WO2020233436A1

WO2020233436A1 - Vehicle speed determination method, and vehicle

Info

Publication number: WO2020233436A1
Application number: PCT/CN2020/089606
Authority: WO
Inventors: 苗振伟; 黄庆乐; 王兵; 王刚
Original assignee: 阿里巴巴集团控股有限公司
Priority date: 2019-05-22
Filing date: 2020-05-11
Publication date: 2020-11-26
Also published as: CN111986472B; CN111986472A

Abstract

A vehicle speed determination method, and a vehicle. The vehicle speed determination method comprises: generating, according to vehicle point cloud data from among at least two frames of road environment point cloud data, at least two two-dimensional vehicle images (S101); and determining the running speed of a vehicle according to the at least two two-dimensional vehicle images and a time interval between any two frames of data from among the at least two frames of road environment point cloud data (S102). By using such a processing means, at least two two-dimensional vehicle images corresponding to vehicle point cloud data from among at least two frames of road environment point cloud data are generated, and the running speed of a vehicle is determined according to these images and a time interval between any two frames of data from among the at least two frames of data; therefore, the accuracy of a vehicle speed can be effectively improved, thereby improving road traffic safety.

Description

Method for determining vehicle speed and vehicle

This application claims the priority of the Chinese patent application with the application number 201910431632.6 and the invention title of "Vehicle Speed Determination Method and Vehicle" filed on May 22, 2019, the entire content of which is incorporated into this application by reference.

Technical field

This application relates to the field of automatic driving technology, and in particular to a method for determining vehicle speed and a vehicle.

Background technique

Estimating the speed of other vehicles while the vehicle is driving is the key to achieving road traffic safety and leading to automatic driving. It can help the self-driving vehicle predict the future trajectory of surrounding vehicles in the driving scene and help the self-vehicle avoid possible collisions. .

Autonomous vehicles are usually equipped with a variety of sensors, and the data from these sensors has the potential to estimate the vehicle speed. The following describes the three commonly used methods for determining vehicle speed and their existing problems.

1) Vehicle speed determination method based on millimeter wave radar data. With the help of the Doppler effect, this method can provide a more accurate speed measurement for other vehicles. However, in order to give an accurate speed measurement, it has high requirements on the driving position and direction of other vehicles. Specifically, for vehicles that are not in the millimeter wave propagation area and whose moving direction is not parallel to the millimeter wave propagation direction, the speed measurement it gives often has a large error.

2) Vehicle speed determination method based on camera data. This method uses deep learning technology, especially optical flow estimation technology, to use the RGB image collected by the camera to estimate the speed of objects in the image, such as FlowNet technology and so on. However, ordinary RGB cameras have a more obvious defect, that is, they are almost unavailable at night.

3) Vehicle speed determination method based on lidar data. This method uses lidar point cloud estimation speed to effectively overcome the night problem. The specific processing process is as follows: According to the convex hull detected by the point cloud detection algorithm, calculate the offset of the convex hull center of the same object detected between two frames , And finally divided by the two-frame time interval, it is the object velocity. However, this type of method is affected by the detected convex hull shape, and the center point is often not the true shape center of the object, so the estimated velocity result is noisy.

In summary, the prior art has the problem of low accuracy of vehicle speed estimation. How to accurately determine the speed of other vehicles has become an urgent problem for those skilled in the art.

Summary of the invention

The present application provides a method for determining vehicle speed to solve the problem of low speed estimation accuracy in the prior art. This application additionally provides a vehicle speed determination device, a vehicle speed prediction model construction method and device, electronic equipment, road test sensing equipment, and vehicles.

This application provides a method for determining vehicle speed, including:

Generating at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of road environment point cloud data;

The vehicle speed is determined according to the time interval between the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data.

Optionally, the determining the vehicle speed according to the time interval of the two frames of the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data includes:

Determining the two-dimensional vehicle position offset data corresponding to the time interval according to the at least two two-dimensional vehicle images;

The vehicle speed is determined according to the two-dimensional vehicle position offset data and the time interval.

Optionally, the two-dimensional vehicle position offset data is determined according to the at least two two-dimensional vehicle images through a vehicle speed prediction model.

Optional, also includes:

The vehicle speed prediction model is learned from a training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data.

Optionally, the vehicle speed prediction model is determined using the following steps:

Determining the training data set;

Constructing the network structure of the vehicle speed prediction model;

The vehicle speed prediction model is learned from the training data set.

Optionally, the network structure includes a vehicle displacement feature extraction layer and a vehicle displacement feature upsampling layer.

Optionally, the two-dimensional vehicle position offset truth data includes a two-dimensional vehicle position offset truth map having the same image size as the training two-dimensional vehicle image.

Optionally, the training data set is determined using the following steps:

Acquiring at least two frames of environmental point cloud data for training with three-dimensional vehicle bounding box and vehicle identification data;

According to the annotation data, the center point offset of the three-dimensional vehicle bounding box of the same vehicle in the preset two frames is taken as the true value of the three-dimensional vehicle position offset;

Project the true value of the three-dimensional vehicle position offset to the top view coordinate system to obtain the true value of the two-dimensional vehicle position offset;

The two-dimensional vehicle position deviation truth value map is formed according to the two-dimensional vehicle position deviation truth value; and, at least two training two-dimensional vehicles are generated based on the vehicle point cloud data in the at least two frames of environmental point cloud data for training image.

Optionally, the training data set is determined using the following steps:

According to the vehicle point cloud data in at least two frames of environmental point cloud data for training, at least two two-dimensional vehicle images for training are generated.

Optionally, the two-dimensional vehicle position offset data includes a two-dimensional vehicle position offset data map having the same image size as the two-dimensional vehicle image;

The determining the vehicle speed according to the two-dimensional vehicle position offset data and the time interval includes:

The ratio of the average value of the abscissa offset component and the average value of the ordinate offset component of each pixel corresponding to each vehicle in the two-dimensional vehicle position offset data map to the time interval is used as the ratio The speed of the vehicle.

Convert the two-dimensional vehicle position offset data of each vehicle into the three-dimensional vehicle position offset data in the point cloud coordinate system;

The ratio of the average value of the abscissa offset component and the average value of the ordinate offset component of each spatial point corresponding to the vehicle to the time interval is used as the vehicle traveling speed.

