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Toward Blockage-Resilient 6G-V2X Connectivity: Semi-Distributed Bandit with Dynamic Arm Set for mmWave HetNets
Weiqi Chi, Bo Qian, Hanlin Wu +3
The vision for 6G vehicle-to-everything (V2X) communications demands reliable, adaptive connectivity for fully autonomous driving across complex dynamic environments. Millimeter-wa…
QueenVIS: Rethinking Image-Only Training for Video Instance Segmentation via Query Enrichment
Arian Kheirandish, Fardin Ayar, Ehsan Javanmardi +2
Video instance segmentation (VIS) requires models to detect, segment, and track object identities across frames, and most methods enforce temporal consistency through video-level s…
DispatchRAG: Grounding Emergency Dispatch Decisions in Real-World Protocols from Traffic Accident Video
Muhammad Sulthan Adhipradhana, Ehsan Javanmardi, Naren Bao +1
Assessing the severity of a traffic accident scenario is important to decide which emergency service to dispatch. Missing an ambulance dispatch on a pedestrian accident is a fatal…
Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving
Yun Li, Jiachen Gong, Simon Thompson +7
Large language models bring instruction following and scene reasoning to end-to-end driving, but their inference latency collides with the control rate a vehicle requires. Existing…
How Do Diffusion Classifiers Decide? A Bias-Centric Evaluation
Saba Fathi, Fardin Ayar, Maryam Abdolali +3
Diffusion models have recently been repurposed for zero-shot classification, giving rise to diffusion classifiers that identify the best-matching text prompt by minimizing the nois…
Blockage-Aware Non-stationary Dynamic Bandit for User Association in mmWave V2X Networks
Weiqi Chi, Manabu Tsukada
In millimeter-wave (mmWave) vehicular networks, dense base station (BS) deployments expand the user association (UA) decision space while dynamic blockages cause link quality fluct…
Causal Scene Narration: Inference-Time Text Restructuring for Vision-Language-Action Driving
Yun Li, Yidu Zhang, Simon Thompson +2
Vision-Language-Action (VLA) models for autonomous driving receive navigation commands and hazard notices as disconnected text fragments, forcing the model to discover on its own w…
Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks
Xiaoyang He, Manabu Tsukada
Timely channel information is necessary for vehicles to determine both the serving base station (BS) and the beamforming vector, but frequent estimation of fast-fading mmWave chann…
Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCI
Xinyue Gui, Ding Xia, Mark Colley +9
Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-…
Trust, Don't Trust, or Flip: Robust Preference-Based Reinforcement Learning with Multi-Expert Feedback
Seyed Amir Hosseini, Maryam Abdolali, Amirhosein Tavakkoli +4
Preference-based reinforcement learning (PBRL) offers a promising alternative to explicit reward engineering by learning from pairwise trajectory comparisons. However, real-world p…
SUG-Occ: Explicit Semantics and Uncertainty Guided Sparse Learning for Efficient 3D Occupancy Prediction
Hanlin Wu, Pengfei Lin, Ehsan Javanmardi +4
3D semantic occupancy prediction has emerged as a critical perception task for autonomous driving due to its ability to offer voxel-level semantic and geometric understanding of th…