Preventing Catastrophic Forgetting in Continuous Online Learning for Autonomous Driving

Autonomous vehicles require online learning ca- pabilities to enable long-term, unattended operation. However, long-term online learning is accompanied by the problem of forgetting previously learned knowledge. This paper introduces an online learning framework that includes a catastrophic for- getting prevention mechanism, named Long-Short-Term Online Learning (LSTOL). The framework consists of a set of short- term learners and a long-term controller, where the former is based on the concept of ensemble learning and aims to achieve rapid learning iterations, while the latter contains a simple yet efficient probabilistic decision-making mechanism combined with four control primitives to achieve effective knowledge maintenance. A novel feature of the proposed LSTOL is that it avoids forgetting while learning autonomously. In addition, LSTOL makes no assumptions about the model type of short-term learners and the continuity of the data. The effectiveness of the proposed framework is demonstrated through experiments across well-known datasets in autonomous driving, including KITTI and Waymo. The source code for the method implementation is publicly available at https: //github.com/epan-utbm/lstol.