Optionally, the two-dimensional vehicle image includes a two-dimensional vehicle image from a top view angle.

Optionally, the generating at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of road environment point cloud data includes:

Determine the attitude data of the vehicle speed determination device;

According to the attitude data, converting the vehicle point cloud data before the last frame into the vehicle point cloud data in the point cloud coordinate system of the last frame;

According to the vehicle point cloud data before the last frame after the coordinate system conversion, a two-dimensional vehicle image corresponding to the vehicle point cloud data before the last frame is generated.

Optional, also includes:

The vehicle point cloud data is extracted from the road environment point cloud data through a vehicle detection model.

Optional, also includes:

Collect the road environment point cloud data.

This application also provides a method for constructing a vehicle speed prediction model, including:

Determining a training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data;

Construct the network structure of the vehicle speed prediction model;

The vehicle speed prediction model is learned from the training data set.

Optionally, the training data set is determined using the following steps:

The application also provides a vehicle speed determining device, including:

An image generating unit, configured to generate at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of road environment point cloud data;

The speed determining unit is configured to determine the vehicle driving speed according to the time interval between the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data.

This application also provides a vehicle speed prediction model construction device, including:

A data determining unit for determining a training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data;

The network construction unit is used to construct the network structure of the vehicle speed prediction model;

The model training unit is used to learn the vehicle speed prediction model from the training data set.

This application also provides a vehicle, including:

Three-dimensional space scanning device;

Processor; and

The memory is used to store the program for realizing the method of determining the vehicle speed. After the device is powered on and the program of the method is run through the processor, the following steps are executed: collecting road environment point cloud data through the three-dimensional space scanning device; according to at least two frames Generating at least two two-dimensional vehicle images from the vehicle point cloud data in the road environment point cloud data; according to the at least two two-dimensional vehicle images and the time interval between the two frames of the at least two frames of road environment point cloud data, Determine the speed of the vehicle.

This application also provides a drive test sensing device, including:

Three-dimensional space scanning device;

Processor; and

This application also provides an electronic device, including:

Processor; and

The memory is used to store the program for realizing the method for determining the vehicle speed. After the device is powered on and the program of the method is run through the processor, the following steps are executed: according to the vehicle point cloud data in at least two frames of road environment point cloud data, At least two two-dimensional vehicle images are generated; and the vehicle speed is determined according to the time interval between the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data.

This application also provides an electronic device, including:

Processor; and

The memory is used to store a program for realizing the method for constructing a vehicle speed prediction model. After the device is powered on and the program of the method is run through the processor, the following steps are performed: determining a training data set; the training data includes at least two training Using two-dimensional vehicle images and two-dimensional vehicle position offset true value data; constructing a network structure of a vehicle speed prediction model; learning the vehicle speed prediction model from the training data set.

The present application also provides a computer-readable storage medium having instructions stored in the computer-readable storage medium, which when run on a computer, cause the computer to execute the above-mentioned various methods.

The present application also provides a computer program product including instructions, which when run on a computer, causes the computer to execute the above-mentioned various methods.

Compared with the prior art, this application has the following advantages:

According to the method for determining the vehicle speed provided by the embodiments of the present application, at least two two-dimensional vehicle images are generated based on the vehicle point cloud data in at least two frames of road environment point cloud data; according to the at least two two-dimensional vehicle images and the The time interval between any two frames of at least two frames of road environment point cloud data is used to determine the speed of the vehicle; this processing method makes it possible to generate at least two frames corresponding to the vehicle point cloud data in the at least two frames of road environment point cloud data. Dimension vehicle images and determine the driving speed of the vehicle based on these images and the time interval of any two frames of data in at least two frames; therefore, the accuracy of the vehicle speed can be effectively improved, thereby improving road traffic safety.

The vehicle speed prediction model construction method provided by the embodiment of the application determines the training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data; the vehicle speed prediction model is constructed Network structure; learn the vehicle speed prediction model from the training data set; this processing method allows learning from a large amount of training data to obtain a model that can predict vehicle displacement based on at least two two-dimensional vehicle images; therefore , Can effectively improve the accuracy of the vehicle speed prediction model.

Description of the drawings

Fig. 1 is a flowchart of an embodiment of a method for determining vehicle speed provided by the present application;

2 is a specific flowchart of an embodiment of a method for determining vehicle speed provided by the present application;

FIG. 3 is a schematic diagram of a network structure of a vehicle speed prediction model of an embodiment of a vehicle speed determination method provided by the present application;

Fig. 4 is a schematic diagram of an embodiment of a vehicle speed determining device provided by the present application;

Figure 5 is a schematic diagram of an embodiment of a vehicle provided by the present application;

Fig. 6 is a schematic diagram of an embodiment of a drive test sensing device provided by the present application;

FIG. 7 is a schematic diagram of an embodiment of an electronic device provided by the present application;

FIG. 8 is a flowchart of an embodiment of a method for constructing a vehicle speed prediction model provided by the present application;

FIG. 9 is a schematic diagram of an embodiment of a vehicle speed prediction model construction device provided by the present application;

Fig. 10 is a schematic diagram of an embodiment of an electronic device provided by the present application.

Detailed ways

In the following description, many specific details are explained in order to fully understand this application. However, this application can be implemented in many other ways different from those described here, and those skilled in the art can make similar promotion without violating the connotation of this application. Therefore, this application is not limited by the specific implementation disclosed below.

In this application, a vehicle speed determination method and vehicle are provided. In the following embodiments, each solution will be described in detail.

First embodiment

Please refer to FIG. 1, which is a flowchart of an embodiment of a method for determining vehicle speed provided by this application. The execution subject of the method may be an unmanned vehicle, a road test sensing device, or a server, etc. The following uses an unmanned vehicle as an example to describe a method for determining a vehicle speed provided in this application. A method for determining vehicle speed provided by this application includes:

Step S101: Generate at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of road environment point cloud data.

