WO2022099526A1

WO2022099526A1 - Method for training lane change prediction regression model, and lane change predicton method and apparatus

Info

Publication number: WO2022099526A1
Application number: PCT/CN2020/128239
Authority: WO
Inventors: 许家妙; 何明; 叶茂胜; 曹通易
Original assignee: 深圳元戎启行科技有限公司
Priority date: 2020-11-12
Filing date: 2020-11-12
Publication date: 2022-05-19
Also published as: CN114945961B; CN114945961A

Abstract

A method for training a lane change prediction regression model, comprising: acquiring a first sample motion feature, the first sample motion feature being obtained according to motion data of a sample motion object collected at a sample collection time; acquiring a standard lane change probability corresponding to the first sample motion feature, the standard lane change probability being determined according to a lane change time interval between an object lane change time corresponding to the first sample motion feature and the sample collection time, and the standard lane change probability having a negative correlation with the lane change time interval; inputting the first sample motion feature into a lane change prediction regression model to be trained, so as to obtain a predicted lane change probability corresponding to the first sample motion feature; obtaining a model loss value according to the probability difference between the predicted lane change probability and the standard lane change probability; and using the model loss value to adjust model parameters in the lane change prediction regression model to obtain a trained lane change prediction regression model.

Description

Lane change prediction regression model training method, lane change prediction method and device

technical field

The present application relates to a lane change prediction regression model training method, lane change prediction method, device, computer equipment and storage medium.

Background technique

With the development of artificial intelligence, self-driving cars have appeared. Self-driving cars are intelligent cars that realize unmanned driving through computer systems. The computer system automatically and safely controls the driving of the car without the active operation of the human being. During the driving process of an autonomous vehicle, it is necessary to detect the motion of moving objects on the way or near the driving path to avoid the moving objects, thereby ensuring the safety of the autonomous vehicle.

However, the inventor realizes that the current method for recognizing the motion state of a moving object is inaccurate, resulting in low obstacle avoidance capability of the autonomous vehicle, and thus low safety of the autonomous vehicle.

SUMMARY OF THE INVENTION

According to various embodiments disclosed in the present application, there are provided a lane change prediction regression model training method, a lane change prediction method, an apparatus, a computer device, and a storage medium.

A lane change prediction regression model training method, comprising:

acquiring a first sample motion feature, the first sample motion feature is obtained according to the motion data of the sample motion object collected at the sample collection time;

Obtain a standard lane change probability corresponding to the motion feature of the first sample, and the standard lane change probability is based on the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time It is determined that the standard lane change probability is negatively correlated with the lane change time interval;

Inputting the first sample motion feature into the lane change prediction regression model to be trained to obtain the predicted lane change probability corresponding to the first sample motion feature;

obtaining a model loss value based on the probability difference between the predicted lane change probability and the standard lane change probability; and

The model parameters in the lane change prediction regression model are adjusted by using the model loss value to obtain the trained lane change prediction regression model, so as to perform lane change prediction according to the trained lane change prediction regression model.

A lane change prediction regression model training device, comprising:

a first sample motion feature acquisition module, configured to acquire a first sample motion feature, the first sample motion feature is obtained according to the motion data of the sample motion object collected at the sample collection time;

The standard lane change probability acquisition module is used to obtain the standard lane change probability corresponding to the motion feature of the first sample, and the standard lane change probability is based on the object lane change time corresponding to the first sample motion feature and the sample A lane change time interval between acquisition times is determined, and the standard lane change probability is negatively correlated with the lane change time interval;

A predicted lane change probability obtaining module, used for inputting the first sample motion feature into the lane change prediction regression model to be trained, to obtain the predicted lane change probability corresponding to the first sample motion feature;

a model loss value obtaining module for obtaining a model loss value according to the probability difference between the predicted lane change probability and the standard lane change probability; and

The trained lane change prediction regression model obtaining module is used to adjust the model parameters in the lane change prediction regression model by using the model loss value to obtain the trained lane change prediction regression model, so as to obtain the trained lane change prediction regression model according to the trained lane change prediction regression model. The lane change prediction regression model is used for lane change prediction.

