External Human Machine Interface Research Articles

Discovering the Power of External Human-Machine Interface: An EEG Study on the Driving Anger Regulation

Driving with anger can result in hazardous driving behaviors, often arising from inadequate communication among Traffic Participants (TPs). Currently, external Human-Machine Interface (eHMI) technology provides a novel channel for enhancing communication among TPs, yet its role in regulating driving emotions remains uncertain. This study investigates the impact of eHMI technology on the regulation of driving anger. We simulated a driving scenario in which the preceding vehicle abruptly applied brakes to elicit anger from the following driver. Three eHMI formats (Symbol, Text, and Symbol + Text) positioned at the rear of the preceding vehicle were implemented to explain the sudden braking event and regulate drivers’ emotions. Objective electroencephalogram (EEG) signals and subjective assessment data were analyzed to assess the effects of eHMIs on driving anger regulation. The results indicate that driving with eHMIs exhibited significant regulatory quality of drivers’ anger. Among the three formats, Symbol + Text eHMI exerted a notable regulatory impact, while both Symbol and Text eHMI contributed to emotional regulation. This study illustrates the positive impact of eHMI on emotion regulation in traffic environments, providing valuable backing for traffic management authorities in developing guidelines and policies, and contributing to the standardization of future eHMI technologies.

The Role of eHMI in Reducing Driver Overtaking Decisions Around Low-speed Automated Vehicles Yielding to Pedestrians

As low speed automated vehicles (LSAVs) are further introduced into our driving fleet, driver-LSAV interactions may put pedestrians and other vulnerable road users at risk, as drivers could be confused by LSAVs and respond inappropriately when a pedestrian is present. A total of 242 participants with a driver’s license participated in an online survey to assess the ambiguity of the external human-machine interface (eHMI) or signaling system used by a recently deployed LSAV. Participants were shown signaling conditions presented as animated GIFs and were told to imagine they were following the LSAV as it stopped and turned on the signal. Participants were more likely to indicate that they would overtake the MCM when presented with the flashing hazards eHMI while the MCM was turning right compared to the alternative signal conditions. Correct shuttle behavior interpretation was found to be an important influence in participants’ choice in overtaking or waiting behaviors.

Hello, is it me you're Stopping for? The Effect of external Human Machine Interface Familiarity on Pedestrians' Crossing Behaviour in an Ambiguous Situation.

We investigated how different deceleration intentions (i.e. an automated vehicle either decelerated for leading traffic or yielded for pedestrians) and a novel (Slow Pulsing Light Band - SPLB) or familiar (Flashing Headlights - FH) external Human Machine Interface (eHMI) informed pedestrians' crossing behaviour. The introduction of SAE Level 4 Automated Vehicles (AVs) has recently fuelled interest in new forms of explicit communication via eHMIs, to improve the interaction between AVs and surrounding road users. Before implementing these eHMIs, it is necessary to understand how pedestrians use them to inform their crossing decisions. Thirty participants took part in the study using a Head-Mounted Display. The independent variables were deceleration intentions and eHMI design. The percentage of crossings, collision frequency and crossing initiation time across trials were measured. Pedestrians were able to identify the intentions of a decelerating vehicle, using implicit cues, with more crossings made when the approaching vehicles were yielding to them. They were also more likely to cross when a familiar eHMI was presented, compared to a novel one or no eHMI, regardless of the vehicle's intention. Finally, participants learned to take a more cautious approach as trials progressed, and not to base their decisions solely on the eHMI. A familiar eHMI led to early crossings regardless of the vehicle's intention but also led to a higher collision frequency than a novel eHMI. To achieve safe and acceptable interactions with AVs, it is important to provide eHMIs that are congruent with road users' expectations.

Evaluating Autonomous Vehicle External Communication Using a Multi-Pedestrian VR Simulator

With the rise of autonomous vehicles (AVs) in transportation, a pressing concern is their seamless integration into daily life. In multi-pedestrian settings, two challenges emerge: ensuring unambiguous communication to individual pedestrians via external Human-Machine Interfaces (eHMIs), and the influence of one pedestrian over another. We conducted an experiment (N=25) using a multi-pedestrian virtual reality simulator. Participants were paired and exposed to three distinct eHMI concepts: on the vehicle, within the surrounding infrastructure, and on the pedestrian themselves, against a baseline without any eHMI. Results indicate that all eHMI concepts improved clarity of communication over the baseline, but differences in their effectiveness were observed. While pedestrian and infrastructure communications often provided more direct clarity, vehicle-based cues at times introduced uncertainty elements. Furthermore, the study identified the role of co-located pedestrians: in the absence of clear AV communication, individuals frequently sought cues from their peers.

Behavioural Gap Assessment of Human-Vehicle Interaction in Real and Virtual Reality-Based Scenarios in Autonomous Driving

In the field of autonomous driving research, the use of immersive virtual reality (VR) techniques is widespread to enable a variety of studies under safe and controlled conditions. However, this methodology is only valid and consistent if the conduct of participants in the simulated setting mirrors their actions in an actual environment. In this paper, we present a first and innovative approach to evaluating what we term the behavioural gap, a concept that captures the disparity in a participant’s conduct when engaging in a VR experiment compared to an equivalent real-world situation. To this end, we developed a digital twin of a pre-existed crosswalk and carried out a field experiment (N = 18) to investigate pedestrian-autonomous vehicle interaction in both real and simulated driving conditions. In the experiment, the pedestrian attempts to cross the road in the presence of different driving styles and an external Human-Machine Interface (eHMI). By combining survey-based and behavioural analysis methodologies, we develop a quantitative approach to empirically assess the behavioural gap, as a mechanism to validate data obtained from real subjects interacting in a simulated VR-based environment. Results show that participants are more cautious and curious in VR, affecting their speed and decisions, and that VR interfaces significantly influence their actions.

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.