つながる知能、広がる現実

@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{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}

}

@inproceedings{Li2025b,

title = {State-Guided Spatial Cross-Attention for Enhanced End-to-End Autonomous Driving},

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

year  = {2025},

date = {2025-09-30},

urldate = {2025-09-30},

booktitle = {IEEE International Automated Vehicle Validation Conference (IAVVC 2025)},

address = {Baden-Baden, Germany},

abstract = {Handling near-accident scenarios is a significant challenge for end-to-end autonomous driving (E2E-AD), as these situations often involve sudden environmental changes, complex interactions with other road users, and high-risk decision-making under uncertainty. Unlike routine driving tasks, near-accident scenarios require rapid and precise responses based on external perception and internal vehicle dynamics. Successfully navigating such situations demands not only a comprehensive understanding of the surrounding environment but also an accurate assessment of the ego vehicle's state, including speed, acceleration, and steering angle, to ensure safe and reliable control. However, conventional E2E-AD models struggle to handle these safety-critical situations effectively. Standard approaches primarily rely on raw sensor inputs to learn driving policies, often overlooking the crucial role of vehicle state information in decision-making. Since many near-accident scenarios involve conditions where the same environmental observation could require vastly different responses depending on the ego vehicle's motion state-such as whether the vehicle is braking, accelerating, or experiencing traction loss-ignoring these internal dynamics can lead to unsafe or suboptimal actions. Furthermore, E2E-AD models typically learn a direct mapping from sensory inputs to control outputs, making it difficult to generalize to highly dynamic and unpredictable interactions, such as emergency evasive maneuvers or sudden braking events. To address these challenges, we propose a state-guided cross-attention mechanism that explicitly models the interaction between the ego vehicle's states and its perception of the environment. By incorporating vehicle state information into the decision-making process, our approach ensures that the model can dynamically adjust its attention to critical sensory inputs based on real-time driving conditions. This allows the autonomous system to make more context-aware decisions, improving its ability to respond effectively to complex and safety-critical scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bao2025,

title = {4D Path Planning via Spatiotemporal Voxels in Urban Airspaces},

author = {Naren Bao and Alex Orsholits and Manabu Tsukada},

year  = {2025},

date = {2025-08-27},

booktitle = {3rd Annual IEEE International Conference on Metaverse Computing, Networking, and Applications (IEEE MetaCom 2025)},

address = {Seoul, Republic of Korea},

abstract = {This paper presents an approach to four-dimensional (4D) path planning for unmanned aerial vehicles (UAVs) in complex urban environments. We introduce a spatiotemporal voxel-based representation that effectively models both spatial and temporal dimensions of urban airspaces. By integrating the 4D spatio-temporal ID framework with reinforcement learning techniques, our system generates efficient and safe flight paths while considering dynamic obstacles and environmental constraints. The proposed method combines off-line pretraining and online fine-tuning of reinforcement learning models to achieve computational efficiency without compromising path quality. Experiments conducted using PLATEAU datasets in various urban scenarios demonstrate that our approach outperforms traditional path planning algorithms by 24% in safety metrics and 18% in efficiency metrics. Our framework advances the state-of-the-art in urban air mobility by providing a scalable solution for airspace management in increasingly congested urban environments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}