A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model (S110); partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result (S120); and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result (S130). The present disclosure effectively solves the problem that a model trained by means of machine learning cannot be locally adjusted and easily modifies the decision of a certain partition without affecting the decision results of other partitions at all. The intuitive nature of a partition decision table can effectively help to find and solve problems in the machine learning process. The partition decision table can speed up the decision process.