External Human Machine Interface Research Articles

EHMI on the Vehicle or on the Infrastructure? A Driving Simulator Study

Automated vehicles (AVs) may require the implementation of an external human-machine interface (eHMI) to communicate their intentions to human-driven vehicles. The optimal placement of the eHMI, either on the AV itself or as part of the road infrastructure, remains undetermined. The current driving simulator study investigated the effect of eHMI positioning on human driving behavior, during the approach and execution of right turns at T-intersections. Forty-three participants drove under three conditions: absence of eHMI, eHMI on the AV (eHMIv), and eHMI integrated into the infrastructure (eHMIi). Participants encountered AVs that either yielded or did not yield to their vehicles. The results regarding the placement of the eHMI showed that both concepts are advantageous, but for different reasons. eHMIv was appreciated because implicit and explicit communication are congruent, although the AV must first be visually identified to respond to it. eHMIi was appreciated because a familiar cue is always at a known location in the environment; as a result, participants braked earlier for the intersection and came less close to the AV (which can be interpreted as a safety advantage or efficiency disadvantage). Although there are limitations to a driving simulator study like this, this research provides important insights into the fundamental question of how information placement affects drivers’ visual attention demands and driving behavior, topics that are important in view of the development of future cities.

Not Always Good: Mitigating Pedestrians’ Less Careful Crossing Behavior by External Human-Machine Interfaces on Automated Vehicles

An external human-machine interface (eHMI) is a novel tool that facilitates efficient interactions between pedestrians and automated vehicles (AVs). However, as pedestrians become accustomed to communicating with AV through eHMI, there is a risk of exhibiting less cautious behavior, which can increase the likelihood of accidents. This study aimed to examine the effects of methods to mitigate the side effects of communication with AV through an eHMI. A total of 132 participants participated in a Virtual Reality experiment to investigate the existence of negative effects and to explore mitigation methods. We designed five experimental conditions to manipulate the tone of text messages (Allocentric: “After you,” Egocentric: “I’ll stop”) and test the effect of mitigation methods, such as hiding the text messages on the eHMI when the AV is stopped (No eHMI, eHMI-A, eHMI-E, HeHMI-A, HeHMI-E). The results revealed that pedestrians were more likely to exhibit less careful behavior in the eHMI condition than in the No eHMI condition. The disappearance of text messages on the eHMI prompted pedestrian attention allocation toward traffic situations, leading to a reduction in accident risk. In particular, the mitigation method was most effective for safe crossing when pedestrians were continuously exposed to the eHMI, presenting an egocentric message. Our findings contribute to the design of text-based eHMIs for pedestrian decision-making when crossing and enhancing traffic safety.

Interpreting pedestrians' head movements when encountering automated vehicles at a virtual crossroad

In the future, Automated Vehicles (AVs) may be able to use pedestrians’ head movement patterns to understand their crossing intentions. This ability of the AV to predict pedestrian crossing intention will improve road safety in mixed traffic situations and may also enhance traffic flow, allowing the vehicle to gradually reduce its speed in advance of a yield, eliminating the need for a complete and erratic halt. To date, most of the work conducted on studying pedestrian head movements has been based on observation studies. To further our understanding in this area, this study examined pedestrians' head movements when interacting with AVs during a range of road crossing scenarios, developed in a VR environment. Thirty-eight participants took part in this CAVE-based pedestrian simulator study. Head movements were recorded using stereoscopic motion-tracking glasses, as pedestrians crossed the road in response to an AV which approached from the right (UK-based road). A zebra crossing was included in half of the trials to understand how it affected crossing behaviour. The effect of different approaching speeds of the AV, and the presence of an external Human-Machine Interface (eHMI), on head movements and crossing behaviour was also studied. Results showed that the absolute head-turning rate (change in pedestrians' head-turning angle, per frame) increased significantly at around 1 s before a crossing initiation, reaching a peak at the crossing initiation, where pedestrians presented a “last-second check” before the crossing decision. Another increase in absolute head-turning rate to the right was seen at the end of the crossing (∼1.5 s after crossing initiation), to check the proximity of the approaching vehicle. A higher rate of head-turning was also seen for AV-non-yielding scenarios. Finally, the least number of head turns was seen for the yielding conditions which included an eHMI, in the presence of the zebra crossing. These results show the value of infrastructural and vehicle-based cues in assisting pedestrians’ crossing decisions and provide an insight into how head-turning behaviour can be used by AVs to better predict pedestrians’ crossing intentions in urban settings.

