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  <front>
    <journal-meta>
      <journal-id/>
      <journal-title-group>
        <journal-title>Research in Autism</journal-title>
      </journal-title-group>
      <issn/>
      <publisher>
        <publisher-name/>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">xxxxx</article-id>
      <title-group>
        <article-title>Mind the Gap: How Autistic Pedestrians Interact with
Autonomous Vehicles</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <contrib-id contrib-id-type="orcid">0000-0002-8068-8217</contrib-id>
          <name>
            <surname>Huang</surname>
            <given-names>Wenjie</given-names>
          </name>
          <string-name>Wenjie Huang</string-name>
          <email>wenjie.huang@univ-lyon2.fr</email>
          <role vocab="https://credit.niso.org" vocab-term="conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
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Curation</role>
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– Original Draft</role>
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          <xref ref-type="corresp" rid="cor-1">*</xref>
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        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-2486-0454</contrib-id>
          <name>
            <surname>Gaujoux</surname>
            <given-names>Vivien</given-names>
          </name>
          <string-name>Vivien Gaujoux</string-name>
          <role vocab="https://credit.niso.org" vocab-term="conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
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        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-4344-1987</contrib-id>
          <name>
            <surname>Fournel</surname>
            <given-names>Arnaud</given-names>
          </name>
          <string-name>Arnaud Fournel</string-name>
          <role vocab="https://credit.niso.org" vocab-term="software" vocab-term-identifier="https://credit.niso.org/contributor-roles/software/">Software</role>
          <role vocab="https://credit.niso.org" vocab-term="formal analysis" vocab-term-identifier="https://credit.niso.org/contributor-roles/formal-analysis/">Formal
analysis</role>
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Curation</role>
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        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-8443-2676</contrib-id>
          <name>
            <surname>Cegarra</surname>
            <given-names>Julien</given-names>
          </name>
          <string-name>Julien Cegarra</string-name>
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        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-5433-0975</contrib-id>
          <name>
            <surname>Reynaud</surname>
            <given-names>Emanuelle</given-names>
          </name>
          <string-name>Emanuelle Reynaud</string-name>
          <role vocab="https://credit.niso.org" vocab-term="conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
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– Review &amp; Editing</role>
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        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-1360-9523</contrib-id>
          <name>
            <surname>Navarro</surname>
            <given-names>Jordan</given-names>
          </name>
          <string-name>Jordan Navarro</string-name>
          <role vocab="https://credit.niso.org" vocab-term="conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
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administration</role>
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acquisition</role>
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– Review &amp; Editing</role>
          <xref ref-type="aff" rid="aff-1">a</xref>
        </contrib>
      </contrib-group>
      <aff id="aff-1">
        <institution-wrap>
          <institution>Université Lumière Lyon 2, Laboratoire d’Études des
Mécanismes Cognitifs</institution>
        </institution-wrap>
        <city>Lyon, France</city>
        <postal-code>69007</postal-code>
        <ext-link ext-link-type="uri" xlink:href="https://emc.univ-lyon2.fr/">https://emc.univ-lyon2.fr/</ext-link>
      </aff>
      <aff id="aff-2">
        <institution-wrap>
          <institution>Laboratoire Sciences de la Cognition, Technologie,
Ergonomie (SCoTE EA 7420), Université de Toulouse, INU
Champollion</institution>
        </institution-wrap>
        <city>Albi, France</city>
      </aff>
      <author-notes>
        <corresp id="cor-1">wenjie.huang@univ-lyon2.fr</corresp>
      </author-notes>
      <pub-date date-type="pub" publication-format="electronic" iso-8601-date="2025-05-23">
        <year>2025</year>
        <month>5</month>
        <day>23</day>
      </pub-date>
      <volume>x</volume>
      <issue>x</issue>
      <history/>
      <permissions>
        <copyright-year>2025</copyright-year>
        <copyright-holder>Wenjie Huang</copyright-holder>
        <license license-type="creative-commons">
          <ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
        </license>
      </permissions>
      <abstract>
        <p>The rise of autonomous vehicles (AVs) necessitates a deeper
understanding of how diverse populations, particularly those with
atypical cognitive profiles, perceive and interact with these systems.
This study assessed behavioral, physiological, and neural differences
between autistic (ASD) and neurotypical (NT) adults during a 3D
simulated road-crossing task involving AVs. Using fNIRS, eye-tracking,
and pupillometry, we examined how participants interpreted dynamic AV
behavior under three conditions: constant, decreasing, and increasing
physical crossing opportunities. Autistic participants showed delayed
crossing initiation and higher collision risk, despite similar crossing
durations. Gaze patterns were condition-dependent but not
group-dependent, and pupil dilation indicated lower baseline arousal in
ASD, with a specific increase under dynamic uncertainty. fNIRS revealed
decreased frontal activation but elevated temporal and parietal
activation in ASD, with significant group divergences in HbO time
courses emerging before crossing onset under the VarPlus condition.
These comparative findings suggest distinct cognitive processing when
interpreting AV behavior in crossing senarios. This study underscores
the need for inclusive urban mobility design and highlights potential
avenues for tailoring AV communication to accommodate
neurodiversity.</p>
      </abstract>
      <kwd-group kwd-group-type="author">
        <kwd>human-machine interaction</kwd>
        <kwd>autism spectrum disorder</kwd>
        <kwd>self-driving cars</kwd>
        <kwd>pedestrian safety</kwd>
        <kwd>eye-tracking</kwd>
        <kwd>fNIRS</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="introduction">
      <title>1. Introduction</title>
      <p>According to the World Health Organization’s global report on road
  safety, 1.19 million people died from road traffic incidents worldwide
  in 2021, with pedestrians accounting for 23% of these fatalities
  (<xref alt="Global Status Report on Road Safety 2023 2023" rid="ref-globals2023" ref-type="bibr"><italic>Global
  Status Report on Road Safety 2023</italic> 2023</xref>). Pedestrian
  vulnerability is particularly pronounced among individuals with
  disabilities, including those with non-visible cognitive or sensory
  conditions such as autism spectrum disorder (ASD)
  (<xref alt="Xiang et al. 2006" rid="ref-xiang2006" ref-type="bibr">Xiang
  et al. 2006</xref>;
  <xref alt="Organization 2019" rid="ref-organization2019" ref-type="bibr">Organization
  2019</xref>;
  <xref alt="Schwartz et al. 2022" rid="ref-schwartz2022" ref-type="bibr">Schwartz
  et al. 2022</xref>). While human drivers may yield to individuals
  displaying visible disabilities—such as those carrying white canes to
  indicate visual impairment
  (<xref alt="Andrew Harrell 1992" rid="ref-andrewharrell1992" ref-type="bibr">Andrew
  Harrell 1992</xref>;
  <xref alt="Guth et al. 2005" rid="ref-guth2005" ref-type="bibr">Guth
  et al. 2005</xref>)—those with invisible disabilities, like ASD, often
  face greater challenges and risks due to the absence of externally
  observable cues
  (<xref alt="“Going Our Own Way: Public Transit Accessibility for Neurodivergent People” 2023" rid="ref-goingou2023" ref-type="bibr">“Going
  Our Own Way: Public Transit Accessibility for Neurodivergent People”
  2023</xref>).</p>
      <p>ASD is a lifelong neurodevelopmental condition characterized by
  difficulties in social communication and interaction, as well as
  sensory sensitivities and restricted behaviors
  (<xref alt="Diagnostic and Statistical Manual of Mental Disorders: DSM-5 2013" rid="ref-diagnost2013" ref-type="bibr"><italic>Diagnostic
  and Statistical Manual of Mental Disorders: DSM-5</italic>
  2013</xref>;
  <xref alt="Lord et al. 2018" rid="ref-lord2018" ref-type="bibr">Lord
  et al. 2018</xref>). Its prevalence has sharply increased, with
  current estimates suggesting that at least 1 in 100 children globally
  are diagnosed with autism
  (<xref alt="Zeidan et al. 2022" rid="ref-zeidan2022" ref-type="bibr">Zeidan
  et al. 2022</xref>;
  <xref alt="Talantseva et al. 2023" rid="ref-talantseva2023" ref-type="bibr">Talantseva
  et al. 2023</xref>). Despite a growing body of research, studies on
  autistic individuals in traffic contexts have mainly focused on
  driving behavior
  (<xref alt="D. Y. Chee et al. 2017" rid="ref-chee2017" ref-type="bibr">D.
