StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets

• URL: http://arxiv.org/abs/2606.04271v1
• Date: Tue, 02 Jun 2026 22:50:55 GMT
• Title: StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets
• Abstract summary: We present StandardE2E, a framework that provides a single unified interface over E2E driving datasets.<n>StandardE2E standardizes per-dataset preprocessing under one shared data schema.<n>It combines multiple datasets in a single PyTorch DataLoader for cross-dataset pretraining, auxiliary-task supervision, and scenario-level filtering.
• Score: 0.2855485723554975
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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