A computer device comprising a memory and one or more processors, the memory having computer-readable instructions stored therein, the computer-readable instructions, when executed by the processor, cause the one or more processors to execute The following steps:

One or more computer storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the following steps:

A lane change prediction method, comprising:

Obtain the target motion feature corresponding to the target motion object;

The target motion feature is input into the trained lane change prediction regression model to obtain the target lane change probability corresponding to the target moving object; the trained lane change prediction regression model is based on the first sample motion feature and the The standard lane change probability corresponding to the first sample motion feature is obtained by training, the first sample motion feature is obtained according to the motion data of the sample moving object collected at the sample collection time, and the standard lane change probability is obtained according to the determining the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time, and the standard lane change probability and the lane change time interval are negatively correlated; and

A target lane change result corresponding to the target moving object is determined according to the target lane change probability.

A lane change prediction device, comprising:

The target motion feature acquisition module is used to acquire the target motion feature corresponding to the target motion object;

The target lane change probability obtaining module is used to input the target motion feature into the trained lane change prediction regression model to obtain the target lane change probability corresponding to the target moving object; the trained lane change prediction regression model Obtained by training according to the first sample motion feature and the standard lane change probability corresponding to the first sample motion feature, and the first sample motion feature is obtained according to the motion data of the sample moving object collected at the sample collection time , the standard lane change probability is determined according to the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time, the standard lane change probability and the lane change time interval negatively correlated; and

A target lane change result determination module, configured to determine the target lane change result corresponding to the target moving object according to the target lane change probability.

Obtain the target motion feature corresponding to the target motion object;

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below. Other features and advantages of the present application will be apparent from the description, drawings, and claims.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

1 is an application scenario diagram of a lane change prediction regression model training method according to one or more embodiments;

2 is a schematic flowchart of a method for training a lane change prediction regression model according to one or more embodiments;

3 is a schematic flowchart of steps of obtaining a target time interval threshold and a target lane change probability generation factor according to one or more embodiments;

4 is a schematic flowchart of a lane change prediction method according to one or more embodiments;

5 is a block diagram of an apparatus for training a lane change prediction regression model according to one or more embodiments;

6 is a block diagram of an apparatus for lane change prediction according to one or more embodiments;

7 is a block diagram of a computer device in accordance with one or more embodiments;

8 is a block diagram of a computer device in accordance with one or more embodiments.

Detailed ways

In order to make the technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

The lane change prediction regression model training method provided by the present application can be applied to the application environment shown in FIG. 1 . The terminal 102 communicates with the server 104 through a network. The server 104 obtains the first sample motion feature, which is obtained according to the motion data of the sample moving object collected at the sample collection time, and obtains the standard lane change probability corresponding to the first sample motion feature, and the standard change The lane change probability is determined according to the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time, and the standard lane change probability is negatively correlated with the lane change time interval. Input the first sample motion feature as input In the lane change prediction regression model to be trained, the predicted lane change probability corresponding to the motion feature of the first sample is obtained, and the model loss value is obtained according to the probability difference between the predicted lane change probability and the standard lane change probability, and the model loss value is used. The model parameters in the lane change prediction regression model are adjusted to obtain a trained lane change prediction regression model, so as to perform lane change prediction according to the trained lane change prediction regression model. The server 104 can use the trained lane change prediction regression model to perform lane change prediction, and control the movement according to the result of the lane change prediction. The server 104 can also transmit the trained lane change prediction regression model to the terminal 102, and the terminal 102 can use the trained lane change prediction regression model. The lane change prediction regression model is used to predict the lane change, and the server or the terminal controls the terminal to move according to the result of the lane change prediction. The terminal 102 can be, but is not limited to, an autonomous vehicle and a mobile robot, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

In some embodiments, as shown in FIG. 2 , a method for training a lane change prediction regression model is provided, and the method is applied to the server 104 in FIG. 1 as an example for description, including the following steps:

S202: Obtain a first sample motion feature, where the first sample motion feature is obtained according to motion data of the sample motion object collected at the sample collection time.

Specifically, a moving object refers to an object in a moving state. The object may be a living object, for example, may include at least one of a human being or an animal, or an inanimate object, such as at least one of a vehicle, an aircraft, or a bicycle. The sample moving object can be any moving object or a specified moving object.

