轮椅、滑板车和自行车等自主微型移动车辆（MMV）有望改善出行可达性，并在行人共享空间中支持安全的低速交通。实现 MMV 的自主性需要真实且可预测的运动模型。然而，许多现有的自动驾驶系统栈依赖于简化的运动学模型，未能捕捉轮胎滑移、摩擦和车轮布局等关键 MMV 特性，从而限制了模型的真实性及基于梯度的优化能力。本文探讨了自主微型移动系统中动力学模型的可微分表述。首先，我们构建了 DiffKBM，即运动学自行车模型（KBM）的可微版本。随后，引入 DiffGM3，这是通用微型移动模型（GM3）的可微表述；GM3 是一种统一的基于轮胎的动力学公式，支持多种 MMV 配置。DiffKBM 和 DiffGM3 实现了通过 MMV 动力学的端到端可微优化，使其适合集成到可微自主系统栈中。我们在开环和闭环环境中评估了这些动力学模型：(1) 开环轨迹匹配，将 DiffKBM 和 DiffGM3 作为动力学层集成到 DiffStack 中，并优化以复现真实世界的 MMV 轨迹；(2) 闭环自主导航，在 CrowdNav 行人场景中，将 DiffKBM 和 DiffGM3 与可微 MPC 控制器配对使用。在开环设置中，DiffGM3 在复现轨迹方面优于 DiffKBM，在自行车、滑板车和摩托车模式下的 ADE 和 NLL 指标均有提升，并降低了自行车和摩托车轨迹的规划损失。此外，我们发现，在闭环设置中，DiffGM3 在 CrowdN...

Autonomous micro-mobility vehicles (MMVs) such as wheelchairs, scooters, and bicycles have the potential to improve mobility access and support safe low-speed transportation in pedestrian-shared spaces. Achieving MMV autonomy will require realistic, predictable MMV motion. However, many existing autonomous vehicle stacks rely on simplified kinematic models that fail to capture key MMV characteristics such as tire slip, friction, and wheel layouts, limiting realism and gradient-based optimization. In this paper, we explore differentiable formulations of dynamics models for autonomous micro-mobility systems. We first construct DiffKBM, a differentiable version of the kinematic bicycle model (KBM). Then, we introduce DiffGM3, a differentiable formulation of the General Micro-Mobility Model (GM3), a unified tire-based dynamics formulation for micro-mobility vehicles that supports a wide range of MMV configurations. DiffKBM and DiffGM3 enable end-to-end differentiable optimization through MMV dynamics, making them suitable for integration into differentiable autonomy stacks. We evaluate these dynamics models in both open-loop and closed-loop settings: (1) open-loop trajectory matching, where DiffKBM and DiffGM3 are integrated as a dynamics layer within DiffStack and optimized to reproduce real-world MMV trajectories, and (2) closed-loop autonomous navigation, where DiffKBM and DiffGM3 are paired with a differentiable MPC controller in CrowdNav pedestrian scenarios. In the open-loop setting, DiffGM3 outperforms DiffKBM in reproducing trajectories with improvements in ADE and NLL across bicycle, scooter, and motorcycle modes, and reductions in planning loss for bicycle and motorcycle trajectories. We also find that, in closed-loop settings, DiffGM3 improves on DiffKBM's CrowdNav performance by producing 55\% fewer collisions and a 75\% lower discomfort frequency for the bicycle mode.