# You Walkin' to Me? How Footstep Sound Primes Anticipation in Virtual Pedestrian Collision Avoidance

Aline Hufschmitt, Agathe Bilhaut, Ludovic Hoyet, Julien Pettré, Anne-Hélène Olivier

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## To cite this version:

Aline Hufschmitt, Agathe Bilhaut, Ludovic Hoyet, Julien Pettré, Anne-Hélène Olivier. You Walkin' to Me? How Footstep Sound Primes Anticipation in Virtual Pedestrian Collision Avoidance. IEEE VR 2026 - IEEE International Conference on Virtual Reality and 3D User Interfaces, Mar 2026, Daegu, South Korea. hal-05514579

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# You Walkin' to Me? How Footstep Sound Primes Anticipation in Virtual Pedestrian Collision Avoidance

Aline Hufschmitt*

Saint-Cyr Coetquidan Military Academy, CReC Saint-Cyr
& Univ Rennes, Inria, CNRS,
IRISA, Rennes, France

Julien Pettré§

Univ Rennes, Inria, CNRS,
IRISA, Rennes, France

Agathe Bilhaut†

LINP2, Univ Paris Nanterre, Paris, France.
& Univ Rennes, Inria, CNRS,
IRISA, Rennes, France

Anne Hélène Olivier¶

Univ Rennes, Inria, CNRS,
IRISA, Rennes, France

Ludovic Hoyet‡

Univ Rennes, Inria, CNRS,
IRISA, Rennes, France

<figure><img src="image_3.png" /><figcaption>Figure 1: This study investigates the role of adding footstep sounds in virtual reality platforms designed to study pedestrian behavior and interactions in VR. We designed a within-subject user study with a collision avoidance task, in which participants encountered a virtual agent approaching from either their left or right on a crossing trajectory. (a) Top view of the virtual environment with dimensions. The participant walked back and forth along the 18.5 m diagonal between the two blue circles, while the virtual character moved along the red dashed line. We compared behavioural data of this interaction including participant's locomotion and gaze in Footstep sound (b) and No Footstep sound conditions (c).</figcaption></figure>

## ABSTRACT

Studying pedestrian navigation in crowded environments is key to many applications, like crowd simulation for urban planning, but is challenging under real-world conditions due to the lack of experimental control. This motivates the use of virtual reality to manipulate variables and recreate complex social interactions. While research has mostly emphasized visual feedback, and eventually haptic rendering of collision, the integration of auditory cues in VR platforms, such as footstep sounds, remains overlooked. The present study investigates the role of adding footstep sounds in a VR collision-avoidance task. Twenty participants completed a within-subject experiment where a virtual pedestrian, initially vi-

sually occluded, crossed their path either with or without audible footsteps. Locomotor adjustments and gaze behavior were analyzed before and during the visual interaction. Results showed that footstep sounds induced earlier gaze orientation and more proactive trajectory adjustments from the participants, without affecting their walking speed. In contrast, the absence of sound led to more collisions and crossing order inversions, indicating reduced efficiency. These findings demonstrate that simple auditory cues can guide attention, facilitate anticipation, and improve interactions with virtual agents. Integrating sound into VR platforms is therefore essential to study pedestrian behavior and social navigation.

**Index Terms:** Pedestrian, Virtual Reality, Non-verbal Interactions, Footstep Sound.

## 1 INTRODUCTION

Navigating public spaces is an essential daily task, fundamental to even the most basic activities, such as picking up bread from the bakery, visiting a doctor, or taking a walk. Locomotion is characterized not only by the repetitive motion of steps to go from point A to point B but also by its adaptive properties, encompassing actions such as steering and collision avoidance, especially with other

<footnote>*e-mail: aline.hufschmitt@irisa.fr</footnote>

<footnote>†e-mail: abilhaut@parisnanterre.fr</footnote>

<footnote>‡e-mail: ludovic.hoyet@inria.fr</footnote>

<footnote>§e-mail: julien.pettre@inria.fr</footnote>

<footnote>¶e-mail: anne-helene.olivier@univ-rennes2.fr</footnote>
pedestrians. A better understanding of human navigation in popu-
lated space can address numerous societal challenges: in health, by
improving the assessment of patients' functional abilities in eco-
logical scenarios; in urban design, by guiding the layout of public
spaces to ensure the harmonious coexistence of diverse users; in
computer science, by contributing to the creation of realistic Virtual
Agents for simulating crowds and analyzing pedestrian behavior in
Virtual Reality (VR).

To achieve this goal, it is essential to investigate human behav-
ior in conditions that closely reproduce natural interactions, such as
those involving encounters and potential collisions between pedes-
trians. While such studies can be conducted in real-world environ-
ments [10, 18, 31, 38, 45], controlling all the relevant experimental
factors in the field remains extremely challenging. This difficulty
has led researchers to increasingly turn to VR as a powerful tool for
studying human navigation. VR offers the possibility to precisely
manipulate environmental variables while ensuring ecological fi-
delity, to reproduce complex social interactions under controlled
conditions [7, 36, 47], and to systematically test hypotheses that
would otherwise be impossible or impractical to evaluate in real
settings such as for example decoupling the age-related appearance
and movement [11].

Pedestrian interactions rely on a continuous perception-action
coupling within dynamic environments, where multiple sensory
systems contribute to adaptive behaviors. Vision plays a central
role by providing information about relative positions and motion
between oneself and others. Proprioception supports the detection
of self-motion, while touch can become relevant in the event of
physical contact in dense crowd situations for example. Audit ion
also contributes, particularly in situations where visual information
is limited or unavailable. For example, when stepping out of an of-
fice into a corridor, the sound of approaching footsteps may allow
one to anticipate the presence of another pedestrian and the poten-
tial risk of collision. While most VR platforms designed to study
pedestrian interactions provide by nature partial-information, inte-
grating primarily visual information [9, 39], and occasionally hap-
tic feedback [3, 32, 64], auditory cues have often been overlooked.
Yet, as highlighted by Serafin et al. [52], auditory perception offers
unique advantages over vision: unlike the directional and limited
field of view of visual perception, hearing is inherently omnidirec-
tional and temporally dynamic. This makes auditory displays not
only a cost-effective addition to VR systems but also a valuable
means of enhancing multimodal representations for more ecologi-
cal VR-based experimental tools.

