Abstract: At present, the continuous change of information technology along with the dramatic explosion of data quantity makes the cloud computing solutions face many problems such as high latency, limited bandwidth, high carbon footprint, high maintenance cost, and privacy concerns. In recent years, the emergence and rapid development of edge computing has effectively alleviated such dilemmas, sinking user demand processing to the edge and avoiding the flow of massive data in the network. As a typical scenario of edge computing, edge intelligence is gaining increasing attention, in which one of the most important stages is the inference phase. Due to the general low performance of resources in edge computing, collaborative inference through resources is becoming a hot topic. By analyzing the trends of edge intelligence development, we conclude that collaborative inference at the edge is still in the increasing phase and has not yet entered a stable phase. We divide edge-edge collaborative inference into two parts: Intelligent methods and collaborative inference architecture, based on a thorough investigation of edge collaborative inference. The involved key technologies are summarized vertically and organized from the perspective of dynamic scenarios. Each key technology is analyzed in more detail, and the different key technologies are compared horizontally and analyzed on the application scenarios. Finally, we propose several directions that deserve further studying in collaborative edge inference in dynamic scenarios.