Understanding Attention Switching with Visual and Audio Cues in Time-Safety Critical Situations

该项目在智能驾驶员支持系统的设计中采用了整体的、以人为本的方法，在该系统中，对情况关键性估计进行持续监控。这需要计算模型来准确估计驾驶员如何感知情况，计划行动以及与车辆及其周围环境的反应和交互。该项目的具体目标是开发计算框架，在时间和安全限制至关重要的环境中，使用多模态线索分析注意力转移。 具体的研究目标是：（1）识别注意转换的身体相关指标。这涉及到利用统计机器学习算法来分析先前收集的人种学数据集，并确定注意力转移的最有用指标，包括头部和眼睛注视、手、脚和其他身体运动。(2)理解驾驶环境中外部视觉和听觉显著性线索对注意力转移的影响。 这涉及到分析这些多模态线索如何影响注意力转移的时间和安全的关键情况下，纳入？自上而下目标导向和？自下而上分散注意力的机制(3)开发一个分层贝叶斯模型和计算框架，用于描述身体线索，外部显着性和驾驶任务之间的关系，以准确地估计注意力和注意力转移。总之，该项目提供了一个可行性评估，检测如何以及为什么注意力转移发生在车辆环境中的多模态传感器套件。项目的研究结果将影响主动安全系统的设计，以减少道路上的碰撞风险。

This project utilizes a holistic, human-centered approach in the design of intelligent driver support systems, where situational criticality estimates are continuously monitored. This requires computational models for accurate estimation of how a driver perceives situations, plans actions, and reacts and interacts with the vehicle and its surround. The specific goal of the project is to develop computational frameworks to analyze attention shifts, using multimodal cues, in an environment where time and safety constraints are critical. Specific research objectives are: (1) Identification of body related indicators of attention switching. This involves utilizing statistical machine-learning algorithms to analyze previously collected ethnographic datasets and determine most useful indicators of attention shifts, including head and eye gaze, hands, feet, and other body motions. (2) Understanding the effect of external visual and audio saliency cues in the driving environment on attention shifts. This involves the analysis of how those multimodal cues affect attention shifts in time- and safety-critical situations, incorporating ?top-down? goal-oriented and ?bottom-up? distraction-based mechanisms. (3) Developing a hierarchical Bayesian model and computational framework for describing the relationship between body cues, external saliency, and driving task, in order to accurately estimate attention and attention shifts. In summary, the project provides a feasibility assessment of detecting how and why attention shifts occur in the vehicular environment with a multimodal sensor suite. Project findings will influence design of active safety systems to reduce crash risk on the roads.