WO2020233436A1 - 车辆速度确定方法及车辆 - Google Patents

车辆速度确定方法及车辆 Download PDF

Info

Publication number
WO2020233436A1
WO2020233436A1 PCT/CN2020/089606 CN2020089606W WO2020233436A1 WO 2020233436 A1 WO2020233436 A1 WO 2020233436A1 CN 2020089606 W CN2020089606 W CN 2020089606W WO 2020233436 A1 WO2020233436 A1 WO 2020233436A1
Authority
WO
WIPO (PCT)
Prior art keywords
vehicle
dimensional
data
point cloud
training
Prior art date
Application number
PCT/CN2020/089606
Other languages
English (en)
French (fr)
Inventor
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 (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 阿里巴巴集团控股有限公司 filed Critical 阿里巴巴集团控股有限公司
Publication of WO2020233436A1 publication Critical patent/WO2020233436A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • This application relates to the field of automatic driving technology, and in particular to a method for determining vehicle speed and a vehicle.
  • 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.
  • 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.
  • RGB image collected by the camera 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.
  • FlowNet technology FlowNet technology
  • ordinary RGB cameras have a more obvious defect, that is, they are almost unavailable at night.
  • 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:
  • 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 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:
  • the vehicle speed is determined according to the two-dimensional vehicle position offset data and the time interval.
  • 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.
  • the vehicle speed prediction model is determined using the following steps:
  • the vehicle speed prediction model is learned from the training data set.
  • the network structure includes a vehicle displacement feature extraction layer and a vehicle displacement feature upsampling layer.
  • 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.
  • the training data set is determined using the following steps:
  • 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
  • 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 training data set is determined using the following steps:
  • 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
  • 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 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 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 spatial point corresponding to the vehicle to the time interval is used as the vehicle traveling speed.
  • the two-dimensional vehicle image includes a two-dimensional vehicle image from a top view angle.
  • 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:
  • 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
  • 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:
  • This application also provides a method for constructing a vehicle speed prediction model, including:
  • 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 learned from the training data set.
  • the network structure includes a vehicle displacement feature extraction layer and a vehicle displacement feature upsampling layer.
  • 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.
  • the training data set is determined using the following steps:
  • 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
  • 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 training data set is determined using the following steps:
  • 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
  • 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 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 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:
  • 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:
  • 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 an electronic device, including:
  • 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:
  • 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.
  • 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.
  • the vehicle speed prediction model construction method 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.
  • Fig. 1 is a flowchart of an embodiment of a method for determining vehicle speed provided by the present application
  • FIG. 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.
  • FIG. 1 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.
  • 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.
  • Point Cloud road environment point cloud
  • 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).
  • RGB color information
  • Intensity reflection intensity information
  • 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).
  • DSM Digital Surface Model
  • 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.
  • 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- ⁇ ,..., t n-1 , A total of ⁇ +1 frames of environmental point cloud data during the driving of the vehicle are collected at t n ⁇ +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 ⁇ +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.
  • 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.
  • 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.
  • 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.
  • two-dimensional vehicle images from other viewing angles such as left view, right view, front view, etc., can also be used.
  • 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.
  • the range of the two-dimensional vehicle image can cover a certain area near the own vehicle.
  • 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.
  • the environmental point cloud data of multiple frames such as frame 0, frame 1...frame 10
  • 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.
  • 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.
  • 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.
  • the 0th to 9th frames since the self-vehicle may move, it is necessary to synchronize the point cloud coordinate system in time.
  • 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.
  • the time interval of the frame data can determine the speed of the vehicle.
  • 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.
  • 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.
  • the self-vehicle collects ⁇ +1 frames of environmental point cloud data during the driving of the vehicle at t n- ⁇ ,..., t n-1 , t n ⁇ +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 .
  • the time interval is t n-1 -t n-2 ; or to determine t n- ⁇ to The distance moved between time t n-3 , in this case the time interval is t n-3 -t n- ⁇ .
  • 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.
  • 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 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.
  • FIG. 2 is a specific flowchart of the method provided in an embodiment of this application.
  • 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.
  • step S201 may include the following sub-steps:
  • Step S2011 Determine the training data set.
  • 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.
  • 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.
  • 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
  • Table 1 shows the annotation data used to determine the training data in this embodiment.
  • 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.
  • FIG. 3 is a schematic diagram of the network structure of the prediction model of the method provided in an embodiment of the application.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • it may include multiple vehicle displacement feature up-sampling layers.
  • 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.
  • the model network structure may not include the deconvolution layer, that is, it does not include the vehicle displacement feature upsampling layer.
  • 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.
  • 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.
  • 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.
  • the weights in the network can be trained according to the training data set.
  • 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
  • the model training can be stopped.
  • a mask image is used.
  • 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.
  • the loss function only pixels with a mask value of 1 participate in the calculation of the loss.
  • 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.
  • 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.
  • 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.
  • the three-dimensional vehicle position offset component divided by the known two-frame time interval is the driving speed of the vehicle.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • this application also provides a device for determining a vehicle speed.
  • This device corresponds to the embodiment of the above method.
  • FIG. 4 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.
  • a method for determining the speed of a vehicle is provided.
  • this application also provides a vehicle. This vehicle corresponds to the embodiment of the above method.
  • FIG. 5 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.
  • 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.
  • 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.
  • FIG. 6 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.
  • 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
  • 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.
  • FIG. 7 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.
  • 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.
  • this application also provides a method for constructing a vehicle speed prediction model. This method corresponds to the embodiment of the above method.
  • FIG. 8 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • the vehicle speed prediction model construction method 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.
  • FIG. 9 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.
  • 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.
  • FIG. 10 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.
  • 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.
  • the computing device includes one or more processors (CPU), input/output interfaces, network interfaces, and memory.
  • processors CPU
  • input/output interfaces network interfaces
  • memory volatile and non-volatile 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.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • 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.
  • computer-readable media does not include non-transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.
  • this 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.
  • a computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Image Analysis (AREA)
  • Traffic Control Systems (AREA)

Abstract

一种车辆速度确定方法及车辆。其中，车辆速度确定方法包括：根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像（S101）；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中任意两帧数据的时间间隔，确定车辆行驶速度（S102）。采用这种处理方式，使得生成与至少两帧道路环境点云数据中的车辆点云数据对应的至少两个二维车辆图像，并根据这些图像和至少两帧数据中任意两帧数据的时间间隔，确定车辆的行驶速度；因此，可以有效提升车辆速度的准确度，从而提升道路交通安全性。

