An intelligent vehicle passable area detection method based on multi-source information fusion

Abstract

The invention discloses an intelligent vehicle passable area detection method based on multi-source information fusion, and the method comprises the steps: S100, collecting obstacle target informationaround a vehicle detected by a vehicle-mounted sensor, and outputting a static obstacle target library; S200, receiving obstacle target information around the vehicle, carrying out time-space synchronization on the obstacle target information detected by the vehicle-mounted sensor, carrying out single-frame target fusion on all detected obstacle information around the vehicle, carrying out continuous inter-frame multi-target tracking by utilizing motion prediction and multi-frame target association, and outputting a dynamic obstacle target library; And S300, receiving the static obstacle target library and the dynamic obstacle target library output in the step S200, and updating the dynamic obstacle target library according to the information of the static obstacle target library to formreal-time obstacle target information and generate a passable area. The position, scale, category and motion information of the obstacle around the vehicle and the binarization rasterized map can be accurately obtained in the vehicle driving process, the motion track of multiple targets is tracked, and the intelligent vehicle passable area including the binarization rasterized map and dynamic obstacle information real-time updating is formed.

Description

technical field [0001] The invention relates to the technical field of automatic driving, in particular to a method for detecting a passable area of ​​an intelligent vehicle based on multi-source information fusion. Background technique [0002] Intelligent vehicles realize automatic driving in various traffic scenarios through technical means such as environmental perception, map navigation, trajectory planning, and decision-making control. The popularization and application of intelligent vehicles plays a positive role in alleviating traffic congestion, improving traffic safety, increasing fuel consumption rate and reducing environmental pollution. Governments, related companies, scientific research institutions, and universities have invested a lot of manpower and material resources in academic theoretical research and engineering practical research on autonomous driving technologies. The benefits of technology. Smart vehicles use on-board sensors such as cameras, milli...

AI Technical Summary

Problems solved by technology

[0005] In general, the current passable area detection for intelligent vehicles has the following problems: 1) It cannot be generally applied to structured roads and unstructured roads; 2) It does not fully consider the movement information of different types of obstacle targets Features, and simple use of linear or nonlinear models to predict target movement cannot effectively update obstacle information in real time; 3) Lack of multi-target direction and trajectory tracking functions, lack of tracking and management of multiple target trajectories in real road scenes; 4) It does not make full use of the on-board sensors that are often equipped with smart vehicles, such as the use of lidar depth maps in target detection, and the use of millimeter-wave radar return width and speed information; 5) On unstructured roads, deep learning-based The road surface pixel segmentation method requires pixel-level marking of the image, and the labor cost is high

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