Crash risk prediction method and system of emergency rescue autonomous vehicle based on hm-trw and hagenn structure search

Abstract

The present disclosure discloses an emergency rescue autonomous driving vehicle collision risk prediction method and a system based on HM-TRW and HAGENN structure search, abstracts the relationship between the emergency rescue vehicle and the surrounding moving objects into a dynamic heterogeneous diagram, captures the importance and dynamic of each surrounding moving object and various interactive relations by using HM-TRW, and merges with the original characteristics of each surrounding moving object. Then input HAGENN to predict the collision risk, and apply the dynamic heterogeneous graph embedding method to the hierarchical attention neural network to further learn the heterogeneous characteristics and dynamic changes of the surrounding moving objects. When calculating the attention force of different levels, the positioning space is used to determine the application position of attention, the parameterized space is used to search the attention function, and multi-stage differential search is introduced to accelerate the above search process. The present disclosure can more comprehensively and accurately predict the collision risk in the operation process of the emergency rescue autonomous driving vehicle.