Action Emergence from Streaming Intent
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 m...
| Authors | Pengfei Jing, Victor Shea-Jay Huang, Hengtong Lu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 May 2026 |
| Last updated at source | 14 May 2026 |
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