2 citations · 5 across the 32 of their papers we have counts for
10 papers · 1 filter
CARE: Camera-Residual Reserves for First Sightings in Adaptive LiDAR Sensing
Jiachen Gong, Yun Li, Ehsan Javanmardi +2
Adaptive LiDAR scanning concentrates a limited sensing budget on regions of interest predicted from past object tracks, lowering data volume in autonomous driving while maintaining…
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…
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…
An Open-Source Modular Benchmark for Diffusion-Based Motion Planning in Closed-Loop Autonomous Driving
Yun Li, Simon Thompson, Yidu Zhang +2
Diffusion-based motion planners have achieved state-of-the-art results on benchmarks such as nuPlan, yet their evaluation within closed-loop production autonomous driving stacks re…
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…