Description

车辆速度确定方法及车辆
本申请要求2019年05月22日递交的申请号为201910431632.6、发明名称为“车辆速度确定方法及车辆”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及自动驾驶技术领域，具体涉及车辆速度确定方法及车辆。
背景技术
车辆在行驶过程中对其他车辆的速度进行估计是实现道路交通安全和通向自动驾驶的关键，它可以帮助自动驾驶车辆预测行驶场景中周围车辆未来的行驶轨迹，帮助自车规避可能发生的碰撞。
自动驾驶车辆通常配备多种传感器，这些传感器的数据都有用来估计车辆速度的潜力。下面对常用的三种车辆速度确定方法及其存在的问题进行说明。
1)基于毫米波雷达数据的车辆速度确定方法。该方法借助于多普勒效应，可以为其他车辆给出一个较为精确的速度测量。然而，为了给出准确的速度测量，它对其他车辆的行驶位置和方向都有较高要求。具体而言，对于未在毫米波传播区域且运动方向不平行于毫米波传播方向的车辆，它给出的速度测量往往有较大的误差。
2)基于相机数据的车辆速度确定方法。该方法通过深度学习技术，尤其是光流估计技术，利用相机采集的RGB图像对图像中物体进行速度估计，如FlowNet技术等等。但是，普通的RGB相机有一个比较明显的缺陷，那就是夜间几乎不可用。
3)基于激光雷达数据的车辆速度确定方法。该方法利用激光雷达点云估计速度，可以有效克服夜间问题，具体处理过程如下所述：根据点云检测算法检测出的凸包，计算两帧间检测出的同一对象凸包中心的偏移量，最后除以两帧时间间隔，即为对象速度。然而，这类方法受检测出的凸包形态影响，中心点往往不是对象真正的形态中心，因此估计的速度结果噪声较大。
综上所述，现有技术存在车辆速度估计准确度较低的问题，如何准确地确定其他车辆的速度，成为本领域技术人员迫切需要解决的问题。
发明内容
本申请提供车辆速度确定方法，以解决现有技术存在的速度估计准确度较低的问题。 本申请另外提供车辆速度确定装置，车辆速度预测模型构建方法和装置，电子设备，路测感知设备，以及车辆。
本申请提供一种车辆速度确定方法，包括：
根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；
根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
可选的，所述根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，并确定车辆行驶速度，包括：
根据所述至少两个二维车辆图像确定与所述时间间隔对应的二维车辆位置偏移数据；
根据所述二维车辆位置偏移数据及所述时间间隔，确定车辆行驶速度。
可选的，通过车辆速度预测模型，根据所述至少两个二维车辆图像确定所述二维车辆位置偏移数据。
可选的，还包括：
从训练数据集中学习得到所述车辆速度预测模型；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据。
可选的，所述车辆速度预测模型采用如下步骤确定：
确定所述训练数据集；
构建所述车辆速度预测模型的网络结构；
从所述训练数据集中学习得到所述车辆速度预测模型。
可选的，所述网络结构包括车辆位移特征提取层和车辆位移特征上采样层。
可选的，所述二维车辆位置偏移真值数据包括与所述训练用二维车辆图像具有相同图像尺寸的二维车辆位置偏移真值图。
可选的，所述训练数据集采用如下步骤确定：
获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；
根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；
将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；
根据二维车辆位置偏移真值形成所述二维车辆位置偏移真值图；以及，根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
可选的，所述训练数据集采用如下步骤确定：
获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；
根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；
将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；
根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
可选的，所述二维车辆位置偏移数据包括与所述二维车辆图像具有相同图像尺寸的二维车辆位置偏移数据图；
所述根据所述二维车辆位置偏移数据及所述时间间隔，确定车辆行驶速度，包括：
将各个车辆在所述二维车辆位置偏移数据图中对应的各个像素点的横坐标偏移分量的平均值和纵坐标偏移分量的平均值分别与所述时间间隔的比值，作为所述车辆行驶速度。
可选的，所述二维车辆位置偏移数据包括与所述二维车辆图像具有相同图像尺寸的二维车辆位置偏移数据图；
所述根据所述二维车辆位置偏移数据及所述时间间隔，确定车辆行驶速度，包括：
将各个车辆的二维车辆位置偏移数据转换为点云坐标系下的三维车辆位置偏移数据；
将所述车辆对应的各个空间点的横坐标偏移分量的平均值和纵坐标偏移分量的平均值分别与所述时间间隔的比值，作为所述车辆行驶速度。
可选的，所述二维车辆图像包括俯视角度的二维车辆图像。
可选的，所述根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像，包括：
确定车辆速度确定装置的姿态数据；
根据所述姿态数据，将最后一帧前的车辆点云数据转换为最后一帧的点云坐标系下的车辆点云数据；
根据坐标系转换后的最后一帧前的车辆点云数据，生成与所述最后一帧前的车辆点云数据对应的二维车辆图像。
可选的，还包括：
通过车辆检测模型从所述道路环境点云数据中提取所述车辆点云数据。
可选的，还包括：
采集所述道路环境点云数据。
本申请还提供一种车辆速度预测模型构建方法，包括：
确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；
构建车辆速度预测模型的网络结构；
从所述训练数据集中学习得到所述车辆速度预测模型。
可选的，所述网络结构包括车辆位移特征提取层和车辆位移特征上采样层。
可选的，所述二维车辆位置偏移真值数据包括与所述训练用二维车辆图像具有相同图像尺寸的二维车辆位置偏移真值图。
可选的，所述训练数据集采用如下步骤确定：
获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；
根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；
将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；
根据二维车辆位置偏移真值形成所述二维车辆位置偏移真值图；以及，根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
可选的，所述训练数据集采用如下步骤确定：
获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；
根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；
将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；
根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
本申请还提供一种车辆速度确定装置，包括：
图像生成单元，用于根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；
速度确定单元，用于根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
本申请还提供一种车辆速度预测模型构建装置，包括：
数据确定单元，用于确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；
网络构建单元，用于构建车辆速度预测模型的网络结构；
模型训练单元，用于从所述训练数据集中学习得到所述车辆速度预测模型。
本申请还提供一种车辆，包括：
三维空间扫描装置；
处理器；以及
存储器，用于存储实现车辆速度确定方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：通过三维空间扫描装置采集道路环境点云数据；根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
本申请还提供一种路测感知设备，包括：
三维空间扫描装置；
处理器；以及
存储器，用于存储实现车辆速度确定方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：通过三维空间扫描装置采集道路环境点云数据；根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
本申请还提供一种电子设备，包括：
处理器；以及
存储器，用于存储实现车辆速度确定方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
本申请还提供一种电子设备，包括：
