Discrepancies in decision-making between Autonomous Driving Systems (ADS) and human drivers underscore the need for intuitive human gaze predictors to bridge this gap, thereby improving user trust and experience. Existing gaze datasets, despite their value, suffer from noise that hampers effective training. Furthermore, current gaze prediction models exhibit inconsistency across diverse scenarios and demand substantial computational resources, restricting their on-board deployment in autonomous vehicles. We propose a novel adaptive cleansing technique for purging noise from existing gaze datasets, coupled with a robust, lightweight convolutional self-attention gaze prediction model. Our approach not only significantly enhances model generalizability and performance by up to 12.13% but also ensures a remarkable reduction in model complexity by up to 98.2% compared to the state-of-the art, making in-vehicle deployment feasible to augment ADS decision visualization and performance.

翻译：自动驾驶系统与人类驾驶员在决策上的差异凸显了对直观人类视线预测模型的需求，以弥合这一差距进而提升用户信任与体验。现有视线数据集虽具价值，但存在干扰有效训练的噪声问题。此外，当前视线预测模型在不同场景中表现不一致且计算资源需求高，限制了其在自动驾驶车辆上的车载部署。本文提出一种自适应清洗技术以消除现有视线数据集的噪声，并设计了一种鲁棒且轻量化的卷积自注意力视线预测模型。该方法不仅将模型泛化能力与性能提升高达12.13%，更将模型复杂度较现有最优方法降低98.2%，使其能够实现在车辆端部署以增强自动驾驶系统的决策可视化与性能表现。