALK, DON’T WALK, allocentric: BRAKING, DRIVING, and ambiguous: GO, STOP) on participants’ (N = 103) crossing decisions, response times, and eye movements. Considering that crossing the road can be cognitively demanding, we added a memory task in two-thirds of the trials. The results showed that egocentric messages yielded higher subjective clarity ratings than the other messages as well as higher objective clarity scores (i.e., more uniform crossing decisions) and faster response times than the allocentric BRAKING and the ambiguous STOP. When participants were subjected to the memory task, pupil diameter increased, and crossing decisions were reached faster as compared to trials without memory task. Regarding the ambiguous messages, most participants crossed for the GO message and did not cross for the STOP message, which points towards an egocentric perspective taken by the participant. More lengthy text messages (e.g., DON’T WALK) yielded a higher number of saccades but did not cause slower response times. We conclude that pedestrians find egocentric eHMI messages clearer than allocentric ones, and take an egocentric perspective if the message is ambiguous. Our results may have important implications, as the consensus among eHMI researchers appears to be that egocentric text-based eHMIs should not be used in traffic.
Future automated vehicles may be equipped with external human-machine interfaces (eHMIs) capable of signaling to pedestrians whether or not they can cross the road. There is currently no consensus on the correct colors for eHMIs. Industry and academia have already proposed a variety of eHMI colors, including red and green, as well as colors that are said to be neutral, such as cyan. A confusion that can arise with red and green is whether the color refers to the pedestrian (egocentric perspective) or the automated vehicle (allocentric perspective). We conducted two crowdsourcing experiments (N = 2000 each) with images depicting an automated vehicle equipped with an eHMI in the form of a rectangular display on the front bumper. The eHMI had one out of 729 colors from the RGB spectrum. In Experiment 1, participants rated the intuitiveness of a random subset of 100 of these eHMIs for signaling 'please cross the road', and in Experiment 2 for 'please do NOT cross the road'. The results showed that for 'please cross', bright green colors were considered the most intuitive. For 'please do NOT cross', red colors were rated as the most intuitive, but with high standard deviations among participants. In addition, some participants rated green colors as intuitive for 'please do NOT cross'. Results were consistent for men and women and for colorblind and non-colorblind persons. It is concluded that eHMIs should be green if the eHMI is intended to signal 'please cross', but green and red should be avoided if the eHMI is intended to signal 'please do NOT cross'. Various neutral colors can be used for that purpose, including cyan, yellow, and purple.
Zusammenfassung
Bereits heute wird die Kommunikation zwischen automatisierten Fahrzeugen und anderen Straßenverkehrsteilnehmenden entwickelt und erforscht, um zukünftig einen sicheren und effizienten Straßenverkehrsfluss zu gewährleisten. Dieser Beitrag zielt darauf ab, Gestaltungsdimensionen der Kommunikation zwischen automatisierten Fahrzeugen und anderen Straßenverkehrsteilnehmenden herzuleiten und zu analysieren. Folgende Fragestellung steht im Vordergrund: Wie kann eine Kommunikationsstrategie zwischen automatisierten Fahrzeugen und anderen Straßenverkehrsteilnehmenden gestaltet werden? Zur Beantwortung dieser Fragestellung werden Gestaltungsdimensionen theoretisch abgeleitet und eine Befragung (N = 30) und Beobachtung (N = 308) von Fußgänger/-innen in Testfeldern von automatisierten Bussen durchgeführt. Aufbauend auf Erkenntnissen der Kommunikationstheorie werden sechs Gestaltungsdimensionen (Wer, Warum, In welcher Situation, Was, Wann, Wie) und dahinterstehende zentrale Fragestellungen identifiziert. Die Ergebnisse der empirischen Studien zeigen Verhaltensweisen und den Kommunikationsbedarf von Fußgänger/-innen bei der Interaktion mit automatisierten Bussen. Die Analyse anhand der Gestaltungsdimensionen legt weiteren Forschungsbedarf offen, wie z. B. nach den zukünftigen Rollen aller Beteiligten im Straßenverkehr.
Praktische Relevanz Die Anwendung des Frameworks von Gestaltungsdimensionen unterstützt bei der Gestaltung der Kommunikation von automatisierten Fahrzeugen, um zukünftig die notwendige Kooperation zwischen Straßenverkehrsteilnehmenden sicherzustellen.
In the future, automated cars may feature external human–machine interfaces (eHMIs) to communicate relevant information to other road users. However, it is currently unknown where on the car the eHMI should be placed. In this study, 61 participants each viewed 36 animations of cars with eHMIs on either the roof, windscreen, grill, above the wheels, or a projection on the road. The eHMI showed ‘Waiting’ combined with a walking symbol 1.2 s before the car started to slow down, or ‘Driving’ while the car continued driving. Participants had to press and hold the spacebar when they felt it safe to cross. Results showed that, averaged over the period when the car approached and slowed down, the roof, windscreen, and grill eHMIs yielded the best performance (i.e., the highest spacebar press time). The projection and wheels eHMIs scored relatively poorly, yet still better than no eHMI. The wheels eHMI received a relatively high percentage of spacebar presses when the car appeared from a corner, a situation in which the roof, windscreen, and grill eHMIs were out of view. Eye-tracking analyses showed that the projection yielded dispersed eye movements, as participants scanned back and forth between the projection and the car. It is concluded that eHMIs should be presented on multiple sides of the car. A projection on the road is visually effortful for pedestrians, as it causes them to divide their attention between the projection and the car itself.
