Accurate driver attention prediction can serve as a critical reference for intelligent vehicles in understanding traffic scenes and making informed driving decisions. Though existing studies on driver attention prediction improved performance by incorporating advanced saliency detection techniques, they overlooked the opportunity to achieve human-inspired prediction by analyzing driving tasks from a cognitive science perspective. During driving, drivers' working memory and long-term memory play crucial roles in scene comprehension and experience retrieval, respectively. Together, they form situational awareness, facilitating drivers to quickly understand the current traffic situation and make optimal decisions based on past driving experiences. To explicitly integrate these two types of memory, this paper proposes an Adaptive Hybrid-Memory-Fusion (AHMF) driver attention prediction model to achieve more human-like predictions. Specifically, the model first encodes information about specific hazardous stimuli in the current scene to form working memories. Then, it adaptively retrieves similar situational experiences from the long-term memory for final prediction. Utilizing domain adaptation techniques, the model performs parallel training across multiple datasets, thereby enriching the accumulated driving experience within the long-term memory module. Compared to existing models, our model demonstrates significant improvements across various metrics on multiple public datasets, proving the effectiveness of integrating hybrid memories in driver attention prediction.

翻译：准确的驾驶员注意力预测可为智能车辆理解交通场景并做出明智驾驶决策提供关键参考。尽管现有关于驾驶员注意力预测的研究通过引入先进的显著性检测技术提升了性能，但它们忽视了从认知科学视角分析驾驶任务以实现类人预测的可能性。在驾驶过程中，驾驶员的工作记忆与长时记忆分别在场景理解和经验检索中发挥关键作用。二者共同构成情境感知，帮助驾驶员快速理解当前交通状况并基于过往驾驶经验做出最优决策。为显式整合这两类记忆，本文提出一种自适应混合记忆融合（AHMF）驾驶员注意力预测模型，以实现更接近人类行为的预测。具体而言，该模型首先编码当前场景中特定危险刺激的信息以形成工作记忆，随后自适应地从长时记忆中检索相似情境经验进行最终预测。通过利用领域自适应技术，模型在多个数据集上进行并行训练，从而丰富长时记忆模块中积累的驾驶经验。与现有模型相比，我们的模型在多个公共数据集的各种指标上均显示出显著提升，证明了混合记忆整合在驾驶员注意力预测中的有效性。