Feature-Generation-Replay Continual Learning Combined with Mixture-of-Experts for Data-Driven Autonomous Guidance

Continual learning (CL) is a key technology for enabling data-driven autonomous guidance systems to operate stably and persistently in complex and dynamic environments. Its core goal is to enable the model to continuously learn new scenarios and tasks after deployment, without forgetting existing knowledge, and finally achieving stable decision-making in the different scenarios over a long period. This paper proposes a continual learning method that combines feature-generation-replay with Mixture-of-Experts and Low-Rank Adaptation (MoE-LoRA). This method retains the key features of historical tasks by feature repla and realizes the adaptive selection of old and new knowledge by the Mixture-of-Experts (MoE), which alleviates the conflict between knowledge while ensuring learning efficiency. In the comparison experiments, we compared the proposed method with the representative continual learning methods, and the experimental results show that our method outperforms the representative continual learning methods, and the ablation experiments further demonstrate the role of each component. This work provides technical support for the long-term maintenance and new task expansion of data-driven autonomous guidance systems, laying a foundation for their stable operation in complex, variable real-world scenarios.