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一种全面的、以人为本的方法，持续监测态势临界评估。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互动。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，使用多模式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，并确定最有用的注意力转移指标，包括头和眼睛的凝视、手、脚和其他身体动作。(2)了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。这包括分析这些多模式线索如何影响时间和安全关键情况下的注意力转移，包括自上而下？目标导向和自下而上？distraction-based机制。(3)建立了描述身体线索、外部显著性和驾驶任务之间关系的层次贝叶斯模型和计算框架，以便准确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，通过多模态传感器套件来检测车辆环境中注意力转移的方式和原因。项目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.