Building on this perspective, an important question is to deter-
mine which functionalities VR platforms should provide to accu-
rately study human behavior in populated environments. While
most systems focus primarily on visual information, it remains un-
clear whether auditory cues, such as footstep sounds, should also
be integrated to better capture the dynamics of pedestrian interac-
tions. The aim of the present study is therefore to investigate the
role of adding footstep sounds during a collision avoidance task in
VR. Importantly, our primary objective is not to claim that foot-
step sounds in their current experimental form fully replicate real-
world auditory conditions, but rather to demonstrate that auditory
cues—specifically footstep-related sounds—can significantly influ-
ence pedestrian behavior even in a minimal and controlled config-
uration. Establishing such effects in a simplified setting represents
a necessary first step, highlighting sound as a modality that should
not be neglected in VR-based studies of pedestrian interactions. To
this end, we designed a within-subject experiment in which partici-
pants encountered a virtual agent approaching from either their left
or right on a crossing trajectory. The onset of the interaction in-
cluded an initial visual occlusion, and two experimental conditions
were compared: with or without the agent's footstep sounds. Both
locomotor adjustments and gaze behavior were analyzed. Our find-

ings revealed that footstep sounds enable participants to estimate
earlier the position and motion of the virtual agent, leading to ear-
lier and safer locomotor adaptations, larger crossing distances, and
fewer occurrences of collisions with the virtual agent.

In summary, the contributions of this paper are

* A preliminary, baseline validation study highlighting the importance of multimodal rendering and establishing the role of auditory cues in collision avoidance behavior.

* Guidelines for improving the design of VR tools used in the study of pedestrian behavior, including recommendations for adding sound to these platforms.

## 2 RELATED WORK

In this section, we review the state of the art on three topics: what we know about interactions between real pedestrians, how these interactions are studied in VR, and finally, what we know about the role of hearing in these interactions. This allows us to contextualize the experiment presented in this paper and to justify its importance.

## 2.1 Collision avoidance between pedestrians in real conditions

A significant body of research has centered on visuomotor coordi-
nations involved during interactions between walkers, with a par-
ticular focus on collision avoidance scenarios. This research has
employed two primary experimental paradigms: (i) a head-on col-
lision avoidance task [12, 48] and (ii) paths crossing with an angle
[25, 27, 45]. Authors were interested in understanding the motion
adaptations performed by walkers [2, 17, 27], predicting motion
adaptations based on gaze behavior [18], identifying personal and
situational influential factors [6, 12, 27, 31, 57], with also the ob-
jective of understanding the visual control variables used by walk-
ers [25, 45]. Results showed that individuals adjust their locomo-
tion only when a future collision risk exists [45] as revealed by the
*minimum predicted distance (mpd)*, which is the predicted clear-
ance distance at the moment of crossing if participants keep a con-
stant velocity. As we use *mpd* in our own analysis, we remind
that the variation of *mpd* in time delimits 3 main stages in colli-
lision avoidance: (1) an *observation phase* – *mpd(t)* is constant, and
predicts that walkers will adapt their trajectory if its value is lower
than 1 *m*; (2) a *reaction phase* – *mpd(t)* increases as participants
make adjustments to avoid future collisions; and (3) a regulation
phase, where no further motion adaptation occur – *mpd(t)* is con-
stant again underscoring the anticipatory nature of collision avoid-
ance. Furthermore, the variation of *mpd(t)* shows that the walker's
role in the interaction, according to crossing order (who passes first,
who gives way), is important. The magnitude and type of trajectory
adjustments depends on this role, which is determined early in the
interaction and not inverted often [44, 31].

## 2.2 Collision avoidance between pedestrians in virtual conditions

VR has been widely used to study walker interactions to face
the challenges of standardizing and controlling experimental con-
ditions in real-world settings (RE). But VR introduces biases in
the collected data. For example, walkers maintain an elliptical-
shaped personal space [24] in VR, but take larger clearance dis-
tances [14, 22, 28, 5, 43]. Interestingly, various factors have simi-
lar influences on walkers interactions in RE and VR: anthropomor-
phic nature of the obstacle [51], locomotion mode (e.g., wheelchair)
[33], eye contact [1], gender and age [28], etc. Furthermore, the dy-
namics of interpersonal distance regulation is also accurately repro-
duced [5, 43]. Gaze allocation is also similar, despite differences in
eye motions [5, 4].

To enhance the VR tools used to study walker interactions, mul-
timodal rendering of the scene has been explored with haptics.
Berton et al. [3] showed that haptics rendering of collisions led to more active avoidance strategies in participants, with an after-effect when haptic rendering was disabled. Koilias et al. [32] found that tactile feedback affected locomotion as well as body ownership and presence. More recently, Yun et al. [64] investigated both haptic and auditory feedback of collisions in bidirectional pedestrian flows. They found significant effects on navigation strategies, local collision-avoidance movements, and subjective experiences of presence and copresence. Auditory cues produced effects on global navigation and further enhanced copresence, while vibrotactile feedback mainly shaped local movement adjustments. Taken together, these studies highlight the value of multisensory integration in VR.

Closer to our topic, Buck et al. proposed to evaluate the effect of spatial audio on the virtual personal space [13], but conclusions have not been reported yet. Other auditory cues, and more specifically footstep sounds, have been included in the study of dyadic or collaborative locomotor interactions in VR. Bönsch et al. [8] investigated social wayfinding with an assistive virtual agent. Footstep sounds were integrated to facilitate localization, enhance spatial awareness, and promote natural interaction with the agent, but their effect was not evaluated. In a 2-participant collaborative VR task, Rios et al. [50] manipulated the synchrony between avatar animations and footstep sounds, and found a small effect on clearance distances. When only collision avoidance tasks are considered, it seems that locomotor adjustments remain primarily visually driven. Silva et al. [54] reported that adding footstep sounds to a virtual pedestrian during a head-on avoidance task did not affect clearance distances. These results align with the principle of inverse effectiveness, suggesting that when visual information is strong and reliable, additional auditory cues provide little behavioral advantage. Nonetheless, in this study, the virtual agent was continuously visible. Therefore, in scenarios where the virtual agent can be occluded, auditory information might become more relevant, pointing to a potential benefit of including sound in specific VR interaction contexts.