处理器；以及
存储器，用于存储实现车辆速度预测模型构建方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：确定训练数据集；所述训练数据包括至少 两个训练用二维车辆图像和二维车辆位置偏移真值数据；构建车辆速度预测模型的网络结构；从所述训练数据集中学习得到所述车辆速度预测模型。
本申请还提供一种计算机可读存储介质，所述计算机可读存储介质中存储有指令，当其在计算机上运行时，使得计算机执行上述各种方法。
本申请还提供一种包括指令的计算机程序产品，当其在计算机上运行时，使得计算机执行上述各种方法。
与现有技术相比，本申请具有以下优点：
本申请实施例提供的车辆速度确定方法，通过根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中任意两帧数据的时间间隔，确定车辆行驶速度；这种处理方式，使得生成与至少两帧道路环境点云数据中的车辆点云数据对应的至少两个二维车辆图像，并根据这些图像和至少两帧数据中任意两帧数据的时间间隔，确定车辆的行驶速度；因此，可以有效提升车辆速度的准确度，从而提升道路交通安全性。
本申请实施例提供的车辆速度预测模型构建方法，通过确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；构建车辆速度预测模型的网络结构；从所述训练数据集中学习得到所述车辆速度预测模型；这种处理方式，使得从大量训练数据中学习得到可以根据至少两个二维车辆图像对车辆位移量进行预测的模型；因此，可以有效提升车辆速度预测模型的准确度。
附图说明
图1是本申请提供的一种车辆速度确定方法的实施例的流程图；
图2是本申请提供的一种车辆速度确定方法的实施例的具体流程图；
图3是本申请提供的一种车辆速度确定方法的实施例的车辆速度预测模型的网络结构示意图；
图4是本申请提供的一种车辆速度确定装置的实施例的示意图；
图5是本申请提供的一种车辆的实施例的示意图；
图6是本申请提供的一种路测感知设备的实施例的示意图；
图7是本申请提供的一种电子设备的实施例的示意图；
图8是本申请提供的一种车辆速度预测模型构建方法的实施例的流程图；
图9是本申请提供的一种车辆速度预测模型构建装置的实施例的示意图；
图10是本申请提供的一种电子设备实施例的示意图。
具体实施方式
在下面的描述中阐述了很多具体细节以便于充分理解本申请。但是本申请能够以很多不同于在此描述的其它方式来实施，本领域技术人员可以在不违背本申请内涵的情况下做类似推广，因此本申请不受下面公开的具体实施的限制。
在本申请中，提供了车辆速度确定方法及车辆。在下面的实施例中逐一对各种方案进行详细说明。
第一实施例
请参考图1，其为本申请提供的一种车辆速度确定方法实施例的流程图，该方法的执行主体可以是无人驾驶车辆，也可以是路测感知设备，或服务器等等。下面以无人驾驶车辆为例，对本申请提供的一种车辆速度确定方法进行说明。本申请提供的一种车辆速度确定方法包括：
步骤S101：根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像。
本申请实施例提供的方法，在车辆(以下简称自车)行驶过程中，可通过安装在车辆上的三维空间扫描装置，获取车辆行驶道路的环境空间物体表面每个采样点的空间坐标，得到点的集合，该海量点数据称为道路环境点云(Point Cloud)数据。通过道路环境点云数据，使得扫描物体表面以点的形式记录，每一个点包含有三维坐标，有些可能含有颜色信息(RGB)或反射强度信息(Intensity)。凭借点云数据，可以在同一空间参考系下表达目标空间。
所述三维空间扫描装置，可以是激光雷达(Light Detection And Ranging，Lidar)，通过激光扫描方式进行激光探测与测量，获得周围环境中障碍物信息，如建筑物、树木、人、车辆等等，其所测得的数据为数字表面模型(Digital Surface Model,DSM)的离散点表示。具体实施时，可采用16线、32线、64线等多线激光雷达，不同激光束数量的雷达采集点云数据的帧频(Frame Rate)不同，如16、32线每秒一般采集10帧点云数据。所述三维空间扫描装置，也可以是三维激光扫描仪或照相式扫描仪等设备。
本实施例中的自车在通过三维空间扫描装置采集道路环境点云数据之后，就可以根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像。
所述道路环境点云数据可包括道路环境空间中各种物体的点云数据，这些物体可以 是树木、建筑物、道路上的行人及其他车辆等等。本申请实施例提供的方法，根据至少两帧道路环境点云数据中的车辆点云数据，确定道路上其他车辆的行驶速度。
所述至少两帧道路环境点云数据，可以是当前车辆(自车)最近采集到的两帧及以上的环境点云数据，例如，当前车辆在t n-τ,…,t n-1,t n这τ+1个时刻共采集了τ+1帧车辆驾驶途中的环境点云数据，每一帧环境点云数据可包括多个车辆的点云数据，因此本申请实施例提供的方法可根据这τ+1帧环境点云数据，确定多个车辆的行驶速度。
所述车辆点云数据，可通过车辆检测模型从所述道路环境点云数据中提取得到。车辆装载的激光雷达扫描得到一帧环境点云数据之后，可将环境点云数据传输到车辆检测模型,通过该模型检测得到车辆及其在环境点云数据中的三维位置数据，也就是说确定出环境点云数据中的车辆点云数据。所述三维位置数据，可以是车辆的矩形立方体包围盒的顶点坐标数据等等。
在本实施例中，所述车辆检测模型可采用基于深度学习的RefineDet方法，该方法在借鉴SSD这类单阶段方法运行速率快的基础上，又结合了Faster R-CNN这类两阶段方法，因此具有车辆检测准确率高的优点。该方法在检测到环境点云数据中的车辆点云数据时，即得到车辆的包围盒(bounding box)坐标，即所述车辆点云数据在环境点云数据中的位置数据。
步骤S101根据至少两帧环境点云数据中的车辆点云数据，生成至少两个二维车辆图像。所述二维车辆图像，可以是二维环境图像(即：根据环境点云数据构建的三维场景图的二维图像)中去除车辆以外的其他物体图像的图像，也就是说，二维车辆图像可以是只包括车辆图像的二维环境图像。
所述二维车辆图像，可以是在俯视图视角下的只包括车辆图像的二维环境图像。采用这种处理方式，使得二维车辆图像可以包括尽量多数量的车辆的二维投影点，根据较为全面的车辆点确定出的车辆行驶速度将更为准确。具体实施时，也可以采用其他视角下的二维车辆图像，如左视图、右视图、正面图等等。
在一个示例中，采集两个相邻帧(前一帧记为第0帧，后一帧记为第1帧)的环境点云数据，并采用俯视视角分别处理前后两帧车辆的点云，生成两张对应的多通道(包括车辆点的密度通道、个数通道等等)二维车辆图像，二维车辆图像的范围可涵盖自车附近的一定区域。在这一过程中，对于第0帧，由于自车可能存在运动，需要对点云坐标系进行时间上的同步，根据自车定位相关传感器给出的姿态信息，将这个第0帧投影到第1帧点云所在的坐标系，再生成二维车辆图像。
在另一个示例中，采集多帧(如第0帧、第1帧…第10帧)的环境点云数据，并采用俯视视角分别处理所有帧车辆的点云，生成多张(如10张)对应的多通道(包括车辆点的密度通道、个数通道等等)二维车辆图像，二维车辆图像的范围同样可涵盖自车附近的一定区域。在这种情况下，步骤S101可包括如下子步骤：1)确定自车的姿态数据；2)根据所述姿态数据，将最后一帧(如第10帧)前的车辆点云数据转换为最后一帧的点云坐标系下的车辆点云数据；3)根据坐标系转换后的最后一帧前的车辆点云数据，生成与所述最后一帧前的车辆点云数据对应的二维车辆图像。在这一过程中，对于第0帧到第9帧，由于自车可能存在运动，需要对点云坐标系进行时间上的同步，根据自车定位相关传感器给出的姿态信息，将第0帧到第9帧投影到第10帧点云所在的坐标系，再生成二维车辆图像。
需要说明的是，如果本申请实施例提供的方法的执行主体为路测感知设备，则由于该设备位置是固定不变的，因此在生成最后一帧前的二维车辆图像时，无需确定路测感知设备的姿态数据，也无需根据所述姿态数据，将最后一帧前的车辆点云数据转换为最后一帧的点云坐标系下的车辆点云数据。
步骤S103：根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中任意两帧数据的时间间隔，确定车辆行驶速度。
在获得与至少两帧道路环境点云数据中的车辆点云数据对应的至少两个二维车辆图像后，根据至少两个二维车辆图像和所述至少两帧道路环境点云数据中任意两帧数据的时间间隔，就可以确定车辆行驶速度。
在一个示例中，根据车辆在每一帧图像中的位置和所述时间间隔，确定车辆行驶速度。例如，在第0帧中车辆A在车辆B的前面，而在第1帧中车辆B超过车辆A，反而在车辆A的前面，这就说明车辆B的速度高于车辆A的速度，根据自车、车辆A、车辆B的位置及这两帧数据的时间间隔，即可确定车辆A和车辆B的行驶速度。
在另一个示例中，步骤S103可包括如下子步骤：
步骤S1031：根据所述至少两个二维车辆图像确定与所述时间间隔对应的二维车辆位置偏移数据。
所述二维车辆位置偏移数据，可以包括车辆在两帧时间间隔的横坐标位置偏移量和纵坐标位置偏移量。所述两帧时间间隔，可以是所述至少两帧道路环境点云数据中任意两帧数据的时间间隔。例如，自车在t n-τ,…,t n-1,t n这τ+1个时刻共采集了τ+1帧车辆驾驶途中的环境点云数据，通过本申请实施例提供的方法可确定其他车辆在时刻t n-1至时 刻t n之间移动的距离，即其他车辆在地面横坐标的位置偏移量和纵坐标的位置偏移量，这种情况下的时间间隔为t n-t n-1。具体实施时，也可以是确定t n-2至时刻t n-1之间移动的距离，这种情况下的时间间隔为t n-1-t n-2；或者是确定t n-τ至时刻t n-3之间移动的距离，这种情况下的时间间隔为t n-3-t n-τ。