Pedestrians’ responses to scalable automated vehicles with different external human-machine interfaces: Evidence from a video-based eye-tracking experiment

To enhance the efficiency and safety of interactions with pedestrians, numerous external Human-Machine Interfaces (eHMIs) concepts for automated vehicles (AVs) have been proposed and evaluated, predominately based on singular pedestrian-AV interaction scenarios. This leaves a gap in comprehending the efficiency and robustness of eHMIs during interactions with scalable AVs. To bridge the gap, this study pioneers an exploration of pedestrians' road-crossing decisions, perceived clarity, and gaze behaviour during synchronous interactions with multiple AVs equipped with different eHMIs. An eye-tracking experiment was conducted, involving 48 video stimuli depicting two AVs (with yielding patterns and eHMIs manipulated) approaching a predetermined road-crossing location from the same side of a two-way, four-lane, un-signalized road. Each yielding AV will present one of four eHMI manipulations (i.e., light band, smiling expression, pedestrian symbol, and no eHMI as baseline), resulting in 16 unique eHMI combinations and video stimuli. Eighty-seven participants were recruited and tasked to respond to approaching AVs, during which their road-crossing decision time, perceived clarity, and gaze metrics were recorded. Findings revealed that different eHMIs across AVs were associated with prolonged road-crossing decision times, diminished perceived clarity, and intensified visual attention as evidenced by increased fixation counts and durations. Regarding AVs equipped with the same eHMIs, the pedestrian symbol-based eHMIs resulted in heightened clarity perception, reduced crossing decision times and visual engagement, in contrast to eHMIs presented in abstract light bands or smiling expressions. Furthermore, for eHMI-different AVs, those featuring only one eHMI within AVL-AVR pairs were perceived as less clear, eliciting heightened visual cognitive load and decision times. Integration of a pedestrian symbol-based eHMI in AVL-AVR configurations garnered superior clarity ratings and facilitated time savings. These findings of this pioneering study provide insights for eHMI design for future AVs in complex traffic scenarios with scalable road users.

Analysis of the Interaction between Humans and Autonomous Vehicles Equipped with External Human–Machine Interfaces: The Effect of an Experimental Reward Mechanism on Pedestrian Crossing Behavior in a Virtual Environment

The advent of autonomous vehicles (AVs) has sparked many concerns about pedestrian safety, prompting manufacturers and researchers to integrate external Human–Machine Interfaces (eHMIs) into AVs as communication tools between vehicles and pedestrians. The evolving dynamics of vehicle–pedestrian interactions make eHMIs a compelling strategy for enhancing safety. This study aimed to examine the contribution of eHMIs to safety while exploring the impact of an incentive system on pedestrian risk behavior. Participants interacted with AVs equipped with eHMIs in an immersive environment featuring two distinct scenarios, each highlighting a sense of urgency to reach their destination. In the first scenario, participants behaved naturally without specific instructions, while in the second scenario, they were informed of an incentive aimed at motivating them to cross the road promptly. This innovative experimental approach explored whether motivated participants could maintain focus and accurately perceive genuine risk within virtual environments. The introduction of a reward system significantly increased road-crossings, particularly when the vehicle was approaching at higher speeds, indicating that incentives encouraged participants to take more risks while crossing. Additionally, eHMIs notably impacted pedestrian risk behavior, with participants more likely to cross when the vehicle signaled it would not stop.