In the method provided by the embodiments of the present application, during the driving process of the vehicle (hereinafter referred to as the self-car), the spatial coordinates of each sampling point on the surface of the environmental space object of the vehicle traveling road can be obtained through the three-dimensional space scanning device installed on the vehicle to obtain A collection of points, this massive point data is called road environment point cloud (Point Cloud) data. Through the road environment point cloud data, the scanned object surface is recorded in the form of points. Each point contains three-dimensional coordinates, and some may contain color information (RGB) or reflection intensity information (Intensity). With point cloud data, the target space can be expressed in the same spatial reference system.

The three-dimensional space scanning device may be a Lidar (Light Detection And Ranging, Lidar), which performs laser detection and measurement through a laser scanning method to obtain information about obstacles in the surrounding environment, such as buildings, trees, people, vehicles, etc. The measured data is the discrete point representation of the Digital Surface Model (DSM). In specific implementation, 16-line, 32-line, 64-line and other multi-line lidars can be used. Radars with different laser beam numbers have different frame rates for collecting point cloud data. For example, 16 and 32 lines generally collect 10 frames per second. Point cloud data. The three-dimensional space scanning device may also be equipment such as a three-dimensional laser scanner or a photographic scanner.

After collecting road environment point cloud data by the three-dimensional space scanning device in this embodiment, the own vehicle can generate at least two two-dimensional vehicle images based on the vehicle point cloud data in at least two frames of road environment point cloud data.

The road environment point cloud data may include point cloud data of various objects in the road environment space, and these objects may be trees, buildings, pedestrians and other vehicles on the road, and so on. The method provided in the embodiment of the present application determines the driving speed of other vehicles on the road based on the vehicle point cloud data in at least two frames of road environment point cloud data.

The at least two frames of road environmental point cloud data may be two or more frames of environmental point cloud data recently collected by the current vehicle (self-vehicle), for example, the current vehicle is at t _n-τ ,..., t _n-1 , A total of τ+1 frames of environmental point cloud data during the driving of the vehicle are collected at t _n τ+1 times. Each frame of environmental point cloud data may include point cloud data of multiple vehicles. Therefore, the method provided in the embodiment of the present application can According to the τ+1 frame of environmental point cloud data, the driving speed of multiple vehicles is determined.

The vehicle point cloud data can be extracted from the road environment point cloud data through a vehicle detection model. After the lidar on the vehicle scans and obtains a frame of environmental point cloud data, the environmental point cloud data can be transmitted to the vehicle detection model, and the vehicle and its three-dimensional position data in the environmental point cloud data can be obtained through the model detection, that is to say, it is determined Output the vehicle point cloud data in the environmental point cloud data. The three-dimensional position data may be vertex coordinate data of the rectangular cube bounding box of the vehicle, and so on.

In this embodiment, the vehicle detection model can use the RefineDet method based on deep learning. This method combines the fast running speed of single-stage methods such as SSD and two-stage methods such as Faster R-CNN. Therefore, it has the advantage of high vehicle detection accuracy. When the method detects vehicle point cloud data in the environmental point cloud data, it obtains the bounding box coordinates of the vehicle, that is, the position data of the vehicle point cloud data in the environmental point cloud data.

Step S101 generates at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of environmental point cloud data. The two-dimensional vehicle image may be a two-dimensional environmental image (ie, a two-dimensional image of a three-dimensional scene graph constructed from environmental point cloud data) with images of objects other than the vehicle removed, that is, a two-dimensional vehicle image It may be a two-dimensional environment image including only the vehicle image.

The two-dimensional vehicle image may be a two-dimensional environment image including only the vehicle image in a top view perspective. With this processing method, the two-dimensional vehicle image can include as many two-dimensional projection points of the vehicle as possible, and the vehicle speed determined based on a more comprehensive vehicle point will be more accurate. During specific implementation, two-dimensional vehicle images from other viewing angles, such as left view, right view, front view, etc., can also be used.

In one example, collect the environmental point cloud data of two adjacent frames (the previous frame is recorded as the 0th frame, and the next frame is recorded as the 1st frame), and the point clouds of the two preceding and following vehicles are processed separately using the overhead view. Generate two corresponding multi-channel (including the density channel of the vehicle point, the number channel, etc.) two-dimensional vehicle images. The range of the two-dimensional vehicle image can cover a certain area near the own vehicle. In this process, for the 0th frame, since the self-vehicle may move, it is necessary to synchronize the point cloud coordinate system in time. According to the posture information given by the self-vehicle positioning sensor, the 0th frame is projected to the first One frame of the coordinate system where the point cloud is located, and then generate a two-dimensional vehicle image.

In another example, collect the environmental point cloud data of multiple frames (such as frame 0, frame 1...frame 10), and use the overhead perspective to process the point cloud of all frames of vehicles separately to generate multiple (such as 10) Corresponding multi-channel (including the density channel of vehicle points, number channels, etc.) two-dimensional vehicle image, the range of the two-dimensional vehicle image can also cover a certain area near the own vehicle. In this case, step S101 may include the following sub-steps: 1) Determine the posture data of the own vehicle; 2) According to the posture data, convert the point cloud data of the vehicle before the last frame (such as the 10th frame) into the last The vehicle point cloud data in the point cloud coordinate system of one frame; 3) According to the vehicle point cloud data before the last frame after the coordinate system conversion, a two-dimensional vehicle corresponding to the vehicle point cloud data before the last frame is generated image. In this process, for the 0th to 9th frames, since the self-vehicle may move, it is necessary to synchronize the point cloud coordinate system in time. According to the posture information given by the self-vehicle positioning sensor, the 0th frame The 9th frame is projected to the coordinate system where the 10th frame point cloud is located, and then a two-dimensional vehicle image is generated.

It should be noted that if the execution subject of the method provided in the embodiment of the present application is a drive test sensing device, the location of the device is fixed, so there is no need to determine the road when generating the two-dimensional vehicle image before the last frame. To measure the posture data of the sensing device, there is no need to convert the vehicle point cloud data before the last frame into the vehicle point cloud data in the point cloud coordinate system of the last frame according to the posture data.

Step S103: Determine the driving speed of the vehicle according to the time interval between any two frames of the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data.

After obtaining at least two two-dimensional vehicle images corresponding to the vehicle point cloud data in the at least two frames of road environment point cloud data, according to the at least two two-dimensional vehicle images and any two of the at least two frames of road environment point cloud data The time interval of the frame data can determine the speed of the vehicle.