需要说明的是，由于一个二维车辆图像中的多个车辆通常具有不同的车辆行驶速度，因此不同车辆在两帧时间间隔的二维车辆位置偏移数据通常不同。
本申请实施例提供的方法，通过车辆速度预测模型确定车辆在两帧时间间隔的二维车辆位置偏移数据。所述车辆速度预测模型，可从大量标注有二维车辆位置偏移真值数据的至少两个二维车辆图像的训练数据集中学习得到，也就是说，所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据。
从时间维度而言，所述二维车辆位置偏移真值数据，可以是车辆在最后两帧时间间隔的二维车辆位置偏移真值，也可以是车辆在任意两帧时间间隔的二维车辆位置偏移真值。
从数据粒度维度而言，所述二维车辆位置偏移真值数据，可以包括与所述训练用二维车辆图像具有相同图像尺寸的二维车辆位置偏移真值图，也可以是比所述训练用二维车辆图像的图像尺寸更小的二维车辆位置偏移真值图，还可以只包括极少量的二维车辆位置偏移真值，极端情况下可以只包括一个横坐标的位移偏移量真值和一个纵坐标的位移偏移量真值，也就是说，训练用二维车辆图像中车辆的位移偏移量真值只包括两个数据，一个是该车辆的横坐标的位移偏移量真值，另一个是该车辆的纵坐标的位移偏移量真值。
请参考图2，其为本申请实施例提供的方法的具体流程图。在本实施例中，所述方法还可包括如下步骤：
步骤S201：从训练数据集中学习得到车辆速度预测模型。
所述训练数据集包括大量训练数据，也就是训练样本。需要注意的是，模型训练时的训练数据包括的二维车辆图像数量，应该与利用模型进行速度预测时的模型输入的二维车辆图像数量相同。
在本实施例中，步骤S201可包括如下子步骤：
步骤S2011：确定所述训练数据集。
在本实施例中，所述训练数据集采用如下步骤确定：1)获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；2)根据所述标注数据，将同一车辆 的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；3)将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；4)根据二维车辆位置偏移真值形成所述二维车辆位置偏移真值图；以及，根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
在训练所述车辆速度预测模型时，模型网络需要标注数据可以提供对连续帧(可以是两帧或两帧以上)跟踪对象(车辆)的3D矩形包围框(包括车辆点云数据)以及它们在帧间的对应关系，以便可以在相邻帧中提取出同一车辆，并生成至少两个训练用二维车辆图像。本实施例将同一车辆相邻帧3D矩形包围框的中心点偏移作为网络回归的偏移量真值，并投影到俯视图坐标系中，将该中心点的偏移量真值填充到第1帧该3D框对应的位置上，形成车辆在两帧时间间隔的二维车辆位置偏移真值图。表1示出了本实施例的确定训练数据用的标注数据。
Figure PCTCN2020089606-appb-000001
Figure PCTCN2020089606-appb-000002
表1、确定训练数据用的标注数据
表1中提供的标注数据可以提供对n个连续帧中的车辆的3D矩形包围框及该框在帧间的对应关系，以便可以在n个连续帧中提取出同一车辆。
步骤S2013：构建所述预测模型的网络结构。
请参考图3，其为本申请实施例提供的方法的预测模型的网络结构示意图。由图3可见，本实施例的模型网络结构为卷积神经网络，可包括多个卷积层和多个反卷积层，所述车辆速度预测模型输出的二维车辆位置偏移数据图与所述训练用二维车辆图像具有相同的图像尺寸。该网络将前后两帧点云生成的二维车辆图像在通道方向上串联起来作为模型的输入数据，模型输出一张两通道、宽高等于输入图像大小的二维车辆位置偏移数据图。由于模型输出图包括二维车辆位置偏移数据，体现了车辆的速度信息，因此也可称为速度图。两个通道分别反映了对应像素位置上存在的点云在图像坐标系下x和y方向上的偏移分量。
在本实施例中，对于输入的合并二维车辆图像，首先借助连续的几层卷积层和最大值池化层抽出特征图尺寸较小的车辆位移高维特征，再经由几层反卷积层恢复到原输入图像的大小，模型输出图包括二维车辆图像中各个车辆的各个像素点在两帧时间间隔的 二维车辆位置偏移数据。本实施例将用于从输入特征图中抽取出特征图尺寸较小的较高维度的车辆位移特征的卷积层称为车辆位移特征提取层，具体实施时，可以包括多个车辆位移特征提取层。相应的，本实施例将用于从输入特征图中上采样出特征图尺寸较大的车辆位移特征的反卷积层称为车辆位移特征上采样层，直至经由最后一个反卷积层上采样出与原输入图像大小一致的二维车辆位置偏移数据图，具体实施时，可以包括多个车辆位移特征上采样层。采用这种处理方式，使得根据车辆在二维车辆图像中的二维位置，即可直接从二维车辆位置偏移数据图中获得输入的二维车辆图像中各个车辆的二维车辆位置偏移数据；因此，可以有效提升车辆速度的准确度，同时可以提升处理速度。
由图3可见，车辆位移特征上采样层的输入数据可包括与其相邻的上一个车辆位移特征上采样层的输出特征图，还可包括与该上一个车辆位移特征上采样层的输出特征图的图像尺寸相同的前面一个车辆位移特征提取层的输出特征图。采用这种处理方式，使得可保留更丰富的与车辆速度有关的特征数据，并从更丰富的特征数据中上采样出二维车辆位置偏移数据；因此，可以有效提升车辆速度的准确度。
在另一个示例中，模型网络结构也可以不包括反卷积层，也就是说，不包括车辆位移特征上采样层，在这样情况下，所述车辆速度预测模型输出的二维车辆位置偏移数据图可能与所述训练用二维车辆图像具有不同的图像尺寸。
在所述车辆速度预测模型的输入图像与输出图像尺寸不同的情况下，所述训练数据集可采用如下步骤确定：1)获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；2)根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；3)将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；4)根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。这种处理方式删减了“根据二维车辆偏移量真值形成所述二维车辆位置偏移真值图”的步骤，因此可以有效提升处理速度。然而，采用这种处理方式得到的模型准确度将低于上述具有车辆位移特征上采样层，且模型输出的二维车辆位置偏移数据图与输入至模型的二维车辆图像具有相同图像尺寸的模型的准确度。
步骤S2015：从所述训练数据集中学习得到所述预测模型。
在获得训练数据集、及构建模型网络结构后，就可以根据训练数据集对网络中的权重进行训练，当网络中的权重使得模型输出的二维车辆位置偏移数据图与二维车辆位置偏移真值图间的差异达到优化目标时，即可停止模型训练。
在本实施例中，为了达到较好的收敛效果，模型训练过程中还可执行以下两种处理：
1)训练中在计算损失函数时，借助于一张掩码图。在模型输入数据为两个二维车辆图像的情况下，该掩码图可对应于第1帧(另一帧为第0帧)二维车辆图像，使得只有第1帧二维车辆图像中对应有车辆的像素，该掩码图的对应像素值才为1，其他位置，该掩码图的像素为0。计算损失函数时，只有掩码值为1的像素才参与计算损失。
2)本实施例的模型网络采用多尺度的思想，在多个反卷积层的输出上计算损失函数，以帮助网络收敛。由于每个反卷积层的输出特征图的大小不一致，需要将所述真值图以及掩码图降采样到对应的大小后再计算损失。
步骤S1033：根据所述二维车辆位置偏移数据及所述时间间隔，确定车辆行驶速度。
在一个示例中，所述二维车辆位置偏移数据包括与所述二维车辆图像具有相同图像尺寸的二维车辆位置偏移数据图；相应的，步骤S1033可包括如下子步骤：1)将各个车辆的二维车辆位置偏移数据转换为点云坐标系下的三维车辆位置偏移数据；2)针对各个车辆，将所述车辆对应的各个空间点的横坐标偏移分量的平均值和纵坐标偏移分量的平均值分别与所述时间间隔的比值，作为该车辆的行驶速度。
在本实施例中，根据车辆最后采集的两帧环境点云数据确定行驶道路中其他车辆的行驶速度，可将最后一帧环境点云数据中的每个车辆的点云数据投影到速度图上，提取出每个点投影后对应像素的二维车辆偏移分量，并将该偏移分量转换回点云坐标系，作为该点在三维空间中x、y方向上的偏移分量，再将同一车辆所有点的三维车辆偏移分量的均值作为该车辆的三维车辆位置偏移分量。最终，该三维车辆位置偏移分量除以已知的两帧时间间隔，即为该车辆的行驶速度。采用这种处理方式，使得综合车辆所有点的位置偏移量确定车辆行驶速度；因此，可以有效提升车辆速度估计的准确度。
在另一个示例中，根据两帧环境点云数据中的车辆点云数据，生成两个二维车辆图像；将两个二维车辆图像作为所述预测模型的输入数据，通过所述预测模型生成与所述二维车辆图像具有相同图像尺寸的二维车辆位置偏移数据图；所述二维车辆位置偏移数据图包括横坐标偏移量图和纵坐标偏移量图；针对所述二维车辆图像中的各个车辆，根据所述横坐标偏移量图、纵坐标偏移量图和所述时间间隔，确定所述行驶速度。采用这种处理方式，使得直接根据车辆的各个点对应的二维车辆位置偏移数据及所述时间间隔，确定车辆行驶速度；因此，可以有效提升速度估计速度。
