Modeling the dynamics of micro-mobility vehicles (MMV) is becoming increasingly important for training autonomous vehicle systems and building urban traffic simulations. However, mainstream tools rely on variants of the Kinematic Bicycle Model (KBM) or mode-specific physics that miss tire slip, load transfer, and rider/vehicle lean. To our knowledge, no unified, physics-based model captures these dynamics across the full range of common MMVs and wheel layouts. We propose the "Generalized Micro-mobility Model" (GM3), a tire-level formulation based on the tire brush representation that supports arbitrary wheel configurations, including single/double track and multi-wheel platforms. We introduce an interactive model-agnostic simulation framework that decouples vehicle/layout specification from dynamics to compare the GM3 with the KBM and other models, consisting of fixed step RK4 integration, human-in-the-loop and scripted control, real-time trajectory traces and logging for analysis. We also empirically validate the GM3 on the Stanford Drone Dataset's deathCircle (roundabout) scene for biker, skater, and cart classes.

翻译：微移动车辆（MMV）的动力学建模对于训练自动驾驶系统和构建城市交通仿真正变得日益重要。然而，主流工具依赖于运动学自行车模型（KBM）的变体或特定模式的物理模型，这些模型忽略了轮胎滑移、载荷转移以及骑手/车辆倾斜等因素。据我们所知，目前尚无统一、基于物理的模型能够捕捉涵盖所有常见微移动车辆类型和车轮布局的完整动力学特性。我们提出了“通用微移动模型”（GM3），这是一种基于轮胎刷表示的轮胎级建模方法，支持任意车轮配置，包括单/双轨和多轮平台。我们引入了一个与模型无关的交互式仿真框架，该框架将车辆/布局规范与动力学解耦，以比较GM3与KBM及其他模型。该框架包含固定步长的RK4积分、人在回路与脚本控制、实时轨迹跟踪以及用于分析的数据记录。我们还在斯坦福无人机数据集的死亡循环（环岛）场景中，针对骑行者、滑板者和手推车类别，对GM3进行了实证验证。