NTR：端到端驾驶中场景令牌瓶颈的神经令牌重建
NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving
AI总结：
针对端到端驾驶中场景令牌瓶颈缺乏视觉监督的问题，提出神经令牌重建（NTR）框架，通过自蒸馏掩码潜在重建约束场景令牌保留更丰富的视觉表示，实现最先进的驾驶性能。
摘要：
最近的无感知端到端自动驾驶方法通过将密集的图像块令牌压缩为紧凑的场景令牌，用于下游轨迹生成和评分，从而绕过了显式的感知输出。虽然这些场景令牌为规划器形成了紧凑的视觉瓶颈，但它们仅从规划目标接收监督，对编码的视觉信息提供了有限的约束。为了解决这一限制，我们引入了神经令牌重建（NTR），一种表示学习框架，直接约束无感知驾驶中的紧凑场景令牌瓶颈。NTR引入了一种自蒸馏掩码潜在重建目标，该目标仅使用紧凑的场景令牌作为重建记忆来重建被掩码的块级潜在特征。这迫使重建梯度仅通过场景令牌瓶颈传递，鼓励场景令牌为规划保留更丰富且更少冗余的视觉表示。我们进一步引入了来自基础模型注释的语义先验，作为弱语义接口，将重建目标偏向于驾驶相关结构，而不引入显式的感知头。所有辅助重建组件在推理时被移除，部署的规划器保持不变。NTR在三个公共自动驾驶基准测试中实现了最先进的性能，包括Waymo E2E上的8.0461 RFS以及NavSim1&2上的94.1 PDMS / 90.9 EPDMS。学习到的场景令牌表现出更低的成对冗余和更高的有效秩，表明有效的瓶颈监督同时改善了紧凑视觉表示学习和规划性能。
英文摘要：
Recent perception-free end-to-end (E2E) autonomous driving methods bypass explicit perception outputs by compressing dense image patch tokens into compact scene tokens for downstream trajectory generation and scoring. While these scene tokens form a compact visual bottleneck for the planner, they receive supervision solely from the planning objective, providing limited constraints on the encoded visual information. To address this limitation, we introduce Neural Token Reconstruction (NTR), a representation learning framework to directly constrain the compact scene-token bottleneck in perception-free driving. NTR introduces a self-distillation masked latent reconstruction objective that reconstructs masked patch-level latent features using only compact scene tokens as reconstruction memory. This forces reconstruction gradients to pass exclusively through the scene-token bottleneck, encouraging scene tokens to preserve richer and less redundant visual representations for planning. We further introduce semantic priors derived from foundation-model annotations as a weak semantic interface biasing reconstruction targets toward driving-related structures without introducing explicit perception heads. All auxiliary reconstruction components are removed at inference time, leaving the deployed planner unchanged. NTR achieves state-of-the-art performance on three public autonomous driving benchmarks, including 8.0461 RFS on Waymo E2E and 94.1 PDMS / 90.9 EPDMS on NavSim1&2. The learned scene tokens exhibit lower pairwise redundancy and higher effective rank, indicating that effective bottleneck supervision improves both compact visual representation learning and planning performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
