Connected Intelligence, Expanded Reality

@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{Li2026c,

title = {V2XState: Intent-aware Spatial Basis Attention for Cooperative End-to-end Driving},

author = {Dongyang Li and Ehsan Javanmardi and Manabu Tsukada},

year  = {2026},

date = {2026-06-09},

urldate = {2026-06-09},

booktitle = {2026 IEEE 103rd Vehicular Technology Conference (VTC2026-Spring)},

address = {Nice, France},

abstract = {Cooperative autonomous driving relies on shared perception, yet most planning models implicitly treat spatial relevance as scene-driven and largely invariant to the ego vehicle's intent. We argue that spatial relevance for planning should be intent-conditioned: which regions matter depends on the specific maneuver being executed. This paper proposes V2XState, a framework that operationalizes this insight by using ego states and commands to modulate spatial attention over cooperative features. By incorporating kinematic-aware inductive biases through a state-gated basis attention mechanism, V2XState yields context-sensitive emphasis that aligns planning with current driving intent. We integrate this mechanism into a lightweight planning stack and observe that the resulting attention maps are both interpretable and intent-consistent. Experiments on cooperative driving benchmarks validate that intent-aware spatial attention leads to more maneuver-consistent planning, achieving a 8.2%/7.2% reduction in ADE/FDE relative to state-of-the-art baselines.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Li2026b,

title = {Aligning What Matters: Object-Centric and Structure-Aware Feature Alignment for Heterogeneous Collaborative Perception},

author = {Donghui Li and Ehsan Javanmardi and Manabu Tsukada},

year  = {2026},

date = {2026-06-09},

booktitle = {2026 IEEE 103rd Vehicular Technology Conference (VTC2026-Spring)},

address = {Nice, France},

abstract = {Collaborative perception enhances autonomous driving by enabling vehicles to jointly reason about the environment. Feature-level sharing is particularly attractive, as it preserves rich spatial information and allows uncertain regions to be jointly refined before object inference. However, in realistic deployments, vehicles often rely on heterogeneous perception backbones and preprocessing pipelines, resulting in substantial domain gaps that make feature fusion unreliable. Recent protocol-domain adapter frameworks mitigate this issue by mapping heterogeneous features into a shared latent space, but their alignment objectives are typically defined over the entire Bird’s Eye View (BEV) grid. Since foreground objects occupy only a small fraction of BEV space, such global reconstruction objectives are dominated by background regions and provide weak supervision for aligning detection-critical features. Moreover, purely global alignment does not explicitly preserve fine-grained spatial structures that are essential for accurate 3D object detection.

In this work, we revisit protocol-domain alignment from the perspective of detection relevance and structural consistency. We propose an object-centric and structure-aware adapter for heterogeneous collaborative perception. Specifically, we introduce BEV foreground masks derived from 3D bounding boxes to emphasize object regions during alignment, and incorporate a Structural Similarity Index Measure (SSIM)–based objective to enforce fine-grained structural consistency across heterogeneous feature domains. Importantly, the proposed method preserves the plug-and-play property of protocol-domain collaboration and requires no modification of the original perception backbones or fusion modules.

Extensive experiments under diverse heterogeneous configurations demonstrate consistent improvements over protocol-domain adapter baselines, achieving up to +0.19 AP@0.7 under challenging modality settings.



},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tao2026,

title = {TacitCollab: A Plug-and-Play V2X Safety Filter via Free-Form LLM Dialogue},

author = {Ye Tao and Manabu Tsukada and Hiroshi Esaki},

year  = {2026},

date = {2026-06-09},

urldate = {2026-06-09},

booktitle = {2026 IEEE 103rd Vehicular Technology Conference (VTC2026-Spring)},

address = {Nice, France},

abstract = {Autonomous vehicles increasingly rely on Vehicle-to-Everything (V2X) communication to coordinate in complex traffic scenarios. However, the existing approaches typically exchange predefined message formats such as waypoints or discrete actions, limiting their ability to handle exotic situations

that require nuanced negotiation. We propose TacitCollab, a V2X dialogue system that enables vehicles to coordinate collision avoidance through free-form natural language communication. TacitCollab leverages vision-language models to interpret traffic situations and engage in human-like dialogue, allowing vehicles

to share observations, negotiate priorities, and reach mutual agreements. The system is designed as a plug-and-play module that can be inserted into existing autonomous driving pipelines, accepting intentions in arbitrary formats and outputting updated intentions in the same format. We evaluate TacitCollab on 238 collision scenarios from the DeepAccident dataset. Results show a Collision Avoidance Ratio of 97.4%, with 84.2% of scenarios achieving coordinated outcomes where vehicles successfully

negotiate right-of-way. The average bandwidth of 1.17 KB per scenario demonstrates that text-based dialogue is practical for bandwidth-constrained V2X networks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chi2026,

title = {Fast-Adapting V2X Networks: A Dynamic Context Clustering Multi-Armed Bandits},

author = {Weiqi Chi and Xiaoyang He and Manabu Tsukada},

year  = {2026},

date = {2026-06-08},

urldate = {2026-06-08},

booktitle = {IEEE Vehicular Networking Conference (VNC2026) },

address = {Montreal, Canada},

abstract = {Only the chairs can edit While contextual multi-armed bandit (CMAB) effectively addresses non-stationary reward for problems such as millimeter-wave (mmWave) Vehicle-to-Everything (V2X) user association (UA), it suffers from slow convergence when dealing with a large-scale context space. This paper proposes a Dynamic Context Clustering Semi-distributed Multi-Armed Bandit (DCC-SMAB) algorithm for the UA problem in mmWave vehicular networks that dynamically clusters vehicle positions as contexts, thereby accelerating convergence in reward estimation without requiring additional channel state information (CSI) compared to the CMAB framework with fixed context partitioning. The algorithm exhibits strong capability in identifying mmWave channel variations, enabling rapid adaptation to non-stationary communication environments for both newly arriving and existing vehicles in the network. Extensive simulations demonstrate over 61% and 76% in average regret reduction and network transmission rate improvement compared to benchmarks using Upper Confidence Bound (UCB) policy, with robustness validated across 40 trials under varying network topologies.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wu2026,

title = {A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X-Enabled Autonomous Driving },

author = {Hanlin Wu and Pengfei Lin and Ehsan Javanmardi and Naren Bao and Bo Qian and Hao Si and Manabu Tsukada},

url = {https://arxiv.org/abs/2506.17004

https://github.com/tlab-wide/Co3SOP},

year  = {2026},

date = {2026-06-01},

urldate = {2026-06-01},

booktitle = {IEEE International Conference on Robotics & Automation (ICRA 2026)},

address = {Vienna, Austria},

abstract = {3D semantic occupancy prediction is an emerging perception paradigm in autonomous driving, providing a voxel-level representation of both geometric details and semantic categories. However, its effectiveness is inherently constrained in single-vehicle setups by occlusions, restricted sensor range, and narrow viewpoints. To address these limitations, collaborative perception enables the exchange of complementary information, thereby enhancing

the completeness and accuracy of predictions. Despite its potential, research on collaborative 3D semantic occupancy prediction is hindered by the lack of dedicated datasets. To bridge this gap, we design a high-resolution semantic voxel sensor in CARLA to produce dense and comprehensive annotations. We further develop a baseline model that performs inter-agent feature fusion via spatial alignment and attention aggregation. In addition, we establish

benchmarks with varying prediction ranges designed to systematically assess the impact of spatial extent on collaborative prediction.  Experimental results demonstrate the superior performance of our baseline, with increasing gains observed as range expands. },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}