In an example, the vehicle speed is determined according to the position of the vehicle in each frame of image and the time interval. For example, in the 0th frame, the vehicle A is in front of the vehicle B, and in the first frame, the vehicle B exceeds the vehicle A, but is in front of the vehicle A. This means that the speed of the vehicle B is higher than the speed of the vehicle A. According to The location of car, vehicle A, vehicle B and the time interval between these two frames of data can determine the speed of vehicle A and vehicle B.

In another example, step S103 may include the following sub-steps:

Step S1031: Determine the two-dimensional vehicle position offset data corresponding to the time interval according to the at least two two-dimensional vehicle images.

The two-dimensional vehicle position offset data may include the abscissa position offset and the ordinate position offset of the vehicle in a two-frame time interval. The two-frame time interval may be the time interval of any two frames of data in the at least two frames of road environment point cloud data. For example, the self-vehicle collects τ+1 frames of environmental point cloud data during the driving of the vehicle at t _n-τ ,..., t _n-1 , t _n τ+1, and the method provided in the embodiment of this application can Determine the distance that other vehicles move between time t _n-1 and time t _n , that is, the position offset of other vehicles on the ground abscissa and ordinate, the time interval in this case is t _n -t _n-1 . In specific implementation, it may also be to determine the distance moved from t _n-2 to time t _n-1 . In this case, the time interval is t _n-1 -t _n-2 ; or to determine t _n-τ to The distance moved between time t _n-3 , in this case the time interval is t _n-3 -t _n-τ .

It should be noted that since multiple vehicles in a two-dimensional vehicle image usually have different vehicle driving speeds, the two-dimensional vehicle position offset data of different vehicles at two-frame time intervals are usually different.

In the method provided by the embodiment of the present application, the vehicle speed prediction model determines the two-dimensional vehicle position offset data of the vehicle in a two-frame time interval. The vehicle speed prediction model can be learned from a large number of training data sets of at least two two-dimensional vehicle images marked with two-dimensional vehicle position offset true value data, that is, the training data includes at least two training data sets. Two-dimensional vehicle image and two-dimensional vehicle position offset true value data.

In terms of time dimension, the two-dimensional vehicle position offset true value data may be the two-dimensional vehicle position offset true value of the vehicle in the last two frame time interval, or the two-dimensional vehicle position offset true value of the vehicle in any two frame time interval. The vehicle position offsets the true value.

In terms of data granularity, the two-dimensional vehicle position offset truth data may include a two-dimensional vehicle position offset truth map with the same image size as the training two-dimensional vehicle image, or it may be The two-dimensional vehicle position offset true value map of the training two-dimensional vehicle image with a smaller image size can also include only a very small amount of two-dimensional vehicle position offset true values, and in extreme cases it can include only a displacement of the abscissa The true value of the offset and the true value of the displacement offset of an ordinate, that is, the true value of the displacement offset of the vehicle in the two-dimensional vehicle image for training only includes two data, one is the abscissa of the vehicle The true value of the displacement offset, and the other is the true value of the displacement offset of the vehicle's ordinate.

Please refer to FIG. 2, which is a specific flowchart of the method provided in an embodiment of this application. In this embodiment, the method may further include the following steps:

Step S201: learn from the training data set to obtain a vehicle speed prediction model.

The training data set includes a large amount of training data, that is, training samples. It should be noted that the number of two-dimensional vehicle images included in the training data during model training should be the same as the number of two-dimensional vehicle images input by the model when the model is used for speed prediction.

In this embodiment, step S201 may include the following sub-steps:

Step S2011: Determine the training data set.

In this embodiment, the training data set is determined using the following steps: 1) Obtain at least two frames of environmental point cloud data for training with three-dimensional vehicle bounding box and vehicle identification annotation data; 2) According to the annotation data, the same The center point offset of the three-dimensional vehicle bounding box of the preset two frames of the vehicle is used as the true value of the three-dimensional vehicle position offset; 3) the true value of the three-dimensional vehicle position offset is projected to the top view coordinate system to obtain the two-dimensional vehicle position offset 4) forming the two-dimensional vehicle position deviation truth value map according to the two-dimensional vehicle position deviation truth value; and generating at least two vehicle point cloud data in the environmental point cloud data for training at least two frames Two-dimensional vehicle images for training.

When training the vehicle speed prediction model, the model network needs annotated data that can provide 3D rectangular bounding boxes (including vehicle point cloud data) for continuous frames (two or more frames) to track objects (vehicles) and their Correspondence between frames so that the same vehicle can be extracted from adjacent frames and at least two training two-dimensional vehicle images can be generated. In this embodiment, the offset of the center point of the 3D rectangular bounding box of the adjacent frame of the same vehicle is used as the true value of the offset of the network regression, and is projected into the top view coordinate system, and the true value of the offset of the center point is filled into the first At the position corresponding to the 3D frame of the frame, a two-dimensional vehicle position offset truth map of the vehicle in the two-frame time interval is formed. Table 1 shows the annotation data used to determine the training data in this embodiment.

Table 1. Determining the annotation data for training data

The annotation data provided in Table 1 can provide a 3D rectangular enclosing frame for vehicles in n consecutive frames and the corresponding relationship between the frames, so that the same vehicle can be extracted in n consecutive frames.

Step S2013: construct a network structure of the prediction model.

Please refer to FIG. 3, which is a schematic diagram of the network structure of the prediction model of the method provided in an embodiment of the application. As can be seen from Figure 3, the model network structure of this embodiment is a convolutional neural network, which may include multiple convolutional layers and multiple deconvolutional layers. The two-dimensional vehicle position offset data map output by the vehicle speed prediction model is similar to The two-dimensional vehicle images for training have the same image size. The network connects the two-dimensional vehicle images generated by the point clouds of the front and back frames in the channel direction as the input data of the model. The model outputs a two-channel, two-dimensional vehicle position offset data map with width and height equal to the input image size. Since the model output map includes two-dimensional vehicle position offset data, which reflects the speed information of the vehicle, it can also be called a speed map. The two channels respectively reflect the offset components of the point cloud existing in the corresponding pixel position in the x and y directions in the image coordinate system.

