任务相关视觉注意机制与非安全驾驶状态分析方法研究

项目名称： 任务相关视觉注意机制与非安全驾驶状态分析方法研究

项目编号： No.61473303

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 安向京

作者单位： 中国人民解放军国防科技大学

项目金额： 80万元

中文摘要： 人类驾驶员注意力不集中是导致交通事故的主因。目前用以缓解这一问题的主动安全系统正由原型系统阶段向产业化方向迈进。而如何评估驾驶员精神状态是制约此类系统走向实用的核心和难点问题。研究表明，人类在正常驾驶时可迅速将注意分配到与行驶安全相关的交通事件上，且分配模式有一定规律可循。因此，安全驾驶状态的判断标准是驾驶员是否及时注意到当前环境中相关交通事件、且没有长期被非相关事件吸引。传统对非安全驾驶状态分析主要是通过间接、外在的特征进行疲劳判断。然而疲劳是渐进过程，当检测到疲劳时驾驶员已有相当长时间处于非安全驾驶状态；另外，不疲劳并不意味着一定处于安全驾驶状态。本项目立足于视觉注意机制建模和智能驾驶领域的工作基础，探索驾驶任务驱动的注意模型，研究基于实测视线分析的驾驶员注意分配模式及其内在规律，从而建立注意计算与眼动实测相匹配的非安全驾驶状态分析方法，为辅助驾驶和自主驾驶技术储备理论基础和关键技术。

中文关键词： 视觉注意；主动安全

英文摘要： Human driver inattention is the leading cause of traffic accidents . Active safety systems, which could be employed to alleviate the mentioned problem above, is currently on the threshold of a prototype system to the process of industrialization. And how to assess the mental state of the driver of such a system is difficult but decisive to make that the Active safety a practical and promising technology. Studies have shown that, in normal driving, human driver has the ability to quickly allocate their attention to safety related traffic events, and the distribution model has certain rules to follow. Thus, a criterion for driving safety status evaluation is that whether the driver pay attention to the related traffic events timely, and do not be attracted by non-related traffic events for a long while. Traditionally, unsafe driving status is mainly analyzed through fatigue detection using indirect, external fatigue features. However, fatigue is a gradual process, and when fatigue occurs, there must has been quite a long time that driving in the unsafe driving condition; Also, no fatigue occurrence does not guarantee current driving status is of safety. According to the former research foundation of human visual attention mechanism, and based on the study of intelligence driving in the recent years, this project plans to research on the modeling of driving-task-driven visual attention mechanism and fixation data analysis based attention allocation pattern and its inherent laws, to propose an direct, rapid and reliable method for unsafe driving status detection based on the fusion of visual attention computation and driver's fixation data analysis. These studies could probably be meaningful in the realization of future advanced driving assistant system and autonomous driving system, and provide new theories and key technologies for such systems.

英文关键词： visual attention;active safety