Estimation method of vehicle sliding parameters

Abstract

The invention relates to a vehicle sliding parameter estimation method, relates to the technical field of intelligent vehicles, and solves the problem that the sliding parameter of a vehicle in an off-road scene is difficult to predict in the prior art. The vehicle sliding parameter estimation method comprises the steps of obtaining point cloud data and image data of a target scene and trajectory data of a vehicle; and inputting the point cloud data of the target scene, the image data and the trajectory data of the vehicle into a pre-trained sliding parameter estimation model to obtain an estimation result of the sliding parameter.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent vehicles, and in particular to a method for estimating vehicle sliding parameters. Background Art

[0002] Slip and turn are the phenomenon that the grounded part of a vehicle moves relative to the ground during driving. Slip and turn (collectively referred to as "slip") are an inevitable parameter that is widely present in the vehicle driving process and have an important impact on the vehicle's driving. Therefore, reasonably estimating the vehicle's sliding parameters is an important consideration for improving the safety of unmanned driving.

[0003] Sliding parameter estimation refers to the process of quickly estimating sliding parameters by using the measurement data of various sensors, which is very helpful for improving vehicle driving stability and safety. The main sliding parameter estimation methods are proprioceptive sensing method and model reasoning sliding method. The proprioceptive sensing method refers to the use of proprioceptive sensing sensors to perceive the ground properties and estimate the sliding parameters through direct contact. Although this method can accurately obtain wheel information for estimating sliding parameters, it has a high risk of silting because it requires contact before measurement. The model reasoning sliding method is a wheel terrain analysis based on terrain mechanics. The sliding parameters are inferred by constructing a wheel-ground interaction model. The disadvantage is that it is only valid for the current sliding parameter values ​​and cannot predict the sliding parameters of future terrain.

[0004] At present, the estimation of sliding parameters mainly focuses on the analysis of sliding parameters in the current state and historical state. The traditional research direction is mainly to use the method of body perception and model reasoning to estimate sliding parameters. Although it has a good performance in estimating real-time sliding parameters, it is difficult to have a reliable estimate of future sliding parameters due to the limitation of the principle. In addition, the current sliding parameter estimation technology is mainly focused on wheeled vehicles and structured roads, and the research on tracked platforms and off-road scenarios that are more significantly affected by sliding parameters is not in-depth. However, in off-road scenarios, tracked platforms can often play an irreplaceable role due to their unique advantages. Therefore, it is very important to study the sliding parameter estimation of tracked platforms in off-road scenarios. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a method for estimating a vehicle sliding parameter, so as to solve the problem that the sliding parameter of a vehicle in an off-road scenario is difficult to predict in the prior art.

[0006] In a first aspect, an embodiment of the present invention provides a method for estimating a vehicle sliding parameter, comprising the following steps:

[0007] Obtain point cloud data, image data and vehicle trajectory data of the target scene;

[0008] The point cloud data, image data and vehicle trajectory data of the target scene are input into a pre-trained sliding parameter estimation model to obtain an estimation result of the sliding parameter; wherein,

[0009] The sliding parameter estimation model includes a feature extraction network, a feature fusion network, a sliding parameter identification network, and a linear layer connected in sequence;

[0010] The feature extraction network includes a first residual network for extracting point cloud features, a second residual network for extracting image features, and a trajectory feature extraction network;

[0011] The feature fusion network includes a first fusion network for fusing point cloud features and trajectory features and a second fusion network for fusing image features and trajectory features;

[0012] The sliding parameter recognition network includes a first convolutional long short-term memory network for learning sliding parameters for fused point cloud features and trajectory features and a second convolutional long short-term memory network for learning sliding parameters for fused image features and trajectory features.

[0013] Based on the further improvement of the above method, the training method of the sliding parameter estimation model includes:

[0014] Obtain the point cloud data, image data, vehicle trajectory data and corresponding sliding parameter labels of the target scene;

[0015] Inputting the point cloud data, image data and vehicle trajectory data of the target scene into an initial model of a sliding parameter estimation model to obtain a preliminary estimation result of the sliding parameter;

[0016] Based on the comparison between the preliminary estimation result of the sliding parameter and the sliding parameter label, an initial model of the sliding parameter estimation model is updated.

