Action Emergence from Streaming Intent
Researchers formalize action emergence for end-to-end autonomous driving and propose Streaming Intent, a mechanism that combines semantic chain-of-thought reasoning with temporal coherence to generate diverse, controllable actions. Their model SI achieves competitive results on the Waymo benchmark and demonstrates first-of-its-kind intent-faithful controllability.
[2605.12622] Action Emergence from Streaming Intent
[Submitted on 12 May 2026]
Title:Action Emergence from Streaming Intent
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Abstract:We formalize action emergence as a target capability for end-to-end autonomous driving: the ability to generate physically feasible, semantically appropriate, and safety-compliant actions in arbitrary, long-tail traffic scenes through scene-conditioned reasoning rather than retrieval or interpolation of learned scene-action mappings. We show that previous paradigms cannot deliver action emergence: autoregressive trajectory decoders collapse the inherently multimodal future into a single averaged output, while diffusion and flow-matching generators express multimodality but are not steerable by reasoned intent. We propose Streaming Intent as a concrete way to approach action emergence: a mechanism that makes driving intent (i) semantically streamed through a continuous chain-of-thought that causally derives the intent from scene understanding, and (ii) temporally streamed across clips so that intent commitments remain coherent along the driving horizon. We realize Streaming Intent in a VLA model we call SI (Streaming Intent). SI autoregressively decodes a four-step chain-of-thought and emits an intent token; the decoded intent then drives classifier-free guidance (CFG) on a flow-matching action head, requiring only two denoising steps to generate the final trajectory. On the Waymo End-to-End benchmark, SI achieves competitive aggregate performance, with an RFS score of 7.96 on the validation set and 7.74 on the test set. Beyond aggregate metrics, the model demonstrates -- to our knowledge for the first time in a fully end-to-end VLA -- intent-faithful controllability: for a fixed scene, varying the intent class at inference yields qualitatively distinct yet consistently high-quality plans, arising purely from data-driven learning without any pre-built trajectory bank or hand-coded post-hoc selector.
Comments: Work in progress. Project page: this https URL
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.12622 [cs.RO]
(or arXiv:2605.12622v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.12622
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Benjin Zhu [view email] [v1] Tue, 12 May 2026 18:09:04 UTC (19,486 KB)
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