Small-sample-in-the-loop learning system and method for autonomous driving environment perception

Abstract

A small-sample-in-the-loop learning system and method for automatic driving environment perception of the present invention, based on the data training set, algorithm training is performed on the target learning model to obtain the target detection algorithm; the small-sample training set is trained and learned in the physical ECU module, and the obtained Target perception algorithm: The target algorithm test of the system is carried out through the acquired target perception algorithm, and the system includes: a sample input module, an image acquisition module, an image display module, a physical ECU module, and a result output module. A small-sample in-the-loop learning system and method for autonomous driving environment perception of the present invention, by proposing a learning method based on small-scale data, setting an in-the-loop experimental simulation platform with simulation software, solves the real vehicle automatic driving environment perception algorithm The problem of difficulty in obtaining large-scale data during development, realize the scene customization function, simulate and generate a large amount of scene data that is difficult to realize in actual situations, and increase the safety of experimental development.

Description

technical field [0001] The invention relates to the field of intelligent driving, in particular to a small-sample-in-the-loop learning system and method for automatic driving environment perception. Background technique [0002] The autonomous driving environment perception system based on camera hardware is one of the most important components of the intelligent system of autonomous driving vehicles. The main functions of the autonomous driving intelligent system include: sensing, perception, decision-making, control, etc. In the development of autonomous vehicles, most of the technical difficulties and challenges come from the environmental perception and computer vision. [0003] At present, the solutions of autonomous driving environment perception technology are mainly based on computer vision and machine learning. Machine learning methods for computer vision are mainly based on deep learning. Deep learning is a data-driven machine learning method, which relies on a lar...

AI Technical Summary

Problems solved by technology

[0005] Taking an autonomous driving scene dataset as an example, there are 200 categories in total, among which the sample size of 10 high-frequency categories covers 45% of the overall dataset, and the sample size of 190 low-frequency categories covers 55% of the overall dataset. Most of them are low-frequency samples, and it is difficult to collect large-scale data for deep learning models for algorithm training

[0006] 2. Difficulty marking

[0007] Deep learning model training for autonomous driving environment perception often requires a data set of 100K scale. Large-scale data sets have the disadvantages of high cost and manual error in manual labeling, which leads to a lot of overhead for automatic driving companies in data labeling. , and cannot get accurate labeled data

[0008] 3. Insufficient data

[0009] In the large-scale sample data collection for algorithm model training, there are problems such as high data collection cost, long cycle, and difficulty in collecting dangerous scene data, which leads to insufficient sample data for autonomous driving companies.

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