Objective:
In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians' crossing intentions.
Background:
Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians' interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI.
Methods:
Twenty-eight participants experienced an urban road environment from a pedestrian's perspective using a head-mounted display. The behavior of approaching vehicles (yielding, nonyielding), vehicle size (small, medium, large), eHMI type (1. baseline without eHMI, 2. front brake lights, 3. Knightrider animation, 4. smiley, 5. text [WALK]), and eHMI timing (early, intermediate, late) were varied. For yielding vehicles, the eHMI changed from a nonyielding to a yielding state, and for nonyielding vehicles, the eHMI remained in its nonyielding state. Participants continuously indicated whether they felt safe to cross using a handheld button, and "feel-safe" percentages were calculated.
Results:
For yielding vehicles, the feel-safe percentages were higher for the front brake lights, Knightrider, smiley, and text, as compared with baseline. For nonyielding vehicles, the feel-safe percentages were equivalent regardless of the presence or type of eHMI, but larger vehicles yielded lower feel-safe percentages. The Text eHMI appeared to require no learning, contrary to the three other eHMIs.
Conclusion:
An eHMI increases the efficiency of pedestrian-AV interactions, and a textual display is regarded as the least ambiguous.
Application:
This research supports the development of automated vehicles that communicate with other road users.
Automated vehicles (AV) are expected to be integrated into mixed traffic environments in the near future. As human road users have established elaborated interaction strategies to coordinate their actions among each other, one challenge that human factors experts and vehicle designers are facing today is how to design AVs in a way that they can safely and intuitively interact with other traffic participants. This paper presents design considerations that are intended to support AV designers in reducing the complexity of the design space. The design considerations are based on a literature review of common human–human interaction strategies. Four categories of information are derived for the design considerations: (1) information about vehicle driving mode; (2) information about AVs’ manoeuvres; (3) information about AVs’ perceptions of the environment; and (4) information about AVs’ cooperation capabilities. In this paper, we apply the four categories to analyse existing research studies of traffic participants’ needs during interactions with AVs and results of the CityMobil2 project. From the CityMobil2 project we present central results from face-to-face interviews, an onsite-survey and two focus groups. To further support the AV designers we describe and rate different design options to present the information of the four categories, including the design of the infrastructure, the vehicle shape, the vehicle manoeuvres and the external human–machine interface of the AV.
In the near future, more vehicles will have automated functions. The traffic system will be a shared space of automated and manually driven vehicles. In our study we focused on the perspective of vulnerable road users, namely pedestrians, in cooperative situations with automated vehicles. Established communication methods, such as eye-contact between pedestrians and drivers, may no longer work when automated vehicles represent the interaction partner. Therefore, we evaluated several human-machine-interfaces (HMI) in order to implement smooth and comfortable communication. We conducted a two-stage study consisting of an explorative focus group discussion with naïve pedestrians (n = 6), followed by an experimental video simulation study (n = 25) based on the results of the focus group discussion. From the focus group we sought member opinion about various HMI, upon presentation of acoustic and visual communication systems such as projections, displays and LED light strips, in addition to portable communication systems, specifically smart watches. On the basis of the focus group discussion, an evaluation criteria was derived. For the video simulation study, HMI designs were created with variations in position, type and coding of the message, and technology. These were assessed by 25 subjects according to the focus discussion derived evaluation criteria: recognizability, unambiguousness, interaction comfort and intuitive comprehensibility. The results show that direct instructions to cross the street are preferred over status information of the vehicle and that large-scale text-based messages from the vehicle to the pedestrian, deliver better results. Design recommendations for HMIs for communication between automated vehicles are derived, and the extent external HMIs may supplement informal communication strategies such as vehicle movement or braking maneuvers, is discussed.
Fully automated self-driving cars, with expected benefits including improved road safety, are closer to becoming a reality. Thus, attention has turned to gauging public perceptions of these autonomous vehicles. To date, surveys have focused on the public as potential passengers of autonomous cars, overlooking other road users who would interact with them. Comparisons with perceptions of other existing vehicles are also lacking. This study surveyed almost 1000 participants on their perceptions, particularly with regards to safety and acceptance of autonomous vehicles. Overall, results revealed that autonomous cars were perceived as a “somewhat low risk“ form of transport and, while concerns existed, there was little opposition to the prospect of their use on public roads. However, compared to human-operated cars, autonomous cars were perceived differently depending on the road user perspective: more risky when a passenger yet less risky when a pedestrian. Autonomous cars were also perceived as more risky than existing autonomous trains. Gender, age and risk-taking had varied relationships with the perceived risk of different vehicle types and general attitudes towards autonomous cars. For instance, males and younger adults displayed greater acceptance. Whilst their adoption of this autonomous technology would seem societally beneficial – due to these groups’ greater propensity for taking road user risks, behaviours linked with poorer road safety – other results suggested it might be premature to draw conclusions on risk-taking and user acceptance. Future studies should therefore continue to investigate people’s perceptions from multiple perspectives, taking into account various road user viewpoints and individual characteristics.
