Update README.md

README.md

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 ---
 license: mit
+tags:
+- autonomous-driving
+- cooperative-perception
+- bird-eye-view
+- uncertainty-estimation
+- hypernetwork
+- segmentation
+- opv2v
 datasets:
 - gqk/opv2v
----
+---
+
+# Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird’s-Eye-View Semantic Segmentation
+
+**IEEE Intelligent Vehicles Symposium (IV) 2026 – Oral Presentation**
+
+[![Project Page](https://img.shields.io/badge/Project-Page-blue.svg)](https://abhishekjagtap1.github.io/HyperV2X/)  
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-red.svg)](https://arxiv.org/abs/2605.21309v1)  
+[![GitHub Repo](https://img.shields.io/badge/GitHub-Code-FFD700?logo=github)](https://github.com/abhishekjagtap1/Hyper-V2X)  
+
+This paper introduces Hyper-V2X, a hypernetwork-based framework for estimating both epistemic and aleatoric uncertainties in V2X-based perception.
+Specifically, we propose 
+1) Bayesian hypernetwork formulation for cooperative perception.
+2) V2X context embedding: that conditions a Bayesian hypernetwork on fused multi-agent features.
+3) Partial weight generation for Bayesian hypernetworks; that avoids generating the full set of model parameters, enabling efficient and scalable uncertainty estimation.
+
+## Overview
+
+Hyper-V2X is a cooperative perception model for autonomous driving that performs **Bird’s-Eye-View (BEV) semantic segmentation** with **uncertainty estimation** under communication constraints.
+
+It leverages a **Bayesian hypernetwork** conditioned on fused multi-agent BEV features to generate stochastic decoder weights, enabling:
+
+- Epistemic uncertainty estimation (model uncertainty)
+- Aleatoric uncertainty estimation (data uncertainty)
+- Robust BEV semantic segmentation in V2X settings
+- Performance under limited communication bandwidth / compression constraints
+
+
+## Training Data
+
+Hyper-V2X is trained on:
+
+- OPV2V cooperative perception dataset
+
+Dataset reference:
+- https://huggingface.co/datasets/gqk/opv2v
+
+
+
+## Quick Start
+
+For full installation instructions please refer to the [Hyper-V2X GitHub repository](https://github.com/abhishekjagtap1/Hyper-V2X).
+
+Once the dependencies are installed, you can use the load the checkpoints from Hugging Face.
+
+
+```python
+from torch.utils.data import DataLoader
+import opencood.hypes_yaml.yaml_utils as yaml_utils
+from opencood.tools import train_utils
+from opencood.data_utils.datasets import build_dataset
+from opencood.utils.seg_utils import (
+    cal_iou_training,
+    cal_ece_brier_score,
+    cal_nll_brier_score
+)
+
+dataset = build_dataset(hypes, visualize=False, train=False)
+
+    loader = DataLoader(
+        dataset,
+        batch_size=1,
+        shuffle=False,
+        num_workers=args.num_workers,
+        collate_fn=dataset.collate_batch,
+        pin_memory=False,
+        drop_last=False
+    )
+
+    print("Loading model...")
+    model = train_utils.create_model(hypes)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    print(model)
+
+    _, model = train_utils.load_saved_model(args.model_dir, model)
+    model.eval()
+
+    # ---------------- RICH PROGRESS ----------------
+    with torch.no_grad():
+        with Live(refresh_per_second=4, console=console) as live:
+            with Progress(
+                TextColumn("Inference"),
+                BarColumn(),
+                TextColumn("{task.completed}/{task.total}"),
+                TimeElapsedColumn(),
+                TimeRemainingColumn(),
+            ) as progress:
+
+                task = progress.add_task("run", total=total)
+
+                for i, batch_data in enumerate(loader):
+
+                    batch_data = train_utils.to_device(batch_data, device)
+
+                    model_out = model(batch_data['ego'])
+                    post_output = dataset.post_process(batch_data['ego'], model_out)
+
+                    # Segmentation Metric
+                    iou_d, iou_s = cal_iou_training(batch_data, post_output)
+
+                    ##### Uncertainty Metrics #########
+                    ece, ece_eqp, _ = cal_ece_brier_score(batch_data, post_output)
+                    nll, brier = cal_nll_brier_score(batch_data, post_output)
+
+  
+```
+
+## Citation
+
+```bibtex
+@article{jagtap2026hyper,
+  title={Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation},
+  author={Jagtap, Abhishek Dinkar and Sadashivaiah, Sanath Tiptur and Festag, Andreas},
+  journal={arXiv preprint arXiv:2605.21309},
+  year={2026}
+}
+```
+