つながる知能、ひろがる現実

@inproceedings{gui2026tap2stop,

title = {Tap2Stop: Pedestrian-Activated Emergency Stop for Autonomous Vehicles},

author = {Xinyue Gui and Ding Xia and Yuan Li and Mark Colley and Chia-Ming Chang and Stela Hanbyeol Seo and Manabu Tsukada and Takeo Igarashi},

year  = {2026},

date = {2026-11-01},

urldate = {2026-11-01},

booktitle = {The 39th Annual ACM Symposium on User Interface Software and Technology (UIST2026)},

address = {Detroit, MI, USA},

series = {UIST '26},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{KEBE2026,

title = {FaymaKash: An NFC-based Interoperable Payment System for Scholarship Access in Senegalese Universities},

author = {Ousmane William KEBE and Manabu Tsukada and Abel DIATTA},

year  = {2026},

date = {2026-10-07},

booktitle = {IEEE Global Humanitarian Technology Conference (GHTC) 2026},

address = {Colorado, United States},

abstract = {Senegalese university students rely on government scholarships delivered through mobile money operators to cover their daily expenses, but accessing these funds at university vendor points is a recurring obstacle. A 600-respondent survey we conducted across six universities found that 61% face difficulties withdrawing their funds. We propose FaymaKash, an NFC-based payment system using Host Card Emulation that unifies four mobile money providers into a single operator-independent wallet. In a controlled evaluation, FaymaKash reduced average transaction time by 46% compared to cash and 38% compared to QR code.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yun2026,

title = {An Open-Source Modular Benchmark for Diffusion-Based Motion Planning in Closed-Loop Autonomous Driving},

author = {Li Yun and Simon Thompson and Yidu Zhang and Ehsan Javanmardi and Manabu Tsukada},

year  = {2026},

date = {2026-09-15},

urldate = {2026-09-15},

booktitle = {The IEEE International Conference on Intelligent Transportation Systems (ITSC2026)

},

address = {Naples, Italy},

abstract = {Diffusion-based motion planners have achieved

state-of-the-art results on benchmarks such as nuPlan, yet

their evaluation within closed-loop production autonomous

driving stacks remains largely unexplored. Existing

evaluations abstract away ROS 2 communication latency and

real-time scheduling constraints, while monolithic ONNX

deployment freezes all solver parameters at export time. We

present an open-source modular benchmark that addresses

both gaps: using ONNX GraphSurgeon, we decompose a

monolithic 18,398-node diffusion planner into three

independently executable modules and reimplement the

DPM-Solver++ denoising loop in native C++. Integrated as a

ROS 2 node within Autoware, the open-source AD stack

deployed on real vehicles worldwide, the system enables

runtime-configurable solver parameters without model

recompilation and per-step observability of the denoising

process, breaking the black box of monolithic deployment.

Unlike evaluations in standalone simulators such as CARLA,

our benchmark operates within a production-grade stack and

is validated through AWSIM closed-loop simulation. Through

systematic comparison of DPM-Solver++ (first- and

second-order) and DDIM across six step-count configurations

(N in {3, 5, 7, 10, 15, 20}), we show that encoder caching

yields a 3.2x latency reduction, and that second-order

solving reduces FDE by 41% at N=3 compared to first-order.

The complete codebase will be released as open-source,

providing a direct path from simulation benchmarks to

real-vehicle deployment. Project page: },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{YANG2026,

title = {Collaborative Semantic Occupancy Prediction with Probabilistic Gaussian},

author = {SIBING YANG and Kimihiko Nakano and Manabu Tsukada and Wei WANG and Ehsan Javanmardi},

year  = {2026},

date = {2026-09-15},

booktitle = {The IEEE International Conference on Intelligent Transportation Systems (ITSC2026)},

address = {Naples, Italy},

abstract = {Vehicle-to-vehicle (V2V) collaborative perception enables

multi-agent information sharing and provides a more

comprehensive understanding of the surrounding environment

for semantic occupancy prediction. However, most existing

collaborative semantic occupancy methods rely on dense

grid-based representations, whose communication cost

increases rapidly due to the explicit modeling of large

amounts of empty space.

