Curriculum Learning for Vehicle Lateral Stability Estimations

Abstract

Precise estimations of the roll and sideslip angles of autonomous vehicles are essential for autonomous driving, which requires further information about the vehicle state. As such, novel deep learning approaches have been introduced for this purpose. However, the majority of deep learning works focusing on vehicle dynamics estimations have yet to delve into learning strategies specifically for this task. Here, we argue that simply applying an adequate learning strategy to the task can boost the estimation performance. In this paper, we propose a simple yet effective curriculum learning strategy for better estimations of the roll and sideslip angles simultaneously. In addition, we compare our curriculum using a self-taught scoring function with a curriculum sorted by prior human knowledge, demonstrating its superiority. The proposed method outperforms the non-curriculum method by a large margin (up to a 16.5% decrease for sideslip as validation and 3.7% on a test), especially with regard to cornering (up to a 4% decrease).

Keywords

• Curriculum learning
• deep learning based estimator
• roll angle
• sensor fusion
• sideslip angle
• vehicle pose estimation