例如，车辆1在第0帧时刻t 0至第1帧时刻t 1的横坐标偏移分量为10米，纵坐标偏移分量为5米，时刻t 0与时刻t 1间隔500毫秒，则车辆1的行驶速度为72迈；车辆2 在第0帧时刻t 0至第1帧时刻t 1的横坐标偏移分量为15米，纵坐标偏移分量为5米，时刻t 0与时刻t 1间隔500毫秒，则车辆2的行驶速度为108迈。
具体实施时，也可以将所述车辆对应的各个像素点的横坐标偏移分量的平均值和纵坐标偏移分量的平均值分别与所述时间间隔的比值，作为所述车辆行驶速度。采用这种方式，综合考虑车辆所有点的位置偏移量；因此，可以有效提升车辆速度估计准确度。
本实施例通过上述步骤S1031和1033的处理方式，使得可通过车辆速度预测模型确定车辆中的点(如所有点或部分点)的位置偏移量，并根据这些位置偏移量确定车辆的行驶速度；因此，可以有效提升车辆速度的准确度。
从上述实施例可见，本申请实施例提供的车辆速度确定方法，通过根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中任意两帧数据的时间间隔，确定车辆行驶速度；这种处理方式，使得生成与至少两帧道路环境点云数据中的车辆点云数据对应的至少两个二维车辆图像，并根据这些图像和至少两帧数据中任意两帧数据的时间间隔，确定车辆的行驶速度；因此，可以有效提升车辆速度的准确度，从而提升道路交通安全性。
第二实施例
在上述的实施例中，提供了一种车辆速度确定方法，与之相对应的，本申请还提供一种车辆速度确定装置。该装置是与上述方法的实施例相对应。
请参看图4，其为本申请的车辆速度确定装置的实施例的示意图。由于装置实施例基本相似于方法实施例，所以描述得比较简单，相关之处参见方法实施例的部分说明即可。下述描述的装置实施例仅仅是示意性的。
本申请另外提供一种车辆速度确定装置，包括：
图像生成单元401，用于根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；
速度确定单元403，用于根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
第三实施例
在上述的实施例中，提供了一种车辆速度确定方法，与之相对应的，本申请还提供一种车辆。该车辆是与上述方法的实施例相对应。
请参看图5，其为本申请的车辆的实施例的示意图。由于车辆实施例基本相似于方 法实施例，所以描述得比较简单，相关之处参见方法实施例的部分说明即可。下述描述的车辆实施例仅仅是示意性的。
本申请另外提供一种车辆，包括：三维空间扫描装置500；处理器501；以及存储器502，用于存储实现车辆速度确定方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：通过三维空间扫描装置采集道路环境点云数据；根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
第四实施例
请参考图6，其为本申请的路测感知设备的实施例的示意图。由于设备实施例基本相似于方法实施例，所以描述得比较简单，相关之处参见方法实施例的部分说明即可。下述描述的设备实施例仅仅是示意性的。
本实施例的一种路测感知设备，该电子设备包括：三维空间扫描装置600；处理器601和存储器602；所述存储器，用于存储实现方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：通过三维空间扫描装置采集道路环境点云数据；根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
第五实施例
请参考图7，其为本申请的电子设备的实施例的示意图。由于设备实施例基本相似于方法实施例，所以描述得比较简单，相关之处参见方法实施例的部分说明即可。下述描述的设备实施例仅仅是示意性的。
本实施例的一种电子设备，该电子设备包括：处理器701和存储器702；所述存储器，用于存储实现方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
第六实施例
在上述的实施例中，提供了一种车辆速度确定方法，与之相对应的，本申请还提供一种车辆速度预测模型构建方法。该方法是与上述方法的实施例相对应。
请参考图8，其为本申请的车辆速度预测模型构建方法的实施例的流程图。由于该方法实施例基本相似于方法实施例一，所以描述得比较简单，相关之处参见方法实施例一的部分说明即可。下述描述的方法实施例仅仅是示意性的。
本实施例的一种车辆速度预测模型构建方法，包括：
步骤S801：确定训练数据集。
所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据。所述二维车辆位置偏移真值数据，可以是与所述训练用二维车辆图像具有相同图像尺寸的二维车辆位置偏移真值图，也可以是与所述训练用二维车辆图像具有不同图像尺寸的二维车辆位置偏移真值图，等等。
在一个示例中，所述二维车辆位置偏移真值数据是与所述训练用二维车辆图像具有相同或不同图像尺寸的二维车辆位置偏移真值图；相应的，所述训练数据集，可采用如下步骤确定：1)获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；2)根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；3)将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；4)根据二维车辆位置偏移真值形成所述二维车辆位置偏移真值图；以及，根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
在另一个示例中，所述二维车辆位置偏移真值数据只包括极少量的二维车辆位置偏移真值，极端情况下可以只包括一个横坐标的位移偏移量真值和一个纵坐标的位移偏移量真值，也就是说，训练用二维车辆图像中车辆的位移偏移量真值只包括两个数据，一个是该车辆的横坐标的位移偏移量真值，另一个是该车辆的纵坐标的位移偏移量真值。相应的，所述训练数据集可采用如下步骤确定：1)获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；2)根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；3)将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；4)根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
步骤S803：构建车辆速度预测模型的网络结构。
所述网络结构，可包括至少一个车辆位移特征提取层和至少一个车辆位移特征上采样层，也可只包括车辆位移特征提取层。所述车辆位移特征提取层可以基于卷积运算实现，所述车辆位移特征上采样层可以基于反卷积运算实现。
步骤S805：从所述训练数据集中学习得到所述车辆速度预测模型。
此处请参见方法实施例一的步骤S2015部分的相关说明，此处不再赘述。
从上述实施例可可见，本申请实施例提供的车辆速度预测模型构建方法，通过确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；构建车辆速度预测模型的网络结构；从所述训练数据集中学习得到所述车辆速度预测模型；这种处理方式，使得从大量训练数据中学习得到可以根据至少两个二维车辆图像对车辆位移量进行预测的模型；因此，可以有效提升车辆速度预测模型的准确度。
第七实施例
请参考图9，其为本申请的车辆速度预测模型构建装置的实施例的示意图。由于装置实施例基本相似于方法实施例，所以描述得比较简单，相关之处参见方法实施例的部分说明即可。下述描述的装置实施例仅仅是示意性的。
本实施例的一种车辆速度预测模型构建装置，包括：
数据确定单元901，用于确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；
网络构建单元903，用于构建车辆速度预测模型的网络结构；
模型训练单元905，用于从所述训练数据集中学习得到所述车辆速度预测模型。
第八实施例
请参考图10，其为本申请的电子设备的实施例的示意图。由于设备实施例基本相似于方法实施例，所以描述得比较简单，相关之处参见方法实施例的部分说明即可。下述描述的设备实施例仅仅是示意性的。
本实施例的一种电子设备，该电子设备包括：处理器1001和存储器1002；所述存储器，用于存储实现方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；构建车辆速度预测模型的网络结构；从所述训练数据集中学习得到所述车辆速度预测模型。
本申请虽然以较佳实施例公开如上，但其并不是用来限定本申请，任何本领域技术人员在不脱离本申请的精神和范围内，都可以做出可能的变动和修改，因此本申请的保护范围应当以本申请权利要求所界定的范围为准。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
1、计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括非暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
2、本领域技术人员应明白，本申请的实施例可提供为方法、***或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。