The motion data may be data related to the motion of the sample moving object collected at the time of sample collection, may include point cloud data collected by a point cloud collection device during the movement of the sample moving object, or may include data collected by an image collection device. at least one of the received image data. A point cloud refers to a collection of three-dimensional data points in a three-dimensional coordinate system. For example, it can be a collection of three-dimensional data points corresponding to the surface of an object in a three-dimensional coordinate system. A point cloud can represent the outer surface shape of an object. Three-dimensional data points refer to points in three-dimensional space. The three-dimensional data points may also include at least one of RGB color, grayscale value, or time. The point cloud can be obtained by scanning with lidar. The point cloud collection device can be any device that can collect point cloud data, and can be, but not limited to, a lidar, for example, a lidar set on the top of an autonomous vehicle. The image acquisition device may be any device that can acquire image data, and may be, but not limited to, a camera. Lidar is an active sensor that emits a laser beam, hits the laser beam on the surface of the object, the laser beam is bounced, and the bounced laser signal is collected to obtain the point cloud of the object. The motion data may be pre-stored in the server. The motion data can be collected by the point cloud collection equipment or image collection equipment installed in the sample moving object, or it can be collected by the point cloud collection equipment or image collection equipment in the surrounding environment of the sample moving object. The distance between objects is less than the distance threshold value collected by the device installed in the object.

Motion features refer to motion-related features that are calculated from motion data. The motion feature may be calculated based on motion feature related data extracted from motion data. For example, the motion feature may be motion feature related data, or may be calculated based on motion feature related data corresponding to motion data at different times. For example, the server can select at least two point cloud frames from the point cloud data, extract motion feature-related data from each of the selected point cloud frames, perform data fitting on the motion feature-related data, obtain a fitting result, and fit the fitting The result is used as a motion feature, for example, a statistical value of data related to the motion feature can be calculated to obtain the motion feature. The statistical value may include at least one of a mean or a variance. The motion feature-related data may be extracted from motion data through a neural network model. For example, the server can input the motion data into the trained motion feature-related data recognition model, and the motion feature-related data recognition model can process the motion data, such as convolution processing, to obtain motion feature-related data.

The motion feature-related data may include at least one of the position, speed, acceleration, distance, motion direction, or relative distance of the motion object, for example, may include at least one of the motion object's position, speed, acceleration, or motion direction in the world coordinate system. A sort of. The world coordinate system can be a three-dimensional coordinate system, and the position in the world coordinate system can be represented by (x, y, z), and x, y, and z are coordinate axes that are perpendicular to each other and intersect. For example, the speed may include at least one of the horizontal speed or the vertical speed of the moving object relative to the road centerline, where the horizontal speed refers to the speed in the direction parallel to the road centerline, and the vertical speed refers to the direction perpendicular to the road centerline. speed in the direction. The direction of movement can be, for example, the direction of the vehicle. The relative distance can be the distance between the sample moving object and the reference object, and the reference object can be an object in a stationary state, for example, it can be at least one of a road boundary or an object set on the road, and the object set on the road can be, for example, trees. The reference object can also be a virtual thing, such as a route, which refers to the route of the aircraft, or a position in a three-dimensional coordinate system, such as the origin of the three-dimensional coordinate system. Road boundaries can also be referred to as lane boundaries. That is, the relative distance may be the distance between the moving object and the road boundary, and the distance between the moving object and the road boundary may be referred to as the road boundary distance. The road boundary distance may include at least one of a road left boundary distance or a road right boundary distance. The distance from the left border of the road refers to the distance between the sample moving object and the left border of the road, and the distance from the right border of the road refers to the distance between the sample moving object and the right border of the road.