[0017] Based on the further improvement of the above method, the step of obtaining the point cloud data of the target scene includes:

[0018] The point cloud block data of the target area is intercepted from the point cloud map according to the geometric size of the vehicle and the driving trajectory of the vehicle.

[0019] Based on the further improvement of the above method, the step of acquiring the image data of the target scene includes:

[0020] Image data representing the same position is determined from the camera image according to the position information of the target area.

[0021] Based on the further improvement of the above method, before inputting the point cloud data of the target scene into the pre-trained sliding parameter estimation model, the following pre-processing operation is performed on the point cloud data of the target scene:

[0022] The point cloud data of the target scene is projected into a two-channel image containing height information and intensity information.

[0023] Based on a further improvement of the above method, the vehicle trajectory data includes: vehicle speed information, acceleration information and steering degree information.

[0024] Based on the further improvement of the above method, the first residual network is a ResNet-18 network model, and the second residual network is a ResNet-34 network model.

[0025] Based on a further improvement of the above method, before inputting the two-channel image and image data of the target scene into the pre-trained sliding parameter estimation model, the following operations are performed on the two-channel image and image data of the target scene:

[0026] Segmenting the two-channel image and image data of the target scene according to the geometric size of the vehicle and the grid resolution to obtain the two-channel image and image data of the target scene in a grid format;

[0027] Encoding the two-channel image of the target scene in a raster format and the raster representing the same position in the image data;

[0028] Furthermore, the estimation method further comprises:

[0029] The estimation result of the sliding parameter is restored to the grid at the corresponding position based on the grid encoding, and the sliding parameter prediction grid map of the target scene is generated.

[0030] Based on the further improvement of the above method, the sliding parameter label is calculated by the following method:

[0031]

[0032] Among them, s is the sliding parameter label, v q is the vehicle drag speed, v s is the winding speed.

[0033] Based on the further improvement of the above method, the loss function used when training the sliding parameter estimation model is the mean square error.

[0034] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0035] 1. The solution of the present invention utilizes image color information and point cloud three-dimensional data, combined with the driving state of the vehicle, to innovatively design a sliding parameter estimation network model. This network model combines the advantages of residual networks and long short-term memory networks, and can accurately predict sliding parameters in a data set constructed in a real off-road environment, thus realizing the prediction and estimation of sliding parameters in off-road scenarios.

[0036] 2. The solution of the present invention can not only predict the sliding parameters by using the data set constructed in the real off-road environment, but also generate a sliding parameter map, thereby improving the accuracy and comprehensiveness of the map, which is conducive to the safe and efficient driving goal of the vehicle platform in a complex off-road environment.

[0037] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0039] Figure 1 A schematic flow chart of a method for estimating vehicle sliding parameters according to an embodiment of the present invention is shown.

[0040] Figure 2 A schematic diagram of acquiring point cloud data of a target scene according to an embodiment of the present invention is shown.

[0041] Figure 3 A schematic diagram showing the temporal synchronization of point cloud data, image data and vehicle trajectory data in an embodiment of the present invention is shown.

[0042] Figure 4 A schematic diagram of a network framework of a sliding parameter estimation model according to an embodiment of the present invention is shown.

[0043] Figure 5 A schematic diagram of the network structure of a sliding parameter estimation model according to another embodiment of the present invention is shown.

[0044] Figure 6 A schematic diagram of a point cloud projection image according to an embodiment of the present invention is shown.

[0045] Figure 7 A schematic diagram of a generation process of a sliding parameter map according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0046] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0047] Figure 1 FIG. 2 shows a flow chart of a method for estimating vehicle sliding parameters according to an embodiment of the present invention. Figure 1 As shown, the method for estimating the vehicle sliding parameters includes the following steps:

[0048] Step S101: Acquire point cloud data, image data of a target scene, and trajectory data of a vehicle.

[0049] In this embodiment, the vehicle's own sensors can be used to collect environmental information around the vehicle and the vehicle's trajectory data. Specifically, sensors such as laser radar, monocular RGB camera, inertial navigation system, GPS receiver, and vehicle bottom controller information can be used to collect point cloud maps and image data of the vehicle's surrounding environment, three-axis acceleration information of the inertial navigation system, positioning information of the GPS system, and speed and steering information of the vehicle bottom layer.