  Y. Chee et al. 2017</xref>;
  <xref alt="D. Y. T. Chee et al. 2019" rid="ref-chee2019" ref-type="bibr">D.
  Y. T. Chee et al. 2019</xref>), leaving a gap in understanding their
  behavior as pedestrians—a particularly vulnerable road user group
  (<xref alt="Earl et al. 2016" rid="ref-earl2016" ref-type="bibr">Earl
  et al. 2016</xref>;
  <xref alt="Earl et al. 2018" rid="ref-earl2018" ref-type="bibr">Earl
  et al. 2018</xref>;
  <xref alt="Wilmut and Purcell 2021" rid="ref-wilmut2021" ref-type="bibr">Wilmut
  and Purcell 2021</xref>).</p>
      <p>Pedestrian interactions, especially at unsignalized crossings,
  heavily rely on non-verbal communication, including eye contact, body
  posture, and subtle gestures that help negotiate right-of-way
  (<xref alt="Hamilton-Baillie 2004" rid="ref-hamilton-baillie2004" ref-type="bibr">Hamilton-Baillie
  2004</xref>;
  <xref alt="Bishop, Biasini, and Stavrinos 2017" rid="ref-bishop2017" ref-type="bibr">Bishop,
  Biasini, and Stavrinos 2017</xref>).</p>
      <p>This process, referred to in cognitive literature as theory of mind
  (<xref alt="Baron-Cohen, Leslie, and Frith 1985" rid="ref-baron-cohen1985" ref-type="bibr">Baron-Cohen,
  Leslie, and Frith 1985</xref>), intention attribution
  (<xref alt="Dennett 1971" rid="ref-dennett1971" ref-type="bibr">Dennett
  1971</xref>), or mentalizing
  (<xref alt="Frith 2001" rid="ref-frith2001" ref-type="bibr">Frith
  2001</xref>), requires quick and accurate interpretation of another’s
  intentions.</p>
      <p>The temporoparietal junction (TPJ) is critically involved in such
  processes
  (<xref alt="R. Saxe and Kanwisher 2003" rid="ref-saxe2003" ref-type="bibr">R.
  Saxe and Kanwisher 2003</xref>;
  <xref alt="Schurz et al. 2014" rid="ref-schurz2014" ref-type="bibr">Schurz
  et al. 2014</xref>) , and atypical TPJ activity has been repeatedly
  observed in individuals with ASD
  (<xref alt="Lombardo et al. 2011" rid="ref-lombardo2011" ref-type="bibr">Lombardo
  et al. 2011</xref>;
  <xref alt="Nijhof et al. 2018" rid="ref-nijhof2018" ref-type="bibr">Nijhof
  et al. 2018</xref>), potentially impacting their ability to navigate
  social negotiation in dynamic environments.</p>
      <p>With the rise of autonomous vehicles (AVs), these traditional
  interaction dynamics are set to change. The AV market is projected to
  expand to $21 billion by 2035, leading to increasing integration with
  human road users
  (<xref alt="Rezwana and Lownes 2024" rid="ref-rezwana2024" ref-type="bibr">Rezwana
  and Lownes 2024</xref>). AVs do not communicate via human social cues,
  potentially reducing ambiguity for those who struggle with traditional
  social signaling
  (<xref alt="Wilmut and Purcell 2021" rid="ref-wilmut2021" ref-type="bibr">Wilmut
  and Purcell 2021</xref>;
  <xref alt="Rezwana and Lownes 2024" rid="ref-rezwana2024" ref-type="bibr">Rezwana
  and Lownes 2024</xref>). While typically developing individuals might
  experience confusion in the absence of human driver cues
  (<xref alt="Van Brummelen et al. 2018" rid="ref-vanbrummelen2018" ref-type="bibr">Van
  Brummelen et al. 2018</xref>), individuals with ASD may benefit from
  the structured, rule-based behavior of AVs. Autistic drivers, for
  example, have been shown to outperform NT counterparts in tasks that
  rely on consistency and predictability
  (<xref alt="D. Y. Chee et al. 2017" rid="ref-chee2017" ref-type="bibr">D.
  Y. Chee et al. 2017</xref>).</p>
      <p>This opens the possibility that autistic pedestrians may also be
  better attuned to inferring intent from the motion kinematics of
  AVs—such as changes in speed or trajectory—rather than social cues
  (<xref alt="Zach Noonan et al. 2023" rid="ref-zachnoonan2023" ref-type="bibr">Zach
  Noonan et al. 2023</xref>). Research supports the idea that ASD
  individuals possess a unique affinity for systematic and technological
  environments, often demonstrating strengths in interaction with
  robotic agents and predictable digital interfaces
  (<xref alt="Diehl et al. 2012" rid="ref-diehl2012" ref-type="bibr">Diehl
  et al. 2012</xref>;
  <xref alt="Scassellati, Admoni, and Matarić 2012" rid="ref-scassellati2012" ref-type="bibr">Scassellati,
  Admoni, and Matarić 2012</xref>;
  <xref alt="Dubois-Sage et al. 2024" rid="ref-dubois-sage2024" ref-type="bibr">Dubois-Sage
  et al. 2024</xref>).</p>
      <p>These capabilities may reflect a different but equally valid form
  of ‘machine theory of mind’
  (<xref alt="P. Pantelis et al. 2011" rid="ref-pantelis2011" ref-type="bibr">P.
