开发自主非公路机动能力通常需要大量特定平台的数据采集，或依赖于简化抽象模型（如单轮或自行车模型），这些模型无法捕捉从轮式到履带式等多种平台的复杂运动学动力学特性。这一局限性阻碍了在不断演化的异构自主机器人机群中实现可扩展性。为解决该挑战，我们提出了一种名为基于移动性表征的跨车辆运动学动力学自适应（CAR）的新框架，实现了向新车辆的快速机动能力迁移。CAR采用带有自适应层归一化的Transformer编码器，将车辆轨迹变化与物理配置嵌入共享的移动性潜在空间。通过识别并提取该潜在空间内最近邻之间的共性，本方法能够在极少数据采集和计算开销的前提下，实现对新型平台的快速运动学动力学自适应。我们在基于Chrono多物理引擎构建的Verti-Bench模拟器上评估了CAR，并在Verti-4-Wheeler平台的四种不同物理配置上验证其性能。仅需一分钟的新轨迹数据，CAR相较于直接邻居迁移，在多种未见过的车辆配置下预测误差降低高达67.2%，证明了跨车辆移动性知识迁移在仿真与真实环境中的有效性。

Developing autonomous off-road mobility typically requires either extensive, platform-specific data collection or relies on simplified abstractions, such as unicycle or bicycle models, that fail to capture the complex kinodynamics of diverse platforms, ranging from wheeled to tracked vehicles. This limitation hinders scalability across evolving heterogeneous autonomous robot fleets. To address this challenge, we propose Cross-vehicle kinodynamics Adaptation via mobility Representation (CAR), a novel framework that enables rapid mobility transfer to new vehicles. CAR employs a Transformer encoder with Adaptive Layer Normalization to embed vehicle trajectory transitions and physical configurations into a shared mobility latent space. By identifying and extracting commonality from nearest neighbors within this latent space, our approach enables rapid kinodynamics adaptation to novel platforms with minimal data collection and computational overhead. We evaluate CAR using the Verti-Bench simulator, built on the Chrono multi-physics engine, and validate its performance on four distinct physical configurations of the Verti-4-Wheeler platform. With only one minute of new trajectory data, CAR achieves up to 67.2% reduction in prediction error compared to direct neighbor transfer across diverse unseen vehicle configurations, demonstrating the effectiveness of cross-vehicle mobility knowledge transfer in both simulated and real-world environments.