Claims (26)

  1. 一种车辆速度确定方法，其特征在于，包括：
    根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；
    根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，并确定车辆行驶速度，包括：
    根据所述至少两个二维车辆图像确定与所述时间间隔对应的二维车辆位置偏移数据；
    根据所述二维车辆位置偏移数据及所述时间间隔，确定车辆行驶速度。
  3. 根据权利要求2所述的方法，其特征在于，
    通过车辆速度预测模型，根据所述至少两个二维车辆图像确定所述二维车辆位置偏移数据。
  4. 根据权利要求3所述的方法，其特征在于，还包括：
    从训练数据集中学习得到所述车辆速度预测模型；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据。
  5. 根据权利要求4所述的方法，其特征在于，所述车辆速度预测模型采用如下步骤确定：
    确定所述训练数据集；
    构建所述车辆速度预测模型的网络结构；
    从所述训练数据集中学习得到所述车辆速度预测模型。
  6. 根据权利要求5所述的方法，其特征在于，
    所述网络结构包括车辆位移特征提取层和车辆位移特征上采样层。
  7. 根据权利要求6所述的方法，其特征在于，
    所述二维车辆位置偏移真值数据包括与所述训练用二维车辆图像具有相同图像尺寸的二维车辆位置偏移真值图。
  8. 根据权利要求7所述的方法，其特征在于，所述训练数据集采用如下步骤确定：
    获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；
    根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；
    将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；
    根据二维车辆位置偏移真值形成所述二维车辆位置偏移真值图；以及，根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
  9. 根据权利要求5所述的方法，其特征在于，所述训练数据集采用如下步骤确定：
    获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；
    根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；
    将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；
    根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
  10. 根据权利要求2所述的方法，其特征在于，
    所述二维车辆位置偏移数据包括与所述二维车辆图像具有相同图像尺寸的二维车辆位置偏移数据图；
    所述根据所述二维车辆位置偏移数据及所述时间间隔，确定车辆行驶速度，包括：
    将各个车辆在所述二维车辆位置偏移数据图中对应的各个像素点的横坐标偏移分量的平均值和纵坐标偏移分量的平均值分别与所述时间间隔的比值，作为所述车辆行驶速度。
  11. 根据权利要求2所述的方法，其特征在于，
    所述二维车辆位置偏移数据包括与所述二维车辆图像具有相同图像尺寸的二维车辆位置偏移数据图；
    所述根据所述二维车辆位置偏移数据及所述时间间隔，确定车辆行驶速度，包括：
    将各个车辆的二维车辆位置偏移数据转换为点云坐标系下的三维车辆位置偏移数据；
    将所述车辆对应的各个空间点的横坐标偏移分量的平均值和纵坐标偏移分量的平均值分别与所述时间间隔的比值，作为所述车辆行驶速度。
  12. 根据权利要求1所述的方法，其特征在于，所述二维车辆图像包括俯视角度的二维车辆图像。
  13. 根据权利要求1所述的方法，其特征在于，所述根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像，包括：
    确定车辆速度确定装置的姿态数据；
    根据所述姿态数据，将最后一帧前的车辆点云数据转换为最后一帧的点云坐标系下的车辆点云数据；
    根据坐标系转换后的最后一帧前的车辆点云数据，生成与所述最后一帧前的车辆点云数据对应的二维车辆图像。
  14. 根据权利要求1所述的方法，其特征在于，还包括：
    通过车辆检测模型从所述道路环境点云数据中提取所述车辆点云数据。
  15. 根据权利要求1所述的方法，其特征在于，还包括：
    采集所述道路环境点云数据。
  16. 一种车辆速度预测模型构建方法，其特征在于，包括：
    确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；
    构建车辆速度预测模型的网络结构；
    从所述训练数据集中学习得到所述车辆速度预测模型。
  17. 根据权利要求16所述的方法，其特征在于，
    所述网络结构包括车辆位移特征提取层和车辆位移特征上采样层。
  18. 根据权利要求17所述的方法，其特征在于，
    所述二维车辆位置偏移真值数据包括与所述训练用二维车辆图像具有相同图像尺寸的二维车辆位置偏移真值图。
  19. 根据权利要求18所述的方法，其特征在于，所述训练数据集采用如下步骤确定：
    获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；
    根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；
    将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；
    根据二维车辆位置偏移真值形成所述二维车辆位置偏移真值图；以及，根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
  20. 根据权利要求16所述的方法，其特征在于，所述训练数据集采用如下步骤确定：
    获取具有三维车辆包围盒及车辆标识标注数据的至少两帧训练用环境点云数据；
    根据所述标注数据，将同一车辆的预设两帧的三维车辆包围盒的中心点偏移量作为三维车辆位置偏移真值；
    将所述三维车辆位置偏移真值投影至俯视图坐标系，得到二维车辆位置偏移真值；
    根据至少两帧训练用环境点云数据中的车辆点云数据，生成至少两个训练用二维车辆图像。
  21. 一种车辆速度确定装置，其特征在于，包括：
    图像生成单元，用于根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；
    速度确定单元，用于根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
  22. 一种车辆速度预测模型构建装置，其特征在于，包括：
    数据确定单元，用于确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；
    网络构建单元，用于构建车辆速度预测模型的网络结构；
    模型训练单元，用于从所述训练数据集中学习得到所述车辆速度预测模型。
  23. 一种车辆，其特征在于，包括：
    三维空间扫描装置；
    处理器；以及
    存储器，用于存储实现车辆速度确定方法的程序，该车辆通电并通过所述处理器运行该方法的程序后，执行下述步骤：通过三维空间扫描装置采集道路环境点云数据；根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
  24. 一种路测感知设备，其特征在于，包括：
    三维空间扫描装置；
    处理器；以及
    存储器，用于存储实现车辆速度确定方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：通过三维空间扫描装置采集道路环境点云数据；根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
  25. 一种电子设备，其特征在于，包括：
    处理器；以及
    存储器，用于存储实现车辆速度确定方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：根据至少两帧道路环境点云数据中的车辆点云数据，生成至少两个二维车辆图像；根据所述至少两个二维车辆图像和所述至少两帧道路环境点云数据中两帧数据的时间间隔，确定车辆行驶速度。
  26. 一种电子设备，其特征在于，包括：
    处理器；以及
    存储器，用于存储实现车辆速度预测模型构建方法的程序，该设备通电并通过所述处理器运行该方法的程序后，执行下述步骤：确定训练数据集；所述训练数据包括至少两个训练用二维车辆图像和二维车辆位置偏移真值数据；构建车辆速度预测模型的网络结构；从所述训练数据集中学习得到所述车辆速度预测模型。
PCT/CN2020/089606 2019-05-22 2020-05-11 车辆速度确定方法及车辆 WO2020233436A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910431632.6 2019-05-22
CN201910431632.6A CN111986472B (zh) 2019-05-22 2019-05-22 车辆速度确定方法及车辆