  Pantelis et al. 2011</xref>;
  <xref alt="P. C. Pantelis et al. 2014" rid="ref-pantelis2014" ref-type="bibr">P.
  C. Pantelis et al. 2014</xref>), where ASD individuals excel in
  interpreting motion-based social cues rather than anthropomorphic
  ones. In fact, ASD participants have been shown to respond more
  accurately to robot-initiated actions than human ones in imitation
  tasks
  (<xref alt="Pierno et al. 2008" rid="ref-pierno2008" ref-type="bibr">Pierno
  et al. 2008</xref>). This suggests that AVs, as predictable and less
  ambiguous agents, may offer a communication context that aligns more
  closely with autistic cognition.</p>
      <p>Given the increasing integration of AVs into public infrastructure,
  it becomes essential to understand how neurodiverse individuals
  interpret AV behavior. While current AV design often incorporates
  social or symbolic cues to improve human comprehension
  (<xref alt="Driggs-Campbell and Bajcsy 2016" rid="ref-driggs-campbell2016" ref-type="bibr">Driggs-Campbell
  and Bajcsy 2016</xref>), few studies consider how such signals are
  <bold>processed</bold> differently across populations. This study
  addresses that gap by investigating behavioral, physiological, and
  neural responses of autistic and neurotypical adults as they interact
  with AVs in a simulated road-crossing scenario.</p>
      <p>To achieve ecological validity and capture multimodal signals, we
  employed functional near-infrared spectroscopy (fNIRS)
  (<xref alt="Villringer and Chance 1997" rid="ref-villringer1997" ref-type="bibr">Villringer
  and Chance 1997</xref>), eye-tracking, and pupillometry. fNIRS has
  proven effective in detecting social brain responses in ASD
  individuals
  (<xref alt="Liu et al. 2019" rid="ref-liu2019" ref-type="bibr">Liu et
  al. 2019</xref>;
  <xref alt="Zhang and Roeyers 2019" rid="ref-zhang2019" ref-type="bibr">Zhang
  and Roeyers 2019</xref>), and its portability makes it ideal for
  mobility studies involving real-time interaction in simulated or
  naturalistic environments.</p>
      <p>By examining how participants respond to AV behavior across
  different traffic conditions, we aim to advance our understanding of
  neurodiverse pedestrian safety and inform the design of more inclusive
  AV systems.</p>
    </sec>
    <sec id="methods">
      <title>2. Methods</title>
      <sec id="participants">
        <title>2.1 Participants</title>
        <p>A total of 53 participants took part in the study, including 12
    adults with autism spectrum disorder (ASD group) and 41 neurotypical
    adults (NT group). All participants had normal or
    corrected-to-normal vision and provided informed written consent
    prior to participation. The study was approved by the ethics
    committee of the Université de Lyon.</p>
        <p>Participants in the ASD group (9 females, 3 males; mean age =
    37.6 years, SD = 14.2) were recruited through autism-related
    communities and forums. All individuals self-identified as autistic
    and reported having received a formal diagnosis of autism spectrum
    disorder (ASD) from a healthcare professional, with no history of
    attention deficit with or without hyperactivity (ADHD). While
    clinical documentation was not independently verified, participants’
    autistic characteristics were further assessed using the Aspie Quiz
    (<ext-link ext-link-type="uri" xlink:href="https://rdos.net/fr/">https://rdos.net/fr/</ext-link>),
    a widely used self-report instrument designed to profile
    neurodivergent and neurotypical traits. The questionnaire provides
    separate scores for autistic and neurotypical dimensions (range:
    0–200). All included participants showed high neurodivergent scores
    (mean = 136, SD = 20.4) and low neurotypical scores (mean = 67.4, SD
    = 21.9), in line with typical profiles observed in diagnosed
    autistic populations
    (<xref alt="Ekblad 2013" rid="ref-ekblad2013" ref-type="bibr">Ekblad
    2013</xref>).</p>
        <p>Participants in the NT group (31 females, 10 males; mean age =
    20.6 years, SD = 1.6) were undergraduate students recruited from the
    Université de Lyon. Although no autism-specific screening instrument
    was administered to this group, participants reported no history of
    developmental or psychiatric disorders.</p>
      </sec>
      <sec id="apparatus-and-procedure">
        <title>2.2 Apparatus and Procedure</title>
        <p>Participants performed a road-crossing task in a virtual 3D
    pedestrian simulator presented on a PC, using a Gamepad for
    realistic movement control, being able to move and view in limited
    range (see
    <xref alt="Figure 1" rid="fig-controls">Figure 1</xref>).</p>
        <fig id="fig-controls">
          <caption>
            <p>Figure 1: Participants’ Control and Virtual
      Environment Setup. Participants navigated the road-crossing
      simulation as pedestrian avatars using a PS5 DualSense controller.
      The virtual environment was presented on a 27-inch screen
      (1920×1080 resolution) positioned approximately 1 meter in front
      of the participant. The pedestrian’s field of view was constrained
      to 60° to minimize visual distortion. The controller allowed for
      intuitive control of the avatar in two primary dimensions: View
      Control: The left joystick enabled lateral head movements (left or
      right), allowing participants to visually scan approaching traffic
      before deciding to cross. Movement Control: The right joystick
      controlled forward and backward locomotion across the road.
      Movement speed was continuous and responsive to the degree of
      joystick tilt, ranging from 3 km/h (minimal tilt) to a maximum of
      6 km/h (full tilt). Releasing the joystick immediately halted
      movement, allowing participants to regulate both the starting time
      and duration of their crossings with precision. The simulated
      environment featured a minimalist rural road scenario designed to
      eliminate unnecessary visual distractions. The road was a one-way,
      single-lane route measuring approximately 200 meters in length and
      5 meters in width. All vehicles were uniform in size and
      appearance.</p>
          </caption>
          <graphic mimetype="image" mime-subtype="png" xlink:href="images/CleanShot%202025-05-04%20at%2012.22.59@2x-02.png"/>
        </fig>
        <p>This task was designed to investigate participants’ behavioral
    strategies and physiological responses during interactions with
    autonomous vehicles (AVs). The experimental environment consisted of
    a gently curved road, providing a clear line-of-sight for
    participants, along which virtual AVs approached continuously.
    Traffic scenarios were presented in three distinct experimental
    conditions, each differing systematically in how the available
    physical crossing gap evolved over time, while maintaining an
    identical temporal crossing window of 3 seconds at the crossing
    point (illustrated in
    <xref alt="Figure 2" rid="fig-4conditions">Figure 2</xref>).</p>
        <fig id="fig-4conditions">
          <caption>
            <p>Figure 2: Illustration of the four traffic conditions.
      1) Baseline: A single vehicle approaches the pedestrian crossing
      at a constant speed of 30 km/h, with no other vehicles present.
