智能车辆中融入视觉注意机制的道路场景理解研究

项目名称： 智能车辆中融入视觉注意机制的道路场景理解研究

项目编号： No.61203250

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 毛燕芬

作者单位： 同济大学

项目金额： 25万元

中文摘要： 道路场景理解RSU利用感知数据进行视觉计算，使车辆具有类似人类的环境认知能力，是智能车辆IV实现自主驾驶的关键，也是国内外学者积极探索的挑战性课题。针对目前研究仅对场景进行对等语义标记、海量数据与计算资源存在矛盾等问题，本课题提出将选择性视觉注意机制VAM有效融入RSU问题求解框架。由紧急度和重要度构建感知优先级任务池，将场景分为危险、正常及辅助性信息，建立目标知识库，为视觉关注及注意力转移提供明确任务。结合自顶向下与自下而上信息，利用VAM聚焦优势及其在解决视觉处理瓶颈效应的独特作用，考虑视频流特点，对"新出现事件"与"时空延续事件"用任务驱动VA及PF概率估计进行并行分析，用证据积累进行预警与确认。达到对场景实施不同关注等级的分析，重要信息优先处理与及时理解，进而给执行机构提供充裕的响应时间，为解决RSU快速、可靠、自适应等问题提供途径，并为任务驱动VA理论及IV研究提供原创性成果。

中文关键词： 道路场景理解；视觉注意；证据积累；深度学习；贝叶斯框架

英文摘要： Road Scene Understanding (RSU) plays a key role in autonomous driving for Intelligent Vehicle (IV). It involves using different sensors combined with automatic reasoning, in order to simulate the human cognitive abilities and create a synthetic representation of the environment around the vehicle. It is also an active and challenging topic for researchers all over the world. Due to the problem for present research which is only making semantic labeling with equivalent priority, confliction about huge amounts of data and limited computation resource, this research proposes a new framework which efficiently fuses selective Visual Attention Mechanism (VAM) into RSU solution. According to the emergency and importance, a priority task pool is built, and the scene data is classified into the dangerous, normal and auxiliary information. Then the corresponding objects' knowledge database is obtained in order to give clear tasks to visual attention model and attention shift. Combing top-down and bottom-up information, VAM focusing advantage and its efficient function on dealing with bottleneck effect in visual computing are ultilized. Considering the characteristic of traffic video streams, new events are recognized by using task-guided visual attention mechanism and temporal-spatial continuation events are verified by u

英文关键词： Road Scene Understanding；Visual Attention；Evidence Accumulation；Deep Learning；Bayesian Framework