  In the field of autonomous driving, even a meticulously trained model can
encounter failures when faced with unfamiliar sceanrios. One of these scenarios
can be formulated as an online continual learning (OCL) problem. That is, data
come in an online fashion, and models are updated according to these streaming
data. Two major OCL challenges are catastrophic forgetting and data imbalance.
To address these challenges, in this paper, we propose an Analytic
Exemplar-Free Online Continual Learning (AEF-OCL). The AEF-OCL leverages
analytic continual learning principles and employs ridge regression as a
classifier for features extracted by a large backbone network. It solves the
OCL problem by recursively calculating the analytical solution, ensuring an
equalization between the continual learning and its joint-learning counterpart,
and works without the need to save any used samples (i.e., exemplar-free).
Additionally, we introduce a Pseudo-Features Generator (PFG) module that
recursively estimates the deviation of real features. The PFG generates offset
pseudo-features following a normal distribution, thereby addressing the data
imbalance issue. Experimental results demonstrate that despite being an
exemplar-free strategy, our method outperforms various methods on the
autonomous driving SODA10M dataset. Source code is available at
https://github.com/ZHUANGHP/Analytic-continual-learning.
