System and method for data-driven decision optimization for autonomous driving

Abstract

A data-driven autonomous driving decision optimization system and method, comprising: a data production module, a data screening module, a model encapsulation module, and a parameter tuning module; the data production module takes human driving data as input, annotation, preprocessing, format conversion, extracts key features and performs standardization, normalization, and encoding to generate raw data for the data-driven process that meets the algorithm input requirements; the data screening module screens corresponding data from training data by the decision-making module and performs effective classification; the model encapsulation module encapsulates C++ decision code and constructs a trajectory-pair evaluation cost map required for training using a ground-truth evaluation method based on trajectory pairs; the parameter tuning module, based on screened data under different scenarios, the encapsulated decision algorithm model, and the trajectory-pair evaluation cost map, employs black-box optimization to obtain decision parameters for the corresponding scenarios under the current decision algorithm.

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