Autonomous Driving Research Requires a Community-Driven Data Paradigm
Quick Answer
The article argues that the future of autonomous driving research hinges on a community-driven data paradigm, as current reliance on limited benchmark datasets hampers progress.
Quick Take
With over 600 datasets available globally, fragmentation and underutilization persist, necessitating collaborative efforts to enhance data discovery and integration for robust autonomous systems.
Key Points
- Current autonomous driving research relies on a few benchmark datasets with limited coverage.
- Over 600 autonomous driving datasets exist, yet many remain underused due to fragmentation.
- A community-driven data paradigm could enhance dataset discovery and integration.
- Collaboration across academia and industry is essential for transforming fragmented datasets.
- The proposed paradigm aims to lower barriers for new contributors in autonomous driving research.
DeepSignal Analysis
What happened
The article emphasizes the need for a community-driven data paradigm in autonomous driving research. It highlights that the current reliance on limited benchmark datasets restricts the field's progress, despite the existence of over 600 datasets globally. The authors argue that fragmentation and underutilization of these datasets hinder the development of robust autonomous systems.
Key evidence
- The research community has produced over 600 autonomous driving datasets across nearly 50 countries, yet many remain underused.
- Current research is heavily reliant on a few benchmark datasets, which limits spatial and scenario coverage.
- Fragmentation, limited visibility, and incompatible protocols contribute to the underutilization of available datasets.
Why it matters
The article's call for a collaborative data paradigm is significant as it aims to enhance the discovery and integration of diverse datasets. This could lead to more robust autonomous systems capable of operating in varied environments. By addressing the issues of fragmentation and underutilization, the research community could potentially accelerate advancements in autonomous driving technology.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Autonomous driving has made remarkable progress, with recent AI advances enabling commercial deployments that are reshaping urban mobility. Yet the field remains far from its universal social promise: autonomous systems that can operate robustly anywhere, anytime, for anyone. We posit that this gap is not merely a modeling problem, but a problem of the prevailing data paradigm. Current research relies heavily on a few benchmark datasets with limited spatial and scenario coverage, even though the community has collectively produced over 600 autonomous driving datasets across nearly 50 countries. However, this abundance has not translated into broad research impact: most datasets remain significantly underused due to fragmentation, limited visibility, incompatible protocols, and benchmark incentives that concentrate attention on a few dominant datasets. We therefore argue that autonomous driving research requires a collaborative, community-driven data paradigm. Such a paradigm would improve the discovery, reuse, integration, and evaluation of diverse datasets; make underexplored data easier and more rewarding to study; and lower the barrier for new contributors. We outline its key principles, illustrate an early realization, and call for collaboration across academia and industry to transform fragmented datasets into shared community infrastructure for anytime-anywhere autonomy.
| Comments: | NeurIPS 2026 Position Paper |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08825 [cs.CV] |
| (or arXiv:2610.08825v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08825 arXiv-issued DOI via DataCite |
Submission history
From: Jinsu Yoo [view email]
[v1]
Fri, 25 Sep 2026 16:44:29 UTC (6,631 KB)
— Originally published at arxiv.org
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