门控驾驶员注意力预测器
Gated Driver Attention Predictor
AI总结：
本文提出门控驾驶员注意力预测器Gate-DAP，通过空间编码网络门控、LSTM门控和信息类型门控的即插即用模块，灵活学习驾驶场景中不同空间、时间与模态信息的重要性，并在DADA-2000和BDDA数据集上验证了优于现有方法的预测性能。
摘要：
驾驶员注意力预测意味着理解驾驶员打算去哪里以及驾驶员关注什么物体，这通常提供了一种驾驶任务引导的交通场景理解。最近的一些工作探索了关键或事故场景中的驾驶员注意力预测，并发现其在帮助事故预测方面具有积极作用，但这种促进能力受到驾驶员注意力图预测精度的制约。在本工作中，我们探索了用于驾驶员注意力预测的网络连接门控机制（Gate-DAP）。Gate-DAP旨在学习不同道路类型、场合以及光照和天气条件下的驾驶场景中不同空间、时间和模态信息的重要性。Gate-DAP中的网络连接门控由空间编码网络门控、长短期记忆网络门控和信息类型门控模块组成。每个连接门控操作都是即插即用的，并且可以灵活组装，这使得Gate-DAP的架构在评估用于驾驶员注意力预测的不同空间、时间和信息类型时是透明的。在DADA-2000和BDDA数据集上的评估通过与最先进方法的比较验证了所提出方法的优越性。代码可在https://github.com/JWFangit/Gate-DAP上获取。
英文摘要：
Driver attention prediction implies the intention understanding of where the driver intends to go and what object the driver concerned about, which commonly provides a driving task-guided traffic scene understanding. Some recent works explore driver attention prediction in critical or accident scenarios and find a positive role in helping accident prediction, while the promotion ability is constrained by the prediction accuracy of driver attention maps. In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP). Gate-DAP aims to learn the importance of different spatial, temporal, and modality information in driving scenarios with various road types, occasions, and light and weather conditions. The network connection gating in Gate-DAP consists of a spatial encoding network gating, long-short-term memory network gating, and information type gating modules. Each connection gating operation is plug-and-play and can be flexibly assembled, which makes the architecture of Gate-DAP transparent for evaluating different spatial, temporal, and information types for driver attention prediction. Evaluations on DADA-2000 and BDDA datasets verify the superiority of the proposed method with the comparison with state-of-the-art approaches. The code is available on https://github.com/JWFangit/Gate-DAP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
