Planning-aligned Token Compression for Long-Context Autonomous Driving
Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed real-time computational budgets when encoding extended temporal context for complex interactions. While approaches like linear transformers and external memory try to make the context lightweight, token compression is most compatible with the architecture as it requires no backbone modifications. Yet existing compression adopts rule-based heuristics like temporal decay, decoupled from planning, risking loss of decision-critical information. We propose COMPACT-VA, a planning-aligned working memory framework built on conditional VQ-VAE...
| Authors | Zhixuan Liang, Yuxiao Chen, Yurong You et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 June 2026 |
| Last updated at source | 19 August 2026 |
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