The motion feature can also be determined according to the sorting result of the motion feature related data, and the sorting result of the motion feature related data can be used as the motion feature. For example, the relative distances at different times can be sorted in chronological order to obtain the distance sorting result, as Motion features, or the motion features are obtained by calculating the sorting results of the data related to the motion features. For example, the data in the distance sorting results can be normalized to obtain the motion features, or the data in the distance sorting results can be statistically calculated or calculated. Fitting to obtain motion features, or splicing the ranking results of motion feature-related data to obtain stitching results. As motion features, for example, distance ranking results and speed ranking results can be spliced to obtain motion features. The speed ranking results refer to different The sorting result obtained by sorting the speed of the moment in chronological order, for example, it can be the sorting result obtained by sorting the horizontal speed at different times in chronological order, for example, the distance sorting result is (s1, s2, s3), and the speed sorting result is (v1, v2, v3), then the result of splicing the distance sorting result and the speed sorting result can be expressed as (s1, s2, s3, v1, v2, v3). Of course, it is also possible to perform statistical calculation or normalization on the data in the splicing result to obtain motion features. The number of relative distances in the distance sorting result and the number of speeds in the speed sorting result may be consistent or inconsistent. The point cloud frame corresponding to the relative distance in the distance sorting result may be consistent with the point cloud frame corresponding to the speed in the speed sorting result, or may be inconsistent.

The sample motion feature refers to the motion feature of the sample moving object. The first sample motion feature can be any sample motion feature or a specified sample motion feature, which is used to train the lane change prediction regression model and obtain the trained model. The lane change prediction regression model. A lane change prediction regression model is a regression model for predicting the probability of a lane change occurring. The input of the lane change prediction regression model can be motion features, and the output can be the probability of lane change. The lane change prediction regression model can be an existing regression model or a custom regression model, such as a four-layer neural network. Regression models can also be referred to as regressors and recurrent neural networks. The sample collection time refers to the time when the motion data is collected, which can be a time point or a time period, such as (t1, t2), where t1 represents the start time of the sample collection time, and t2 represents the start time of the sample collection time. The termination time, the duration of the time period may be, for example, 5 minutes.

In some embodiments, the server may acquire the motion point cloud of the sample moving object during the motion from the point cloud acquisition device, acquire the first number of point cloud frames from the point cloud frames included in acquiring the motion point cloud, and determine the first number The motion feature-related data of the sample moving objects in each point cloud frame of the point cloud frame, sort the motion feature-related data corresponding to each point cloud frame according to the time sequence of the point cloud frames, and obtain the sorting result, according to the sorting result Obtain the sample motion characteristics, such as calculating the statistical value of the data in the sorting result, obtain the sample motion characteristics, or perform data fitting on the data in the sorting result, and obtain the sample motion characteristics according to the data fitting results. The result is used as the sample motion feature, or the result of data fitting is normalized to obtain the sample motion feature. The point cloud frames in the first number of point cloud frames may be continuous or discontinuous point cloud frames. The first number may be preset or set as required, for example, it may be 10.

S204: Obtain a standard lane change probability corresponding to the motion feature of the first sample, the standard lane change probability is determined according to the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample collection time, and the standard lane change probability There is a negative correlation with the lane change interval.

Specifically, the object lane change time refers to the time when the sample moving object changes lanes. The object lane change time corresponding to the first sample motion feature refers to the time when the sample moving object changes lanes for the first time after the sample acquisition time. For example, after the sample collection time (t1, t2), the sample moving object changes lanes at time t2+a1, then t2+a1 is the time for the object to change lanes. The lane change time interval refers to the time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time. The standard lane change probability corresponding to the motion feature of the first sample refers to the real lane change probability of the motion feature of the first sample. For any lane change time interval, the standard lane change probability can be negatively correlated with the lane change time interval. Of course, when the lane change time interval satisfies the time interval condition, the standard lane change probability is negatively correlated with the lane change time interval. The time interval condition may include that the lane change time interval is less than or equal to the target time interval threshold. The target time interval threshold may be preset, or may be determined by training a lane change prediction regression model, for example, may be determined by cross-validation. When the standard lane change probability is negatively correlated with the lane change time interval, the standard lane change probability may be calculated according to the square value of the lane change time interval, for example, it may be calculated according to the ratio of the square value of the lane change time interval to a specific value The obtained specific value may be preset or determined by training the lane change prediction regression model.

In some embodiments, when the lane change time interval is greater than the target time interval threshold, the server may obtain a fixed lane change probability as a standard lane change probability. The fixed lane change probability may be set as required, or may be preset, for example, it may be 0.

S206: Input the motion feature of the first sample into the lane change prediction regression model to be trained to obtain a predicted lane change probability corresponding to the motion feature of the first sample.