Therefore, existing literature only partially addresses the role of auditory cues, such as footstep sounds, in VR-based investigations of pedestrian collision avoidance, particularly when potentially colliding avatars are initially occluded.

## 2.3 Auditory information in interactions with the environment

Beyond dyadic interactions and collision avoidance, a growing body of research has explored how auditory information contributes to perceiving and predicting movement in the environment, as well as to shaping users' sense of immersion and presence in VR. This broader perspective enables us to situate footstep sounds within a wider literature on the functional and experiential roles of sound in human-environment interactions.

Sound design in VR requires multiple elements to create an immersive sonic experience [52]. A distinction is often made between action sounds, generated by the user's own movements (e.g., walking, hitting, throwing), and environmental sounds, produced by objects and the surrounding soundscape. As mentioned by Serafin et al. [52], interactivity is also central in this dynamical context.

Several studies have evaluated the effect of auditory cues on perceived realism and presence in immersive environments. Kern et al. [29] showed that adding some ambient soundscape has a strong effect on the perceived realism and presence, stronger than the addition of the participants' own footstep sounds (possibly due to residual imperfections in the reproduction of steps). Nordahl et al. [40] also investigated one's footstep sounds in VR, showing that participants reported a significantly higher sense of presence in an audio-visual condition (with pressure-triggered step sounds) compared to a purely visual one. Further refinements have adapted the feedback

to the properties of the stepped surface [53, 41] or to the walker's weight [56].

Other studies have investigated how auditory information contributes to perceiving and predicting motion which is a fundamental ability when interacting in a dynamic environment. Oberfeld et al. [42] examined Time-To-Collision (TTC) estimation of an approaching vehicle in VR. Humans can estimate TTC based on auditory cues alone, with higher accuracy when combined to vision. Moreover, louder sounds lead to overestimations in the TTC judgments. Complementarily, Wessels et al. [58] investigated TTC estimation for visually presented accelerating vehicles and found that adding auditory information significantly decreased errors. More recently, Yuan et al. [63] explored how hearing and vision contribute together to motion prediction, perception, and localization without, however, showing evidence of multisensory integration. This suggests that humans flexibly adapt their sensory strategies to the context, integrating modalities when beneficial but prioritizing one modality when integration is less reliable. In walking scenarios, Young et al. [61] investigated the perception and reenactment of spatiotemporal characteristics of walking sounds. While participants could accurately discriminate large differences in stride length, their perceptions were significantly influenced by temporal factors. This suggests that although participants could imitate both spatial and temporal gait parameters from auditory cues, their judgments were more heavily biased by timing variations than by spatial differences.

Finally, sound plays a central role in non-verbal communication and collaboration. Yu et al. [62] showed in a real environment that footstep sounds can influence social perception, such as perceived status and persuasion, highlighting the role of sound in shaping how users interpret and engage with others. In a remote collaboration task, Yang et al. [60] showed that combining visual information with spatialized auditory cues significantly enhanced local workers' task performance, sense of social presence, and spatial understanding of the shared environment. Similarly, Fink et al. [21] investigated collaboration in augmented reality, focusing on both the visual features of avatars and the auditory characteristics of the environment, revealing preferences for spatial audio (which also improved task performance) over mono audio, regardless of the avatar's visual representation.

## 3 OBJECTIVES AND HYPOTHESES

While interactions between pedestrians rely primarily on vision, the previous related work section highlights that auditory information can also influence these behaviors, because it affects attention mechanisms or because it contributes to the estimation of others' motion in interactions. The objective of this preliminary study is thus to evaluate the importance, or even the necessity, of generating the footstep sounds of agents we interact with in VR platforms dedicated to analyzing pedestrian interactions. To meet this objective, the presented experiment examines a case of collision avoidance between a real participant and a virtual pedestrian. Two conditions are compared: i) with the rendering of the sound generated by the footsteps of the virtual pedestrian (noted FS) and ii) without the sound rendering of footsteps (NFS). Importantly, the NFS condition is not considered a purely artificial baseline but is explicitly framed as a partial-information condition, enabling the investigation of pedestrian collision avoidance under reduced sensory input. This framing is consistent with the literature on multisensory perception [63, 60, 21] and anticipatory control [43, 45, 25], and it provides a meaningful context for assessing the specific contribution of auditory cues to anticipation mechanisms during interaction. We use a proven experimental paradigm that has been employed in various previous studies, in which the participants encounter a virtual pedestrian with an orthogonal trajectory that they can visually perceive a few seconds before their crossing point [5, 43, 44, 45].
We analyse and explore behavioral measures to reveal the impor-
tance of sound rendering of footsteps.

In light of the literature, our main hypothesis is that auditory
footstep cues facilitate the anticipation of the virtual pedestrian's
trajectory, potentially even before visual contact occurs, thereby
enabling participants to adopt safer and more successful avoidance
strategies. We formulate the following expectations to check this
hypothesis:

E1 If auditory information supports successful task execution, fewer collisions should be observed in the **FS** condition compared to the **NFS** condition.

E2 If auditory cues enhance anticipatory motion estimation, par-
ticipants should initiate avoidance maneuvers earlier and
maintain larger avoidance distances in the FS condition. This
effect is expected to be reflected in *mpd* values that increase
earlier (E2a) and to greater values (E2b) under the FS condi-
tion than under the NFS condition.

E3 If auditory information affects visual attention allocation, gaze-tracking data should reveal earlier gaze allocation toward the virtual pedestrian in the FS condition (E3a), and a reduced proportion of gaze directed toward the agent in the NFS condition (E3b), where the absence of auditory cues may delay or even prevent timely visual detection.

