针对目前无人驾驶“类人决策”中学习内容表达不清晰、学习对象参差不齐、学习泛化能力弱三个问题，以驾驶行为大数据为背景，以驾驶员感知-决策-行为的隐性耦合关系刻画为特点，以图谱迁移学习为核心，重点研究特征图谱建模表征中注意感知图、策略决策图和行为规划谱的交互式动态影响；提出图谱相似性距离测度，采用成对约束半监督聚类算法发现理想的“完美驾驶员”；提出图谱迁移学习算法，根据域空间共享特征对源样本（完美驾驶员）的决策图谱进行细粒度渐近关联聚合将其学习迁移到目标样本（无人驾驶）。最终形成涵盖决策图谱刻画（内容清晰化）-完美驾驶员提取（对象完美化）-图谱迁移学习（提升泛化能力）全过程的学习体系，为无人驾驶类人学习的推广应用奠定基础，进而期望无人驾驶具备优良的驾驶素质、灵活的决策能力、高超的驾驶技能和绿色的驾驶行为，对营造健康、和谐、高效、绿色的无人驾驶交通环境具有重要意义。

Aim at three problems, unclear expression of learning content, uneven learning object and weak generalization ability in the human-like decision-making for the autonomous vehicle, the key of this study is to transfer Learning of the characteristic graph, through describing the coupling relationship between driver's perception and decision behavior, on the basis of big data of driving behavior. This study mainly aims to: 1)study the interactive dynamic influence of perceptual map, strategy of decision diagrams and behavior planning spectrum. 2)propose the similarity distance measure of the characteristic graph, using pairwise constrained semi-supervised clustering algorithm to find the ideal "perfect driver". 3)propose transfer learning of the characteristic graph. According to the sharing characteristics of the domain, the graph of source samples (perfect driver) is asymptotically converged using fine-grained correlation and then is transferred to the target sample(the autonomous vehicle).Finally, a learning system, considering the whole process of the depiction of the decision map (sharpening the content) - perfect driver extraction (perfecting the object) - graph transferring learning (improving the generalization ability), is formed. It will lay the foundation of the popularization and application of the autonomous vehicle' learning, and further expect that the autonomous vehicle would have excellent driving quality, flexible decision-making ability, high driving skills, and green driving behavior. It can be of great significance for building a healthy, harmonious, efficient and green driverless traffic environment.