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Vehicle behavior prediction method and device, equipment and medium

A prediction method and vehicle technology, applied in the direction of prediction, image data processing, 3D image processing, etc., can solve the problems that sub-module uncertainty cannot be effectively compensated, affect the performance of the scheme, and waste computing resources, so as to reduce computing power. The effect of resource consumption, improving calculation speed, and ensuring prediction accuracy

Active Publication Date: 2021-10-22
CHINA FIRST AUTOMOBILE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, each module in this type of solution operates independently, so the uncertainty of its sub-modules cannot be effectively compensated during the propagation process, which in turn affects the performance of the overall solution
In addition, since the sub-modules are serially connected, computational resources are wasted

Method used

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  • Vehicle behavior prediction method and device, equipment and medium
  • Vehicle behavior prediction method and device, equipment and medium
  • Vehicle behavior prediction method and device, equipment and medium

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Experimental program
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Embodiment 1

[0037] figure 1 It is a flow chart of a vehicle behavior prediction method provided in Embodiment 1 of the present invention. This embodiment is applicable to vehicle behavior prediction in unmanned driving technology. The method can be executed by the vehicle behavior prediction device provided in the embodiment of the present invention, which can be realized by software and / or hardware, and can be integrated on electronic equipment.

[0038] Specifically, such as figure 1 As shown, the vehicle behavior prediction method provided by the embodiment of the present invention may include the following steps:

[0039] S110. Acquire three-dimensional point cloud data collected by the vehicle sensor.

[0040] Among them, the 3D point cloud data is collected by vehicle-mounted sensors mainly based on lidar. 3D point cloud data is not RGB image data, in addition to containing the collection position, it also contains rich information, such as color information and intensity informa...

Embodiment 2

[0059] figure 2 It is a flow chart of a vehicle behavior prediction method provided by Embodiment 2 of the present invention. This method is further optimized on the basis of the above embodiments, and a specific introduction of how to extract spatial dimension features is given.

[0060] Specifically, such as figure 2 As shown, the method includes:

[0061] S210. Acquire three-dimensional point cloud data collected by the vehicle sensor.

[0062] Among them, the 3D point cloud data is collected by vehicle-mounted sensors mainly based on lidar. 3D point cloud data is not RGB image data, in addition to containing the collection position, it also contains rich information, such as color information and intensity information.

[0063] In order to improve the calculation speed, the input data can be simplified. After obtaining the 3D point cloud data collected by the vehicle sensor, the 3D point cloud data can be quantified based on the 3D voxel grid to obtain the quantized ...

Embodiment 3

[0077] image 3 It is a network structure diagram of the vehicle behavior prediction model provided in Embodiment 3 of the present application. The embodiment of the present application provides a preferred implementation mode on the basis of the technical solutions of the foregoing embodiments.

[0078] This embodiment is implemented on the basis of the laser radar as the main vehicle sensor, so different from the traditional target detection algorithm, the input data is 3D point cloud data rather than RGB image data. In this embodiment, the input data is simplified, and the specific process can be divided into two parts: 1) quantify the 3D point cloud data based on the 3D voxel grid to obtain the quantified point cloud data; 2) through two Valued data encodes quantized point cloud data, that is, if there is original input data in this grid, it is set to 1, otherwise it is 0. Since the input data obtained through simplified processing is a sparse three-dimensional tensor, t...

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Abstract

The invention discloses a vehicle behavior prediction method, apparatus and device, and a medium. The method comprises the steps of obtaining three-dimensional point cloud data collected by a vehicle-mounted sensor; based on the vehicle behavior prediction model, extracting spatial dimension features in spatial dimensions; based on a vehicle behavior prediction model, determining a compressed four-dimensional tensor according to the three-dimensional point cloud data; performing group convolution on the compressed four-dimensional tensor, and performing feature extraction on a time dimension to obtain a simplified feature map; according to the simplified feature map, performing fusion on group convolution layer results, and determining time dimension features; fusing the spatial dimension feature and the time dimension feature to obtain a fused feature; and predicting the motion state of the vehicle in the three-dimensional point cloud data according to the fusion features. According to the technical scheme, detection, tracking and behavior prediction are processed in parallel, consumption of computing resources is reduced while the prediction precision is guaranteed, the input data are compressed, and the computing speed is increased.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of unmanned driving, and in particular to a vehicle behavior prediction method, device, equipment and medium. Background technique [0002] Vehicle driving behavior prediction mainly serves applications related to the safety of the Internet of Vehicles, such as vehicle anti-collision monitoring at intersections. [0003] In the research field of unmanned driving technology at this stage, most methods divide the problem into four sub-modules, namely target detection, trajectory tracking, target behavior prediction and planning decision-making. The relationship between each module is: the output of the detection module is as The input of the tracking module, and then input the target motion curve obtained by the tracking module to the behavior prediction module, and finally output the decision signal. [0004] However, each module in this type of solution operates independently, so the u...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06T15/00G06K9/62
CPCG06Q10/04G06T15/00G06F18/253
Inventor 王祎男曹容川张天奇关瀛洲
Owner CHINA FIRST AUTOMOBILE
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