@inproceedings{Zhang2025,

title = {AWSIM-VR: A Tightly-Coupled Virtual Reality Extension for Human-in-the-Loop Pedestrian-Autonomous Vehicle Interaction},

author = {Shangkai Zhang and Alex Orsholits and Ehsan Javanmardi 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 = {Effective communication between autonomous vehicles (AVs) and pedestrians is crucial for ensuring future urban traffic safety. While external Human-Machine Interfaces (eHMIs) have emerged as promising solutions, current evaluation methodologies — particularly Virtual Reality (VR)-based studies — typically rely on scripted or pre-defined autonomous vehicle behaviors, limiting realism and neglecting pedestrians' active role in interactions. To address this, we introduce AWSIM-VR, a tightly-coupled VR extension of the AWSIM autonomous driving simulator, enabling real-time, human-in-the-loop pedestrian-AV interactions by directly integrating unmodified, real autonomous driving software (Autoware) into the simulation loop. Unlike previous systems, AWSIM-VR provides authentic, bidirectional interaction: pedestrians' actions dynamically influence vehicle decision-making and eHMI responses in real-time, closely emulating real-world AV scenarios. In user studies directly comparing AWSIM-VR to existing methodologies, participants reported significantly higher perceived realism and immersion, underscoring the importance of authentic autonomous behaviors in VR-based pedestrian interaction research. By directly utilizing production-level autonomous driving stacks, AWSIM-VR represents a significant methodological advancement, enabling more realistic, effective, and safer development and evaluation of eHMIs and AV technologies.},

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}

}