커뮤니케이션 의도와 주행 시나리오에 따른 자율주행차량 eHMI 디자인 컨셉 제안

커뮤니케이션 의도와 주행 시나리오에 따른 자율주행차량 eHMI 디자인 컨셉 제안

Is Silent External Human–Machine Interface (eHMI) Enough? A Passenger-Centric Study on Effective eHMI for Autonomous Personal Mobility Vehicles in the Field

Autonomous personal mobility vehicle (APMV) is a miniaturized autonomous vehicle designed for short-distance mobility to everyone. Due to its open design, APMV’s passengers are exposed to communications between the external human-machine interface (eHMI) on APMV and pedestrians. Therefore, effective eHMI designs for APMV need to consider potential impacts of APMV-pedestrian interactions on passengers’ subjective feelings. This study from the perspective of APMV passengers discussed three eHMI designs: (1) graphical user interface (GUI)-based eHMI with text message (eHMI-T), (2) multimodal user interface (MUI)-based eHMI with neutral voice (eHMI-NV), and (3) MUI-based eHMI with affective voice (eHMI-AV). In a riding field experiment (N = 24), eHMI-T made passengers feel awkward during the “silent time” when eHMI-T conveyed information exclusively to pedestrians, not passengers. MUI-based eHMIs with voice cues showed advantages, with eHMI-NV excelling in pragmatic quality and eHMI-AV in hedonic quality. Considering passengers’ personalities and genders in APMV eHMI design is also highlighted.

Effects of a frontal brake light on pedestrians’ willingness to cross the street

Effects of a frontal brake light (FBL, a potential external human–machine interface for automated vehicles) on participants’ self-reported willingness to cross a vehicle’s path were investigated. In a mixed design online study (vehicles in the experimental group were equipped with FBLs, there were no FBLs in the control group), participants observed videos of a vehicle approaching at different speeds from the perspective of a pedestrian standing at the curb. The vehicles exhibited either yielding behavior (braking onset 55 m or 32 m before standstill in front of the pedestrian’s position) or non-yielding behavior (approach speed was maintained). Participants specified their willingness to cross the vehicle’s path at different distances. When the vehicle yielded (i.e., FBL was activated), willingness to cross was significantly higher in the experimental group than the control group. Notably, we further observed a significantly lower willingness to cross in the experimental group than the control group when the vehicle did not yield (i.e., FBL was deactivated). Novel external human–machine interfaces might therefore influence the interaction with vehicles not only when they are activated but also when they are deactivated.

Characteristics of Rear-End Collisions: A Comparison between Automated Driving System-Involved Crashes and Advanced Driving Assistance System-Involved Crashes

With the increasing number of vehicles equipped with automated driving systems (ADS) being tested on public roads and the expanding market share of vehicles equipped with advanced driving assistance systems (ADAS), the number of ADS- or ADAS-involved crashes increases. Thus, it is necessary to investigate the distribution of ADS- and ADAS-involved crashes and the factors leading to them. The rear-end collision has been found to dominate ADS-involved crashes. However, no research has explored the conditions when ADS-involved rear-end collisions are more likely to happen and no research has investigated ADAS-involved rear-end crashes. Based on 130 ADS-involved crashes and 84 ADAS-involved crashes extracted from a dataset collected by the National Highway Traffic Safety Administration (NHTSA) between July 2021 and May 2022, this study explored the crash patterns, especially rear-end crashes, of ADS- and ADAS-controlled vehicles. Results show that rear-end collisions dominate both ADS- and ADAS-involved crashes, especially ADAS-involved crashes. The type of both ADS-involved and ADAS-involved crashes was affected by the speed of the ego-vehicle relative to the posted speed limit. Further, the type of ADS-involved crash was affected by the pre-crash movement of the crash partner, while the type of ADAS-involved crash was further associated with the road type. The findings can provide insights into the design of ADAS and ADS control algorithms, the external human-machine interface design of the vehicles with ADS or ADAS, and the training program of human road users to improve traffic safety in mixed traffic.

Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization

In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors.

Pedestrian interactions with automated vehicles: Does the presence of a zebra crossing affect how eHMIs and movement patterns are interpreted?