4 METHODS

4.1 Participants

Twenty unpaid participants, recruited via mailing lists among stu-
dents, staff, and the general public, volunteered for the experiment
(7 females, 13 males, age: mean = 31 ± 9 years, range = 22–59).
All participants were naïve to the purpose of the study, had normal
or corrected-to-normal vision (verified while wearing the headset),
self-reported no mobility or hearing issues, and provided written
informed consent. These self-reports were collected prior to partic-
ipation to ensure that no participant with vision or mobility impair-
ments took part. 15 out of 20 participants had previously used a VR
headset

The study adhered to the Declaration of Helsinki and was ap-
proved on June 17th 2025, by our local ethics committee (CO-
ERLE) under the number 2025-34.

4.2 Task

Participants were asked to walk in a virtual environment toward
a target while avoiding any collision with a virtual agent (VA)
crossing their path. The experiment was conducted, following the
methodology described in [5], in a room large enough to allow par-
ticipants to actually walk along the corridor marked out by barriers
(Figure 1a) without fear of bumping into a wall. In this setup, the
VA followed a straight trajectory, orthogonal to that of the partici-
pant, approaching from either the right or left side. The VA walked
at constant speed and made no adjustment to its trajectory. The tar-
get was positioned in front of the participant and visible from the
start. Walls were set to hide the VA at the beginning of trials, so that
participants reached their comfortable walking speed before being
able to see the VA.

4.3 Virtual Environment

The virtual environment was developed using Unreal Engine 5.4. It consists of a square of 15m × 15m, with four 5m walls dividing the space into four equal square areas, leaving participants with a 18.5m diagonal passing through the center (see Figure 1a). Participants were instructed to reach a target represented by the statue of a fox, located in the corner opposite to their starting position. The VA, illustrated on Figure 1b, was a female agent animated with

a simple state machine animation blueprint. The animations did
not include eye movements or facial expressions. The agent walk-
ing speed was tailored according to each participant mean walking
speed measured during unobstructed walking trials performed at the
beginning of the experiment (see Section 4.4.3). Participants were
immersed in the virtual environment using a HTC VIVE Focus Vi-
sion headset (90 Hz<fnref n="1" />, 120°fov) equipped with an integrated eye-
tracking system (120 Hz). They performed the collision-avoidance
task with the VA while physically walking within the co-localized
physical setup.

4.4 Experimental Design

We designed a within-subject experiment manipulating the auditory
environment, with two conditions: Footstep Sound (FS) and No
Footstep Sound (NFS), as well as the risk of collision.

4.4.1 Auditory Environment

Two experimental conditions were implemented: one during which the VA footsteps generated sound (FS), and one which did not (NFS). An environmental ambient sound was present in both conditions. All sound processing, including sound calibration was implemented using Audiokinetic Wwise. For each participant, the sound levels were individually calibrated. Participants first calibrated the environmental sound (simulating an outdoor courtyard with faint birdsong) to a moderate level using a slider (see accompanying video). They then adjusted the footstep volume, to the minimum level at which the footsteps became inaudible, and to the maximum level corresponding to footsteps perceived as very close to them. The maximum possible volume corresponds to the maximum volume that can be emitted by the VR headset. Footstep sound volume was dynamically adjusted during the experiment (the environmental sound volume remains unchanged): a sine-shaped curve with a constant-power crossfade gradually increased volume as the participant approached the VA. Footstep sound positioning was achieved using 3D spatialization with listener relative routing. An occlusion effect simulated footstep sounds attenuation when the VA was hidden behind a wall, implemented via a 20 kHz low-pass filter. Sound volume was maximal when the participant and the VA occupied the same location and minimal at the starting position with maximal offset of the VA's position (see next section). Since the other starting positions of the VA were beyond this distance, the sound appeared shortly after the VA started walking. The evolution of the sound level before the participant is able to see the VA (the occluded phase, see Section 5.4), is illustrated on Figure 2.

4.4.2 Risk of Collision

The participant and the VA move along paths that are perpendicu-
lar to each other. However, it is necessary to synchronize the start
times and speeds of the two to set them on a collision course. The
VA's speed is calibrated to match the participant's pace. To en-
sure this calibration, we measure the participant's comfort speed
by having them perform the task in the absence of the VA during
familiarization trials, as detailed in the following section. We are
also interested in observing situations involving low or high risk
of collision, as well as instances where the participant occasionally
passes first or gives way. As in previous work using similar tasks
[5, 43, 44, 45], we observe a palette of collision trajectories, not
only a unique situation. This allows for a better understanding of
how participants adjust their trajectories.

As proposed by Berton et al. [5], the risk of collision was ma-
nipulated by defining offset positions for the virtual agent's start-
ing point. We defined 26 offsets: 13 producing an advance in the
trajectory of the VA at the moment it became visible to the par-
ticipant, 13 producing a delay in the trajectory. More precisely,

<footnote n="1">¹45Hz with one image for each eye</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 2: Footstep sound level in percentage of maximum level (0% = no footstep sound, 100% = maximal footstep sound level, reached at $T_{cross}$), during the occluded phase (see Section 5.4 for definitions).</figcaption></figure>

offsets were controlled so that the VA started between −2*m* (delay) and +2*m* (advance) relative to the participant, with values normally distributed around 0*m*. Note that the offset refers to the starting position; the collision risk at the time the participant will interact with the VA will also depend on the participant's locomotion: a slightly faster or slower walk compared to the previously measured comfort speed will affect it.

### 4.4.3 Experimental Procedure

Upon arrival, participants were welcomed, signed informed consent was obtained, and questions were answered if needed. After filling a demographics questionnaire, the head-mounted display was then fitted on their head, and the eye tracker calibrated. Participants first proceed to the sound calibration stage, then perform four walking trials without interaction with the VA to familiarize themselves with the virtual environment. The last familiarization trial was also used to compute their median comfort speed, that was then used to individualize the VA speed during the experiment. They then performed the collision avoidance task under the two main experimental conditions (FS and NFS), organized into two blocks. The order of these blocks was randomized across participants. Within each block, participants were presented with 26 trials (one per offset), presented in a random order. They were also presented with an additional familiarization trial before each block with the VA. In each trial, the VA was randomly presented approaching from the left or the right of participants. In total, each participant therefore completed 58 trials, among which 52 were part of the experimental trials: 4 familiarization trials without interaction + 2 sound conditions × (1 familiarization trial with VA + 26 experimental trials). In total, we recorded for analysis 20 × 52 = 1040 experimental trials, of which 8 trials were lost due to technical issues resulting in a total of 1032 trials.