[0050] In this embodiment, the point cloud data of the target scene refers to the point cloud data located in the target area in the point cloud map collected by the laser radar of the vehicle itself. For example, the target area can be a block area located at a certain distance in front of the vehicle.

[0051] In some embodiments, the point cloud data of the target scene can be obtained by the following method:

[0052] The point cloud block data of the target area is intercepted from the point cloud map according to the geometric size of the vehicle and the driving trajectory of the vehicle.

[0053] Figure 2 FIG. 2 shows a schematic diagram of obtaining point cloud data of a target scene according to an embodiment of the present invention. Figure 2 As shown, the geometric center of the vehicle is the origin o, the forward direction of the vehicle is the x-axis, the speed of the vehicle is v (on the x-axis), and the vehicle coordinate system xoy is established with the geometric center of the vehicle as the origin. In this embodiment, the corresponding point cloud block can be extracted according to the geometric size of the vehicle. The size of the point cloud block is obtained according to the rectangular frame surrounding the outer contour of the left and right tracks of the vehicle. In order to ensure that the vehicle can completely fall within the point cloud block and reserve a certain error range, the length and width of the vehicle can be rounded up. Figure 2 In the figure, the length of the rectangular box is [length] and the width is [width]. According to the vehicle's driving trajectory, the point cloud block at a distance d in front of the vehicle can be intercepted. It is known that the vehicle's geometric center point o is the current discrete trajectory point, and the heading angle at this point is θ. The vertex coordinates of the target area can be obtained as A(xa ,y a )、B(x b ,y b )、C(x c ,y c ) and D(x d ,y d ), as shown in the following table:

[0054]

[0055]

[0056] It is known that the coordinates of the origin o in the xoy coordinate system are (x o ,y o ), the heading angle is θ, and the point A(x A ,y A ),B(x B ,y B ),C(x C ,y C ),D(x D ,y D ) is shown in the following formula:

[0057]

[0058] Finally, according to the geodetic coordinate system, point A(x A ,y A ),B(x B ,y B ),C(x C ,y C ),D(x D ,y D )’s real coordinates are used to intercept the point cloud and obtain the point cloud data of the target scene.

[0059] In this embodiment, by extracting corresponding point cloud blocks according to the geometric dimensions of the vehicle, the quality and relevance of the features expressed by the point cloud data can be improved.

[0060] In this embodiment, the image data of the target scene and the track data of the vehicle after time and space alignment can be obtained by performing time alignment and space alignment on the point cloud data, the image data and the track data of the vehicle. The time and space alignment method of the point cloud data, the image data and the track data of the vehicle is described below.

[0061] When collecting data, the camera acquires image frames through instantaneous exposure, so the image data frequency is relatively high, while the lidar acquires point cloud data through laser beam rotation, so the point cloud data frequency is relatively low. In addition, the GPS and inertial navigation systems also collect data at a high frequency, so the vehicle trajectory data frequency is also relatively high. Since there are certain differences in the sampling frequencies of these three types of data, it is first necessary to synchronize the point cloud data, image data, and vehicle trajectory data in time.

[0062] Figure 3 FIG. 1 is a schematic diagram showing the time synchronization of point cloud data, image data and vehicle trajectory data in an embodiment of the present invention. Figure 3 As shown, a multi-threading and queue caching method can be used to simultaneously open three threads to receive point cloud, image and trajectory data. The timestamp of the lower-frequency point cloud data can be used as the message synchronization benchmark, and the image and trajectory data can be compared with the benchmark timestamp until the time difference is within the set threshold, thus completing the time synchronization of a frame of data.

[0063] After completing the time synchronization of the point cloud data, image data, and vehicle trajectory data, it is necessary to obtain the pose transformation relationship of the laser radar and camera coordinate systems, and then realize the alignment of the spatial positions of the image data and the point cloud data. It should be noted that the pose transformation relationship of the laser radar and camera coordinate systems is a hyperparameter, which can be learned in the training phase of the sliding parameter estimation model to be described below. In the application phase of the sliding parameter estimation model, as long as the positions of the laser radar and camera on the vehicle do not change, the pose transformation relationship of the laser radar and camera coordinate systems learned in the training phase can be used.