Specifically, the lane change prediction regression model to be trained refers to a lane change prediction regression model that needs to be trained. The server may use the motion feature of the first sample as the input of the lane change prediction regression model to be trained, and the lane change prediction regression model to be trained may calculate the motion feature of the first sample, such as convolution calculation, to obtain the first sample The predicted lane change probability corresponding to this motion feature. The predicted lane change probability is the lane change probability output by the lane change prediction regression model.

S208, obtain a model loss value according to the probability difference between the predicted lane change probability and the standard lane change probability.

Specifically, the probability difference refers to the difference between the predicted lane change probability and the standard lane change probability. The model loss value is positively correlated with the probability difference. The model loss value may be, for example, any one of the square of the probability difference or a multiple of the square of the probability difference, and the multiple is, for example, one-half.

In some embodiments, the model loss value may be determined according to the magnitude relationship between the probability difference and the difference threshold. For example, when the probability difference is smaller than the difference threshold, the model loss value may be determined according to the square of the probability difference, and the model loss value is positively correlated with the square of the probability difference. When the probability difference is greater than or equal to the difference threshold, the model loss value can be calculated according to the probability difference and the difference threshold. The difference threshold can be preset or set as required.

S210 , using the model loss value to adjust model parameters in the lane change prediction regression model to obtain a trained lane change prediction regression model, so as to perform lane change prediction according to the trained lane change prediction regression model.

Specifically, the model parameter refers to the variable parameter inside the lane change prediction regression model, and for the neural network model, it may also be referred to as a neural network weight (weight). The trained lane change prediction regression model may be obtained through one or more trainings. For example, the server can adjust the model parameters in the lane change prediction regression model in the direction of decreasing the loss value, and can obtain the trained lane change prediction regression model after multiple iterations of training.

In some embodiments, the server may perform backpropagation according to the model loss value, and in the process of backpropagation, update the model parameters of the lane change prediction regression model along the gradient descent direction to obtain the trained lane change prediction regression model. Among them, reverse means that the direction of parameter update and lane change prediction is opposite. Since the parameter update is back-propagated, the descending gradient can be obtained according to the model loss value, and the last layer of the regression model can be predicted from the lane change. At the beginning, the gradient update of the model parameters is started according to the descending gradient, until the first layer of the lane change prediction regression model is reached. The gradient descent method can be either stochastic gradient descent or batch gradient descent. It can be understood that the training of the model can be iterated multiple times, that is, the trained lane change prediction regression model can be obtained by iterative training, and the training is stopped when the model convergence condition is satisfied. Set the loss value, or it can be that the change of the model parameters is smaller than the preset parameter change value.

In some embodiments, the server may transmit the trained lane change prediction regression model to the terminal, and the terminal uses the trained lane change prediction regression model to predict the lane change of the moving objects in the surrounding environment, thereby controlling the movement of the terminal, realizing Avoidance.

In the above lane change prediction regression model training method, a first sample motion feature is obtained, and the first sample motion feature is obtained according to the motion data of the sample moving object collected at the sample collection time, and the obtained first sample motion feature corresponds to. The standard lane-change probability of The probability difference between the two is obtained, the model loss value is obtained, the model parameters in the lane change prediction regression model are adjusted by the model loss value, and the trained lane change prediction regression model is obtained, and the lane change prediction is performed according to the trained lane change prediction regression model. Since the standard lane change probability is determined according to the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time, the standard lane change probability is negatively correlated with the lane change time interval, thereby improving the trained The accuracy of the regression model of lane change prediction improves the accuracy of lane change prediction.

In some embodiments, obtaining the standard lane change probability corresponding to the motion feature of the first sample includes: when the lane change time interval is less than or equal to the target time interval threshold, obtaining the target lane change probability generation factor; and, according to the lane change time interval and the target lane change probability generation factor to calculate the standard lane change probability corresponding to the motion feature of the first sample.