      This condition served as a physiological baseline for fNIRS
      recordings. 2) Const: Four groups of vehicles (each composed of
      three cars) approach at a uniform speed of 30 km/h. The
      inter-group gaps are temporally and physically consistent,
      resulting in stable crossing opportunities. 3) VarMinus: Vehicles
      are organized into four groups, with each successive group
      traveling faster than the previous one—30, 40, 50, and 60 km/h,
      respectively. This acceleration pattern leads to progressively
      shorter physical gaps between the groups, while maintaining a
      constant temporal gap (3s) at the crossing point. 4) VarPlus: All
      four vehicle groups initially travel at 60 km/h. The first group
      maintains this speed throughout. The second group decelerates to
      50 km/h over 3 seconds and then maintains that speed. The third
      group slows further, from 60→50→40 km/h in two 3-second stages.
      The fourth group continues the same pattern until it reaches 30
      km/h. This progressive deceleration results in increasingly
      widened physical gaps over time, despite the temporal gap
      remaining constant at 3s.</p>
          </caption>
          <graphic mimetype="image" mime-subtype="png" xlink:href="images/CleanShot%202025-05-04%20at%2011.48.53@2x.png"/>
        </fig>
        <p>Being explicitly informed that the vehicles were all
    fully-automated, participants encountered three sequential
    inter-group gaps (Gap #1 to Gap #3) in each trial, simulating
    recurring crossing opportunities. They were instructed to cross as
    soon as possible, reflecting real-world pedestrian decision-making
    behaviors. However, only those trials in which participants
    initiated crossing during the first gap (Gap #1) were retained for
    subsequent analyses. The sequence of traffic conditions was
    randomized between trials to mitigate potential learning effects or
    carryover biases.</p>
        <p>Eye-tracking data were captured at a sampling rate of 1000 Hz
    using an Eyelink Portable Duo eye tracker in remote tracking mode.
    Recorded metrics included gaze positions, fixation duration, and
    pupil diameter changes, precisely synchronized with key traffic
    events (e.g., the onset of the initial inter-group gap).</p>
        <p>Pupillometry has been widely used to assess cognitive load and
    emotional arousal, making it a suitable tool to examine how traffic
    conditions translated on participants’ workload in different
    group. To account for the physiological delay in pupil dilation
    responses to cognitive load, we applied a 1000 milliseconds offset
    when extracting pupil size data relative to the onset of crossing
    decision-making. A 0.5s baseline sliding window was used for
    normalization.</p>
        <p>Concurrently, cerebral hemodynamic responses were monitored using
    a fNIRS system with a 12×10 optode array covering frontal, temporal
    and parietal cortical regions, sampling at approximately 5.5 Hz (see
    <xref alt="Figure 3" rid="fig-layout">Figure 3</xref>). fNIRS
    signals were acquired using an 26-channel continuous wave fNIRS
    system (Cortivision Photo Cap C20).</p>
        <p>The placement of fNIRS optodes was determined using a systematic
    and refined procedure based on an automated meta-analytic approach
    (<xref alt="Yarkoni et al. 2011" rid="ref-yarkoni2011" ref-type="bibr">Yarkoni
    et al. 2011</xref>). Specifically, we conducted an automated
    meta-analysis via the Neurosynth database, targeting brain regions
    consistently activated during tasks involving decision-making and
    Theory of Mind (ToM). Using the “fNIRS Optodes’ Location Decider”
    (fOLD) toolbox
    (<xref alt="Zimeo Morais, Balardin, and Sato 2018" rid="ref-zimeomorais2018" ref-type="bibr">Zimeo
    Morais, Balardin, and Sato 2018</xref>), we mapped these identified
    activation clusters onto the international 10–10 EEG electrode
    positioning standard. The epoch was determined as [-5s,30s] with the
    moment when the first vehicle entered onto the 200m road as the t=0
    reference in time course analysis.</p>
        <fig id="fig-layout">
          <caption>
            <p>Figure 3: fNIRS layout consisted of 12 emitters (red)
      and 10 detectors (purple), forming 4 short channels and 21
      measurement channels covering the frontal, temporal, and parietal
      regions. The optode numbers were color-coded, with channels
      represented by blue arrows indicating connections from source to
      receptor optodes.</p>
          </caption>
          <graphic mimetype="image" mime-subtype="png" xlink:href="images/layout.png"/>
        </fig>
        <p>The simulation environment was developed using Unity 3D, with
    precise synchronization between the virtual environment,
    eye-tracking data, and fNIRS recordings achieved via the Lab
    Streaming Layer (LSL)
    protocol(<xref alt="“LabStreamingLayer’s Documentation  Labstreaminglayer 1.13 Documentation,” n.d." rid="ref-labstrea" ref-type="bibr">“LabStreamingLayer’s
    Documentation Labstreaminglayer 1.13 Documentation,” n.d.</xref>).
    Prior to experimental trials, each participant underwent individual
    calibrations for both eye-tracking and fNIRS systems, followed by a
    brief familiarization phase for vibration feedback if collision
    happens. The entire experimental session, including calibration,
    data collection, and participant debriefing, lasted approximately 45
    minutes. Each participants went through 6 trials per conditions,
    resulting in 24 trials per participant.</p>
      </sec>
      <sec id="measures">
        <title>2.3 Measures</title>
        <p><xref alt="3.1.1 Success Rate:" rid="success-rate">3.1.1 Success
    Rate:</xref> Binary indicator of collision avoidance in each
    trial</p>
        <p><xref alt="3.1.2 Crossing Start Time:" rid="crossing-start-time">3.1.2
    Crossing Start Time:</xref> Time from trial onset to engaging with
    potential collision area</p>
        <p><xref alt="3.1.3 Crossing duration:" rid="crossing-duration">3.1.3
    Crossing duration:</xref> Time spent in crossing</p>
        <p><xref alt="3.1.4 Current Deviation:" rid="current-deviation">3.1.4
    Current Deviation:</xref> Time difference between pedestrian and gap
    center trajectories</p>
        <p><xref alt="3.1.5 Pedestrian Crossing Speed:" rid="pedestrian-crossing-speed">3.1.5
    Pedestrian Crossing Speed:</xref> Speed of the pedestrian avatar in
    meter per second</p>
        <p><xref alt="3.1.6 Temporal Error:" rid="temporal-error">3.1.6
    Temporal Error:</xref> Temporal offset from ideal crossing
    moment</p>
        <p><xref alt="3.2.1 Gaze Preference:" rid="gaze-preference">3.2.1
    Gaze Preference:</xref> Standardized gaze position projected between
    gap-related vehicles</p>
        <p><xref alt="3.2.1 Pupil Dilatation:" rid="pupil-dilatation">3.2.1
    Pupil Dilatation:</xref> Proxy for autonomic arousal</p>
        <p><xref alt="3.3.1 Channel-level activation" rid="channel-level-activation">3.3.1
    Channel-level activation</xref> Proxy for cortex activation</p>
        <p><xref alt="3.3.2. Permutation cluster test" rid="permutation-cluster-test">3.3.2.
