In recent years, predicting driver's focus of attention has been a very
active area of research in the autonomous driving community. Unfortunately,
existing state-of-the-art techniques achieve this by relying only on human gaze
information, thereby ignoring scene semantics. We propose a novel Semantics
Augmented GazE (SAGE) detection approach that captures driving specific
contextual information, in addition to the raw gaze. Such a combined attention
mechanism serves as a powerful tool to focus on the relevant regions in an
image frame in order to make driving both safe and efficient. Using this, we
design a complete saliency prediction framework - SAGE-Net, which modifies the
initial prediction from SAGE by taking into account vital aspects such as
distance to objects (depth), ego vehicle speed, and pedestrian crossing intent.
Exhaustive experiments conducted through four popular saliency algorithms show
that on $\mathbf{49/56\text{ }(87.5\%)}$ cases - considering both the overall
dataset and crucial driving scenarios, SAGE outperforms existing techniques
without any additional computational overhead during the training process. The
augmented dataset along with the relevant code are available as part of the
supplementary material.

该论文提出了一种基于场景语义和注视信息相结合的自动驾驶注意力预测方法 SAGE，并通过修改自动驾驶的关键预测参数，设计了一个完整的预测框架 SAGE-Net，实验证明 SAGE 在不增加计算量的情况下，可以优于现有技术 87.5% 的案例。