Specifically, the time interval threshold may be a period of time, such as 10 seconds. The target time interval threshold may be selected from candidate time interval thresholds. The candidate time interval threshold may be a time interval threshold pre-stored in the server, or may be a time interval threshold acquired or generated by the server. The lane change probability generation factor is used to generate the lane change probability. The lane change probability refers to the probability of the moving object changing lanes. The target lane change probability generation factor may be selected from the candidate lane change probability generation factors. The candidate lane change probability generation factor may be a lane change probability generation factor pre-stored in the server, or may be a lane change probability generation factor acquired or generated by the server. The target lane change probability generation factor may be determined by training a lane change prediction regression model. For example, the server may generate the candidate lane change probability according to the candidate lane change probability generation factor and the lane change time interval, use the candidate lane change probability as the probability label of the motion feature of the second sample, and use the motion feature of the second sample and the corresponding probability label to compare the The lane change prediction regression model is trained, and the target lane change probability generation factor is determined according to the accuracy of the trained lane change prediction regression model. The second sample motion feature may be any sample motion feature, or may be a specified sample motion feature, for example, may be a sample motion feature different from the first sample motion feature. Probabilistic labels can also be called regression ground truth.

In some embodiments, the server may use a lane change probability calculation formula to calculate the lane change time interval and the target lane change probability generation factor to obtain the standard lane change probability corresponding to the motion feature of the first sample. In the calculation formula of lane change probability, when the value of the independent variable is greater than or equal to the value of the independent variable, the dependent variable decreases with the increase of the independent variable, and the value of the dependent variable is greater than 0 and less than 1. The server can replace the independent variable in the lane change probability calculation formula with the lane change time interval, replace the constant in the lane change probability calculation formula with the target lane change probability generation factor, and calculate the standard lane change corresponding to the motion feature of the first sample. probability. The calculation formula of the lane change probability may be, for example, a Gaussian function. The server may use a Gaussian function to calculate the lane change time interval and the target lane change probability generation factor to obtain the standard lane change probability corresponding to the motion feature of the first sample. For example, the server can replace the independent variable of the Gaussian function with the lane change time interval, replace the variance in the Gaussian function with the target lane change probability generation factor, set the mean value of the Gaussian function to 0, and calculate the corresponding motion feature of the first sample. Standard lane change probability.

In the above embodiment, when the lane change time interval is less than or equal to the target time interval threshold, the target lane change probability generation factor is obtained, and the calculation is performed according to the lane change time interval and the target lane change probability generation factor, so as to obtain the corresponding motion characteristics of the first sample. The standard lane change probability can be ensured that the lane change time interval corresponding to the standard lane change probability is not too large, the validity of the lane change time interval is ensured, and the accuracy of the standard lane change probability is improved.

In some embodiments, calculating according to the lane change time interval and the target lane change probability generation factor to obtain the standard lane change probability corresponding to the motion feature of the first sample includes: performing a square operation on the lane change time interval to obtain a squared value of the interval; Calculate the ratio of the interval squared value to the target lane change probability generation factor to obtain the target ratio; and, take the first value as the base, and use the negative number of the target ratio as the index to perform exponential calculation to obtain the standard lane change corresponding to the motion characteristic of the first sample probability.

Specifically, the squared value of the interval is the square of the lane-change time interval. For example, if the lane-change time interval is d, the squared value of the interval is d ² . The target ratio refers to the ratio of the interval squared value to the target lane change probability generation factor, for example, it may be d ² /C, where C represents the target lane change probability generation factor. The first value can be any positive number greater than 1, for example, it can be the base of the natural logarithm e≈2.71828.

In some embodiments, the server may use the first value as a base, and perform index calculation according to the target ratio determination index to obtain the standard lane change probability. For example, the server may use the first value as the base, and use the negative number of the target ratio as the index to perform exponential calculation to obtain the standard lane change probability corresponding to the motion feature of the first sample. The server may use the first value as the base, and use the ratio of the negative number of the target ratio to the second value as an index to perform exponential calculation to obtain the standard lane change probability. The second value can be any positive number, for example, it can be 2. The standard lane change probability can be expressed as formula (1), for example, where c ² represents the target lane change probability generation factor. f(d) represents the standard lane change probability.

