@inproceedings{fredriksson-etal-2026-vanishing,
    title = "Vanishing point of attention: A platform for adaptive driver dialogue experiments",
    author = "Fredriksson, Morgan  and
      Yaici, Yanis  and
      Lam, Kevin  and
      Konigsmann, Jurgen  and
      Edlund, Jens",
    editor = "Riccardi, Giuseppe  and
      Mousavi, Seyed Mahed  and
      Torres, Maria Ines  and
      Yoshino, Koichiro  and
      Callejas, Zoraida  and
      Chowdhury, Shammur Absar  and
      Chen, Yun-Nung  and
      Bechet, Frederic  and
      Gustafson, Joakim  and
      Damnati, G{\'e}raldine  and
      Papangelis, Alex  and
      D{'}Haro, Luis Fernando  and
      Mendon{\c{c}}a, John  and
      Bernardi, Raffaella  and
      Hakkani-Tur, Dilek  and
      Di Fabbrizio, Giuseppe {''}Pino{''}  and
      Kawahara, Tatsuya  and
      Alam, Firoj  and
      Tur, Gokhan  and
      Johnston, Michael",
    booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
    month = feb,
    year = "2026",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard-stats/2026.iwsds-1.24/",
    pages = "231--238",
    abstract = "Current in-vehicle conversational agents lack awareness of the driving situation, treating all dialogue alike regardless of cognitive demands. This paper presents a modular experimental platform that integrates the {CARLA} driving simulator with a real-time spatial-reasoning engine to support research on situation-aware dialogue. The system enables {W}izard-of-{O}z studies in which human operators control conversational agents informed by live spatial-semantic analysis of the traffic environment. As initial validation, a controlled study (n = 10) tested the platform{'}s sensitivity to conversational load effects, examining whether increasing conversational complexity produces a vanishing point of attention, a threshold where combined conversational and driving demands lead to a non-linear collapse in performance. Results revealed a sharp rise in collisions and missed hazard detections under high cognitive load, confirming the platform{'}s sensitivity to conversational strain. The platform provides a reproducible testbed for investigating how dialogue timing, content, and environmental demands interact, offering a foundation for designing adaptive, cognitively safe in-vehicle conversational systems."
}