  Developing autonomous vehicles (AVs) helps improve the road safety and
traffic efficiency of intelligent transportation systems (ITS). Accurately
predicting the trajectories of traffic participants is essential to the
decision-making and motion planning of AVs in interactive scenarios. Recently,
learning-based trajectory predictors have shown state-of-the-art performance in
highway or urban areas. However, most existing learning-based models trained
with fixed datasets may perform poorly in continuously changing scenarios.
Specifically, they may not perform well in learned scenarios after learning the
new one. This phenomenon is called "catastrophic forgetting". Few studies
investigate trajectory predictions in continuous scenarios, where catastrophic
forgetting may happen. To handle this problem, first, a novel continual
learning (CL) approach for vehicle trajectory prediction is proposed in this
paper. Then, inspired by brain science, a dynamic memory mechanism is developed
by utilizing the measurement of traffic divergence between scenarios, which
balances the performance and training efficiency of the proposed CL approach.
Finally, datasets collected from different locations are used to design
continual training and testing methods in experiments. Experimental results
show that the proposed approach achieves consistently high prediction accuracy
in continuous scenarios without re-training, which mitigates catastrophic
forgetting compared to non-CL approaches. The implementation of the proposed
approach is publicly available at https://github.com/BIT-Jack/D-GSM
