Understanding occupant-vehicle interactions by modeling control transitions is important to ensure safe approaches to passenger vehicle automation. Models which contain contextual, semantically meaningful representations of driver states can be used to determine the appropriate timing and conditions for transfer of control between driver and vehicle. However, such models rely on real-world control take-over data from drivers engaged in distracting activities, which is costly to collect. Here, we introduce a scheme for data augmentation for such a dataset. Using the augmented dataset, we develop and train take-over time (TOT) models that operate sequentially on mid and high-level features produced by computer vision algorithms operating on different driver-facing camera views, showing models trained on the augmented dataset to outperform the initial dataset. The demonstrated model features encode different aspects of the driver state, pertaining to the face, hands, foot and upper body of the driver. We perform ablative experiments on feature combinations as well as model architectures, showing that a TOT model supported by augmented data can be used to produce continuous estimates of take-over times without delay, suitable for complex real-world scenarios.
翻译:通过模拟控制过渡来理解机动车辆的相互作用对于确保安全地处理客车自动化非常重要。包含驾驶员所属国家具有内在意义的背景表现模型可用于确定驾驶员和车辆之间转移控制的适当时间和条件。然而,这些模型依赖于从事转移注意力活动的驾驶员的真实世界控制接收数据,收集成本很高。这里,我们为这样一个数据集引入了一个数据增强计划。我们利用增强的数据集,开发和培训接收时间(TOT)模型,这些模型在以不同驱动器成像相机视图操作的计算机视觉算法产生的中高层次特征上按顺序运行,显示在增强的数据集上经过培训的模型,以超越最初数据集。所显示的模型特征将驱动器的不同方面与驱动器的面部、手部、脚部和上部联系起来。我们在特征组合和模型结构上进行模拟实验,表明在增强数据的支持下,可以毫不拖延地使用TOT模型来连续估计占用的时间,以适应复杂的现实世界的情景。