通过元学习实现越野自动驾驶动力学模型实时自适应。
Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving
- 结合卡尔曼滤波与元学习，实现动态模型在线更新。
- 实车测试显示预测精度与安全性显著提升。
- 适合需要应对未知复杂地形的自动驾驶系统。
高速越野自动驾驶面临复杂多变的地形挑战，且难以准确建模车辆-地形交互关系。虽然基于模型控制的动力学模型可从真实数据中学习，但往往无法泛化到未见地形，因此实时自适应至关重要。本文提出一种新框架，将基于卡尔曼滤波的在线自适应机制与元学习参数相结合：离线阶段优化自适应所依赖的基函数及参数，线上阶段实时调整车载动力学模型以支持模型预测控制。通过大量实验验证，包括全尺寸自动驾驶越野车辆的真实道路测试，结果表明该方法在预测精度、性能和安全指标上均优于基线方案，尤其在安全关键场景中表现突出。实验结果证明了元学习动力学模型自适应的有效性，推动了可在多样未知环境中可靠运行的自主系统发展。视频展示见：https://youtu.be/cCKHHrDRQEA
原文摘要 · Abstract (English)
High-speed off-road autonomous driving presents unique challenges due to complex, evolving terrain characteristics and the difficulty of accurately modeling terrain-vehicle interactions. While dynamics models used in model-based control can be learned from real-world data, they often struggle to generalize to unseen terrain, making real-time adaptation essential. We propose a novel framework that combines a Kalman filter-based online adaptation scheme with meta-learned parameters to address these challenges. Offline meta-learning optimizes the basis functions along which adaptation occurs, as well as the adaptation parameters, while online adaptation dynamically adjusts the onboard dynamics model in real time for model-based control. We validate our approach through extensive experiments, including real-world testing on a full-scale autonomous off-road vehicle, demonstrating that our method outperforms baseline approaches in prediction accuracy, performance, and safety metrics, particularly in safety-critical scenarios. Our results underscore the effectiveness of meta-learned dynamics model adaptation, advancing the development of reliable autonomous systems capable of navigating diverse and unseen environments. Video is available at: https://youtu.be/cCKHHrDRQEA
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