自主微出行导航的可微分动力学
Differentiable Dynamics for Autonomous Micro-Mobility Navigation
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中文总结 AI 辅助
本文提出可微分的MMV动力学模型DiffKBM和DiffGM3，支持端到端优化，在开环轨迹匹配和闭环导航中优于传统模型，显著减少碰撞和不适。
中文摘要 AI 辅助
自主微出行车辆（MMV），如轮椅、滑板车和自行车，有潜力改善出行便利性，并支持在行人共享空间中安全低速的交通。实现MMV的自主性需要真实、可预测的MMV运动。然而，许多现有的自动驾驶系统依赖简化的运动学模型，这些模型无法捕捉关键的MMV特性，如轮胎滑移、摩擦和车轮布局，限制了真实性和基于梯度的优化。在本文中，我们探索了自主微出行系统动力学模型的可微分公式。我们首先构建了DiffKBM，即运动学自行车模型（KBM）的可微分版本。然后，我们引入了DiffGM3，即通用微出行模型（GM3）的可微分公式，这是一种统一的基于轮胎的微出行车辆动力学公式，支持广泛的MMV配置。DiffKBM和DiffGM3实现了通过MMV动力学的端到端可微分优化，使其适合集成到可微分自动驾驶系统中。我们在开环和闭环设置中评估了这些动力学模型：（1）开环轨迹匹配，其中DiffKBM和DiffGM3作为动力学层集成到DiffStack中，并优化以重现真实的MMV轨迹；（2）闭环自主导航，其中DiffKBM和DiffGM3与CrowdNav行人场景中的可微分MPC控制器配对。在开环设置中，DiffGM3在重现轨迹方面优于DiffKBM，在自行车、滑板车和摩托车模式下，ADE和NLL均有改善，并且自行车和摩托车轨迹的规划损失减少。我们还发现，在闭环设置中，DiffGM3通过产生55%更少的碰撞和75%更低的自行车模式不适频率，改善了DiffKBM在CrowdNav中的性能。
英文摘要
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.
