Recent analysis of incidents involving Autonomous Driving Systems (ADS) has
shown that the decision-making process of ADS can be significantly different
from that of human drivers. To improve the performance of ADS, it may be
helpful to incorporate the human decision-making process, particularly the
signals provided by the human gaze. There are many existing works to create
human gaze datasets and predict the human gaze using deep learning models.
However, current datasets of human gaze are noisy and include irrelevant
objects that can hinder model training. Additionally, existing CNN-based models
for predicting human gaze lack generalizability across different datasets and
driving conditions, and many models have a centre bias in their prediction such
that the gaze tends to be generated in the centre of the gaze map. To address
these gaps, we propose an adaptive method for cleansing existing human gaze
datasets and a robust convolutional self-attention gaze prediction model. Our
quantitative metrics show that our cleansing method improves models'
performance by up to 7.38% and generalizability by up to 8.24% compared to
those trained on the original datasets. Furthermore, our model demonstrates an
improvement of up to 12.13% in terms of generalizability compared to the
state-of-the-art (SOTA) models. Notably, it achieves these gains while
conserving up to 98.12% of computation resources.

该论文提出了一种自适应的方法来清洗现有的人类凝视数据集，并提出了一个健壮的卷积自我注意凝视预测模型，实验证明我们的清洗方法和模型能够显著提高 ADS 性能和泛化能力。