StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets
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 pr...
| Authors | Stepan Konev |
|---|---|
| Primary category | cs.CV |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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