The invention belongs to the technical field of
software engineering, particularly relates to an automatic driving
system performance enhancement method based on an expert
hybrid architecture, and aims to solve the core problems that an end-to-end automatic driving
system is confronted with semantic fuzziness to cause unreliable decision, multi-task interference hinders optimization planning, too long reasoning
delay increases driving risks and the like. According to the method, an ExpertAD framework is provided, task key features are amplified through a
perception adapter (PA), and the relevance of scene context understanding is guaranteed; related driving tasks are dynamically activated through a sparse expert mixture (MoSE), and task interference is minimized; and in combination with a customized training
loss function, collaborative optimization of planning effectiveness and reasoning efficiency is realized. Experiments show that compared with an existing method, the method has the advantages that the average
collision rate is reduced by 20%, the reasoning
delay is reduced by 25%, higher multi-skill planning capacity is achieved in rare scenes (such as accident handling and first-aid vehicle avoiding), and good generalization is achieved for unseen urban environments.