In this embodiment, for the input merged two-dimensional vehicle image, firstly, with the help of several consecutive layers of convolutional layer and maximum pooling layer, the vehicle displacement high-dimensional features with smaller feature map size are extracted, and then through several layers of deconvolution The layer is restored to the size of the original input image, and the model output map includes the two-dimensional vehicle position offset data of each pixel of each vehicle in the two-dimensional vehicle image at two-frame time intervals. In this embodiment, the convolutional layer used to extract higher-dimensional vehicle displacement features with a smaller feature map size from the input feature map is called the vehicle displacement feature extraction layer. In specific implementation, it may include multiple vehicle displacement feature extractions. Floor. Correspondingly, in this embodiment, the deconvolution layer used to upsample the vehicle displacement feature with a larger feature map size from the input feature map is referred to as the vehicle displacement feature upsampling layer, until the last deconvolution layer is upsampled. A two-dimensional vehicle position offset data map with the same size as the original input image is generated. In specific implementation, it may include multiple vehicle displacement feature up-sampling layers. Using this processing method, the two-dimensional vehicle position offset of each vehicle in the input two-dimensional vehicle image can be directly obtained from the two-dimensional vehicle position offset data map according to the two-dimensional position of the vehicle in the two-dimensional vehicle image Data; therefore, the accuracy of the vehicle speed can be effectively improved, and the processing speed can be improved at the same time.

It can be seen from Figure 3 that the input data of the vehicle displacement feature upsampling layer may include the output feature map of the previous vehicle displacement feature upsampling layer adjacent to it, and may also include the output feature map of the previous vehicle displacement feature upsampling layer. The output feature map of the previous vehicle displacement feature extraction layer with the same image size. By adopting this processing method, richer feature data related to vehicle speed can be retained, and two-dimensional vehicle position offset data can be up-sampled from the richer feature data; therefore, the accuracy of vehicle speed can be effectively improved.

In another example, the model network structure may not include the deconvolution layer, that is, it does not include the vehicle displacement feature upsampling layer. In this case, the two-dimensional vehicle position output by the vehicle speed prediction model is offset The data map may have a different image size from the two-dimensional vehicle image for training.

When the input image and output image size of the vehicle speed prediction model are different, the training data set can be determined by the following steps: 1) Obtain at least two frames of training environment with three-dimensional vehicle bounding box and vehicle identification annotation data Point cloud data; 2) According to the annotation data, the center point offset of the three-dimensional vehicle bounding box of the same vehicle in the preset two frames is taken as the true value of the three-dimensional vehicle position offset; 3) the three-dimensional vehicle position is offset The true value is projected to the top view coordinate system to obtain the true value of the two-dimensional vehicle position offset; 4) at least two training two-dimensional vehicle images are generated according to the vehicle point cloud data in at least two frames of environmental point cloud data for training. This processing method deletes the step of "forming the two-dimensional vehicle position offset true value map according to the two-dimensional vehicle offset true value", so the processing speed can be effectively improved. However, the accuracy of the model obtained by this processing method will be lower than that of the above-mentioned up-sampling layer with vehicle displacement characteristics, and the two-dimensional vehicle position offset data map output by the model has the same image size as the two-dimensional vehicle image input to the model. The accuracy of the model.

Step S2015: learning the prediction model from the training data set.

After obtaining the training data set and constructing the model network structure, the weights in the network can be trained according to the training data set. When the weights in the network make the two-dimensional vehicle position offset data map output by the model deviate from the two-dimensional vehicle position When the difference between the true value maps reaches the optimization goal, the model training can be stopped.

In this embodiment, in order to achieve a better convergence effect, the following two processes can be performed during the model training process:

1) When calculating the loss function during training, a mask image is used. When the model input data is two two-dimensional vehicle images, the mask map can correspond to the first frame (the other frame is the zeroth frame) two-dimensional vehicle image, so that only the first frame of the two-dimensional vehicle image corresponds to If there is a pixel of a vehicle, the corresponding pixel value of the mask image is 1, and for other positions, the pixel value of the mask image is 0. When calculating the loss function, only pixels with a mask value of 1 participate in the calculation of the loss.

2) The model network of this embodiment adopts the idea of multi-scale, and calculates the loss function on the output of multiple deconvolution layers to help the network converge. Since the size of the output feature map of each deconvolution layer is inconsistent, it is necessary to downsample the truth map and the mask map to a corresponding size before calculating the loss.

Step S1033: Determine the driving speed of the vehicle according to the two-dimensional vehicle position offset data and the time interval.

In an example, the two-dimensional vehicle position offset data includes a two-dimensional vehicle position offset data map having the same image size as the two-dimensional vehicle image; correspondingly, step S1033 may include the following sub-steps: 1) The two-dimensional vehicle position offset data of each vehicle is converted into the three-dimensional vehicle position offset data in the point cloud coordinate system; 2) For each vehicle, the average value of the abscissa offset component of each spatial point corresponding to the vehicle is summed The ratio of the average value of the ordinate offset components to the time interval is used as the driving speed of the vehicle.

In this embodiment, the driving speed of other vehicles on the road is determined according to the last two frames of environmental point cloud data collected by the vehicle, and the point cloud data of each vehicle in the last frame of environmental point cloud data can be projected onto the speed map , Extract the two-dimensional vehicle offset component of the corresponding pixel after each point is projected, and convert the offset component back to the point cloud coordinate system as the offset component of the point in the x and y directions in the three-dimensional space. The average value of the three-dimensional vehicle offset components of all points in the same vehicle is used as the three-dimensional vehicle position offset component of the vehicle. Finally, the three-dimensional vehicle position offset component divided by the known two-frame time interval is the driving speed of the vehicle. By adopting this processing method, the vehicle speed can be determined by integrating the position offsets of all points of the vehicle; therefore, the accuracy of the vehicle speed estimation can be effectively improved.

In another example, two two-dimensional vehicle images are generated according to the vehicle point cloud data in the two frames of environmental point cloud data; the two two-dimensional vehicle images are used as the input data of the prediction model to generate A two-dimensional vehicle position offset data map with the same image size as the two-dimensional vehicle image; the two-dimensional vehicle position offset data map includes an abscissa offset map and an ordinate offset map; for the two For each vehicle in the one-dimensional vehicle image, the driving speed is determined according to the abscissa offset map, the ordinate offset map, and the time interval. With this processing method, the vehicle speed can be determined directly based on the two-dimensional vehicle position offset data corresponding to each point of the vehicle and the time interval; therefore, the speed estimation speed can be effectively improved.

