{"ID":3053272,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-05T22:37:20.23433632Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04271","arxiv_id":"2606.04271","title":"StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets","abstract":"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.","short_abstract":"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 sen...","url_abs":"https://arxiv.org/abs/2606.04271","url_pdf":"https://arxiv.org/pdf/2606.04271v1","authors":"[\"Stepan Konev\"]","published":"2026-06-02T22:50:55Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":612804,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-04T04:41:36.695875263Z","DeletedAt":null,"paper_id":3053272,"paper_url":"https://arxiv.org/abs/2606.04271","paper_title":"StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets","repo_url":"https://github.com/stepankonev/StandardE2E","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}