{"slug": "standarde2e-a-unified-framework-for-end-to-end-autonomous-driving-datasets", "title": "StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets", "summary": "Researchers have released StandardE2E, a unified open-source framework that standardizes preprocessing and data loading across six major autonomous driving datasets, including Waymo and Argoverse. The framework eliminates the need for per-project reimplementation by providing a single PyTorch DataLoader interface for cross-dataset pretraining and auxiliary-task supervision. StandardE2E reduces adding a new dataset to a single mapping step, enabling researchers to combine multiple sensor-rich driving datasets for end-to-end autonomous driving model development.", "body_md": "arXiv:2606.04271v1 Announce Type: new\nAbstract: Autonomous driving has shifted from modular perception-prediction-planning stacks toward end-to-end (E2E) models that map sensor inputs directly to vehicle control, often regularized by auxiliary tasks such as 3D detection, motion forecasting, and HD-map perception. Progress is driven by a fast-growing ecosystem of sensor-rich driving datasets, yet each ships its own file formats, APIs, coordinate conventions, and modality coverage, leaving cross-dataset experimentation and even basic per-dataset preprocessing to be re-implemented per project. We present StandardE2E, a framework that provides a single unified interface over E2E driving datasets. StandardE2E (i) standardizes per-dataset preprocessing under one shared data schema; (ii) combines multiple datasets in a single PyTorch DataLoader for cross-dataset pretraining, auxiliary-task supervision, and scenario-level filtering; and (iii) reduces adding a new dataset to a single per-dataset mapping from raw frames to the canonical schema, leaving the entire downstream pipeline unchanged. The framework supports six datasets out of the box: Waymo End-to-End, Waymo Perception, Argoverse 2 Sensor, Argoverse 2 LiDAR, NAVSIM (OpenScene-v1.1), and WayveScenes101, and is released as the open-source standard-e2e Python package, available at https://github.com/stepankonev/StandardE2E.", "url": "https://wpnews.pro/news/standarde2e-a-unified-framework-for-end-to-end-autonomous-driving-datasets", "canonical_source": "https://arxiv.org/abs/2606.04271", "published_at": "2026-06-04 04:00:00+00:00", "updated_at": "2026-06-04 04:19:44.679290+00:00", "lang": "en", "topics": ["autonomous-vehicles", "machine-learning", "computer-vision", "artificial-intelligence", "ai-research"], "entities": ["StandardE2E", "Waymo", "Argoverse 2", "NAVSIM", "WayveScenes101", "PyTorch", "stepankonev", "GitHub"], "alternates": {"html": "https://wpnews.pro/news/standarde2e-a-unified-framework-for-end-to-end-autonomous-driving-datasets", "markdown": "https://wpnews.pro/news/standarde2e-a-unified-framework-for-end-to-end-autonomous-driving-datasets.md", "text": "https://wpnews.pro/news/standarde2e-a-unified-framework-for-end-to-end-autonomous-driving-datasets.txt", "jsonld": "https://wpnews.pro/news/standarde2e-a-unified-framework-for-end-to-end-autonomous-driving-datasets.jsonld"}}