Publications (1)

Publication Number Publication Date
WO2020233436A1 true WO2020233436A1 (zh) 2020-11-26

Family

ID=73436392

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/089606 WO2020233436A1 (zh) 2019-05-22 2020-05-11 车辆速度确定方法及车辆

Country Status (2)

Country Link
CN (1) CN111986472B (zh)
WO (1) WO2020233436A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112634343A (zh) * 2020-12-23 2021-04-09 北京百度网讯科技有限公司 图像深度估计模型的训练方法、图像深度信息的处理方法
CN112652016A (zh) * 2020-12-30 2021-04-13 北京百度网讯科技有限公司 点云预测模型的生成方法、位姿估计方法及其装置
CN114648886A (zh) * 2022-03-07 2022-06-21 深圳市腾运发电子有限公司 一种新能源汽车的管控方法、装置、电子设备及存储介质

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114612500B (zh) * 2022-01-28 2024-06-18 广州文远知行科技有限公司 基于点云的车辆速度估计方法、装置、设备及存储介质
CN115049698B (zh) * 2022-08-17 2022-11-04 杭州兆华电子股份有限公司 一种手持声成像设备的云图显示方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108196535A (zh) * 2017-12-12 2018-06-22 清华大学苏州汽车研究院（吴江） 基于增强学习和多传感器融合的自动驾驶***
CN108460791A (zh) * 2017-12-29 2018-08-28 百度在线网络技术（北京）有限公司 用于处理点云数据的方法和装置
US20180357773A1 (en) * 2017-06-13 2018-12-13 TuSimple Sparse image point correspondences generation and correspondences refinement system for ground truth static scene sparse flow generation
CN109271880A (zh) * 2018-08-27 2019-01-25 深圳清创新科技有限公司 车辆检测方法、装置、计算机设备和存储介质
CN109376664A (zh) * 2018-10-29 2019-02-22 百度在线网络技术（北京）有限公司 机器学习训练方法、装置、服务器和介质

