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

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.

arXiv:2606.04271v1 Announce Type: new 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.