At different levels of driving automation, driver’s gaze maintains great indispensable importance on semantic perception of the surround. In this work, we model gaze dynamics and clarify its relationship with driver’s maneuver behaviors from personalized driving style. Firstly, this paper proposes an Occlusion-immune Face Detector (OFD) for facial landmark detection, which can adaptively solve the facial occlusion introduced by the body and glasses frame in the real-world driving scenarios. Meanwhile, an Eye-head Coordination Model is brought up to bridge the error gap in gaze direction through determining eye pose and head pose fused pattern. Then, a vectorized spatiotemporal guidance feature (STGF) descriptor combining gaze accumulation and gaze transition frequency is proposed to construct gaze dynamics within a time window. Finally, we predict driver’s maneuver behavior through STGF descriptor considering different driving styles to clarify the relationship between gaze dynamics and driving maneuver task. Natural driving data are sampled in a dedicated instrumented vehicle testbed, on which 15 drivers with three kind of driving styles participated. Experimental results show that the prediction model achieves the best performance, estimating driver’s behavior an average of 1 s ahead of actual behavior with 83.6% accuracy considering driving style, compared with other approaches.

在不同级别的驾驶自动化中，驾驶员的视线对周围的语义感知保持着极为重要的不可或缺的重要性。在这项工作中，我们对凝视动力学建模，并从个性化的驾驶风格中阐明其与驾驶员操纵行为的关系。首先，本文提出了一种用于面部界标检测的遮挡免疫人脸检测器（OFD），可以在现实世界的驾驶场景中自适应地解决由身体和眼镜框引入的面部遮挡。同时，提出了眼头协调模型，通过确定眼睛的姿势和头部的姿势融合模式来弥合注视方向的误差间隙。然后，提出了一种结合凝视积累和凝视过渡频率的矢量化时空引导特征（STGF）描述符，以构造一个时间窗口内的凝视动力学。最后，我们通过考虑不同驾驶方式的STGF描述子来预测驾驶员的操纵行为，以阐明凝视动力学与驾驶操纵任务之间的关系。自然驾驶数据是在专用的仪器仪表测试台上采样的，其中有15种具有三种驾驶方式的驾驶员参加了测试。实验结果表明，与其他方法相比，该预测模型达到了最佳性能，将驾驶员的行为估计比实际行为平均提前1 s，考虑驾驶方式的准确性为83.6％。共有15种具有三种驾驶风格的驾驶员参加了比赛。实验结果表明，与其他方法相比，该预测模型达到了最佳性能，将驾驶员的行为估计比实际行为平均提前1 s，考虑驾驶方式的准确性为83.6％。共有15种具有三种驾驶风格的驾驶员参加了比赛。实验结果表明，与其他方法相比，该预测模型达到了最佳性能，将驾驶员的行为估计比实际行为平均提前1 s，考虑驾驶方式的准确性为83.6％。