Online Adaptation of Learned Vehicle Dynamics Model with Meta-Learning Approach

TL;DR

This paper introduces a meta-learning approach for online adaptation of vehicle dynamics models, enabling autonomous vehicles to quickly adjust to new environments without forgetting past experiences, improving control performance.

Contribution

It applies Continual-MAML to vehicle dynamics modeling, allowing rapid online adaptation while retaining knowledge from previous environments, which is a novel integration in autonomous driving.

Findings

Continual-MAML outperforms fixed and gradient descent models in test loss.

Enhanced online tracking performance of MPPI controller.

Effective adaptation to unseen road conditions.

Abstract

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.