    Permutation cluster test</xref> Activation on time course</p>
      </sec>
      <sec id="data-analysis">
        <title>2.4 Data Analysis</title>
        <p>Linear mixed-effects models assessed group and condition effects
    across behavioral and physiological variables. Eye-tracking data
    were processed via dynamic dot-product alignment to AVs’ screen
    positions (see
    <xref alt="Figure 4" rid="fig-gazePorportion">Figure 4</xref>).</p>
        <fig id="fig-gazePorportion">
          <caption>
            <p>Figure 4: Illustration of the calculation for Gaze
      Preference. It was computed using a standardized dot product
      projection along the axis defined by the gap-opening and
      gap-closing vehicles. Values range beyond the [0, 1] interval: a
      score of 0 indicates gaze precisely aligned with the gap-opening
      vehicle (V3), while a score of 1 reflects alignment with the
      gap-closing vehicle (V1). Intermediate values within [0, 1]
      indicate gaze directed between the two vehicles. When either
      vehicle fell outside the visual field, projection endpoints were
      adjusted accordingly: the origin was set to (0, 0) for missing V3
      (left-bottom corner of the screen) and to (0, 0.5) for missing V1
      (right edge of the screen).</p>
          </caption>
          <graphic mimetype="image" mime-subtype="png" xlink:href="images/CleanShot%202025-05-04%20at%2013.12.29@2x.png"/>
        </fig>
        <p>fNIRS data preprocessing was completed using the MNE package in
    Python. The raw intensity data were first converted to optical
    density (OD) changes, then underwent TDDR correction
    (<xref alt="Fishburn et al. 2019" rid="ref-fishburn2019" ref-type="bibr">Fishburn
    et al. 2019</xref>) and bandpass filtering (0.01–0.09 Hz) to filter
    out motion and other physiological artefacts. Finally the OD data
    were converted to hemodynamic responses (HDR) using the modified
    Beer-Lambert Law
    (<xref alt="Kocsis, Herman, and Eke 2006" rid="ref-kocsis2006" ref-type="bibr">Kocsis,
    Herman, and Eke 2006</xref>). The oxygenated hemoglobin (HbO) data
    of six trials under Baseline traffic condition were averaged for
    each participant and then used for steady-state control and
    normalized by z-score methods to eliminate the effect of data units
    and facilitate comparison between different traffic conditions.
    Finally, each channel’s HbO amplitude under the six trials of each
    participant in the three different traffic conditions (Const,
    VarMinus, VarPlus) was calculated to compare the effect of
    conditions and groups (ASD, NT).</p>
      </sec>
    </sec>
    <sec id="results">
      <title>3. Results</title>
      <sec id="behavioral-outcomes">
        <title>3.1 Behavioral Outcomes</title>
        <sec id="success-rate">
          <title>3.1.1 Success Rate:</title>
          <p>A linear mixed-effect model showed that the ASD group
      demonstrated a significantly higher likelihood of collisions
      compared to the NT group, as indicated by a positive fixed effect
      estimate for the group comparison (<italic>β = 0.206, SE = 0.086,
      t(51) = 2.379, p = .021</italic>).</p>
          <p>Notably, in the VarPlus condition, both groups showed a
      numerical reduction in risk-taking behaviors such as crossing
      during intra-group gaps, compared to Const and VarMinus
      conditions. However, this reduction did not reach statistical
      significance in either group (<italic>ASD: z = -1.29, p = .20; NT:
      z = -0.99, p = .32</italic>), suggesting a trend toward more
      cautious behavior in the presence of physically expanding crossing
      gaps.</p>
          <fig id="fig-success-rate">
            <caption>
              <p>Figure 5: Success rate breakdown by Group and
        Condition. Each panel displays one condition. Within each panel,
        each bar displays one group. The bars represent the proportion
        of trial outcomes across each Group × Condition combination, the
        full bar height corresponds to 100% of the trials in the
        given Group × Condition combination. Trials were categorized
        into three mutually exclusive outcomes: Successful crossings
        (green), Failed attempts at inter-group gaps (blue), and Failed
        attempts at intra-group gaps (red). Inter-group gap failures
        reflect unsuccessful yet strategically appropriate decisions,
        whereas intra-group gap failures suggest riskier decision-making
        patterns.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="notebooks/fig-success_rate.png"/>
          </fig>
        </sec>
        <sec id="crossing-start-time">
          <title>3.1.2 Crossing Start Time:</title>
          <p>We examined the effects of traffic condition and group on
      pedestrian crossing start time using a two-way mixed-design ANOVA
      with Condition as a within-subjects factor and Group as a
      between-subjects factor.</p>
          <p>Crossing start time revealed a significant main effect on
      Condition (<italic>F(2,96) = 9085.91, p &lt; .001,
      ηp2=.995)</italic>, indicating that crossing decisions varied
      strongly across traffic conditions. Post-hoc tests on Condition
      showed that the difference majorly came from VarPlus (see
      <xref alt="Figure 6" rid="fig-varpost">Figure 6</xref>).</p>
          <fig id="fig-varpost">
            <caption>
              <p>Figure 6: Post-hoc comparisons following the
        significant main effect of Condition on Crossing Start Time
        revealed that the VarPlus condition significantly differed from
        both the Const and VarMinus conditions (ps &lt; .001).
        Specifically, participants in both the ASD and NT groups
        initiated crossing earlier under the VarPlus condition compared
        to the other two conditions. No significant difference was
        observed between the Const and VarMinus conditions (p &gt; .05).
