Automatic driving scene self-recognition method and storage medium

Abstract

The invention relates to an automatic driving scene self-recognition method and a storage medium. The method comprises the steps of data acquisition and storage, data cleaning, events, scene recognition and point burying, data cutting, classification and label labeling, format conversion and data use. According to the invention, automatic identification and extraction, format conversion and labeled classified storage management are carried out on all collected whole vehicle network signals and sensor original signal data; a scene library established according to layering and classification of real vehicle data covers a natural traffic flow scene, an automatic driving failure working condition, driver takeover data, whole vehicle driving data, a working condition outside an operation area range, a traffic accident, traffic participant interaction data and man-machine interaction data. Big data support can be provided for automatic automobile product definition, function planning, demand analysis, software and hardware development, system integration, simulation and real automobile verification, function and performance rapid iteration updating, automatic driving standard regulation making and the like, and the utilization efficiency and the repeated utilization rate of data are improved.

Description

technical field [0001] The invention belongs to the technical field of data collection and analysis, and in particular relates to a self-identification method and a storage medium of an automatic driving scene. Background technique [0002] The diversity, complexity, uncertainty, inability to fully reproduce, etc. of traffic scene elements such as road types, lane lines, signs, obstacles, etc., make it difficult for the test and verification of automatic driving functions to cover all traffic scenes or require huge costs. At the same time, it is also a limiting factor for the real mass production of unmanned autonomous driving. Continuous iterative update of data-driven software functions and performance is undoubtedly the most cost-effective way, the fastest iteration speed, and the strongest self-growth ability. [0003] In order to ensure the safety of the automatic driving function, any optimization and upgrade of the software requires hundreds of thousands of kilometer...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a self-identification method and storage medium for automatic driving scenes, so as to avoid the problem of low feasibility of the traditional real vehicle data collection method in the face of positioning and analysis problems, and obtain Improve the efficiency of data utilization and the effect of repeated utilization

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