Understanding the decision-making process of drivers is one of the keys to
ensuring road safety. While the driver intent and the resulting ego-motion
trajectory are valuable in developing driver-assistance systems, existing
methods mostly focus on the motions of other vehicles. In contrast, we focus on
inferring the ego trajectory of a driver's vehicle using their gaze data. For
this purpose, we first collect a new dataset, GEM, which contains high-fidelity
ego-motion videos paired with drivers' eye-tracking data and GPS coordinates.
Next, we develop G-MEMP, a novel multimodal ego-trajectory prediction network
that combines GPS and video input with gaze data. We also propose a new metric
called Path Complexity Index (PCI) to measure the trajectory complexity. We
perform extensive evaluations of the proposed method on both GEM and DR(eye)VE,
an existing benchmark dataset. The results show that G-MEMP significantly
outperforms state-of-the-art methods in both benchmarks. Furthermore, ablation
studies demonstrate over 20% improvement in average displacement using gaze
data, particularly in challenging driving scenarios with a high PCI. The data,
code, and models can be found at this https URL

我们研究了司机的决策过程对确保道路安全的重要性，通过利用司机的注视数据来推测司机自身的车辆行驶轨迹，提出了 G-MEMP 方法，利用 GPS、视频和注视数据进行多模态的自身车辆行驶轨迹预测，并通过提出的路径复杂度指数（PCI）来评估轨迹复杂性，在多个基准数据集上验证了该方法的有效性。