# 5 ANALYSIS

## 5.1 Collected Data

During the experiment, the trajectories of both the participant and the VA were recorded, along with participants' head rotation and gaze behavior (origin and direction). The trajectory and eye movement data were recorded at a frequency of 20 Hz and filtered using a 2 Hz second-order low-pass Butterworth filter.

## 5.2 Interaction Phases and Temporal Normalization

To characterize the interaction, we defined three key time points: (i) the initiation of the participant's movement ($T_0$), (ii) the moment when the VA becomes visible after the wall is no longer occluding it ($T_{see}$), and (iii) the moment of closest approach between the participant and the VA ($T_{cross}$). Note that $T_{see}$ means that the VA is no more occluded by walls, but not that it is necessarily in the field of view of the participants. This definition matches the literature [45]. Based on these time points, we considered two main phases: from $T_0$ to $T_{see}$, that we note the *occluded phase*, during which the VA is audible but visually occluded by walls in the FS condition, while no information about the VA is available in the NFS condition; and from $T_{see}$ to $T_{cross}$, that we name the *visible phase*, during which the VA provides both auditory and visual information in the FS condition, but only visual information in the NFS condition. The time within these two interaction phases was normalized from 0% to 100% in both cases.

## 5.3 Trial exclusion

The task guiding this experiment is collision avoidance, and we controlled the experimental conditions to cause a high or low risk of collision between the participant and the VA. However, in some trials, this control did not effectively produce the desired conditions: a late or excessively slow start by the participant for reasons unrelated to the experiment may be the cause. In these trials, the participant's distance to the VA is very high (above 2*m*): the participants' trajectories are therefore in no way influenced by the VA, and we preferred to eliminate these trials from the analysis, as done in previous work [5].

We could simply eliminate trials with high $mpd(T_{see})$ values, but in order to objectively decide on a filtering out threshold, we adopted the analysis principle introduced by Berton et al. [5]. Based on piecewise linear regression, the analysis consists of calculating the set of trials of highest $mpd(T_{see})$ value and for which the $mpd(T_{cross})$ value is similar to $mpd(T_{see})$. This therefore corresponds to trials were participants did not perform trajectory adaptations, as there was no potential collision with the VA. To do this, as detailed in [5], we seek the optimal inflexion point (in terms of residuals square error) of a two-piece linear regression, where the second piece is forced to be an identity ($mpd(T_{cross})=mpd(T_{see})$). The trials which correspond to the second (identity) part of the regression are then filtered out of the subsequent analyses.

## 5.4 Kinematics of Collision Avoidance

For both conditions, we analyzed the kinematic characteristics of the collision avoidance task by computing for each trial:

* **Collisions:** we defined a collision as occurring when the distance between the participant and the virtual agent is less than 50 cm, computed from the center of the headset to the center of the VA. In the analysis, we computed the proportion of trials with collisions per participant.

For trials where there was a collision avoidance, we computed:

* $\bar{s}_{vis}$: the average participant's speed over the visible phase.

* **mpd:** to characterize trajectory adaptations, we computed $mpd$ (in m) during the normalized occluded and visible phases. We also extract $mpd$ at two key instants:

- $T_{see}$ the predicted minimum clearance distance between the participants and the VA at $T_{see}$ (i.e., $mpd(T_{see})$).

- $T_{cross}$ the actual distance between the participants and the VA at $T_{cross}$ (i.e., $mpd(T_{cross})$). Note that $mpd(T_{cross})$ can be also named minimum clearance distance.
## 5.5 Gaze Behaviour

The gaze vector was considered for each frame of the occluded and visible phases. In the visible phase, when this vector was directed toward the VA, the VA was categorized as being looked at. In the occluded phase, the same computation was performed assuming no wall obstruction, i.e., as if the gaze was directed toward the VA. Two analyses were then carried out:

1. we calculated for each participant and in 10% bins of normalized time, the percentage of time the VA was looked at during the occluded and visible phases.

2. we identified the first instant at which the VA was actually seen in the visible phase.

## 5.6 Statistics

Statistics were performed using R Studio and Matlab. We set the level of significance to $\alpha = 0.05$. A Shapiro Wilk test was performed to evaluate whether our data followed a normal distribution, and Levene's test was conducted to evaluate the homogeneity of variances across groups. If the distribution was not normal or if the assumption of homogeneity of variances was violated ($p < 0.05$), a Wilcoxon signed-rank test, a non-parametric test for paired data, was performed to compare our dependent variables across the two conditions of footstep sounds. If the distribution was normal and the assumption of homogeneity of variances was met, a paired Student's t-test was performed.

For the variables describing an evolution during the normalized occluded or visible phases, we evaluated the effect of the sound condition using paired Statistical Parametric Mapping (SPM) methods [23]. This analysis allows comparing time-series data of different trials taking into account their variability at each time-step.

# 6 RESULTS

## 6.1 Trial exclusion

We applied the trial exclusion rule described in Section 5.3. This resulted into the removal of a total of 40 out of 1032 recorded trials (27 in the FS condition and 13 in the NFS condition). Specifically, in the excluded trials, participants passed at a minimum clearance distance greater than 2.22m from the VA in FS condition, and greater than 2.72m in the NFS condition.

## 6.2 Locomotion

The analyzed kinematic characteristics of the collision avoidance task are summarized in Table 1. First, there was a significant effect of the condition on the percentage of collisions ($W = 12.0, p = 0.01, r = -0.85$), with more collisions occurring in the NFS condition. We also observed a significant effect of the condition on the $mpd(T_{see})$ (predicted minimum clearance distance) ($W = 32, p = 0.005, r = -0.80$) and $mpd(T_{cross})$ (minimum clearance distance) ($W = 45, p = 0.02, r = -0.78$), where participants had both a smaller predicted clearance distance at $T_{see}$, and a smaller clearance distance at $T_{cross}$, in the NFS condition, suggesting a higher future risk of collision and smaller adaptations when no footstep sound was presented. The walking average speed $\bar{s}_{vis}$ was however not affected by the condition (paired Student's t-test, $p = 0.38$).

