Obstacle trajectory prediction method and device, equipment and medium

A trajectory prediction and obstacle technology, which is applied in the direction of measuring devices, character and pattern recognition, radio wave measurement systems, etc., can solve the problems of long time consumption, low accuracy of obstacle trajectory prediction, and unreasonable path planning of unmanned equipment To achieve the effect of improving forecasting accuracy, optimizing forecasting methods, and taking into account real-time forecasting and forecasting accuracy

Pending Publication Date: 2021-01-22
NEOLIX TECH CO LTD
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Problems solved by technology

[0004] Since the prediction accuracy of the prediction model can be high or low, if the prediction accuracy of the prediction model is set relatively high, it will take a long time to predict the trajectory of the obstacle, which will affect the real-time path planning of the unmanned equipment; if The prediction accuracy setting of the prediction model is relatively low, which will make the obstacle trajectory prediction accuracy low, resulting in unreasonable path planning for unmanned equipment

Method used

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  • Obstacle trajectory prediction method and device, equipment and medium
  • Obstacle trajectory prediction method and device, equipment and medium
  • Obstacle trajectory prediction method and device, equipment and medium

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Embodiment 1

[0034] figure 1 It is a schematic flow chart of a method for predicting obstacle trajectories provided by Embodiment 1 of the present invention. This embodiment can be applied to the scene of predicting the trajectory of dynamic obstacles identified during the driving of unmanned equipment. The method can be composed of Obstacle trajectory prediction device can be implemented, the device can be composed of hardware and / or software, and can be integrated in electronic equipment. In this embodiment, the electronic device may be a vehicle-mounted terminal, an intelligent driving device, or a server device. The method specifically includes the following:

[0035] S101. Acquire the type and image or point cloud data of at least one dynamic obstacle recognized during driving of the unmanned device.

[0036] In this embodiment, the unmanned equipment may be, but not limited to: equipment capable of automatic driving, such as automatic driving vehicles, unmanned vehicles, unmanned a...

Embodiment 2

[0059] It can be known from the above introduction that the number of dynamic obstacles identified in the embodiment of the present invention is at least one. Combine below figure 2 , taking the number of dynamic obstacles as an example, the trajectory prediction of obstacles in the embodiment of the present invention will be further described. Such as figure 2 As shown, the method is as follows:

[0060] S201. Obtain the type and image or point cloud data of at least one dynamic obstacle recognized during driving of the unmanned device.

[0061] S202. If there are multiple dynamic obstacles, according to the type of each dynamic obstacle, select a first-level prediction model for each dynamic obstacle from multiple levels of prediction models of the same type.

[0062] Optionally, when there are multiple dynamic obstacles and the types of each dynamic obstacle are different, the electronic device in this embodiment may select from multiple prediction model types based on...

Embodiment 3

[0099] image 3 It is a schematic flow chart of an obstacle trajectory prediction method provided by Embodiment 3 of the present invention. On the basis of the above embodiments, in this embodiment, "upgrade the prediction models of dynamic obstacles in the sorting results sequentially until all The prediction duration and value of the multiple prediction models are greater than or equal to the duration threshold" for further optimization. Such as image 3 As shown, the method is as follows:

[0100] S301. Upgrade the first-level prediction model of the dynamic obstacle at the first position in the sorting result to a second-level prediction model, and determine the prediction duration and value of the second-level prediction model and other first-level prediction models Relationship to the duration threshold.

[0101] Since the dynamic obstacles in the sorting results are sorted according to the order of the obstacle scores from high to low, the dynamic obstacles at the fi...

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Abstract

The invention discloses an obstacle trajectory prediction method and device, equipment and a medium. The obstacle trajectory prediction method and device are suitable for unmanned equipment (or calledunmanned vehicle or automatic driving equipment). The method comprises the following steps: acquiring a dynamic obstacle type and image or point cloud data recognized in the driving process of the unmanned equipment; selecting a first-level prediction model from a plurality of level prediction models of the same type according to the type of the dynamic obstacle; adjusting the level of the prediction model or the number of the dynamic obstacles according to a relationship between a prediction duration sum value of the first-level prediction model and a duration threshold value to obtain the optimal level of the prediction model or the optimal number of the dynamic obstacles; enabling the optimal level prediction model to perform trajectory prediction based on the image or point cloud dataof the dynamic obstacle; or enabling the prediction model to perform trajectory prediction based on the image or point cloud data of the optimal number of dynamic obstacles. According to the invention, the obstacle trajectory prediction mode is optimized, and the trajectory prediction real-time performance and prediction precision can be both considered.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of unmanned driving, and in particular to a method, device, device and medium for predicting obstacle trajectories. Background technique [0002] In unmanned driving technology, the automatic driving system in the unmanned equipment will perform path planning based on the acquired surrounding conditions of the equipment. [0003] When planning the path of the unmanned equipment, in order to enable the unmanned equipment to avoid surrounding obstacles, it is necessary to predict the trajectory of the obstacles. Obstacles generally include static obstacles and dynamic obstacles, and dynamic obstacles can be roughly classified as vehicles, pedestrians or other moving objects. At present, when predicting the movement trajectory of the dynamic obstacle, the prediction model is usually used to predict the movement trajectory of the dynamic obstacle based on the movement state of the dyna...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S13/931G06K9/00
CPCG01S13/931G06V20/58
Inventor 王永聪
Owner NEOLIX TECH CO LTD
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