Publication

@inproceedings{Chauhan2026,

title = {Colored Shared Spaces (CSS): How Visual Design Transforms Pedestrian Experiences},

author = {Vishal Chauhan and Anubhav Anubhav and Chia Ming Chang and Ehsan Javanmardi and Takeo Igarashi and Alex Orsholits and Kantaro Fujiwara and Manabu Tsukada},

year  = {2026},

date = {2026-07-26},

urldate = {2026-07-16},

booktitle = {28th International Conference on Human-Computer Interaction (HCII2026)},

address = {Montreal, Canada},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chauhan2026b,

title = {Don't Worry, Just Follow Me: Prototyping and In-the-Wild Evaluation of Smart Pole Interaction Unit with Mobility},

author = {Vishal Chauhan and Anubhav Anubhav and Mark Colley and Chia-Ming Chang and Xinyue Gui and Ding Xia and Ehsan Javanmardi and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada},

url = {https://youtu.be/PLmCgmTwsHQ},

doi = {10.1145/3772318.3790882},

year  = {2026},

date = {2026-04-13},

urldate = {2026-04-13},

booktitle = {ACM CHI conference on Human Factors in Computing Systems 2026},

address = {Barcelona, Spain},

abstract = {Pedestrian–automated vehicle(AV) encounters in shared spaces often involve hesitation and ambiguity. Vehicle-mounted external human–machine interfaces(eHMIs) can help, but obscured or poorly timed communications create significant challenges. To address this, we present a mobile smart pole interaction unit(SPIU) with integrated cameras and LED displays, designed as a pedestrian-side system to deliver explicit cues(``WALK,'' ``STOP''). An in-the-wild evaluation of the SPIU(N=21) using a four-factor analysis (CarBehavior, Mobility, eHMI, SPIU) showed that the SPIU improved understandability, trust, and perceived safety, and reduced workload compared with the baseline, with a combination(eHMI+SPIU) yielding the strongest results. Beyond these quantitative benefits, participants appreciated the mobility of the SPIU for its ``clear'' and ``easy to decide'' mediation. This work contributes to(1) a design and deployment framework for a mobile SPIU and(2) an in-the-wild evaluation protocol for pedestrian–AV interactions in nonsignalized spaces. Our work sparks discussions on real world evaluations involving detailed vehicle kinematics and accessible multimodality(e.g., audio), focusing on the role of personal robots as user-side eHMIs.},

note = {Honourable Mention Award},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gui2026,

title = {Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCI},

author = {Xinyue Gui and Ding Xia and Mark Colley and Yuan Li and Vishal Chauhan and Anubhav Anubhav and Zhongyi Zhou and Ehsan Javanmardi and Stela Hanbyeol Seo Social and Chia-Ming Chang and Manabu Tsukada and Takeo Igarashi},

url = {https://arxiv.org/abs/2602.16157

https://youtu.be/PORfzWr2v5c},

doi = {10.1145/3772318.3790537},

year  = {2026},

date = {2026-04-13},

urldate = {2026-04-13},

booktitle = {ACM CHI conference on Human Factors in Computing Systems 2026},

address = {Barcelona, Spain},

abstract = {Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.},

note = {Honourable Mention Award},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chauhan2025b,

title = {A Silent Negotiator? Cross-cultural VR Evaluation of Smart Pole Interaction Units in Dynamic Shared Spaces},

author = {Vishal Chauhan and Anubhav Anubhav and Robin Sidhu and Yu Asabe and Kanta Tanaka and Chia-Ming Chang and Xiang Su and Dr. Ehsan Javanmardi and Takeo Igarashi and Alex Orsholits and Kantaro Fujiwara and Manabu Tsukada},

url = {https://github.com/tlab-wide/Smartpole-VR-AWSIM.git},

doi = {10.1145/3756884.3765991},

year  = {2025},

date = {2025-11-12},

urldate = {2025-11-12},

booktitle = {The ACM Symposium on Virtual Reality Software and Technology (VRST2025) },

address = {Montreal, Canada},

abstract = {As autonomous vehicles (AVs) enter pedestrian-centric environments, existing vehicle-mounted external human–machine interfaces (eHMIs) often fall short in shared spaces due to line-of-sight limitations, inconsistent signaling, and increased cognitive burden on pedestrians. To address these challenges, we introduce the Smart Pole Interaction Unit (SPIU), an infrastructure-based eHMI that decouples intent signaling from vehicles and provides context-aware, elevated visual cues. We evaluate SPIU using immersive VR-AWSIM simulations in four high-risk urban scenarios: four-way intersections, autonomous mixed traffic, blindspots, and nighttime crosswalks. The experiment was developed in Japan and replicated in Norway, where forty participants engaged in 32 trials each under both SPIU-present and SPIU-absent conditions. Behavioral (response time) and subjective (acceptance scale) data were collected. Results show that SPIU significantly improves pedestrian decision-making, with reductions ranging from 40% to over 80% depending on scenario and cultural context, particularly in complex or low-visibility scenarios. Cross-cultural analyses highlight SPIU's adaptability across differing urban and social contexts. We release our open-source Smartpole-VR-AWSIM framework to support reproducibility and global advancement of infrastructure-based eHMI research through reproducible and immersive behavioral studies.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chauhan2024b,

title = {Connected Shared Spaces: Expert Insights into the Impact of eHMI and SPIU for Next-Generation Pedestrian-AV Communication},