[0064] The following is an explanation of the process of learning the pose transformation relationship of the laser radar and camera coordinate systems. Existing calibration methods can be used here to quickly calibrate the camera and laser radar together. For example, you can first place feature objects at the key corner points on the ground and obtain real-time image data online; then use the corner detection algorithm based on Harris corner points and boundary tracking in the OpenCV library to obtain the camera's calibration internal parameters; then select the four corner points of the calibration object as seed points for the point cloud data, and obtain the point cloud data of the key point area scanned by the regional growing algorithm; then, through plane fitting based on the random consistency algorithm, you can get multiple sets of pose data representations for the same position area of ​​the image data and point cloud data, and then solve the spatial transformation relationship between the image data and the point cloud data.

[0065] After learning the spatial transformation relationship between image data and point cloud data, we can use the position information of key points in the point cloud data of the target scene (e.g. Figure 2The matching positions of the key points in the image data can be obtained through the spatial transformation relationship between the two, and then the image data representing the same position area can be obtained.

[0066] For the sliding parameters, in addition to being able to be reflected to a certain extent through image data and point cloud data, the driving state of the vehicle will also have a certain impact on the sliding parameters. Therefore, in order to achieve accurate estimation of the sliding parameters, this embodiment also takes into account the trajectory data of the vehicle, such as the speed and acceleration information of the vehicle. Since the image data and point cloud data of the target scene obtained according to this embodiment are low-frequency signals, and the inertial navigation signal and GPS signal are high-frequency signals, when matching the trajectory data, the speed information, acceleration information, and vehicle steering degree information in a fixed time window near the most recent time corresponding to a frame of point cloud data of the target scene can be used. The speed information, acceleration information, and vehicle steering degree information in the fixed time window are the trajectory data of the vehicle after spatiotemporal alignment. For the speed, acceleration, and steering degree information x(i) of N sampling signals in the time window, the following 5 commonly used statistics can be used to characterize its characteristics:

[0067] (1) Mean:

[0068]

[0069] (2) Standard deviation:

[0070]

[0071] (3) Square root amplitude:

[0072]

[0073] (4) Root mean square value:

[0074]

[0075] (5) Maximum absolute value:

[0076] f5=max|x(i)|

[0077] In some embodiments, the acceleration information may include longitudinal acceleration and vertical acceleration for analysis. Longitudinal acceleration reflects the acceleration or deceleration of the vehicle, during which the sliding parameter changes greatly. Vertical acceleration is a measure of the acceleration of the vehicle in the vertical direction, and the gravity acceleration a needs to be eliminated in advance. g =9.8m / s 2The influence of represents the degree of vibration during vehicle driving and can reflect the current ground properties to a certain extent. The speed information can be analyzed by selecting the three-axis speeds, where the longitudinal speed represents the vehicle's driving speed, the lateral speed represents the vehicle's steering and other operations, and the vertical speed represents the vehicle's vertical climb. The steering degree information is the real-time steering curvature of the vehicle calculated by the left and right speeds of the underlying motor combined with the inertial navigation system. For the above six variables, each variable can generate 5 statistical features, and a total of 30 statistical features can be generated.

[0078] Step S102: inputting the point cloud data, image data and vehicle trajectory data of the target scene into a pre-trained sliding parameter estimation model to obtain an estimation result of the sliding parameter.

