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arxiv: 2606.04271 · v1 · pith:V4CWWBUEnew · submitted 2026-06-02 · 💻 cs.CV · cs.AI

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

Stepan Konev This is my paper

Pith reviewed 2026-06-28 10:19 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords end-to-end autonomous drivingunified dataset frameworkdata schema standardizationPyTorch DataLoadercross-dataset pretrainingWaymoArgoverseNAVSIM
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The pith

StandardE2E unifies six end-to-end driving datasets under one shared schema and PyTorch DataLoader.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents StandardE2E as a response to the incompatible file formats, coordinate systems, and APIs that currently force researchers to rewrite preprocessing code for every new autonomous driving dataset. It defines a single canonical schema that all supported datasets map into, then exposes the combined collection through one DataLoader. Adding any future dataset therefore collapses to writing one adapter that produces the canonical form, after which the entire training, filtering, and auxiliary-task pipeline stays unchanged. The framework ships with support for Waymo End-to-End, Waymo Perception, Argoverse 2 Sensor and LiDAR, NAVSIM, and WayveScenes101.

Core claim

StandardE2E standardizes per-dataset preprocessing under one shared data schema, combines multiple datasets in a single PyTorch DataLoader for cross-dataset pretraining, auxiliary-task supervision, and scenario-level filtering, and reduces adding a new dataset to a single per-dataset mapping from raw frames to the canonical schema, leaving the entire downstream pipeline unchanged.

What carries the argument

The canonical data schema together with lightweight per-dataset adapters that convert raw frames into it while preserving the information required by end-to-end control, detection, forecasting, and map tasks.

If this is right

  • A single DataLoader can now draw training batches from any combination of the six datasets for joint pretraining.
  • Scenario-level filtering and auxiliary supervision become uniform operations rather than dataset-specific code.
  • Extending the collection with a seventh dataset requires only one new mapping file and no further pipeline edits.
  • Cross-dataset experiments no longer incur repeated reimplementation of coordinate transforms or modality handling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Models trained on the combined collection may generalize better across sensor configurations because the schema normalizes coordinate and modality differences.
  • Future dataset releases could adopt the canonical schema directly, further lowering the cost of integration.
  • Benchmarking protocols that mix data sources become reproducible without hidden per-project preprocessing differences.

Load-bearing premise

A single canonical schema can be defined that preserves all information needed for the supported end-to-end tasks across the six datasets without requiring task-specific adjustments or data loss.

What would settle it

A new dataset whose raw annotations or sensor data cannot be losslessly mapped into the canonical schema for at least one of the supported auxiliary tasks, forcing either information loss or changes to the downstream pipeline.

Figures

Figures reproduced from arXiv: 2606.04271 by Stepan Konev.

Figure 1
Figure 1. Figure 1: StandardE2E dataflow. The only per-dataset code a contributor writes is the dataset￾specific Raw → StandardFrameData processing (left). All downstream nodes — the adapter chain, the npz cache, the parquet index, the UnifiedE2EDataset, and the PyTorch DataLoader — are dataset-agnostic and reused as-is when adding a new dataset. indexing, segment-context aggregation, modality defaults, the PyTorch Dataset, a… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces StandardE2E, an open-source Python package providing a unified interface over six end-to-end autonomous driving datasets (Waymo End-to-End, Waymo Perception, Argoverse 2 Sensor, Argoverse 2 LiDAR, NAVSIM OpenScene-v1.1, WayveScenes101). It claims to standardize per-dataset preprocessing under one shared data schema, support combining datasets in a single PyTorch DataLoader for cross-dataset pretraining and auxiliary supervision, and reduce new-dataset integration to a single raw-to-canonical mapping that leaves downstream pipelines unchanged.

Significance. If the described schema and loader function as claimed, the framework addresses a recurring practical bottleneck in E2E driving research by eliminating repeated per-project data-handling code. The open-source release at the cited GitHub repository is a concrete strength that could enable community extensions and reproducible multi-dataset experiments.

major comments (1)
  1. [Abstract] Abstract and framework description: the central claim that a single canonical schema preserves all information required for the supported E2E tasks (3D detection, motion forecasting, HD-map perception) across the six datasets is asserted by construction but not supported by an explicit field-level definition of the schema or example mappings; without this, the sufficiency claim cannot be evaluated.
minor comments (2)
  1. The manuscript would benefit from a table or section listing the canonical schema fields and their coverage per original dataset.
  2. Include at least one minimal usage example (DataLoader instantiation and batch structure) to demonstrate the unified interface.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment. We address it point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract and framework description: the central claim that a single canonical schema preserves all information required for the supported E2E tasks (3D detection, motion forecasting, HD-map perception) across the six datasets is asserted by construction but not supported by an explicit field-level definition of the schema or example mappings; without this, the sufficiency claim cannot be evaluated.

    Authors: We agree that an explicit field-level definition of the canonical schema, together with concrete example mappings, is necessary to substantiate the claim that all required information for the listed tasks is preserved. In the revised manuscript we will insert a new subsection (or expanded table) that enumerates every field in the shared schema, states its type and semantics, and supplies at least one worked mapping from each of the six datasets. This material will be placed in the main body so that readers can directly verify sufficiency for 3D detection, motion forecasting, and HD-map perception. revision: yes

Circularity Check

0 steps flagged

No circularity; software framework with no derivations or fitted claims

full rationale

The manuscript describes a data standardization library and PyTorch loader. No equations, predictions, fitted parameters, or theorems are present. The central claim (a canonical schema plus unified loader) is realized by explicit per-dataset mapping code rather than derived from prior results or self-citations. All six datasets are stated to be mapped, so sufficiency is shown by construction of the implementation itself. No load-bearing self-citation or ansatz appears. This is a normal non-finding for an engineering tool paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the existence and sufficiency of a canonical schema that can be populated from each dataset; no free parameters, axioms, or invented entities are stated in the abstract.

pith-pipeline@v0.9.1-grok · 5780 in / 1070 out tokens · 21207 ms · 2026-06-28T10:19:40.653031+00:00 · methodology

discussion (0)

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Reference graph

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