author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada},

doi = {10.1145/3732437.3732752},

year  = {2024},

date = {2024-11-28},

urldate = {2024-11-28},

booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},

pages = {16 - 20},

address = {Tokyo, Japan},

abstract = {Increasing prevalence of Autonomous Vehicles (AVs) necessitates efficient communication with susceptible road users, especially pedestrians, in communal urban areas. To improve pedestrians’ trust and safety, Smart Pole Interaction Units (SPIU) and external Human-Machine Interfaces (eHMI) have become crucial interfaces. In this study, we ask 12 automotive UI design experts to evaluate eHMI, SPIU, and eHMI+SPIU through an online survey. They evaluated the interfaces’ effects on five key parameters: Safety, Seamless, Adaptability, Accessibility, and Trust. Our findings show that eHMI stands out for its smooth integration (Seamless), whereas SPIU is favoured for fostering Safety, Adaptability, Accessibility, and Trust. Furthermore, an integrated eHMI+SPIU solution is rated higher than individual eHMI and SPIU. In particular, when several AVs interact, the best way to promote pedestrian trust is to employ eHMI in conjunction with SPIU. This study highlights the benefits of SPIU as a centralised information hub for reliable pedestrian communication and presents innovative design considerations for eHMI on AVs in shared spaces. The results provide a framework for more practical testing of these systems to create safe, inclusive, and human-centric pedestrian-AV interactions in various urban environments beyond shared spaces.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chauhan2024,

title = {Transforming Pedestrian and Autonomous Vehicles Interactions in Shared Spaces: A Think-Tank Study on Exploring Human-Centric Designs},

author = {Vishal Chauhan and Anubhav Anubhav and Chia-Ming Chang and Jin Nakazato and Ehsan Javanmardi and Alex Orsholits and Takeo Igarashi and Kantaro Fujiwara and Manabu Tsukada



},

doi = {10.1145/3641308.3685037},

year  = {2024},

date = {2024-09-22},

urldate = {2024-09-22},

booktitle = {16th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2024), Work in Progress (WiP)},

pages = {1-8},

address = {California, USA},

abstract = {Our research focuses on the smart pole interaction unit (SPIU) as an infrastructure external human-machine interface (HMI) to enhance pedestrian interaction with autonomous vehicles (AVs) in shared spaces. We extensively study SPIU with external human-machine interfaces (eHMI) on AVs as an integrated solution. To discuss interaction barriers and enhance pedestrian safety, we engaged 25 participants aged 18-40 to brainstorm design solutions for pedestrian-AV interactions, emphasising effectiveness, simplicity, visibility, and clarity. Findings indicate a preference for real-time SPIU interaction over eHMI on AVs in multiple AV scenarios. However, the combined use of SPIU and eHMI on AVs is crucial for building trust in decision-making. Consequently, we propose innovative design solutions for both SPIU and eHMI on AVs, discussing their pros and cons. This study lays the groundwork for future autonomous mobility solutions by developing human-centric eHMI and SPIU prototypes as ieHMI.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chauhan2023,

title = {Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load},

author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Koki Toda and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374582122_Keep_Calm_and_Cross_Smart_Pole_Interaction_Unit_for_Easing_Pedestrian_Cognitive_Load/links/6525681eb32c91681fb2e1b5/Keep-Calm-and-Cross-Smart-Pole-Interaction-Unit-for-Easing-Pedestrian-Cognitive-Load.pdf},

doi = {10.1109/WF-IoT58464.2023.10539511},

year  = {2023},

date = {2023-10-12},

urldate = {2023-10-12},

booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},

address = {Aveiro, Portugal},

abstract = {Recently, there has been a growing emphasis on autonomous vehicles (AVs), and as they coexist with pedestrians, ensuring pedestrian safety at crosswalks has become paramount. While AVs exhibit commendable performance on traditional roads with established traffic infrastructure, their interaction in different environments, such as shared spaces lacking traffic lights or sign rules (also known as naked streets), can present significant challenges, including right-of-way and accessibility concerns. To address these challenges, this study proposes a novel approach to enhance pedestrian safety in shared spaces, focusing on the proposed smart pole interaction unit (SPIU) combined with an external human-machine interface (eHMI). By evaluating the proposal of SPIU developed by a virtual reality system, we explore its usability and effectiveness in facilitating vehicle-to-pedestrian (V2P) interactions at crosswalks. Our findings from this study showed that SPIU facilitates safe, quicker decision-making to stop and pass at crosswalks in shared space and reduces cognitive load compared to scenarios where an SPIU is absent for pedestrians and reduce the need for eHMI to see on multiple AVs. The SPIU addition with the eHMI in vehicles yields a noteworthy 21 % improvement in response time, enhancing efficiency during pedestrian stops. In both scenarios, whether with a single AV (1-way) or multiple AVs (2-way), SPIU has a positive impact on interaction dynamics and statistically demonstrates a significant improvement (p = 0.001). },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lin2023,

title = {Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles},

author = {Pengfei Lin and Ehsan Javanmardi and Ye Tao and Vishal Chauhan and Jin Nakazato and Manabu Tsukada},

url = {https://arxiv.org/abs/2306.06981},

year  = {2023},

date = {2023-06-04},

urldate = {2023-06-04},

booktitle = {2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023)},

address = {Anchorage, Alaska, USA},

abstract = {Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.

},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}