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 propose \textit{IcRegress}, a novel regression-based incremental learning approach that adapts to changing behavior and the unique characteristics of drivers engaged in the dual task of driving and referencing objects. We suggest a more personalized and adaptable solution for multimodal gestural interfaces, employing continuous lifelong learning to enhance driver experience, safety, and convenience. Our approach was evaluated using an outside-the-vehicle object referencing use case, highlighting the superiority of the incremental learning models adapted over a single trained model across various driver traits such as handedness, driving experience, and numerous driving conditions. Finally, to facilitate reproducibility, ease deployment, and promote further research, we offer our approach as an open-source framework at \url{https://github.com/amrgomaaelhady/IcRegress}.

翻译：汽车行业向自动化与半自动化车辆领域的快速发展，使得传统车辆交互方式（如触控和语音命令系统）已无法满足日益增长的非驾驶相关任务需求，例如参照车外物体。因此，研究转向手势输入（如手部、视线和头部姿态手势）作为驾驶过程中更适宜的交互模式。然而，由于驾驶的动态特性和个体差异，驾驶员在手势输入性能上存在显著差异。理论上，这种固有的变异性可通过大规模数据驱动的机器学习模型加以调节，但主流方法倾向于采用约束性的单实例训练模型进行物体参照，这些模型在持续适应个体驾驶员差异行为及多样化驾驶场景方面能力有限。针对这一问题，我们提出\textit{IcRegress}——一种新颖的基于回归的增量学习方法，能够适应同时执行驾驶与参照物体双任务的驾驶员的行为变化及个体特征。我们通过持续终身学习，为多模态手势界面提供更个性化和自适应的解决方案，以提升驾驶体验、安全性和便利性。本研究利用车外物体参照用例进行评估，结果显示，相较于单一训练模型，针对不同驾驶员特征（如惯用手、驾驶经验及多种驾驶条件）进行适配的增量学习模型具有显著优越性。最后，为促进可复现性、简化部署并推动后续研究，我们将本方法作为开源框架发布于\url{https://github.com/amrgomaaelhady/IcRegress}。