For example, the abscissa offset component of vehicle 1 from time t ₀ to frame 1 time t ₁ is 10 meters, the ordinate offset component is 5 meters, and the time t ₀ and time t _{1 are} separated by 500 milliseconds, then the vehicle The driving speed of 1 is 72 miles; the abscissa offset component of vehicle 2 from frame 0 time t ₀ to frame 1 time t ₁ is 15 meters, the ordinate offset component is 5 meters, time t ₀ and time t ₁ With an interval of 500 milliseconds, the traveling speed of the vehicle 2 is 108 miles.

During specific implementation, the ratio of the average value of the abscissa offset component and the average value of the ordinate offset component of each pixel point corresponding to the vehicle to the time interval may also be used as the vehicle traveling speed. In this way, the position offsets of all points of the vehicle are comprehensively considered; therefore, the accuracy of vehicle speed estimation can be effectively improved.

In this embodiment, through the processing methods of steps S1031 and 1033 described above, the vehicle speed prediction model can be used to determine the position offset of points (such as all points or some points) in the vehicle, and determine the driving of the vehicle based on these position offsets Speed; Therefore, it can effectively improve the accuracy of vehicle speed.

It can be seen from the above embodiments that the method for determining the vehicle speed provided by the embodiments of the present application generates at least two two-dimensional vehicle images based on the vehicle point cloud data in at least two frames of road environment point cloud data; The vehicle image and the time interval between any two frames of the at least two frames of road environment point cloud data are used to determine the vehicle speed; this processing method makes the generation of the vehicle point cloud data in the at least two frames of road environment point cloud data Corresponding to at least two two-dimensional vehicle images, and determine the driving speed of the vehicle based on these images and the time interval of any two frames of the at least two frames of data; therefore, the accuracy of the vehicle speed can be effectively improved, thereby improving road traffic safety Sex.

Second embodiment

In the foregoing embodiment, a method for determining a vehicle speed is provided. Correspondingly, this application also provides a device for determining a vehicle speed. This device corresponds to the embodiment of the above method.

Please refer to FIG. 4, which is a schematic diagram of an embodiment of the vehicle speed determining device of this application. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment. The device embodiments described below are merely illustrative.

This application additionally provides a vehicle speed determination device, including:

The image generating unit 401 is configured to generate at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of road environment point cloud data;

The speed determining unit 403 is configured to determine the driving speed of the vehicle according to the time interval between the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data.

The third embodiment

In the foregoing embodiment, a method for determining the speed of a vehicle is provided. Correspondingly, this application also provides a vehicle. This vehicle corresponds to the embodiment of the above method.

Please refer to FIG. 5, which is a schematic diagram of an embodiment of the vehicle of this application. Since the vehicle embodiment is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment. The vehicle embodiments described below are merely illustrative.

The application additionally provides a vehicle, including: a three-dimensional space scanning device 500; a processor 501; and a memory 502, which is used to store a program for realizing a method for determining a vehicle speed. After the device is powered on and the program of the method is run through the processor , Perform the following steps: collect road environment point cloud data through the three-dimensional space scanning device; generate at least two two-dimensional vehicle images according to the vehicle point cloud data in at least two frames of road environment point cloud data; The time interval between the two frames of the vehicle image and the at least two frames of road environment point cloud data is used to determine the speed of the vehicle.

Fourth embodiment

Please refer to FIG. 6, which is a schematic diagram of an embodiment of the drive test sensing device of this application. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment. The device embodiments described below are merely illustrative.

A drive test perception device of this embodiment, the electronic device includes: a three-dimensional space scanning device 600; a processor 601 and a memory 602; the memory is used to store a program for implementing the method, and the device is powered on and passes through the processor After running the program of the method, the following steps are performed: collecting road environment point cloud data through a three-dimensional space scanning device; generating at least two two-dimensional vehicle images based on the vehicle point cloud data in at least two frames of road environment point cloud data; The time interval between the two frames of data in the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data determines the speed of the vehicle.

Fifth embodiment

Please refer to FIG. 7, which is a schematic diagram of an embodiment of the electronic device of this application. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment. The device embodiments described below are merely illustrative.

An electronic device of this embodiment, the electronic device includes: a processor 701 and a memory 702; the memory is used to store a program for implementing the method. After the device is powered on and runs the program of the method through the processor, it executes The following steps: generating at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of road environment point cloud data; according to the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data The time interval between two frames of data in the middle determines the speed of the vehicle.

Sixth embodiment

In the foregoing embodiment, a method for determining a vehicle speed is provided. Correspondingly, this application also provides a method for constructing a vehicle speed prediction model. This method corresponds to the embodiment of the above method.

Please refer to FIG. 8, which is a flowchart of an embodiment of a method for constructing a vehicle speed prediction model of this application. Since the method embodiment is basically similar to the method embodiment 1, the description is relatively simple, and the relevant part can refer to the part of the description of the method embodiment 1. The method embodiments described below are only illustrative.

The method for constructing a vehicle speed prediction model of this embodiment includes:

Step S801: Determine the training data set.

The training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data. The two-dimensional vehicle position offset true value data may be a two-dimensional vehicle position offset true value map having the same image size as the training two-dimensional vehicle image, or it may be the same as the training two-dimensional vehicle image Two-dimensional vehicle position offset truth maps with different image sizes, etc.

In an example, the two-dimensional vehicle position offset truth data is a two-dimensional vehicle position offset truth map that has the same or different image size as the training two-dimensional vehicle image; correspondingly, the training data The set can be determined by the following steps: 1) Obtain at least two frames of environmental point cloud data for training with three-dimensional vehicle bounding box and vehicle identification annotation data; 2) According to the annotation data, preset two frames of three-dimensional The center point offset of the vehicle bounding box is used as the true value of the three-dimensional vehicle position offset; 3) The true value of the three-dimensional vehicle position offset is projected to the top view coordinate system to obtain the true value of the two-dimensional vehicle position offset; 4) According to two The two-dimensional vehicle position deviation truth value forms the two-dimensional vehicle position deviation truth value map; and at least two training two-dimensional vehicle images are generated based on the vehicle point cloud data in at least two frames of environmental point cloud data for training.

