Image information and radar information fusion method and system for traffic scene

Abstract

The invention provides an image information and radar information fusion method and system for a traffic scene, and the method comprises the following steps: preprocessing the image information, obtained by a camera, of the front of a vehicle; extracting the feature information in the image information, and comparing the feature information with the pre-stored traffic scene information; classifying the current traffic scene according to a comparison judgment result, performing a corresponding fusion algorithm according to a preset fusion method of image information and radar information whichis adapted to the current traffic scene category, and outputting the result of the fusion algorithm. The method judges a scene according to the collected image information, switches between differentfusion algorithms, effectively utilizes resources, and improves scene adaptability. The method fully utilizes redundancy and complementary characteristics between different sensor data to improve system robustness and reliability. The method adopts a deep learning algorithm in image information processing, which has higher real-time performance and is more accurate in target recognition.

Description

technical field [0001] The present invention relates to the field of safe driving, in particular to a fusion method and system for image information of traffic scenes and radar information. Background technique [0002] With the continuous improvement of vehicle electrification, intelligence and networking, advanced vehicle assistance driving (ADAS) has become the key research direction of major enterprises, universities and research institutes, and environmental perception is the most basic in ADAS system. key technologies. Accurate acquisition of effective target information in front of the road can provide strong technical support for active safety technologies such as adaptive cruise control (ACC) and automatic emergency braking system (AEB), and is of great significance to the development of ADAS systems. Existing environmental perception technologies mostly use a single sensor, or simple superposition of multi-sensor data. Such a method cannot meet the high-precision...

AI Technical Summary

Problems solved by technology

Although these machine learning methods improve the accuracy of detection while reducing the computational intensity, their accuracy largely depends on the training data set in the experimental environment.

In 2015, Alencar used millimeter-wave radar and camera for data fusion to realize road multi-target identification and classification, and used k-means cluster analysis, support vector machine and nuclear principal component analysis to comprehensively analyze camera data and millimeter-wave radar data to obtain road Target, high accuracy but only suitable for the identification of close-range targets in good weather

[0004] To sum up, most of the existing research only discusses the detection problem in specific traffic scenarios, without considering how to use the advantages of different sensors in different traffic scenarios, the fusion method has poor adaptability to application scene changes, and does not pay attention to the construction of the fusion structure and overall performance optimization

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