开发自主移动机器人系统通常需依赖大量平台专属数据采集，或采用简化抽象模型（如单轮车模型或自行车模型），而此类模型无法准确刻画从轮式到履带式等多样化平台的复杂运动学与动力学特性。这一局限制约了自主机器人异构车队在演进过程中的可扩展性。为应对该挑战，我们提出“基于运动表征的跨车辆运动学动力学自适应方法”（Cross-vehicle kinodynamics Adaptation via mobility Representation, CAR），一种支持新车辆快速迁移运动能力的新型框架。CAR采用带自适应层归一化（Adaptive Layer Normalization）的Transformer编码器，将车辆轨迹转移与物理构型嵌入至共享的运动潜在空间。通过在此潜在空间中识别并提取最近邻样本的共性，本方法仅需极少的数据采集与计算开销，即可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多物理场引擎构建的Verti-Bench仿真器上评估CAR，并在Verti-4-Wheeler平台四种不同物理构型上验证其性能。仅需一分钟的新轨迹数据，CAR在多种未见车辆构型上的预测误差相较直接邻域迁移方法最多降低67.2%，证明了跨车辆运动知识迁移在仿真与真实环境中的有效性。

Developing autonomous mobile robot systems 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.