**mpd evolution** during both the occluded and visible phases, is illustrated in Figure 3. First, SPM analysis showed that $mpd$ values were significantly higher for the FS condition during the whole visible phase, as well as during the 20-80% period of the occluded phase ($p < 0.001$). The 0-20% occluded phase corresponds to the initiation of walking, this result suggests that participants initiated the interaction in a very similar manner, but maintained a higher predicted clearance distance very early in the interaction. Nevertheless, we can observe that both conditions lead to qualitatively similar $mpd$ curves, with similar shapes leading to a progressive

<table><caption>Table 1: Kinematic data: Mean (SD) participant speed, median (IQR) trial collision rate, median (IQR) $mpd(T_{see})$ in meters, and median (IQR) $mpd(T_{cross})$ in meters across experimental conditions.</caption><thead><tr><th>Conditions</th><th>Speed (m.s<sup>-1</sup>)</th><th>Collisions (%)</th><th>mpd(T<sub>see</sub>) (m)</th><th>mpd(T<sub>cross</sub>) (m)</th></tr></thead><tbody><tr><td>Footstep Sound</td><td>1.08 (0.27)</td><td>0.00 (4.02)</td><td>0.78 (0.30)</td><td>0.99 (0.48)</td></tr><tr><td>No Footstep Sound</td><td>1.05 (0.25)</td><td>5.84 (15.38)</td><td>0.65 (0.20)</td><td>0.83 (0.39)</td></tr></tbody></table>

increase of the predicted clearance distance converging towards a plateau at the end of the visible phase (see Figure 3 Bottom).

## 6.3 Gaze Behaviour

The results about gaze behavior are presented in Figure 4. Participants looked significantly less often toward the VA in the NFS condition, both before the wall was crossed (occluded phase) and once the VA became visible (visible phase), compared to the FS condition. During the occluded phase, significant differences emerged in the 80-90% interval ($W = 1, p = 0.003, r = -0.86$), and in the 90-100% interval ($W = 9, p < 0.001, r = -0.79$). Once the VA was visible (visible phase), statistical analysis revealed significant differences during the interaction in the intervals 0-10% ($W = 4, p < 0.001, r = -0.84$), 10-20% ($W = 1, p < 0.001, r = -0.87$), 20-30% ($W = 6, p < 0.001, r = -0.83$), 30-40% ($W = 15, p < 0.001, r = -0.75$), 40-50% ($W = 30, p = 0.009, r = -0.60$), 50-60% ($W = 36, p = 0.02, r = -0.54$), and 90-100% ($W = 23.5, p = 0.03, r = -0.54$). From 60% to 90% of the interaction, no significant effects were observed.

# 7 DISCUSSION

## 7.1 Results interpretation

The main objective of this study is to investigate the importance of considering the rendering of auditory cues such as footstep sounds when studying pedestrian interactions in VR. Our general hypothesis (see Section 3) is that footstep sounds facilitate anticipatory mechanisms by providing early cues about the presence and trajectory of an approaching agent, thereby supporting predictive control during collision avoidance. We here discuss the obtained results.

### 7.1.1 Task success

We first expected that auditory cues under the FS condition would lead to more successful achievement of the task. The percentage of trials with collision is indeed significantly lower under the FS condition (4.02% vs. 15.38% under the NFS condition). This validates E1. Given the dimensions of the environment and depending on the exact trial conditions in terms of collision risk (see Section 4.4.2), the time between when the VA can get visible, and when it is likely to collide can be very short, a few seconds (typically between 1.5 and 2s.). In this short period, it is crucial for the participant to make the best possible estimate of the VA's movement and to carry out the avoidance maneuvers accordingly. The improved performance observed with auditory cues can be interpreted in two ways: first, the sound of footsteps may have facilitated earlier visual detection by providing spatial information about the VA's position in the environment, particularly indicating whether it approached from the left or the right (consistent with the conclusions of Yuan et al. [63]); second, the sound may have contributed directly to the estimation of the VA's movement (in line with the conclusions of [58, 42]).

### 7.1.2 Collision avoidance

We expected earlier adjustments (E2a) and larger clearance E2b in the collision avoidance. The analysis of $mpd(t)$ validates these two expectations. Two interesting elements are particularly noteworthy. First, the reaction period as defined by [45] ($mpd$ increases to safe values) occurs earlier when auditory cues are present. Under the
<figure><img src="image_5.png" /><figcaption>Figure 3: Kinematics adaptations to avoid collisions during the interaction are illustrated using the minimum predicted distance (*mpd*). Paired t-test SPM analysis revealed an effect of the sound condition, indicated by horizontal black lines showing the time periods during which *mpd(t)* significantly differs between Footstep Sound and No Footstep Sound conditions (*p* < 0.001). Top: Full illustration of the evolution of *mpd*; Bottom: Zoomed-in view of the evolution</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 4: Percentage of time participants look at the virtual agent (or in the direction of the virtual agent, as evidenced by a beam cast) per 10% time interval during the normalized *occluded* and *visible* phases.</figcaption></figure>
<table><caption>Table 2: Comparison of collisions according to footstep sounds condition when the virtual agent was never seen.</caption><thead><tr><td>Condition</td><td>Collisions</td><td>No collision</td></tr></thead><tbody><tr><td>Footstep Sound (n = 30)</td><td>1</td><td>29</td></tr><tr><td>No Footstep Sound (n = 64)</td><td>9</td><td>55</td></tr></tbody></table>