Automated vehicles (AVs) will be introduced on public roads in the future, meaning that traditional vehicles and AVs will be sharing the urban space. There is currently little knowledge about the interaction between pedestrians and AVs from the point of view of the pedestrian in a real-life environment. Pedestrians may not know with which type of vehicle they are interacting, potentially leading to stress and altered crossing decisions. For example, pedestrians may show elevated stress and conservative crossing behavior when the AV driver does not make eye contact and performs a non-driving task instead. It is also possible that pedestrians assume that an AV would always yield (leading to short critical gaps). This study aimed to determine pedestrians’ crossing decisions when interacting with an AV as compared to when interacting with a traditional vehicle. We performed a study on a closed road section where participants (N = 24) encountered a Wizard of Oz AV and a traditional vehicle in a within-subject design. In the Wizard of Oz setup, a fake ‘driver’ sat on the driver seat while the vehicle was driven by the passenger by means of a joystick. Twenty scenarios were studied regarding vehicle conditions (traditional vehicle, ‘driver’ reading a newspaper, inattentive driver in a vehicle with ‘‘self-driving” sign on the roof, inattentive driver in a vehicle with ‘‘self-driving” signs on the hood and door, attentive driver), vehicle behavior (stopping vs. not stopping), and approach direction (left vs. right). Participants experienced each scenario once, in a randomized order. This allowed assessing the behavior of participants when interacting with AVs for the first time (no previous training or experience). Post-experiment interviews showed that about half of the participants thought that the vehicle was (sometimes) driven automatically. Measurements of the participants’ critical gap (i.e., the gap below which the participant will not attempt to begin crossing the street) and self-reported level of stress showed no statistically significant differences between the vehicle conditions. However, results from a post-experiment questionnaire indicated that most participants did perceive differences in vehicle appearance, and reported to have been influenced by these features. Future research could adopt more fine-grained behavioral measures, such as eye tracking, to determine how pedestrians react to AVs. Furthermore, we recommend examining the effectiveness of dynamic AV-to-pedestrian communication, such as artificial lights and gestures.
Agent transparency has been proposed as a solution to the problem of facilitating operators’ situation awareness in human-robot teams. Sixty participants performed a dual monitoring task, monitoring both an intelligent, autonomous robot teammate and performing threat detection in a virtual environment. The robot displayed four different interfaces, corresponding to information from the Situation awareness-based Agent Transparency (SAT) model. Participants’ situation awareness of the robot, confidence in their situation awareness, trust in the robot, workload, cognitive processing, and perceived usability of the robot displays were assessed. Results indicate that participants using interfaces corresponding to higher SAT level had greater situation awareness, cognitive processing, and trust in the robot than when they viewed lower level SAT interfaces. No differences in workload or perceived usability of the display were detected. Based on these findings, we observed that transparency has a significant effect on situation awareness, trust, and cognitive processing..
A preliminary theoretical model is developed for the mechanism of behavioural adaptations to changes in the road transport system, which describes the process of behavioural change, explains the nature of adaptation phenomenon, and predicts its effects on road safety programmes. It can be used as a basis to organize the body of adaptation research and to explain empirical results. Les auteurs développent un modèle préliminaire théorique pour décrire le mécanisme des adaptations comportementales aux modifications dans le système de transport routier; il décrit le processus du changement de comportement, explique la nature du phénomène d'adaptation et prévoit ses effets sur les programmes de sécurité routière. Il peut être utilisé comme une base pour l'organisation des recherches sur l'adaptation et pour expliquer des résultats empiriques. Ein erstes theoretisches Modeli des Mechanismus von Verhaltensanpassungen an Änderungen im Straßenverkehrssystem ist entwickelt worden. Es beschreibt den Prozeß der Verhaltensänderung, erklärt die Natur des Anpassungsphänomens und sagt dessen Auswirkungen auf Straßenverkehrssicherheitsprogramme voraus. Es kann als Ansatz benutzt werden, um eine Institution für Anpassungsforschung zu organisieren, aber auch um empirische Befunde zu erläutern. Se desarrolla un modelo teórico preliminar para el mecanismo de adaptación de comportamiento frente a cambios en el sistema de transporte caminero. Este describe los procesos de cambio en el comportamiento, explica la naturaleza de los fenómenos de adaptación, y predice sus efectos en programas de seguridad vial. El modelo puede ser usado como base para organizar el material de investigación sobre adaptación y para explicar resultados empíricos.
Neue Ansätze der Human Factors Forschung im Zeitalter des Hochautomatisierten Fahrens
- P Joisten
- A Müller
- J Walter
- B Abendroth
- R Bruder
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(eds.) Hands off, Human Factors off? Welche Rolle spielen Human Factors in der Fahrzeugautomation? 9. Darmstädter Kolloquium, pp. 69--88. Darmstadt (2019)