Looking for an even better fit? A Personalized Incremental Reinforcement Learning Multimodal Object Referencing Framework

The rapid advancement of the automotive industry towards automated and semi-automated vehicles has rendered traditional methods of vehicle interaction, such as touch-based and voice command systems, inadequate for a widening range of non-driving related tasks, such as referencing objects outside of the vehicle. Consequently, research has shifted toward gestural input (e.g., hand, gaze, and head pose gestures) as a more suitable mode of interaction during driving. However, due to the dynamic nature of driving and individual variation, there are significant differences in drivers’ gestural input performance. While, in theory, this inherent variability could be moderated by substantial data-driven machine learning models, prevalent methodologies lean towards constrained, single-instance trained models for object referencing. These models show a limited capacity to continuously adapt to the divergent behaviors of individual drivers and the variety of driving scenarios. To address this, we proposed, in our previous work, an incremental learning approach, adapting to changing user behavior. Although this method proved superior over state-of-the-art approaches, it still lacked in performance and had some computational resource limitations, mainly the small-sized memory needed to store a subset of old data for incremental learning. Therefore, in this work, we propose an alternative solution using reinforcement learning that can be used to overcome memory storage limitations, achieving a zero data storage approach for incremental learning without performance loss. Our newly enhanced solution has been added to our previous framework at https://github.com/amrgomaaelhady/IcRegress .