Family Cites Families (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101778623B1 (ko) * 2016-01-06 2017-09-27 (주)안세기술 2d 레이저 센서와 모터를 구비하는 스캐너를 이용하여 항공기를 주기장에 안전하게 유도하는 방법
CN107193011A (zh) * 2016-03-15 2017-09-22 山东理工大学 一种用于快速计算无人驾驶车感兴趣区域内车辆速度的方法
JPWO2018020680A1 (ja) * 2016-07-29 2019-05-16 パイオニア株式会社 測定装置、測定方法、及び、プログラム
CN106205126B (zh) * 2016-08-12 2019-01-15 北京航空航天大学 基于卷积神经网络的大规模交通网络拥堵预测方法及装置
WO2018039871A1 (zh) * 2016-08-29 2018-03-08 北京清影机器视觉技术有限公司 三维视觉测量数据的处理方法和装置
EP3324209A1 (en) * 2016-11-18 2018-05-23 Dibotics Methods and systems for vehicle environment map generation and updating
CN106951847B (zh) * 2017-03-13 2020-09-29 百度在线网络技术（北京）有限公司 障碍物检测方法、装置、设备及存储介质
CN107194957B (zh) * 2017-04-17 2019-11-22 武汉光庭科技有限公司 智能驾驶中激光雷达点云数据与车辆信息融合的方法
CN106872722B (zh) * 2017-04-25 2019-08-06 北京精英智通科技股份有限公司 一种速度的测量方法和装置
CN109425365B (zh) * 2017-08-23 2022-03-11 腾讯科技（深圳）有限公司 激光扫描设备标定的方法、装置、设备及存储介质
CN108470159B (zh) * 2018-03-09 2019-12-20 腾讯科技（深圳）有限公司 车道线数据处理方法、装置、计算机设备和存储介质
CN108985171B (zh) * 2018-06-15 2023-04-07 上海仙途智能科技有限公司 运动状态估计方法和运动状态估计装置
CN109345510A (zh) * 2018-09-07 2019-02-15 百度在线网络技术（北京）有限公司 物体检测方法、装置、设备、存储介质及车辆
CN109345829B (zh) * 2018-10-29 2021-11-23 百度在线网络技术（北京）有限公司 无人车的监控方法、装置、设备及存储介质
CN109631915B (zh) * 2018-12-19 2021-06-29 百度在线网络技术（北京）有限公司 轨迹预测方法、装置、设备及计算机可读存储介质
CN109683170B (zh) * 2018-12-27 2021-07-02 驭势科技（北京）有限公司 一种图像行驶区域标注方法、装置、车载设备及存储介质
CN109726692A (zh) * 2018-12-29 2019-05-07 重庆集诚汽车电子有限责任公司 基于深度学习的高清摄像头3d目标检测***
CN109782015A (zh) * 2019-03-21 2019-05-21 同方威视技术股份有限公司 激光测速方法、控制装置和激光测速仪

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180357773A1 (en) * 2017-06-13 2018-12-13 TuSimple Sparse image point correspondences generation and correspondences refinement system for ground truth static scene sparse flow generation
CN108196535A (zh) * 2017-12-12 2018-06-22 清华大学苏州汽车研究院（吴江） 基于增强学习和多传感器融合的自动驾驶***
CN108460791A (zh) * 2017-12-29 2018-08-28 百度在线网络技术（北京）有限公司 用于处理点云数据的方法和装置
CN109271880A (zh) * 2018-08-27 2019-01-25 深圳清创新科技有限公司 车辆检测方法、装置、计算机设备和存储介质
CN109376664A (zh) * 2018-10-29 2019-02-22 百度在线网络技术（北京）有限公司 机器学习训练方法、装置、服务器和介质

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112634343A (zh) * 2020-12-23 2021-04-09 北京百度网讯科技有限公司 图像深度估计模型的训练方法、图像深度信息的处理方法
CN112652016A (zh) * 2020-12-30 2021-04-13 北京百度网讯科技有限公司 点云预测模型的生成方法、位姿估计方法及其装置
CN112652016B (zh) * 2020-12-30 2023-07-28 北京百度网讯科技有限公司 点云预测模型的生成方法、位姿估计方法及其装置
CN114648886A (zh) * 2022-03-07 2022-06-21 深圳市腾运发电子有限公司 一种新能源汽车的管控方法、装置、电子设备及存储介质

Also Published As

Publication number Publication date
CN111986472A (zh) 2020-11-24
CN111986472B (zh) 2023-04-28

Similar Documents

Publication Publication Date Title
WO2020233436A1 (zh) 车辆速度确定方法及车辆
CN111024040B (zh) 距离估计方法和设备
EP3745158B1 (en) Methods and systems for computer-based determining of presence of dynamic objects
US11780465B2 (en) System and method for free space estimation
JP2023523243A (ja) 障害物検出方法及び装置、コンピュータデバイス、並びにコンピュータプログラム
JP2021523443A (ja) Ｌｉｄａｒデータと画像データの関連付け
WO2021027710A1 (zh) 物体检测方法、装置及设备
KR20160123668A (ko) 무인자동주차 기능 지원을 위한 장애물 및 주차구획 인식 장치 및 그 방법
KR101864127B1 (ko) 무인 차량을 위한 주변 환경 매핑 방법 및 장치
CN111209825A (zh) 一种用于动态目标3d检测的方法和装置
EP4213128A1 (en) Obstacle detection device, obstacle detection system, and obstacle detection method
CN112184799A (zh) 车道线空间坐标确定方法、装置、存储介质和电子设备
CN114519772A (zh) 一种基于稀疏点云和代价聚合的三维重建方法及***
Berriel et al. A particle filter-based lane marker tracking approach using a cubic spline model
CN113449692A (zh) 一种基于无人机的地图车道信息更新方法及其***
CN115311646A (zh) 一种障碍物检测的方法及装置
KR20220110100A (ko) 카메라 보정 없는 속도 추정 시스템들 및 방법들
WO2020135325A1 (zh) 移动设备定位方法、装置、***及移动设备
WO2024015891A1 (en) Image and depth sensor fusion methods and systems
Hirata et al. Real-time dense depth estimation using semantically-guided LIDAR data propagation and motion stereo
CN114648639B (zh) 一种目标车辆的检测方法、***及装置
CN112835063B (zh) 物体动静属性的确定方法、装置、设备及存储介质
Teutsch et al. 3d-segmentation of traffic environments with u/v-disparity supported by radar-given masterpoints
CN112183378A (zh) 一种基于颜色和深度图像的道路坡度估计方法及装置
CN114170267A (zh) 目标跟踪方法、装置、设备及计算机可读存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20810685

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20810685

Country of ref document: EP

Kind code of ref document: A1