arXiv:2606.07464v1 Announce Type: cross Abstract: 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 plan
The increasing complexity of autonomous driving systems requires more efficient processing of long temporal contexts to enable robust and real-time decision-making, driving innovation in token compression.
Efficient long-context processing is critical for the reliable deployment of autonomous vehicles, impacting safety, performance, and the commercial viability of AI-driven mobility solutions.
This research proposes a method for token compression that is planning-aligned, potentially leading to more effective and less computationally intensive autonomous driving models that can handle complex, real-world scenarios.
- · Autonomous vehicle development companies
- · AI hardware manufacturers
- · Logistics and transportation sectors
- · Robotics companies
- · Companies reliant on less efficient, high-compute AI architectures
- · Legacy automotive suppliers slow to adopt AI
Improved performance and safety metrics for autonomous vehicles through better real-time decision-making capabilities.
Accelerated deployment and broader adoption of autonomous driving technologies in various applications, from transport to delivery services.
Reduced computational costs and energy consumption for AI in robotics, potentially influencing data center infrastructure and sustainability efforts.
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Read at arXiv cs.AI