        These findings suggest that the increasing physical crossing
        opportunity in the VarPlus condition—despite identical temporal
        crossing opportunities across all conditions—promoted earlier
        initiation of crossing behavior.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="images/CleanShot%202025-05-04%20at%2015.34.22@2x-01.png"/>
          </fig>
          <p>The Group effect was also significant (<italic>F(1,48) = 5.997,
      p = .018, ηp2​ = .111)</italic>, showing that overall, ASD
      participants started to cross later than NT participants across
      conditions.</p>
          <p>To evaluate potential group differences in motor execution
      during the road-crossing task, we analyzed also the Crossing
      duration that participants spent within the potential collision
      zone—the centered area in crossing, superposed with the trajectory
      of the approaching vehicles.</p>
        </sec>
        <sec id="crossing-duration">
          <title>3.1.3 Crossing Duration:</title>
          <p>Notably, we found no significant group differences in crossing
      duration <italic>(F(1, 669) = 0, p = .99</italic>), suggesting
      intact motor execution in ASD group, once started crossing.</p>
        </sec>
        <sec id="current-deviation">
          <title>3.1.4 Current Deviation:</title>
          <p>We examined <italic>current deviation</italic>—a behavioral
      measure reflecting whether pedestrians were adjusting their speed
      appropriately relative to vehicle positions—using a mixed-design
      ANOVA with Group as a between-subjects factor and Condition and
      Time (–5 s to 0 s relative to crossing midpoint) as
      within-subjects factors. The results revealed no significant main
      effect of Group <italic>( F(1, 6) = .007, p = .934)</italic>, no
      significant main effect of Condition <italic>(F(2, 12) = .693, p =
      .519)</italic>, and no significant Group × Condition interaction
      <italic>(F(2, 12) = .133, p = .877)</italic>.</p>
          <p>Descriptively, some group- and condition-level differences were
      observed (see
      <xref alt="Figure 7" rid="fig-currentDev">Figure 7</xref>), but
      these did not reach statistical significance. One potential reason
      for the null results lies in the data quality: the current
      deviation metric is calculated based on the instantaneous spatial
      relation between pedestrians and approaching vehicles. In our
      simulation, vehicle position data were updated at a resolution of
      4-meter intervals, which introduced temporal imprecision and
      resulted in sparse or missing current deviation estimates in many
      trials, especially in ASD group where we already have limited
      number of participants.</p>
          <fig id="fig-currentDev">
            <caption>
              <p>Figure 7: Current deviation trajectories aligned to
        the pedestrian’s midpoint crossing time. The y-axis indicates
        current deviation (in seconds)—the temporal offset between the
        pedestrian’s projected crossing time (if they maintained their
        current speed) and the ideal physical midpoint of the
        inter-vehicle gap. A current deviation of 0 means the pedestrian
        was on track to cross precisely at the midpoint of the physical
        gap. Negative values reflect delayed timing (crossing would
        occur <italic>after</italic> the optimal midpoint), while
        positive values reflect anticipatory timing (crossing
        <italic>before</italic> the midpoint). The x-axis represents a
        5-second countdown leading up to the actual midpoint crossing,
        allowing observation of real-time adjustments. Mean trajectories
        for each group and condition are shown as solid lines, with
        shaded ribbons denoting ±1 standard deviation. This
        visualization captures participants’ anticipatory or delayed
        adjustments to their speed as vehicles approached, reflecting
        their ability to dynamically align with moving
        gaps.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="notebooks/fig-currentDev.png"/>
          </fig>
          <p>To circumvent this limitation, we further analyzed pedestrian
      crossing speed, which does not depend on vehicle position data and
      thus offers a more continuous behavioral time course.</p>
        </sec>
        <sec id="pedestrian-crossing-speed">
          <title>3.1.5 Pedestrian Crossing Speed:</title>
          <p>The pedestrian crossing speed could be interpreted as the slope
      factor in
      <xref alt="Figure 7" rid="fig-currentDev">Figure 7</xref>. A
      mixed-design ANOVA on pedestrian speed revealed a significant main
      effect of Condition, <italic>F</italic>(2, <italic>94</italic>) =
      3.43, <italic>p</italic> = .036, <italic>ηp2=0.068</italic>,
      suggesting that, despite the limitations in current deviation
      measures, time-resolved modulations of movement speed captured
      condition-dependent strategies in crossing decisions. follow-up
      pairwise comparisons did not yield statistically significant
      differences.</p>
        </sec>
        <sec id="temporal-error">
          <title>3.1.6 Temporal Error:</title>
          <p>The temporal error represented the deviation from the ideal
      mid-gap. For our 3-second gaps, the ideal mid-gap would be at
      1.5-second. A mixed-design ANOVA revealed significant main effects
      of Condition (<italic>F(2,96) = 19.323, p &lt; .001, ηp2 =
      .287</italic>) and of Group <italic>(F(1,48) = 6.226, p = .016,
      ηp2 = .115).</italic>
      <xref alt="Figure 8" rid="fig-crossingTiming">Figure 8</xref>
      showed that ASD participants crossed the mid-point significantly
      later than NT participants. The result was congruent with our
      previous finding on Crossing Start Time stating that ASD
      participants started later their crossing.</p>
          <fig id="fig-crossingTiming">
            <caption>
              <p>Figure 8: Temporal deviation from ideal mid-gap
        crossing time by group and condition. The y-axis indicates the
        temporal deviation (in seconds) between the actual pedestrian
        crossing time and the ideal temporal midpoint of a 3-second
        inter-group gap (i.e., 1.5 s after the gap-opening vehicle and
        1.5 s before the gap-closing vehicle). A value of 0 represents a
        crossing precisely at the temporal mid-gap. Positive values
        reflect delayed crossings occurring in the second half of the
        available gap, whereas negative values indicate early crossings
        occurring before the temporal midpoint. Boxplots summarize the
        distribution of deviations for each condition and group, with
        error bars showing standard error.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="notebooks/fig-crossingTiming.png"/>
          </fig>
        </sec>
      </sec>
      <sec id="eye-tracking-and-pupillometry">
        <title>3.2 Eye-Tracking and Pupillometry</title>
        <sec id="gaze-preference">
          <title>3.2.1 Gaze Preference:</title>
          <fig id="fig-gazePref">
            <caption>
              <p>Figure 9: Standardized Gaze Projection onto the Gap
        Vector. This figure depicts the standardized projection of
        participants’ gaze positions onto a directional vector defined
        by two vehicles: the <italic>gap-opening</italic> vehicle (V3)
        and the <italic>gap-closing</italic> vehicle (V1). The
        projection translates each gaze point into a scalar value along
        this vector, indicating where attention was spatially oriented
        during the decision phase leading up to road crossing. To
        facilitate interpretation, the projection values were segmented
        into three meaningful ranges: [-0.25, 0.25]: gaze concentrated
        near the <italic>gap-opening</italic> vehicle (V3), [0.25,
        0.75]: gaze oriented toward the <italic>inter-group gap</italic>
        — the physical space potentially available for crossing, [0.75,
        1.25]: gaze concentrated near the <italic>gap-closing</italic>
        vehicle (V1). The x-axis represents time (in seconds) counting
        backward from the pedestrian reaching the road center (t = 0).