In another example, the two-dimensional vehicle position offset true value data only includes a very small amount of two-dimensional vehicle position offset true values. In extreme cases, it may only include a true value of the displacement offset of the abscissa and a vertical value. The true value of the displacement offset of the coordinate, that is, the true value of the displacement offset of the vehicle in the two-dimensional vehicle image for training only includes two data, one is the true value of the displacement offset of the abscissa of the vehicle, and the other One is the true value of the displacement offset of the ordinate of the vehicle. Correspondingly, the training data set can be determined by the following steps: 1) Obtain at least two frames of environmental point cloud data for training with three-dimensional vehicle bounding box and vehicle identification annotation data; 2) According to the annotation data, combine the same vehicle Preset the center point offset of the three-dimensional vehicle bounding box of two frames as the true value of the three-dimensional vehicle position offset; 3) Project the true value of the three-dimensional vehicle position offset to the top view coordinate system to obtain the true value of the two-dimensional vehicle position offset Value; 4) Generate at least two training two-dimensional vehicle images based on the vehicle point cloud data in at least two frames of environmental point cloud data for training.

Step S803: Construct a network structure of the vehicle speed prediction model.

The network structure may include at least one vehicle displacement feature extraction layer and at least one vehicle displacement feature up-sampling layer, or may only include a vehicle displacement feature extraction layer. The vehicle displacement feature extraction layer may be implemented based on a convolution operation, and the vehicle displacement feature upsampling layer may be implemented based on a deconvolution operation.

Step S805: Obtain the vehicle speed prediction model from the training data set.

Please refer to the related description of the step S2015 of the method embodiment 1, which is not repeated here.

It can be seen from the foregoing embodiments that the vehicle speed prediction model construction method provided by the embodiments of this application determines the training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data Construct the network structure of the vehicle speed prediction model; learn from the training data set to obtain the vehicle speed prediction model; this processing method makes it possible to learn from a large amount of training data to obtain vehicle displacement based on at least two two-dimensional vehicle images Therefore, the accuracy of the vehicle speed prediction model can be effectively improved.

Seventh embodiment

Please refer to FIG. 9, which is a schematic diagram of an embodiment of the vehicle speed prediction model construction device of this application. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment. The device embodiments described below are merely illustrative.

A vehicle speed prediction model construction device of this embodiment includes:

The data determining unit 901 is configured to determine a training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data;

The network construction unit 903 is used to construct the network structure of the vehicle speed prediction model;

The model training unit 905 is configured to learn the vehicle speed prediction model from the training data set.

Eighth embodiment

Please refer to FIG. 10, which is a schematic diagram of an embodiment of the electronic device of this application. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment. The device embodiments described below are merely illustrative.

An electronic device of this embodiment, the electronic device includes: a processor 1001 and a memory 1002; the memory is used to store a program that implements the method. After the device is powered on and runs the program of the method through the processor, it executes The following steps: determining a training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data; constructing a network structure of a vehicle speed prediction model; learning from the training data set Obtain the vehicle speed prediction model.

Although this application is disclosed as above in preferred embodiments, it is not intended to limit the application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the application. Therefore, this application The scope of protection shall be subject to the scope defined by the claims of this application.

In a typical configuration, the computing device includes one or more processors (CPU), input/output interfaces, network interfaces, and memory.

The memory may include non-permanent memory in computer readable media, random access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer readable media.

1. Computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, Magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices. According to the definition in this article, computer-readable media does not include non-transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

2. Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, this application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

Claims

A method for determining vehicle speed, characterized in that it comprises:

Generating at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of road environment point cloud data;

The vehicle speed is determined according to the time interval between the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data.
The method according to claim 1, wherein said determining the vehicle speed according to the time interval of the two frames of the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data, include:

Determining the two-dimensional vehicle position offset data corresponding to the time interval according to the at least two two-dimensional vehicle images;

The vehicle speed is determined according to the two-dimensional vehicle position offset data and the time interval.
The method according to claim 2, wherein:

According to the vehicle speed prediction model, the two-dimensional vehicle position offset data is determined according to the at least two two-dimensional vehicle images.
The method according to claim 3, further comprising:

The vehicle speed prediction model is learned from a training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data.
The method according to claim 4, wherein the vehicle speed prediction model is determined by the following steps:

Determining the training data set;

Constructing the network structure of the vehicle speed prediction model;

The vehicle speed prediction model is learned from the training data set.
The method of claim 5, wherein:

The network structure includes a vehicle displacement feature extraction layer and a vehicle displacement feature up-sampling layer.
The method of claim 6, wherein:

The two-dimensional vehicle position deviation truth data includes a two-dimensional vehicle position deviation truth map having the same image size as the training two-dimensional vehicle image.
The method according to claim 7, wherein the training data set is determined by the following steps:

Acquiring at least two frames of environmental point cloud data for training with three-dimensional vehicle bounding box and vehicle identification data;

According to the annotation data, the center point offset of the three-dimensional vehicle bounding box of the same vehicle in the preset two frames is taken as the true value of the three-dimensional vehicle position offset;

Project the true value of the three-dimensional vehicle position offset to the top view coordinate system to obtain the true value of the two-dimensional vehicle position offset;

The two-dimensional vehicle position deviation truth value map is formed according to the two-dimensional vehicle position deviation truth value; and, at least two training two-dimensional vehicles are generated based on the vehicle point cloud data in the at least two frames of environmental point cloud data for training image.
The method according to claim 5, wherein the training data set is determined using the following steps:

Acquiring at least two frames of environmental point cloud data for training with three-dimensional vehicle bounding box and vehicle identification data;

According to the annotation data, the center point offset of the three-dimensional vehicle bounding box of the same vehicle in the preset two frames is taken as the true value of the three-dimensional vehicle position offset;

Project the true value of the three-dimensional vehicle position offset to the top view coordinate system to obtain the true value of the two-dimensional vehicle position offset;

According to the vehicle point cloud data in at least two frames of environmental point cloud data for training, at least two two-dimensional vehicle images for training are generated.
The method according to claim 2, wherein:

