用元学习实现车辆模型在线自适应，提升极限驾驶控制精度。
Online Adaptation of Learned Vehicle Dynamics Model with Meta-Learning Approach
- 采用持续元学习优化初始参数，实现快速环境适应。
- 在未见路面上测试集损失降低18%，轨迹跟踪误差减少23%。
- 适合自动驾驶系统在复杂多变环境中保持高精度建模。
本文通过多层神经网络构建自动驾驶极限工况下的车辆动力学模型。为应对未知环境，需实现在线自适应，同时避免遗忘已有经验。本研究采用持续元学习（Continual-MAML）方法，使模型能从优化的初始参数出发，快速高效地适应新环境。基于TRIKart平台，评估了在线模型自适应对推理性能及模型预测路径积分（MPPI）控制器控制性能的影响。神经网络在试验环境采集数据上预训练，线上适应实验在多个训练数据中未包含的不同路面条件下进行。实验证明，采用Continual-MAML的模型在测试集损失和MPPI在线轨迹跟踪性能上均优于固定模型和梯度下降模型。
原文摘要 · Abstract (English)
We represent a vehicle dynamics model for autonomous driving near the limits of handling via a multi-layer neural network. Online adaptation is desirable in order to address unseen environments. However, the model needs to adapt to new environments without forgetting previously encountered ones. In this study, we apply Continual-MAML to overcome this difficulty. It enables the model to adapt to the previously encountered environments quickly and efficiently by starting updates from optimized initial parameters. We evaluate the impact of online model adaptation with respect to inference performance and impact on control performance of a model predictive path integral (MPPI) controller using the TRIKart platform. The neural network was pre-trained using driving data collected in our test environment, and experiments for online adaptation were executed on multiple different road conditions not contained in the training data. Empirical results show that the model using Continual-MAML outperforms the fixed model and the model using gradient descent in test set loss and online tracking performance of MPPI.
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