        Each dot represents the group-level mean projection value within
        a time bin; error ribbons indicate the standard error. The ASD
        group is shown as purple dots overlaid on red ribbons, while the
        NT group is represented by orange dots with teal
        ribbons.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="notebooks/fig-gazePreference.png"/>
          </fig>
          <p>Since the gaze data were sparse, we used a mixed linear model
      on gaze projections. With Subject as the random effect, Group,
      Condition, and Time as fixed-effect variables, we found a main
      effect of Condition <italic>(F(2,40.33) = 9.297, p &lt;
      .01).</italic> However, no significant main effect of Group
      emerged, although descriptive visualizations (see
      <xref alt="Figure 10" rid="fig-gazeCond">Figure 10</xref>)
      suggested subtle group-level differences in attentional allocation
      patterns.</p>
          <fig id="fig-gazeCond">
            <caption>
              <p>Figure 10: Overall Gaze Preference Across Conditions
        by Group. This figure summarizes the overall spatial
        distribution of gaze allocation in participants from the ASD and
        NT groups, aggregated across all experimental conditions and
        across a 12-second time window preceding the pedestrian’s
        arrival at the center of the road (t = 0). Gaze projection
        values were standardized and bounded within the range
        [−0.25,1.25]; values outside this interval were excluded to
        focus the analysis on task-relevant visual targets. The solid
        line represents the mean gaze projection for the ASD group,
        while the dotted line indicates the mean for the NT group. Error
        bars show 95% confidence intervals estimated from a linear
        mixed-effects model.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="notebooks/fig-gazeCond.png"/>
          </fig>
        </sec>
        <sec id="pupil-dilatation">
          <title>3.2.1 Pupil Dilatation:</title>
          <fig id="fig-pupil">
            <caption>
              <p>Figure 11: Pupil dilatation changes across
        conditions and groups.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="notebooks/fig-pupil.png"/>
          </fig>
          <p>Pupil dilation responses indicated reduced sensitivity in ASD
      participants across conditions, aligning with hypo-arousal
      theories
      (<xref alt="Zhao, Liu, and Wei 2022" rid="ref-zhao2022" ref-type="bibr">Zhao,
      Liu, and Wei 2022</xref>). Under the VarPlus condition, however,
      ASD participants showed significantly increased pupil dilation,
      reflecting heightened cognitive demand and higher sensitivity to
      the manipulated variable in VarPlus condition (i.e. absolute speed
      variability).</p>
        </sec>
      </sec>
      <sec id="fnirs-findings">
        <title>3.3 fNIRS Findings</title>
        <p>As HbO has been shown to correlate better with blood flow
    compared to HbR and total hemoglobin values
    (<xref alt="Hoshi, Kobayashi, and Tamura 2001" rid="ref-hoshi2001" ref-type="bibr">Hoshi,
    Kobayashi, and Tamura 2001</xref>), we focused on HbO signals for
    analysis. We performed two complementary statistical analyses.
    First, a time-averaged channel-level comparison assessed the overall
    activation difference between groups for each channel, regardless of
    temporal dynamics. Second, a time-resolved cluster-based permutation
    test was conducted to identify whether group differences occurred as
    contiguous temporal clusters within each channel under a specific
    condition.</p>
        <sec id="channel-level-activation">
          <title>3.3.1 Channel-level activation</title>
          <p>For each subject, and for each channel, we computed the
      contrast value as the GLM beta estimate of each Condition of
      interest (Const, VarMinus, VarPlus) minus the Baseline condition
      data. These contrast values were then entered into a linear
      mixed-effects model with group, three contrasted condition, and
      channels as fixed effects, and subject as a random effect. The
      model revealed a significant main effect of <bold>Group</bold>
      (<italic>F(1,52.7) = 6.10, p = 0.017</italic>), indicating that
      ASD and NT participants showed different mean activation contrasts
      across all channels and conditions. No significant main effect of
      <bold>Condition</bold> or three-way interactions were observed.
      The <bold>Group × Channel</bold> interaction was significant
      (<italic>F(24,2978) = 3.41, p &lt; 0.001</italic>), highlighting
      spatial heterogeneity in group differences. Post-hoc examination
      of fixed effects showed that channels <monospace>Fz_F1</monospace>
      (frontal, <italic>p = 0.00318</italic>) and
      <monospace>F7_FT7</monospace> (left temporal; <italic>p =
      0.0022</italic>) exhibited especially pronounced group
      differences.</p>
          <p>Pairwise t-tests across all channels identified a broader set
      of significant group differences (see
      <xref alt="Figure 12" rid="fig-channels">Figure 12</xref>). The
      significant or marginal group differences were mapped onto the
      initial layout (see
      <xref alt="Figure 13" rid="fig-signiftheta">Figure 13</xref>
      ).</p>
          <p>In <xref alt="Figure 12" rid="fig-channels">Figure 12</xref>,
      we found channel <monospace>Fz_F1</monospace> showing that ASD
      have significantly less HbO in frontal area; while the channel
      <monospace>F7_FT7</monospace> showing the opposite pattern, ASD
      having more HbO in the left temporal area.</p>
          <fig id="fig-channels">
            <caption>
              <p>Figure 12: <bold>Channel-wise directional group
        differences in task-evoked HbO response (ASD - NT).</bold> Each
        point represents the mean difference in HbO (ASD minus NT) for a
        given channel, estimated via paired t-test after FDR correction.
        Red stars denote statistically significant differences
        <italic>(p &lt; 0.05)</italic>, while red dots indicate marginal
        significance (0.05 ≤ p &lt; 0.1). A negative mean difference
        (left side) reflects higher HbO in the NT group, whereas a
        positive value (right side) indicates higher HbO in the ASD
        group.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="images/group%20diff%20on%20theta%20contrast%20by%20channel.png"/>
          </fig>
          <p>ASD participants in general exhibited lower HbO compared to NT
      across most channels, especially in the frontal region
      (e.g. <monospace>Fz_F1</monospace>). In contrast, increased HbO
      was observed in the ASD group relative to NT in the left temporal
      area (i.e. <monospace>F7_FT7</monospace>)</p>
          <fig id="fig-signiftheta">
            <caption>
              <p>Figure 13: <bold>Spatial visualization of group
        differences in channel-level theta contrasts (ASD vs NT).</bold>
        Blue arrows represent the anatomical locations of channels based
        on the international 10-10 system. Channel markers with red
        stars indicate statistically significant group differences (p
        &lt; 0.05), and those with red dots indicate marginal
        significance (0.05 ≤ p &lt; 0.1) based on independent t-tests
        (FDR-corrected). Channels with a red background highlight sites
        (e.g., <monospace>Fz_F1, F7_FT7</monospace>) where robust group
        effects were also confirmed using linear mixed-effects
        modeling.</p>
            </caption>
            <graphic mimetype="image" mime-subtype="png" xlink:href="images/signiftheta.png"/>
          </fig>
          <p>Only double-confirmed effects (<monospace>Fz_F1</monospace> in
      the frontal area, and <monospace>F7_FT7</monospace> in left
      temporal) are interpreted as robust findings; additional
      significant channels identified by t-tests alone are considered
      exploratory.</p>
        </sec>
