Planning-aligned Token Compression for Long-Context Autonomous Driving

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, extended

Summary

COMPACT-VA introduces a planning-aligned token compression framework for monolithic vision-action (VA) models in autonomous driving. This architecture addresses the challenge of token sequences exceeding real-time computational budgets when encoding extended temporal context for complex interactions. Built on a conditional VQ-VAE, COMPACT-VA compresses historical context into bounded representations, conditioned on both past trajectory and a learned planning intent. Evaluated on high-signal dynamic scenarios like four-way stops, it achieved a 68.3% success rate, representing a >6% improvement over baselines under comparable token budgets. Closed-loop evaluation confirmed general driving performance with a 3.3× speedup and 2.7× memory reduction compared to uncompressed processing.

Key takeaway

For AI Scientists and Machine Learning Engineers developing end-to-end autonomous driving systems, you should integrate planning-aligned token compression to efficiently manage long-context computational demands. This approach, exemplified by COMPACT-VA, significantly improves decision correctness in complex scenarios like four-way stops by preserving critical historical cues. Adopting such a framework can yield substantial speedups and memory reductions, enhancing real-time performance without sacrificing safety-critical information.

Key insights

Planning-aligned token compression, via conditional VQ-VAE, retains decision-critical historical cues for autonomous driving policies.

Principles

• Couple compression with planning objectives.
• Prioritize recent history with higher token density.
• Evaluate memory on high-signal dynamic scenarios.

Method

COMPACT-VA uses a Q-former for learned hierarchical compression, a cVAE with VQ to distill driving intent, and end-to-end optimization to align compression with trajectory prediction.

In practice

• Implement hierarchical FIFO buffers for temporal context.
• Use behavioral metrics like Stop SR for decision correctness.
• Condition compression on historical trajectory and planning intent.

Topics

• Autonomous Driving
• Token Compression
• Vision-Action Models
• Variational Autoencoders
• Long-Context Transformers
• Real-time Systems
• Planning Alignment

Code references

• NVlabs/alpasim

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.