In this paper, we propose CoPG, a collaborative semantic

occupancy prediction framework built upon a probabilistic

Gaussian representation. The proposed representation

explicitly models non-empty regions while suppressing

redundant primitives in empty space, enabling a compact yet

expressive scene encoding. Moreover, we introduce a

Gaussian communication strategy that selectively transmits

semantically confident and spatially relevant Gaussians,

substantially reducing communication redundancy. For

multi-agent integration, we employ 3D sparse convolution to

implicitly model spatial interactions among neighboring

Gaussians at the Gaussian level.

Experimental results demonstrate that the proposed method

reduces communication volume by 18.5% while improving mIoU

by 11.8% over strong baselines, validating the

effectiveness and communication efficiency of

Gaussian-level fusion for collaborative semantic occupancy

prediction.

The source code will be made publicly available},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Asabe2026,

title = {At the Limits: Sim-to-Real and Community Lessons from the Autonomous Driving AI Challenge},

author = {Yu Asabe and Shintaro Tomie and Taiki Tanaka and Masahiro Kubota and Shinpei Kato and Manabu Tsukada and Hiroshi Esaki},

year  = {2026},

date = {2026-09-15},

booktitle = {The IEEE International Conference on Intelligent Transportation Systems (ITSC2026)},

address = {Naples, Italy},

abstract = {Developing software-defined vehicles (SDV) requires an

integrated mastery of automotive control, software

engineering, and machine learning. As participants in the

Autonomous Driving AI Challenge, we navigated this

multi-domain integration within a high-speed autonomous

racing context. This paper documents our technical journey

in bridging the "sim-to-real" gap, moving from

high-fidelity digital twins to physical EV racing karts

operating at their dynamic limits. A unique feature of this

challenge is its professional-level development

infrastructure, incorporating cloud-native Continuous

Integration/Continuous Delivery (CI/CD) pipelines and

Over-The-Air (OTA) deployment. We detail how this framework

enabled a rapid PDCA (Plan-Do-Check-Act) cycle, allowing us

to transition from virtual validation to real-world

performance tuning in minutes. We detail the methodologies

employed to operate at the vehicle's physical limits,

specifically focusing on the challenges of robust

simulation-to-real transfer and the implementation of

hybrid architectural strategies. Beyond the individual

technical challenges, we highlight how a collaborative

community ecosystem driven by open-source heuristics,

real-time telemetry, and "edutainment" broadcasting

accelerated our technical growth. Our findings serve as a

participant-led case study on the efficacy of autonomous

racing as a catalyst for both technical innovation and the

democratization of SDV engineering expertise. },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {HERO: Heterogeneous Evidential Robust Object-Level Collaborative Perception},

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

year  = {2026},

date = {2026-09-08},

booktitle = {The 19th European Conference on Computer Vision (ECCV 2026)},

address = {Malmö, Sweden},

abstract = {Real-world collaborative perception agents typically deploy diverse sensor configurations and network architectures. While feature-level fusion methods address this heterogeneity by aligning feature spaces, they require collaborative training and large communication overhead. In contrast, object-level fusion sidesteps these constraints, as transmitted bounding boxes are directly usable across varying architectures. However, standard late fusion relies only on box coordinates and confidence scores, which provide insufficient information for robust association and leave the system vulnerable to localization noise and asynchronous delays. To address this, we propose HERO, an uncertainty-guided object-level collaborative perception method that equips proposals with evidential statistics and uses the uncertainty signals to perform robust association and fusion under pose noise and asynchrony. Experiments on OPV2V-H and DAIR-V2X show that HERO matches the SOTA feature-level fusion methods while reducing bandwidth by over 1000 times. Under localization error and latency scenarios, HERO further outperforms feature-level SOTA models.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}