Previous research has shown that the use of an eHMI can lead pedestrians to make earlier, and more, crossing decisions in front of an automated vehicle (AV). However, there has been little exploration of the impact of crossing infrastructure or AV approach direction on pedestrian behaviour. This CAVE-based pedestrian simulator study investigated the individual, and combined, effects of a pedestrian crossing, AV approach direction, AV yielding behaviour, and a novel external Human Machine Interface (eHMI) on pedestrian crossing decisions at a four-way crossroads. Thirty eight participants took part in a multi-method study consisting of a pedestrian simulator experiment, an online interview, and a short questionnaire. The main independent variables were: (1) presence or absence of a zebra crossing; (2) the direction from which the AV approached (oncoming/right); (3) the AV’s yielding behaviour (yielding/not yielding); and (4) the presence or absence of a light-based eHMI. The AV’s yielding behaviour was the most important source of information for pedestrians, followed by the crossing infrastructure. Participants showed a greater willingness to cross in front of yielding than non-yielding vehicles, and were more likely to cross in the presence of a zebra crossing. The eHMI had the most impact in the absence of a zebra crossing, promoting earlier crossings, and encouraging more participants to cross while the approaching AV was still moving. The results of this study show the importance of eHMIs for situations associated with uncertainty about right-of-way between an AV and other road users, and highlights the interaction between formal traffic infrastructure and explicit forms of communication for future AVs. This knowledge increases our knowledge of when and where explicit communication from AVs can reduce the likelihood of pedestrian misunderstanding of AV intentions, thus reducing the likelihood of accidents occurring around these vehicles.

Please, Go Ahead! Fostering Prosocial Driving with Sympathy-Eliciting Automated Vehicle External Displays

Road traffic is strongly regulated, however informal communication is essential whenever formal rules are flexibly treated. Consequently, conflict-avoidant automated vehicles (AVs) can be disadvantaged when humans do not behave prosocially towards them. This can lead to disruptions of mixed traffic, where human and automated driving co-exists. Equipping AVs with sympathy-eliciting external Human-Machine Interfaces (eHMI) mimicking informal communication cues could mitigate this challenge by fostering the prosocial behavior of drivers. This work contributes video vignettes that are experimentally validated in an online survey (N=90). While we found participants to not behave differently towards human-controlled and baseline automated vehicles, eHMIs were potent in eliciting sympathy and encouraged yielding behavior. This effect was more pronounced when the interface signaled an urgent situation or indicated prolonged waiting times. Non-yielding behavior was rationalized based on priority rules. These results emphasize how fostering prosocial behavior in traffic can be achieved via sympathy-eliciting external displays.

Perceptions and attitudes of bicyclists towards self-driving cars: a mixed methods approach

Efforts to advance Autonomous Vehicles (AVs) have taken on a central role in research and development in recent years and will have a significant influence on road traffic in the future. Research on AVs has mainly focused on the technology itself and the direct users of AVs and their acceptance. However, the role of bicyclists, interacting with AVs in traffic, is not yet researched as thoroughly. Using a mixed methods approach, we combine quantitative results from a survey among bicyclists (N = 889) and qualitative results from a focus group (N = 19) to give insights into bicyclists’ attitudes and expectations towards self-driving cars. The results showed that bicyclists’ affinity for technology is a significant predictor for both their trust and perceived safety towards self-driving cars, as well as an effect of age and gender on these variables. Both from the quantitative and qualitative results, it is clear that flawless functioning of the technology of AVs is a prerequisite for bicyclists encountering and interacting with AVs in traffic, and that the status of the vehicle (autonomous vs. non-autonomous) is very important as well as easy to understand signals that indicate the next manoeuvres of the AV. For supporting interaction with AVs, we found that bicyclists are open to External Human Machine Interface (eHMI) solutions, as long as these ensure inclusion and support the easily-accessible nature of bicycling. Our findings can inform the design of eHMIs that help shape the interaction between bicyclists and AVs in the future, and provide insights on which factors determine the perception of AVs and, ultimately, the acceptance of AVs as part of road traffic.

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.

