The research area of behavior-based driver identification and indoor localization is witnessing significant advancements, particularly in the application of Continual Learning (CL) techniques. These methods are proving effective in addressing the dynamic and resource-constrained environments typical of real-world deployments. Innovations in CL are enabling models to adapt to new data and changing conditions without significant loss of performance, which is crucial for scalable and robust solutions. Notably, the integration of multi-surrogate teacher assistance for representation alignment is advancing the field of indoor localization by enhancing the transferability of learned representations across diverse datasets. Additionally, the development of continual domain expansion methods for absolute pose regression is demonstrating substantial improvements in handling long-term data changes and novel scene conditions. These advancements collectively push the boundaries of what is possible in real-time, adaptive learning systems, making them more suitable for deployment in various practical scenarios.

Noteworthy papers include one that introduces novel CL methods for driver identification, achieving near-optimal performance with minimal accuracy reduction, and another that proposes a plug-and-play framework for knowledge transfer in indoor localization, significantly boosting the potential of specialized networks. Furthermore, a study on continual domain expansion for absolute pose regression showcases a 7x reduction in localization error under challenging conditions.