自动驾驶新框架Lagrange用掩码潜在场和VLM处理开放世界异常，比密集模型更高效，在nuScenes和CODA上表现不错。
Lagrange提出基于掩码潜在场（MLF）的开放词汇稀疏驾驶框架，利用视觉语言模型（VLM）编码类无关目标提议为连续语义视觉标记。通过意图驱动的掩码交叉注意力模块过滤无关实体，将注意力解码为空间坐标上的隐式连续能量场。将决策制定为跨越该能量场的拉格朗日动作最小化问题，强制遵守车辆运动学并执行碰撞避免。在nuScenes和CODA基准上的离线评估显示，该框架实现了鲁棒、可解释且运动学可行的开放世界自主性。
Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving
Scaling end-to-end autonomous driving to complex, open-world environments requires perceptual models that generalize to anomalous scenarios and planners that produce kinematically valid trajectories. Existing paradigms face a distinct dichotomy between representational efficiency and generalization capacity. Dense models (e.g., occupancy networks), while geometrically robust, incur critical computational bottlenecks and struggle with high-level semantic reasoning. Conversely, sparse, query-based planners are efficient but reliant on closed-set definitions, rendering them vulnerable to out-of-distribution (OOD) events. Although recent Vision-Language-Action (VLA) models offer open-vocabulary reasoning, their autoregressive, discrete token generation fundamentally conflicts with the continuous, high-frequency control requirements of vehicle dynamics. To address this, we propose Lagrange, an open-vocabulary, computationally sparse driving framework based on Masked Latent Fields (MLF). Rather than relying on dense volumetric reconstructions or closed-set query mechanisms, Lagrange exploits Vision-Language Models (VLMs) to encode class-agnostic object proposals into continuous semantic visual tokens. We introduce an intent-driven masked cross-attention module that temporally filters irrelevant entities, decoding the attended tokens into an implicit continuous energy field defined over spatial coordinates. By framing decision-making as a Lagrangian action minimization problem spanning this energy field, we enforce strict compliance with vehicle kinematics while executing collision avoidance. Extensive offline evaluations on both standard (nuScenes) and long-tail (CODA) benchmarks demonstrate that Lagrange establishes a promising framework for robust, interpretable, and kinematically feasible open-world autonomy.