The invention provides an intelligent driving closed-
loop control method based on a pre-training
large model. The intelligent driving closed-
loop control method comprises the following steps: S1, obtaining full-quantity heterogeneous input data in a
vehicle driving process; s2, performing
feature coding on the total heterogeneous input data to generate
feature data suitable for pre-training
large model processing; s3, inputting the encoded
feature data into a pre-trained
large model for
feature extraction, and outputting a key value pair; s4, constructing a plurality of task head sharing key value pairs in a
cascade form, and generating intention-level and track-level outputs; s5, outputting and generating a
vehicle control instruction according to the intention level and the track level to control the driving state of the vehicle; according to the method, full-quantity heterogeneous input features in a closed-loop form are constructed, so that the use efficiency of data is remarkably improved, and the most comprehensive feature representation is extracted from multi-source input, so that an output result with higher quality is generated; according to the method, the problems of
information loss and accumulative errors in a traditional
modular system are effectively solved, and the accuracy and reliability of decision planning are improved.