For a Level 3 automated vehicle, according to the SAE International Automation Levels definition (J3016), the identification of non-driving activities (NDAs) that the driver is engaging with is of great importance in the design of an intelligent take-over interface. Much of the existing literature focuses on the driver take-over strategy with associated Human-Machine Interaction design. This paper proposes a dual-camera based framework to identify and track NDAs that require visual attention. This is achieved by mapping the driver's gaze using a nonlinear system identification approach, on the object scene, recognised by a deep learning algorithm. A novel gaze-based region of interest (ROI) selection module is introduced and contributes about a 30% improvement in average success rate and about a 60% reduction in average processing time compared to the results without this module. This framework has been successfully demonstrated to identify five types of NDA required visual attention with an average success rate of 86.18%. The outcome of this research could be applicable to the identification of other NDAs and the tracking of NDAs within a certain time window could potentially be used to evaluate the driver's attention level for both automated and human-driving vehicles.

对于3级自动驾驶汽车，根据SAE International Automation Levels定义（J3016），在智能接管界面的设计中，识别驾驶员正在从事的非驾驶活动（NDA）至关重要。现有的许多文献都集中在驾驶员接管策略以及相关的人机交互设计上。本文提出了一种基于双摄像头的框架来识别和跟踪需要视觉关注的NDA。这是通过在深度学习算法识别的对象场景上使用非线性系统识别方法将驾驶员的视线映射到地图上来实现的。引入了一种新颖的基于视线的关注区域（ROI）选择模块，与不使用该模块的结果相比，该方法可将平均成功率提高约30％，并将平均处理时间缩短约60％。该框架已成功证明可识别需要视觉关注的五种NDA，平均成功率为86.18％。这项研究的结果可能适用于其他NDA的识别，并且在一定时间范围内对NDA进行跟踪可能会被用于评估驾驶员对自动驾驶和人工驾驶汽车的注意力水平。