1 citations · 1 across the 23 of their papers we have counts for
23 papers
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…
UTrice: Unifying Primitives in Differentiable Ray Tracing and Rasterization via Triangles for Particle-Based 3D Scenes
Changhe Liu, Ehsan Javanmardi, Naren Bao +2
Ray tracing 3D Gaussian particles enables realistic effects such as depth of field, refractions, and flexible camera modeling for novel-view synthesis. However, existing methods tr…
Evict3R: Training-Free Token Eviction for Memory-Bounded Streaming Visual Geometry Transformers
Soroush Mahdi, Fardin Ayar, Ehsan Javanmardi +2
Streaming visual transformers like StreamVGGT achieve strong 3D perception but suffer from unbounded growth of key value (KV) memory, which limits scalability. We propose a trainin…
Towards Robust LiDAR Localization: Deep Learning-based Uncertainty Estimation
Minoo Dolatabadi, Fardin Ayar, Ehsan Javanmardi +2
LiDAR-based localization and SLAM often rely on iterative matching algorithms, particularly the Iterative Closest Point (ICP) algorithm, to align sensor data with pre-existing maps…
You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception
Hao Si, Ehsan Javanmardi, Manabu Tsukada
Collaborative perception enables vehicles to overcome individual perception limitations by sharing information, allowing them to see further and through occlusions. In real-world s…
A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X-Enabled Autonomous Driving
Hanlin Wu, Pengfei Lin, Ehsan Javanmardi +4
3D semantic occupancy prediction is an emerging perception paradigm in autonomous driving, providing a voxel-level representation of both geometric details and semantic categories.…
Where Do You Go? Pedestrian Trajectory Prediction using Scene Features
Mohammad Ali Rezaei, Fardin Ayar, Ehsan Javanmardi +2
Accurate prediction of pedestrian trajectories is crucial for enhancing the safety of autonomous vehicles and reducing traffic fatalities involving pedestrians. While numerous stud…
Neural Error Covariance Estimation for Precise LiDAR Localization
Minoo Dolatabadi, Fardin Ayar, Ehsan Javanmardi +2
Autonomous vehicles have gained significant attention due to technological advancements and their potential to transform transportation. A critical challenge in this domain is prec…
LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training
Fardin Ayar, Ehsan Javanmardi, Manabu Tsukada +2
Panoptic segmentation, which combines instance and semantic segmentation, has gained a lot of attention in autonomous vehicles, due to its comprehensive representation of the scene…
eRSS-RAMP: A Rule-Adherence Motion Planner Based on Extended Responsibility-Sensitive Safety for Autonomous Driving
Pengfei Lin, Ehsan Javanmardi, Yuze Jiang +3
Driving safety and responsibility determination are indispensable pieces of the puzzle for autonomous driving. They are also deeply related to the allocation of right-of-way and th…
Large Language Models for Human-like Autonomous Driving: A Survey
Yun Li, Kai Katsumata, Ehsan Javanmardi +1
Large Language Models (LLMs), AI models trained on massive text corpora with remarkable language understanding and generation capabilities, are transforming the field of Autonomous…
A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety
Pengfei Lin, Ehsan Javanmardi, Yuze Jiang +1
Lane merging is one of the critical tasks for self-driving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards in measuri…
RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning
Raphael Trumpp, Ehsan Javanmardi, Jin Nakazato +2
The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of pr…