Fisher's exact test : *p* = 0.16

NFS condition, the average *mpd* function is continuously increasing from time *T*<sub>see</sub>. This means that on average, participants were able to start adjustments that solved the risk of collision from the moment they were able to see the VA. But under the FS condition, this happened earlier, starting from 90% of the occluded phase. This shows that on average, participants were able to adjust their trajectory in an efficient way even before the VA got fully visible to them. This is also in line with the conclusions of Oberfeld et al. [42] that states that humans can estimate the risk of collision only from auditory cues. Note that the reaction period also ends earlier under the FS condition (at 75% of the visible phase) than in the NFS condition (90%). Second, the SPM analysis shows that *mpd* values are higher under the FS condition compared to the NFS condition starting from the 20% of the first interaction phase. This could be due to the earlier occurrence of the reaction phase, but the closest approach (*mpd*(*T*<sub>cross</sub>)) is also significantly higher under FS. In this context, footstep sounds in the occluded phase may prime anticipation in virtual pedestrian collision avoidance, providing the participant with the opportunity to perform earlier adjustments to increase their clearance distance to the VA. In contrast to the work of Silva et al.[54], which did not show any effect of footstep sounds on clearance distance in a head-on collision avoidance situation where the virtual agent was always visible, the visual occlusion of the VA in our case allowed auditory cues to induce a behavioral change.

An additional point of discussion concerns the initial evolution of the *mpd* curves in the initial occluded phase between 0 and 20% of normalized time. We do not take this evolution into account in our analysis and discussion, as it is an artifact related to the way we compute the VA trajectory. While the participant starts from zero speed and gradually accelerates until reaching their comfort speed, the VA follows a constant speed. As explained in Section 4, this trajectory is calculated to control the risk of collision: this risk is reached when the participant is walking at their comfort speed. This explains the fast decreasing of the *mpd* curve during the participant's acceleration phase, before reaching values that correspond to an existing risk of collision.

### 7.1.3 Gaze Behavior

Our third expectation was about the effect of auditory cues on the visual attention. The activity of the gaze, and more specifically the proportion of time spent looking in the direction of the VA (whether visible or not due to the wall), is shown in Figure 4. Clearly, and as confirmed by our statistics, the gaze is directed toward the VA earlier in the FS condition. This confirms our expectation E3a. In particular, we see that the gaze is directed towards the VA even before it is visible: only auditory cues can lead to detecting the direction of the VA before *T*<sub>see</sub>. We also note that the proportion of time during which the gaze is fixed in the direction of the VA is greater in the FS condition throughout the entire reaction period, validating E3b. This supports the interpretation that the presence of the sound of footsteps in the sound rendering serves as a guide for visual attention, in addition to possibly helping to better estimate the movement of the first instant at which the VA was actually seen. These findings extend previous research on the role of sound in visual attention (e.g., [37, 34, 35]) by highlighting its contribution in the specific context of pedestrian interactions.

## 7.2 Additional Analyses

In order to adequately address the potential influence of moderating factors on our conclusions, we conducted two complementary analyses, related to the actual value of *mpd*(*T*<sub>see</sub>) and the fact that in some trials the participants did not look at the VA.

First, we aimed to evaluate whether the difference observed between the two sound conditions could simply be explained by differences in the associated *mpd*(*T*<sub>see</sub>). To address this, we restricted the analysis to trials in which participants actually looked at the VA. We introduced the variable *T*<sub>gaze</sub>, the time at which the participants first looked at the VA in the visible phase. Then, we specifically tested: (1) whether the experimental conditions (FS vs. NFS) influenced *T*<sub>gaze</sub>, (2) whether *mpd*(*T*<sub>see</sub>) itself was associated with *T*<sub>gaze</sub>, and (3) whether an interaction between experimental conditions and *mpd*(*T*<sub>see</sub>) was present. A linear regression model was fitted with the time of the first gaze *T*<sub>gaze</sub> toward the VA as the dependent variable, the sound conditions, *mpd*(*T*<sub>see</sub>), and their interaction as predictors. The model was significant, *F*(3, 36) = 9.00, *p* < 0.001, with an adjusted *R*<sup>2</sup> of 0.38, indicating that 38% of the variance was explained. The NFS condition was associated with a significant increase in the moment of the first gaze (*β* = 29.33, *t* = 2.20, *p* = 0.034), whereas neither the main effect of *mpd*(*T*<sub>see</sub>) (*β* = 7.17, *p* = 0.562) nor its interaction with the sound condition (*β* = -11.80, *p* = 0.522) were significant. This analysis shows that the sound condition is indeed the factor influencing *T*<sub>gaze</sub>.

Second, we observed that in some trials (30 and 64 respectively in the FS and NFS conditions), participants did not look at the VA during the visible phase, which may have contributed to an increased number of collisions. To account for this, we performed a Fisher's exact test on these trials (Table 2). The analysis revealed no significant effect of the sound condition on the number of observed collisions (*p* = 0.16).

## 7.3 Guidelines for VR platforms to study pedestrian behaviors

Our study provides clear recommendations for the design of virtual reality platforms for studying pedestrian behavior. Increasingly, we are seeing virtual reality platforms being used to acquire rich, accurate and detailed data on certain pedestrian behaviors, including in particular gaze activity and body trajectories (global and articulatory ones) to decipher behavior. Here we demonstrate the significant influence that auditory cues can have on pedestrian behavior. For example, we showed that recorded trajectories are modified, the distances maintained during interaction change significantly (> 10%), and the timing of these interactions is also greatly affected. The difference in the visual attention is very important too. It is therefore clear that if detailed spatio-temporal data is expected, the introduction of sound into the simulation will have a significant influence on the results, and is probably a requirement. This is in line with the recent book of Genorazzo et al. [26] highlighting the importance of sonic interactions in virtual environments.

## 7.4 Limitations and Future work

However, our study has limitations that open up avenues for future work on this topic.

