つながる知能、広がる現実

@inproceedings{Chen2026,

title = {Spatial ID-Driven Edge-Cloud Architecture for Real-Time Urban Digital Twins},

author = {Yanru Chen and Sami Brahim Djelloul and Alex Orsholits and Manabu Tsukada and Hiroshi Esaki},

year  = {2026},

date = {2026-01-08},

urldate = {2026-01-08},

booktitle = {IEEE Consumer Communications & Networking Conference (CCNC2026)},

address = {Las Vegas, USA},

abstract = {Only the chairs can edit The integration of static geospatial datasets and real-time IoT streams is essential for responsive and scalable urban Digital Twins (DTs). However, current infrastructures remain fragmented across domains, formats, and reference systems, limiting interoperability and city-scale deployment. This paper presents the first city-scale implementation of a Spatial ID-driven edge-cloud architecture that unifies heterogeneous static and dynamic urban data under a hierarchical four-dimensional identifier. Unlike prior DT systems that rely on ad hoc tiling or local schemas, our design operationalizes Spatial ID as a universal indexing layer across batch and streaming pipelines, enabling multi-resolution queries, real-time synchronization, and cross-domain interoperability. A prototype deployment in Tokyo's Chiyoda and Bunkyo wards demonstrates the approach, integrating 3D city models with live IoT streams. Static data are encoded into Spatial IDs and distributed via a geospatial database and vector tiles, while dynamic streams are processed at the edge and synchronized with a cloud backend using a publish/subscribe model. The system supports real-time encoding, querying, distribution, and web/Mixed Reality (MR)-based visualization. Evaluation shows millisecond-to-second query performance over 148 million records, sub-100 ms vector tile delivery, and real-time IoT stream processing at 30 fps. These results establish Spatial ID not only as a conceptual framework but as a practical, deployable foundation for interoperable, low-latency, and scalable Digital Twin infrastructures aligned with the vision of Society 5.0.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Meng2025b,

title = {Patch Exploration-Based Route Planning for Autonomous Vehicles},

author = {Huan Meng and Jinhui Zhang and Xiaobing Huang and Ehsan Javanmardi and Manabu Tsukada},

year  = {2025},

date = {2025-11-18},

urldate = {2025-11-18},

booktitle = {28th IEEE International Conference on Intelligent Transportation Systems (ITSC2025)},

address = {Gold Coast, Australia},

abstract = {Route planning is a crucial component of autonomous

driving, making it essential to develop efficient planning

methods tailored to different scenarios. To address the

limitations of the existing RRT* methods, we propose a

Patch Exploration RRT* (PE-RRT*). First, separating

hyperplanes and half-spaces are utilized to construct patch

for each node in the tree structure, allowing adaptation to

the environmental density. Additionally, during the

sampling process, the patches guide the expansion of the

random tree, enabling rapid searches and efficient use of

sampled points. Moreover, the patches are applied for route

pruning, simplifying the route representation. Simulation

results demonstrate that the proposed method achieves high

search efficiency across various scenarios. },

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{Si2025,

title = {You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception},

author = {Hao Si and Ehsan Javanmardi and Manabu Tsukada},

url = {https://sihaoo1.github.io/PHCP_Page/

https://arxiv.org/abs/2509.09310

https://github.com/sihaoo1/PHCP},

year  = {2025},

date = {2025-10-19},

urldate = {2025-10-19},

booktitle = {International Conference on Computer Vision (ICCV2025)},

address = {Honolulu, Hawai'i},

abstract = {Collaborative perception enables vehicles to overcome individual perception limitations by sharing information, allowing them to see further and through occlusions. In real-world scenarios, models on different vehicles are often heterogeneous due to manufacturer variations. Existing methods for heterogeneous collaborative perception address this challenge by fine-tuning adapters or the entire network to bridge the domain gap. However, these methods are impractical in real-world applications, as each new collaborator must undergo joint training with the ego vehicle on a dataset before inference, or the ego vehicle stores models for all potential collaborators in advance. Therefore, we pose a new question: Can we tackle this challenge directly during inference, eliminating the need for joint training? To answer this, we introduce Progressive Heterogeneous Collaborative Perception (PHCP), a novel framework that formulates the problem as few-shot unsupervised domain adaptation. Unlike previous work, PHCP dynamically aligns features by self-training an adapter during inference, eliminating the need for labeled data and joint training. Extensive experiments on the OPV2V dataset demonstrate that PHCP achieves strong performance across diverse heterogeneous scenarios. Notably, PHCP achieves performance comparable to SOTA methods trained on the entire dataset while using only a small amount of unlabeled data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Li2025d,

title = {Multi-PrefDrive: Optimizing Large Language Models for Autonomous Driving Through Multi-Preference Tuning},

author = {Yun Li and Ehsan Javanmardi and Simon Thompson and Kai Katsumata and Alex Orsholits and Manabu Tsukada},

url = {https://liyun0607.github.io/},

year  = {2025},

date = {2025-10-19},

booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

address = {Hangzhou, China},

abstract = {This paper introduces Multi-PrefDrive, a framework that significantly enhances LLM-based autonomous driving through multidimensional preference tuning. Aligning LLMs with human driving preferences is crucial yet challenging, as driving scenarios involve complex decisions where multiple incorrect actions can correspond to a single correct choice. Traditional binary preference tuning fails to capture this complexity. Our approach pairs each chosen action with multiple rejected alternatives, better reflecting real-world driving decisions. By implementing the Plackett-Luce preference model, we enable nuanced ranking of actions across the spectrum of possible errors. Experiments in the CARLA simulator demonstrate that our algorithm achieves an 11.0% improvement in overall score and an 83.6% reduction in

infrastructure collisions, while showing perfect compliance with traffic signals in certain environments. Comparative analysis against DPO and its variants reveals that Multi-PrefDrive’s superior discrimination between chosen and rejected actions, which achieving a margin value of 25, and such ability has been directly translates to enhanced driving performance. We implement memory-efficient techniques including LoRA and 4-bit quantization to enable deployment on consumer-grade hardware and will open-source our training code and multi-rejected dataset to advance research in LLM-based autonomous driving systems. Project Page (https://liyun0607.github.io/)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Meng2025,

title = {Robust composite control strategy for constrained continuous-time nonlinear systems},

author = {Huan Meng and Jinhui Zhang and Manabu Tsukada},

year  = {2025},

date = {2025-10-05},

booktitle = {IEEE International Conference on Systems, Man, and Cybernetics (SMC2025)},

address = {Vienna, Austria},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}