Promoting trust in HAVs of following manual drivers through implicit and explicit communication during minimal risk maneuvers

The successful integration of highly automated vehicles (HAV) in future mixed traffic environments will depend, among other things, on their seamless, safe, and accepted interaction with other road users. Therefore, appropriate combination of light signals, as external human-machine interface (eHMI), and driving behavior, as dynamic human-machine interface (dHMI), is required consistently in order to develop trust of following manual drivers in HAVs. Especially, in borderline traffic scenarios where HAVs are confronted with challenges, such as loss of connectivity, so-called minimal risk maneuvers (MRMs) are performed abruptly. Here, understanding communication via eHMI and dHMI is crucial for road safety, as drivers need to prepare for maneuvers themselves. Therefore, two consecutive, explorative online video studies were conducted. Firstly, the appropriate braking dynamics for an MRM were evaluated. Secondly, insights into the eHMI communication strategy of an HAV during an MRM were gained. The overall aim of this work is to present strategies for implicit and explicit communication channels of an HAV in order to promote learned trust during MRMs from the perspective of drivers who follow them. The results show that adding novel eHMI designs (e.g., warning sign, 360° LED light-band) to conventional light signals positively affects the user experience in a first contact interaction. The findings could have a positive impact on the development of trust in HAVs. In conclusion, specific eHMI communication strategies can be highly supportive for following manual drivers in MRM scenarios, which may lead to legislative considerations in the future.

Should an external human-machine interface flash or just show text? A study with a gaze-contingent setup

Automated vehicles need to prioritize pedestrian safety. One way to achieve this is through external human–machine interfaces (eHMIs) that send visual signals to pedestrians. eHMIs can be either text-based or light-based. However, there has been limited research on the effects of these types of eHMI on human information processing and attention allocation. This study aimed to fill this gap by using a gaze-contingent approach, which blurs the view outside a circular aperture, to test the hypothesis that text-based eHMIs, which require focused or foveal attention, result in longer response times compared to light-based eHMIs, which can be understood using peripheral vision. In this study, 23 participants watched animated video clips of traffic situations involving automated vehicles with either no eHMI, a flashing-light eHMI, or a text-based eHMI. Their eye movements were tracked, and they were asked to press the spacebar when they felt it was safe to cross the road. The results showed faster response times when an eHMI was present, with no significant difference between the two types of eHMIs. Further analysis suggested that the flashing-light eHMI captured attention briefly, while the text-based eHMI held attention for a longer period. When no eHMI was present, participants focused on the approaching vehicle for the longest time. The gaze-contingent window resulted in fewer eye movements and slower response times. In conclusion, the study showed that the gaze-contingent window negatively affected response times and eye movements, emphasizing the importance of considering peripheral vision when designing eHMIs for pedestrian safety.

Using eHMI, acoustic signal, and pitch motion to communicate the intention of automated vehicles to pedestrians: A Wizard of Oz study

Communication between automated vehicles (AVs) and pedestrians is crucial to coordinate traffic maneuvers and hence maintain road safety. Automated driving changes the driver’s role to that of a mere passenger, who can perform non-driving related tasks and therefore may not interact with other road users. In this novel traffic environment, both explicit and implicit cues might contribute to an effective communication between AVs and pedestrians by providing relevant information.So far, physical prototypes of AV communication have rarely been evaluated under real world conditions. The few studies managed to do so used a Wizard of Oz technique, where a human operator simulates the behavior of an AV. To measure the communication effectiveness, these studies instructed participants to explicitly state their crossing decision, but not to cross. While safety is assured, the generalizability of the results to pedestrians’ actual crossing behavior is unclear. We established a more realistic experimental setup for a Wizard of Oz study that allows pedestrians to actually start crossing while a of simulated AV is approaching.In a Wizard of Oz study, participants (N = 35) interacted with a simulated AV and had to infer its intention. In a fully factorial design, the effects of three different communication cues were explored: an external human-machine interface (eHMI, absent vs. present), an acoustic signal (absent vs. present), and pitch motion (active, normal, or none). The eHMI and acoustic signal were considered as explicit and the pitch motion as implicit cues. Participants were asked to exhibit natural crossing behavior which was captured by two optical tracking systems.Consistently to previous work, explicit AV communication showed a positive effect on pedestrians’ crossing behavior and safety feeling. Pedestrians initiated their crossings earlier and reported to feel safer when approaching an AV with an eHMI or an acoustic signal, compared to only having only a pitch motion. There was no main effect of pitch motion on crossing behavior and safety feeling. Nevertheless, there was an interaction between eHMI and active pitch motion. The effect of eHMI on crossing initiation time (CIT) was smaller in combination with an active pitch motion compared to normal pitch conditions. These results underline the importance of implementing appropriate AV communication.

