寻找更佳适配？一种适应个体驾驶员的增量学习多模态对象引用框架
Looking for a better fit? An Incremental Learning Multimodal Object Referencing Framework adapting to Individual Drivers
AI总结：
针对驾驶员手势引用车外对象时个体差异大、传统模型难以适应的问题，提出基于回归的增量学习框架IcRegress，通过持续学习实现个性化适配，实验证明其优于单一训练模型，并开源代码。
摘要：
汽车行业向自动化和半自动化车辆快速发展，使得传统的车辆交互方式（如基于触摸和语音命令的系统）已不足以应对日益广泛的非驾驶相关任务，例如引用车辆外部的对象。因此，研究已转向手势输入（如手部、视线和头部姿势手势），将其视为驾驶过程中更合适的交互模式。然而，由于驾驶的动态性和个体差异，驾驶员在手势输入表现上存在显著差异。虽然理论上，这种固有变异性可以通过大量数据驱动的机器学习模型加以调节，但主流方法倾向于使用受限的、单实例训练的模型进行对象引用。这些模型在持续适应个体驾驶员的差异行为和各种驾驶场景方面能力有限。为解决这一问题，我们提出了IcRegress，一种新颖的基于回归的增量学习方法，能够适应驾驶员在驾驶和引用对象双重任务中不断变化的行为和独特特征。我们为多模态手势界面提出了一种更个性化和自适应的解决方案，采用持续终身学习来增强驾驶体验、安全性和便利性。我们的方法通过一个车外对象引用用例进行了评估，突显了增量学习模型相比单一训练模型在不同驾驶员特征（如惯用手、驾驶经验和多种驾驶条件）下的优越性。最后，为促进可复现性、简化部署并推动进一步研究，我们在https://github.com/amrgomaelhady/IcRegress 上以开源框架形式提供我们的方法。
英文摘要：
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}.
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