[0079] Figure 4 FIG. 4 shows a schematic diagram of a network framework of a sliding parameter estimation model according to an embodiment of the present invention. Figure 4 As shown, the sliding parameter estimation model includes a sensor data feature extraction network, a feature fusion network, a parameter (i.e., sliding parameter) identification network, and a hierarchical linearization layer connected in sequence. The sensor data feature extraction network includes a point cloud data residual network, a camera data residual network, and a trajectory feature extraction network. The point cloud data residual network is used to extract point cloud features from the point cloud data of the target scene. The camera data residual network is used to extract image features from the image data of the target scene. The trajectory feature extraction network is used to extract trajectory features from the trajectory data of the vehicle. The feature fusion network includes a first fusion network and a second fusion network ( Figure 4 The first fusion network is used to fuse the point cloud features and the trajectory features. The second fusion network is used to fuse the image features and the trajectory features. The parameter (i.e., sliding parameter) recognition network includes a point cloud data convolutional long short-term memory network (i.e., Figure 4 Point cloud data ConvLSTM) and camera data convolutional long short-term memory network (i.e. Figure 4 The output of the first fusion network is input to the point cloud data convolutional long short-term memory network, and the output of the second fusion network is input to the camera data convolutional long short-term memory network. The point cloud data convolutional long short-term memory network is used to learn the sliding parameters of the fused point cloud features and trajectory features, and the camera data convolutional long short-term memory network is used to learn the sliding parameters of the fused image features and trajectory features. The output results of the point cloud data ConvLSTM and the camera data ConvLSTM are input to the hierarchical linearization layer, and the estimation results of the sliding parameters are finally output through the fusion and extraction of multiple linearization layers.

[0080] Figure 5FIG. 4 shows a schematic diagram of a network structure of a sliding parameter estimation model according to another embodiment of the present invention. Figure 5 As shown, the input data of the sliding parameter estimation model consists of three parts, namely: three-channel image information (RGB) representing the image data of the target scene, two-channel image information (height and reflection intensity) representing the point cloud data of the target scene, and speed, acceleration, and steering information representing the trajectory data of the vehicle.

[0081] In this embodiment, before inputting the point cloud data of the target scene into the sliding parameter estimation model, the point cloud data of the target scene may be preprocessed to obtain two-dimensional image information of the point cloud data. Specifically, each point cloud block may be projected into a two-channel image containing height and intensity information. Figure 6 FIG. 4 is a schematic diagram showing a point cloud projection image according to an embodiment of the present invention. Figure 6 As shown in Figure 2, each point in the 3D point cloud block is assigned to the nearest pixel in the image, and each pixel unit corresponds to many point cloud data points p. i , called a point cloud cluster. Calculate the height mean h of the point cloud cluster in each pixel mean and the mean reflection intensity i mean , and assign it to the red and blue channels of the corresponding pixels of the image. For the missing areas of the point cloud, linear interpolation is performed through the surrounding pixel blocks to fill them.

[0082] In this embodiment, before the trajectory data of the vehicle is input into the sliding parameter estimation model, feature extraction may be performed on the trajectory data of the vehicle. For example, for the longitudinal acceleration and vertical acceleration in the acceleration information, the following five features are calculated respectively:

[0083] (1) mean, (2) standard deviation, (3) root amplitude, (4) root mean square value, (5) maximum absolute value.

[0084] For the longitudinal velocity, lateral velocity, and vertical velocity in the velocity information, the following five features are calculated respectively:

[0085] (1) mean, (2) standard deviation, (3) root amplitude, (4) root mean square value, (5) maximum absolute value.

[0086] For the steering degree information, the following five features are calculated respectively:

[0087] (1) mean, (2) standard deviation, (3) root amplitude, (4) root mean square value, (5) maximum absolute value.

[0088] For the above six variables, each variable can generate 5 statistical features, and a total of 30 statistical features can be generated.

[0089] Back to Figure 5, the input parameters of the three-channel image information are [b,3,a,b'], the first dimension parameter b represents the size of a batch, the second dimension parameter represents that the image has three channels, and the third and fourth dimension parameters represent the size information of the image. The input parameters of the vehicle trajectory data are [b,6*5], the first dimension parameter b represents the size of a batch, and 6*5 represents "six variables, each variable can generate 5 statistical features". The input parameters of the point cloud data are [b,3,a,b'], the first dimension parameter b represents the size of a batch, the second dimension parameter represents that the image has two channels, and the third and fourth dimension parameters represent the size information of the image.