First, concerning participants, this was a preliminary study, with an open recruitment strategy intended to be as inclusive as possible, allowing participants of any profile to take part. Participant gender, age, and cultural background were not controlled. Previous research has shown that these factors can influence pedestrian dynamics: for instance, age affects sensitivity to interpersonal spacing when adjusting walking speed [15, 49], and gender-related differences in walking speed have been observed in real-world crowd settings [46], although these effects are not consistently replicated in virtual reality [19, 65]. Few studies have investigated cultural
factors, but existing results suggest cross-country differences, par-
ticularly regarding gender effects [16]. Our study focused on a spe-
cific VR-based 90° collision avoidance scenario, and broader gen-
eralization remains limited. Moreover, the paradigms in prior work
are heterogeneous, ranging from crowd dynamics studies [59] with
varying densities [16] to dyadic pedestrian interactions [31, 48, 57],
which further constrains generalizability. The aim of our study was
specifically to examine the influence of footstep sounds on interac-
tions between two walkers. As such, it constitutes a preliminary
investigation, warranting further research in future work. Other
factors, such as individual differences in danger perception, were
not controlled. However, since the task was representative and had
already been validated in previous studies, we did not explicitly
account for these factors [5]. We did, however, record prior VR ex-
perience (15 out of 20 participants had previously used a VR head-
set) and monitored potential VR sickness using the FMS [30]. No
significant intra-participant differences were observed between pre-
and post-task FMS scores (pre-task FMS mean = 0.5 ± 1.14, range
= 0–4; post-task FMS mean = 1.5 ± 1.92, range = 0–6). In addition,
we also acknowledge our relatively small sample size (20 partici-
pants) which could affect the generalizability of the findings.

Secondly, all trials included the presence of a virtual agent, and
participants were therefore aware that a potential interaction could
occur. This may have reduced behavioral uncertainty and lim-
ited exploratory strategies that could emerge in scenarios where
the presence of another pedestrian is unpredictable. In addition,
a block design was used, with prior familiarization before each
block, which likely increased anticipation regarding both the pres-
ence of the virtual agent and the modality of sensory cues. While
this choice helped reduce carryover effects between conditions, it
may have influenced participants’ navigation strategies. Finally,
the study focused on a single type of auditory cue in a relatively
simple interaction scenario. Future work should consider fully ran-
domized designs, the inclusion of empty trials, and more complex
auditory and crowd configurations to assess the generalizability of
the observed effects.

Thirdly, we compared the effect of the sound rendering of foot-
steps in VR, but we do not dispose of real-world data to observe
the compare those effects on real-world behavior. We could de-
sign a real-world experiment using headphones to block out sound
and recreate the effect of the NFS condition in interactions between
two participants. It would be interesting to see if this has a simi-
lar effect on eye-gaze behaviors and *mpd*. We would not expect an
effect of similar magnitude though. In VR, HMDs limits periph-
eral vision which has a great effect on visual attention. Thus, in
a real-world situation, the visual attention would be probably less
affected by suppressing auditory cues. Gaze allocation would prob-
ably differ less, and similarly, the delay in reaction would be prob-
ably less important, and so the *mpd* values. We also chose to make
the VA passive, leaving the participant alone to play the avoidance
role. In a real-life experiment, it would also be difficult to repro-
duce this passivity and allow the interaction to reach high colli-
sion risks. We used simple sampled sounds with a low-pass filter
and simulated sound attenuation over distance by adjusting the vol-
ume, without employing the more advanced spatial audio features
of the Wwise sound engine. We could imagine that the quality of
the sound rendering technique has an effect on the precision of the
localisation. This question remains open for future investigation. If
we assume that auditory cues mainly affect visual attention, basic
vs. high quality sound rendering may not have a big impact. If we
assume that the auditory channel substantially enhances the accu-
racy of motion estimation, sound quality may play an even greater
role. However, our experiment was not designed to investigate the
contribution of audition or the broader question of sensory integra-
tion mechanisms in pedestrian interactions.

Finally, regarding ecological validity and applicability to real-

world scenarios, the present study should be considered a first,
foundational step. While the controlled VR setup allowed us to
isolate the contribution of auditory cues, it does not yet capture
the full complexity of real-world pedestrian environments. Fu-
ture work will therefore extend these findings to more realistic sit-
uations, including dense crowd settings and multiple simultane-
ous sound sources, such as footsteps from several agents as well
as environmental sounds. Such extensions will make it possible
to investigate under which conditions auditory cues play a criti-
cal role in pedestrian interactions, particularly in situations involv-
ing multiple simultaneous interactions as encountered in crowds.
For instance, auditory information may contribute differently in
crowded bright but noisy environments compared to darker but qui-
eter contexts. More broadly, these extensions will enable a sys-
tematic investigation of how auditory cues interact with visual in-
formation, ambient noise, cognitive load, and attentional demands
-including distraction effects [20, 55]- and how these multidimen-
sional factors jointly support navigation and decision-making pro-
cesses that can be meaningfully transferred to real-world environ-
ments. This preliminary study is then a baseline validation of au-
ditory rendering in VR pedestrian interaction paradigms as an im-
portant partial-information for anticipation, a necessary step toward
future work on multisensory interaction, sensory deprivation, and
hearing-impaired populations.

8 CONCLUSION

This study investigates the influence of auditory cues on pedestrian
behavior within virtual reality platforms. The results demonstrate
that the rendering of footstep sounds exerts a significant effect on
participants’ interactions with a virtual agent, notably modifying vi-
sual attention, anticipatory processes, and interpersonal clearance.
These findings support the systematic integration of auditory feed-
back in experimental platforms where the objective is to obtain re-
liable and ecologically valid data on human locomotor behavior.

The implications extend beyond methodological considerations.
Our results suggest immediate applications, such as examining the
behaviors of hearing-impaired populations, where the absence of
auditory cues may critically alter interaction dynamics. Further-
more, they underscore the need for deeper investigation into mul-
timodal integration and its role in the control of locomotor trajec-
tories, an area that remains underexplored in the literature. Finally,
broadening this line of inquiry to the study of collective phenom-
ena, particularly the mechanisms by which pedestrians prioritize
interactions in crowd contexts, constitutes a promising direction for
future research.

ACKNOWLEDGMENTS

This work was supported by French government funding managed
by the National Research Agency under the Investments for the Fu-
ture program (PIA) with the grant ANR-21-ESRE-0030 (CONTIN-
UUM project) as well by the Inria Associate team programme (Soc-
Nav).

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