Pedestrians’ road-crossing behavior towards eHMI-equipped autonomous vehicles driving in segregated and mixed traffic conditions

Pedestrians’ road-crossing behavior can be influenced by eHMIs (external Human-Machine Interfaces) on autonomous vehicles (AVs). In this research, we developed a novel eHMI concept that aimed to support pedestrians’ risk evaluation by displaying predicted real-time risk levels. In a virtual reality environment, we measured pedestrians’ road-crossing behavior when they encountered AVs with this eHMI and manual-driven vehicles (MVs) in the same lane. Results showed that pedestrians exhibited typical crossing behaviors based on gap size for both vehicle types. In segregated traffic conditions, compared to MVs, eHMI-equipped AVs made pedestrians more sensitive to the changes in gap size by rejecting more small gaps and accepting more large gaps. Pedestrians also walked faster and kept larger safety margins for smaller gaps. Similar results were observed for AVs in mixed traffic conditions. However, in mixed traffic conditions, pedestrians faced more challenges when interacting with MVs as they tended to accept smaller gaps, walk more slowly, and maintain smaller safety margins. These findings indicate that dynamic risk information could be conducive to pedestrians’ road-crossing behavior, but the use of eHMIs on AVs might disrupt pedestrians’ interactions with MVs in complex traffic conditions. This potential risk shift among vehicles also poses the question of whether AVs should use segregated lanes to reduce their indirect impacts on pedestrian-MV interactions.

How to improve pedestrians' trust in automated vehicles: new road infrastructure, external human-machine interface with anthropomorphism, or conventional road signaling?

Automated vehicles need to gain the trust of all road users in order to be accepted. To make technology trustworthy, automated vehicles must transmit crucial information to pedestrians through a human-machine interface, allowing pedestrians to accurately predict and act on their next behavior. However, the unsolved core issue in the field of vehicle automation is to know how to successfully communicate with pedestrians in a way that is efficient, comfortable, and easy to understand. This study investigated the impact of three human-machine interfaces specifically designed for pedestrians' trust during the street crossing in front of an automated vehicle. The interfaces used different communication channels to interact with pedestrians, i.e., through a new road infrastructure, an external human-machine interface with anthropomorphism, or with conventional road signaling. Mentally projected in standard and non-standard use cases of human-machine interfaces, 731 participants reported their feelings and behavior through an online survey. Results showed that human-machine interfaces were efficient to improve trust and willingness to cross the street in front of automated vehicles. Among external human-machine interfaces, anthropomorphic features showed significant advantages in comparison with conventional road signals to induce pedestrians' trust and safer crossing behaviors. More than the external human-machine interfaces, findings highlighted the efficiency of the trust-based road infrastructure on the global street crossing experience of pedestrians with automated vehicles. All of these findings support trust-centered design to anticipate and build safe and satisfying human-machine interactions.

External Human-Machine Interfaces for Automated Vehicles in Shared Spaces: A Review of the Human-Computer Interaction Literature.

Given the rise of automated vehicles from an engineering and technical perspective, there has been increased research interest concerning the Human and Computer Interactions (HCI) between vulnerable road users (VRUs, such as cyclists and pedestrians) and automated vehicles. As with all HCI challenges, clear communication and a common understanding-in this application of shared road usage-is critical in order to reduce conflicts and crashes between the VRUs and automated vehicles. In an effort to solve this communication challenge, various external human-machine interface (eHMI) solutions have been developed and tested across the world. This paper presents a timely critical review of the literature on the communication between automated vehicles and VRUs in shared spaces. Recent developments will be explored and studies analyzing their effectiveness will be presented, including the innovative use of Virtual Reality (VR) for user assessments. This paper provides insight into several gaps in the eHMI literature and directions for future research, including the need to further research eHMI effects on cyclists, investigate the negative effects of eHMIs, and address the technical challenges of eHMI implementation. Furthermore, it has been underlined that there is a lack of research into the use of eHMIs in shared spaces, where the communication and interaction needs differ from conventional roads.