[0090] The three-channel image information is input into a residual network module (i.e. Figure 5 The two-channel point cloud information is input into another residual network module (i.e. Figure 5 The trajectory features are expanded to [b,w*h] through continuous linearization layers and reshaped to [b,w,h] through reshape operations. The upper residual network module can extract image features from three-channel image information, and the lower residual network module can extract point cloud features from two-channel point cloud information. After that, the image features and point cloud features are fused with the trajectory features through element-wise multiplication, and the fusion results are convolved, batch normalized, activated, and pooled. Figure 5 The "CBRP" modules in the figure represent the convolution layer (C), batch normalization layer (B), activation layer (R), and pooling layer (P). The fused features output by the "CBRP" module are input into the ConvLSTM module for learning the sliding parameters. Finally, the outputs of the two ConvLSTM modules are flattened and concatenated, and then the data is linearized layer by layer and then channel by channel, and finally regressed to the estimated sliding parameter value.

[0091] In some embodiments, the image feature extraction uses the ResNet-34 network, and the point cloud feature extraction uses the ResNet-18 network. Image data contains rich environmental features, and the features contained in the image can be fully utilized by using a more complex ResNet-34 network.

[0092] In some embodiments, before inputting the two-channel image and image data of the target scene into the pre-trained sliding parameter estimation model, the following operations may be performed on the two-channel image and image data of the target scene:

[0093] Step S111: segmenting the two-channel image and image data of the target scene according to the geometric size of the vehicle and the grid resolution, and obtaining the two-channel image and image data of the target scene in a grid format.

[0094] Step S222: Encode the two-channel image of the target scene in the raster format and the raster representing the same position in the image data.

[0095] and, Figure 1 The estimation methods in also include:

[0096] Step S103: restoring the estimation result of the sliding parameter to the grid at the corresponding position based on the grid coding, and generating a sliding parameter prediction grid map of the target scene.

[0097] Figure 7 A schematic diagram of a generation process of a sliding parameter map according to an embodiment of the present invention is shown.

[0098] like Figure 7 As shown, the color image on the left represents the two-channel image information of the point cloud data representing the target scene, the gray image on the left represents the three-channel image information of the image data representing the target scene, and the function "f 1.2.3.4.5 (v x.v , α x.v.z , δ steer )" represents the "six variables, each of which can generate five statistical features" in the trajectory features described above. The middle module "module" represents the sliding parameter estimation model in the embodiment of the present invention. First, the two images on the left can be segmented in position in combination with the vehicle size and grid resolution to obtain a series of point cloud data and image data representing the same position. In the process of generating the grid map, each position can be encoded to ensure that the sliding parameter prediction result output by the sliding parameter estimation model can match the corresponding position. Finally, the point cloud data and image data are combined with the trajectory features of the vehicle as input, and the trained sliding parameter estimation model is used for estimation, and the estimation result is restored to the corresponding grid position based on the position encoding to generate the sliding parameter prediction grid map of the current scene.

[0099] The following describes the training process of the sliding parameter estimation model in the embodiment of the present invention.

[0100] The training method of the sliding parameter estimation model in the embodiment of the present invention includes:

[0101] Step S100: Obtain point cloud data, image data, vehicle trajectory data and corresponding sliding parameter labels of the target scene.

[0102] The method for acquiring the point cloud data, image data, and vehicle trajectory data of the target scene has been described in step S101 and will not be repeated here. The processing process of the sliding parameter label in this embodiment is as follows:

[0103] The generation of sliding parameters is expressed in terms of the absolute value of the slip, as follows:

[0104]

[0105] Among them, s is the sliding parameter label; v q is the vehicle drag speed, i.e. the actual driving speed of the vehicle; v s is the winding speed, is the driving speed of the vehicle motor. When the vehicle does not have any slip, v q =v s , at this time the sliding parameter is 0; when the driving wheel is locked and fully braked, v s =0, it is a pure slip state and the sliding parameter is 1.

[0106] Step S200: inputting the point cloud data, image data and vehicle trajectory data of the target scene into the initial model of the sliding parameter estimation model to obtain a preliminary estimation result of the sliding parameter.

[0107] Step S300: Based on the comparison between the preliminary estimation result of the sliding parameter and the sliding parameter label, the initial model of the sliding parameter estimation model is updated.

[0108] In this embodiment, the training process of the initial model of the sliding parameter estimation model can use the mean square error (MSE). The mean square error is a loss function in a regression task. It is the mean of the sum of squares of the difference between the predicted value f(x) and the target value y. It is used to measure the difference between the predicted value and the true value of the initial model of the sliding parameter estimation model. The calculation formula is as follows:

[0109]

[0110] Here, n is the number of samples.

