arXiv:2606.04271v1 Announce Type: cross 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.
The proliferation of end-to-end autonomous driving models and diverse datasets necessitates a unified framework to overcome integration challenges and accelerate research and development.
A standardized approach to autonomous driving datasets will significantly reduce development friction, foster better data utilization, and accelerate the progression towards robust and commercial E2E autonomous systems.
Cross-dataset experimentation and preprocessing, previously complex and time-consuming, will become more streamlined and efficient, fostering faster iteration and innovation in autonomous driving.
- · Autonomous driving research institutions
- · Open-source AI developers
- · Tier 1 automotive suppliers
- · AI software companies
- · Companies with proprietary, non-standardized dataset handling
- · Fragmented data annotation services
- · Legacy modular ADAS developers
A common framework will make it easier to compare and benchmark different end-to-end autonomous driving models.
Accelerated development cycles could bring safer and more capable autonomous vehicles to market sooner.
The widespread adoption of such standards might eventually shape regulatory frameworks for autonomous vehicle data and testing.
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Read at arXiv cs.AI