Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture
arXiv:2606.28383v1 Announce Type: cross Abstract: Identifying complex and safety-critical driving scenarios in large unlabelled datasets is an important but expensive problem. Existing approaches rely on human annotators, supervised classifiers, or carefully engineered rule sets, all of which require substantial prior knowledge about what constitutes a difficult scenario. We ask whether a model can discover scenario complexity on its own, with no labels at any stage. We train a minimal Joint Embedding Predictive Architecture (JEPA) on structured agent state data from the nuPlan mini dataset an
This research leverages recent advancements in self-supervised learning architectures and large unlabelled datasets, pushing the boundaries of autonomous learning for critical safety applications.
This development could significantly reduce the cost and human effort required to identify dangerous scenarios for autonomous systems, accelerating their deployment and safety validation.
The ability of AI models to autonomously discover complex scenarios without human labels changes the paradigm for training and validating AI in safety-critical domains.
- · Autonomous vehicle developers
- · AI model developers
- · Robotics companies
- · Insurance companies
- · Manual annotation services
- · Traditional safety validation engineers
Autonomous driving systems could achieve higher levels of robustness and safety with reduced development costs.
This capability could extend to other safety-critical AI applications beyond driving, like industrial robotics or medical diagnostics.
A shift towards purely autonomous AI discovery of critical situations could redefine human roles in AI oversight and safety engineering.
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Read at arXiv cs.LG