[0111] Compared with the prior art, the embodiments of the present invention can achieve at least one of the following beneficial effects:

[0112] 1. The solution of the present invention utilizes image color information and point cloud three-dimensional data, combined with the driving state of the vehicle, to innovatively design a sliding parameter estimation network model. This network model combines the advantages of residual networks and long short-term memory networks, and can accurately predict sliding parameters in a data set constructed in a real off-road environment, thus realizing the prediction and estimation of sliding parameters in off-road scenarios.

[0113] 2. The solution of the present invention can not only predict the sliding parameters by using the data set constructed in the real off-road environment, but also generate a sliding parameter map, thereby improving the accuracy and comprehensiveness of the map, which is conducive to the safe and efficient driving goal of the vehicle platform in a complex off-road environment.

[0114] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for estimating vehicle sliding parameters, characterized in that: The following steps are involved: Obtain point cloud data, image data and vehicle trajectory data of the target scene; The point cloud data, image data and vehicle trajectory data of the target scene are input into a pre-trained sliding parameter estimation model to obtain an estimation result of the sliding parameter; wherein, The sliding parameter estimation model includes a feature extraction network, a feature fusion network, a sliding parameter identification network, and a linear layer connected in sequence; The feature extraction network includes a first residual network for extracting point cloud features, a second residual network for extracting image features, and a trajectory feature extraction network; The feature fusion network includes a first fusion network for fusing point cloud features and trajectory features and a second fusion network for fusing image features and trajectory features; The sliding parameter recognition network includes a first convolutional long short-term memory network for learning sliding parameters for fused point cloud features and trajectory features and a second convolutional long short-term memory network for learning sliding parameters for fused image features and trajectory features.

2. The estimation method according to claim 1, characterized in that The training method of the sliding parameter estimation model includes: Obtain the point cloud data, image data, vehicle trajectory data and corresponding sliding parameter labels of the target scene; Inputting the point cloud data, image data and vehicle trajectory data of the target scene into an initial model of a sliding parameter estimation model to obtain a preliminary estimation result of the sliding parameter; Based on the comparison between the preliminary estimation result of the sliding parameter and the sliding parameter label, an initial model of the sliding parameter estimation model is updated.

3. The estimation method according to claim 1, characterized in that The step of acquiring the point cloud data of the target scene includes: The point cloud block data of the target area is intercepted from the point cloud map according to the geometric size of the vehicle and the driving trajectory of the vehicle.

4. The estimation method according to claim 3, characterized in that: The acquiring of the image data of the target scene comprises: Image data representing the same position is determined from the camera image according to the position information of the target area.

5. The estimation method according to claim 3, characterized in that: Before inputting the point cloud data of the target scene into the pre-trained sliding parameter estimation model, the following pre-processing operations are performed on the point cloud data of the target scene: The point cloud data of the target scene is projected into a two-channel image containing height information and intensity information.

6. The estimation method according to claim 1, characterized in that: The vehicle trajectory data includes: vehicle speed information, acceleration information and steering degree information.

7. The estimation method according to claim 1, characterized in that: The first residual network is a ResNet-18 network model, and the second residual network is a ResNet-34 network model.

8. The estimation method according to claim 5, characterized in that: Before inputting the two-channel image and image data of the target scene into the pre-trained sliding parameter estimation model, the following operations are performed on the two-channel image and image data of the target scene: Segmenting the two-channel image and image data of the target scene according to the geometric size of the vehicle and the grid resolution to obtain the two-channel image and image data of the target scene in a grid format; Encoding the two-channel image of the target scene in a raster format and the raster representing the same position in the image data; Furthermore, the estimation method further comprises: The estimation result of the sliding parameter is restored to the grid at the corresponding position based on the grid encoding, and the sliding parameter prediction grid map of the target scene is generated.

9. The estimation method according to claim 2, characterized in that: The sliding parameter label is calculated as follows: Among them, s is the sliding parameter label, v q is the vehicle drag speed, v s is the winding speed.

10. The estimation method according to claim 2 or 9, characterized in that: The loss function used when training the sliding parameter estimation model is the mean square error.

Images