        <sec id="permutation-cluster-test">
          <title>3.3.2. Permutation cluster test</title>
          <fig id="fig-permutation">
            <caption>
              <p>Figure 14</p>
            </caption>
            <graphic id="fig-permutation" mimetype="image" mime-subtype="png" xlink:href="images/permutation.png"/>
          </fig>
          <p>To identify group-level differences in time courses, we
      conducted a cluster-based permutation test on HbO signals across
      conditions. The test was applied to each channel and each
      condition individually, using participant-level HbO time courses
      as input.</p>
          <p>Cluster-based permutation testing revealed significant
      (<italic>p &lt; .05</italic>, cluster-corrected) group differences
      under <bold>VarPlus</bold> condition at frontal channel
      <monospace>AF4_F2</monospace> and left parietal channel
      <monospace>CP5_P5</monospace>. ASD participants exhibited
      increased HbO signals in these channels prior to crossing
      attempts, suggesting the cognitive differences were determined
      even before crossing. ASD pedestrians showed a differential
      engagement in neural processing than NT pedestrians under VarPlus
      condition.</p>
        </sec>
      </sec>
    </sec>
    <sec id="discussion">
      <title>4. Discussion</title>
      <p>Our multimodal findings illuminate distinctive cognitive responses
  from ASD pedestrians during interactions with autonomous vehicles,
  resonating strongly with predictive coding models of autism
  (<xref alt="Van de Cruys et al. 2014" rid="ref-vandecruys2014" ref-type="bibr">Van
  de Cruys et al. 2014</xref>). The heightened hesitation (i.e. Crossing
  Start Time) and sensitivity to speed variability (i.e. specific
  performance under VarPlus) observed in ASD participants likely arise
  from atypical sensory processing and decision-making processes, rather
  than deficits in motor execution.</p>
      <p>The reduced HbO activation in frontal areas coupled with increased
  temporal and parietal activations in ASD could reflect diminished
  engagement of regions involved in rapid social inference and executive
  control (frontal cortex) alongside enhanced reliance on regions
  implicated in sensory integration and motion perception (temporal and
  parietal cortices). The increased pupil dilation specifically under
  VarPlus conditions further corroborates increased cognitive load and
  autonomic arousal
  (<xref alt="Zadok et al. 2024" rid="ref-zadok2024" ref-type="bibr">Zadok
  et al. 2024</xref>) associated with unpredictability and sensory
  uncertainty.</p>
      <p>Hemodynamic response data from fNIRS further enriched our
  understanding by revealing condition-specific cortical activation
  patterns. Reduced frontal cortex activation (channel
  <monospace>Fz_F1</monospace>) in the ASD group potentially indicates
  diminished reliance on executive control regions typically engaged
  during rapid social inference or cognitive conflict resolution. </p>
      <p>Conversely, enhanced activations observed in temporal
  (<monospace>F7_FT7</monospace>) and parietal areas
  (<monospace>CP5_P5</monospace>) are particularly intriguing, as these
  regions are known to play critical roles in social perception, motion
  prediction, and mentalizing processes
  (<xref alt="Rebecca Saxe and Powell 2006" rid="ref-saxe2006" ref-type="bibr">Rebecca
  Saxe and Powell 2006</xref>;
  <xref alt="Van Overwalle and Baetens 2009" rid="ref-vanoverwalle2009" ref-type="bibr">Van
  Overwalle and Baetens 2009</xref>). This distinct activation profile
  suggests that ASD individuals may engage alternative neural pathways,
  possibly compensatory mechanisms, to process dynamic social cues and
  motion-related environmental changes presented by AVs.</p>
      <p>The permutation-based cluster analysis revealed temporally specific
  neural activation differences in frontal
  (<monospace>AF4_F2</monospace>) and parietal
  (<monospace>CP5_P5</monospace>) regions before crossing onset in the
  VarPlus condition. These early divergences suggest that autistic
  participants may allocate more anticipatory resources when faced with
  dynamically changing environments. Such a pattern is consistent with
  theories of altered sensory precision in autism, implying that under
  uncertainty, autistic individuals may engage more cortical processing
  to integrate sensory cues and guide decision-making.</p>
      <p>These findings collectively underscore the importance of designing
  AV communication systems that explicitly account for neurodiverse
  cognitive and perceptual processing styles. Incorporating clear,
  explicit, and dynamically adaptive signals into AV-pedestrian
  interactions can significantly improve safety and inclusivity. Our
  study also emphasizes the potential utility of multimodal
  physiological signals, such as fNIRS and pupillometry, as biomarkers
  for adaptive AV systems, enhancing responsiveness to the varied
  cognitive strategies employed by different pedestrian populations.</p>
      <p>However, several limitations should be acknowledged, including
  modest statistical power due to the smaller ASD sample, and age
  discrepancies between groups. Future research with larger,
  better-matched samples and refined measures (e.g., higher temporal
  resolution for vehicle position tracking) is essential to strengthen
  and generalize these findings.</p>
      <p>Ultimately, our multimodal approach reveals that the distinct
  behaviors and neural responses observed in autistic pedestrians are
  predominantly driven by unique sensory processing and decision-making
  characteristics rather than motor deficits. This highlights critical
  considerations for creating inclusive AV technology, facilitating
  safer interactions for neurodiverse populations in urban mobility
  environments.</p>
    </sec>
    <sec id="conclusion">
      <title>5. Conclusion</title>
      <p>Autistic individuals exhibit distinct behavioral and cognitive
  patterns in crossing decisions involving AVs. Their challenges do not
  stem from motor execution but from upstream decision processes tied to
  sensory processing, social perception and arousal. Designing AV
  systems that accommodate such differences can promote safer, more
  inclusive mobility ecosystems.</p>
    </sec>
    <sec id="author-contributions">
      <title>Author contributions</title>
      <p>Wenjie Huang: Conceptualization, Methodology, Software, Formal
  analysis, Investigation, Visualization, Data Curation, Writing –
  Original Draft.
  Emanuelle Reynaud: Conceptualization, Methodology, Validation,
  Resources, Supervision, Writing – Review &amp; Editing.
  Julien Cegarra: Software, Conceptualization, Funding acquisition.
  Arnaud Fournel: Software, Formal analysis, Resources, Data Curation.
  Vivien Gaujoux: Conceptualization, Methodology, Investigation.
  Jordan Navarro: Conceptualization, Supervision, Project
  administration, Funding acquisition, Writing – Review &amp;
  Editing.</p>
    </sec>
    <sec id="declaration-of-competing-interest">
      <title>Declaration of Competing Interest</title>
      <p>The authors declare no competing interests.</p>
    </sec>
    <sec id="funding">
      <title>Funding</title>
      <p>This research was supported by the a public funding from Délégation
  à la sécurité routière (DSR) (Grant no.xxxxx) for Jordan Navarro.</p>
    </sec>
    <sec id="declaration-of-generative-ai-in-the-writing-process">
      <title>Declaration of Generative AI in the Writing Process</title>
      <p>During the preparation of this article, ChatGPT was used to assist
  with language refinement and formatting. The final content was
  reviewed and verified by the authors.</p>
    </sec>
    <sec id="data-availability">
      <title>Data Availability</title>
      <p>Data underlying this article will be made available upon request
  due to privacy constraints regarding sensitive participant
  information.</p>
    </sec>
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