Robust Meta-Learning of Vehicle Yaw Rate Dynamics via Conditional Neural Processes

TL;DR

This paper introduces a data-driven meta-learning approach using conditional neural processes to predict vehicle yaw rate dynamics, aiming for accuracy, efficiency, and robustness over traditional physical models in autonomous vehicle planning.

Contribution

It proposes a novel application of CNP for yaw rate prediction, enhancing physical models with data-driven robustness and computational efficiency.

Findings

CNP achieves low prediction errors across diverse scenarios.

The approach demonstrates robustness to environmental and operational changes.

It enables safer and more reliable autonomous vehicle planning.

Abstract

Trajectory planners of autonomous vehicles usually rely on physical models to predict the vehicle behavior. However, despite their suitability, physical models have some shortcomings. On the one hand, simple models suffer from larger model errors and more restrictive assumptions. On the other hand, complex models are computationally more demanding and depend on environmental and operational parameters. In each case, the drawbacks can be associated to a certain degree to the physical modeling of the yaw rate dynamics. Therefore, this paper investigates the yaw rate prediction based on conditional neural processes (CNP), a data-driven meta-learning approach, to simultaneously achieve low errors, adequate complexity and robustness to varying parameters. Thus, physical models can be enhanced in a targeted manner to provide accurate and computationally efficient predictions to enable safe planning in autonomous vehicles. High fidelity simulations for a variety of driving scenarios and different types of cars show that CNP makes it possible to employ and transfer knowledge about the yaw rate based on current driving dynamics in a human-like manner, yielding robustness against changing environmental and operational conditions.