The two-dimensional vehicle position offset data includes a two-dimensional vehicle position offset data map having the same image size as the two-dimensional vehicle image;

The determining the vehicle speed according to the two-dimensional vehicle position offset data and the time interval includes:

The ratio of the average value of the abscissa offset component and the average value of the ordinate offset component of each pixel corresponding to each vehicle in the two-dimensional vehicle position offset data map to the time interval is used as the ratio The speed of the vehicle.
The method according to claim 2, wherein:

The two-dimensional vehicle position offset data includes a two-dimensional vehicle position offset data map having the same image size as the two-dimensional vehicle image;

The determining the vehicle speed according to the two-dimensional vehicle position offset data and the time interval includes:

Convert the two-dimensional vehicle position offset data of each vehicle into the three-dimensional vehicle position offset data in the point cloud coordinate system;

The ratio of the average value of the abscissa offset component and the average value of the ordinate offset component of each spatial point corresponding to the vehicle to the time interval is used as the vehicle traveling speed.
The method according to claim 1, wherein the two-dimensional vehicle image comprises a two-dimensional vehicle image at a top view angle.
The method according to claim 1, wherein said generating at least two two-dimensional vehicle images according to vehicle point cloud data in at least two frames of road environment point cloud data comprises:

Determine the attitude data of the vehicle speed determination device;

According to the attitude data, converting the vehicle point cloud data before the last frame into the vehicle point cloud data in the point cloud coordinate system of the last frame;

According to the vehicle point cloud data before the last frame after the coordinate system conversion, a two-dimensional vehicle image corresponding to the vehicle point cloud data before the last frame is generated.
The method according to claim 1, further comprising:

The vehicle point cloud data is extracted from the road environment point cloud data through a vehicle detection model.
The method according to claim 1, further comprising:

Collect the road environment point cloud data.
A method for constructing a vehicle speed prediction model, characterized in that it comprises:

Determining a training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data;

Construct the network structure of the vehicle speed prediction model;

The vehicle speed prediction model is learned from the training data set.
The method of claim 16, wherein:

The network structure includes a vehicle displacement feature extraction layer and a vehicle displacement feature up-sampling layer.
The method of claim 17, wherein:

The two-dimensional vehicle position deviation truth data includes a two-dimensional vehicle position deviation truth map having the same image size as the training two-dimensional vehicle image.
The method according to claim 18, wherein the training data set is determined by the following steps:

Acquiring at least two frames of environmental point cloud data for training with three-dimensional vehicle bounding box and vehicle identification data;

According to the annotation data, the center point offset of the three-dimensional vehicle bounding box of the same vehicle in the preset two frames is taken as the true value of the three-dimensional vehicle position offset;

Project the true value of the three-dimensional vehicle position offset to the top view coordinate system to obtain the true value of the two-dimensional vehicle position offset;

The two-dimensional vehicle position deviation truth value map is formed according to the two-dimensional vehicle position deviation truth value; and, at least two training two-dimensional vehicles are generated based on the vehicle point cloud data in the at least two frames of environmental point cloud data for training image.
The method according to claim 16, wherein the training data set is determined using the following steps:

Acquiring at least two frames of environmental point cloud data for training with three-dimensional vehicle bounding box and vehicle identification data;

According to the annotation data, the center point offset of the three-dimensional vehicle bounding box of the same vehicle in the preset two frames is taken as the true value of the three-dimensional vehicle position offset;

Project the true value of the three-dimensional vehicle position offset to the top view coordinate system to obtain the true value of the two-dimensional vehicle position offset;

According to the vehicle point cloud data in at least two frames of environmental point cloud data for training, at least two two-dimensional vehicle images for training are generated.
A vehicle speed determining device, characterized in that it comprises:

An image generating unit, configured to generate at least two two-dimensional vehicle images according to the vehicle point cloud data in the at least two frames of road environment point cloud data;

The speed determining unit is configured to determine the vehicle driving speed according to the time interval between the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data.
A vehicle speed prediction model construction device, characterized in that it comprises:

A data determining unit for determining a training data set; the training data includes at least two training two-dimensional vehicle images and two-dimensional vehicle position offset true value data;

The network construction unit is used to construct the network structure of the vehicle speed prediction model;

The model training unit is used to learn the vehicle speed prediction model from the training data set.
A vehicle, characterized by comprising:

Three-dimensional space scanning device;

Processor; and

The memory is used to store the program for realizing the method of determining the speed of the vehicle. After the vehicle is powered on and the program of the method is run through the processor, the following steps are executed: collecting road environment point cloud data through the three-dimensional space scanning device; according to at least two frames Generating at least two two-dimensional vehicle images from the vehicle point cloud data in the road environment point cloud data; according to the at least two two-dimensional vehicle images and the time interval between the two frames of the at least two frames of road environment point cloud data, Determine the speed of the vehicle.
A drive test sensing device, characterized in that it comprises:

Three-dimensional space scanning device;

Processor; and

The memory is used to store the program for realizing the method of determining the vehicle speed. After the device is powered on and the program of the method is run through the processor, the following steps are executed: collecting road environment point cloud data through the three-dimensional space scanning device; according to at least two frames Generating at least two two-dimensional vehicle images from the vehicle point cloud data in the road environment point cloud data; according to the at least two two-dimensional vehicle images and the time interval between the two frames of the at least two frames of road environment point cloud data, Determine the speed of the vehicle.
An electronic device, characterized in that it comprises:

Processor; and

The memory is used to store the program for realizing the method for determining the vehicle speed. After the device is powered on and the program of the method is run through the processor, the following steps are executed: according to the vehicle point cloud data in at least two frames of road environment point cloud data, At least two two-dimensional vehicle images are generated; and the vehicle speed is determined according to the time interval between the at least two two-dimensional vehicle images and the at least two frames of road environment point cloud data.
An electronic device, characterized in that it comprises:

Processor; and

The memory is used to store a program for realizing the method for constructing a vehicle speed prediction model. After the device is powered on and the program of the method is run through the processor, the following steps are performed: determining a training data set; the training data includes at least two training Using two-dimensional vehicle images and two-dimensional vehicle position offset true value data; constructing a network structure of a vehicle speed prediction model; learning the vehicle speed prediction model from the training data set.