In the above embodiment, a square operation is performed on the lane change time interval to obtain the interval square value, the ratio of the interval square value to the target lane change probability generation factor is calculated, and the target ratio value is obtained, the first value is used as the base, and the negative number of the target ratio value is used as The index performs exponential calculation to obtain the standard lane change probability corresponding to the motion feature of the first sample, so that the value of the standard lane change probability can be guaranteed to be between 0 and 1, and the accuracy of the standard lane change probability is improved.

In some embodiments, as shown in FIG. 3 , the steps of obtaining the target time interval threshold and the target lane change probability generation factor include:

S302: Determine at least two candidate parameter combinations, where the candidate parameter combinations include a candidate time interval threshold and a candidate lane change probability generation factor.

Specifically, the server can arbitrarily select a candidate time interval threshold from a plurality of candidate time interval thresholds, as a candidate time interval threshold in the candidate parameter combination, and can arbitrarily select a candidate lane change from a plurality of candidate lane change probability generation factors Probability generation factor as the candidate interval threshold in the candidate parameter combination. If the number of candidate time interval thresholds is N, the number of candidate lane change probability generation factors is M, and the number of candidate parameter combinations may be N×M. For example, if the candidate time interval thresholds are r1 and r2, and the candidate lane change probability generation factors are w1 and w2, the candidate parameter combinations may include (r1,w1), (r1,w2), (r2,w1) and (r2,w2) ).

S304: Obtain the motion feature of the second sample, and determine the lane change probability corresponding to the motion feature of the second sample according to the candidate parameter combination, as a probability label corresponding to the motion feature of the second sample.

Specifically, the second sample motion feature may be any sample motion feature, or may be a specified sample motion feature, for example, may be a sample motion feature different from the first sample motion feature. There may be multiple second sample motion features. The probability label refers to the label of the motion feature of the second sample, and the probability label can also be called the regression true value. The probability label is obtained in a similar way to the standard lane change probability. The probability label is determined according to the lane change time interval corresponding to the motion feature of the second sample. When the lane change time interval is less than or equal to the candidate time interval threshold in the candidate parameter combination, according to the lane change time interval The interval and the candidate lane change probability generation factor in the candidate parameter combination are calculated to obtain the probability label corresponding to the motion feature of the second sample. When the lane change time interval is greater than the candidate time interval threshold in the candidate parameter combination, the probability label corresponding to the motion feature of the second sample is determined as a fixed probability. The fixed probability can be set as required or preset, such as 0.

In some embodiments, the server may obtain a candidate sample motion feature set, and select a second number of candidate sample motion features from the candidate sample motion feature set to obtain a second sample motion feature set. A third sample motion feature set is composed of candidate sample motion features other than the second sample motion feature set in the candidate sample motion feature set. The second number can be set as required, for example, determined according to the number of candidate sample motion features in the candidate sample motion feature set (referred to as the total number of features), and the second number is, for example, one-fifth of the total number of features.

S306 , train the lane change prediction regression model based on the motion feature of the second sample and the probability label corresponding to the motion feature of the second sample, to obtain a trained lane change prediction regression model corresponding to the candidate parameter combination.

Specifically, the server may input the motion feature of the second sample into the lane change prediction regression model to obtain the predicted lane change probability corresponding to the motion feature of the second sample, and the server may obtain the probability difference between the predicted lane change probability and the probability label according to the probability difference between the predicted lane change probability and the probability label The model loss value is used to adjust the model parameters in the lane change prediction regression model by using the model loss value to obtain the trained lane change prediction regression model. For example, each second sample motion feature in the second sample motion feature set may be input into the lane change prediction regression model to obtain a trained lane change prediction regression model.

In some embodiments, the server may obtain multiple second sample motion feature sets from the candidate sample motion feature sets, and the second sample motion features in different second sample motion feature sets are different. Using the same candidate parameter combination, the probability labels of the second sample motion features in each second sample motion feature set are determined. Each second sample motion feature set is used to train the lane change prediction regression model, respectively, to obtain the trained lane change prediction regression model obtained from different second sample motion feature sets under the same candidate parameter combination.

S308: Determine the model accuracy of the trained lane change prediction regression model corresponding to the candidate parameter combination, select a target parameter combination that satisfies the accuracy condition from at least two candidate parameter combinations based on the model accuracy, and select the target parameter combination in the target parameter combination. The candidate time interval threshold is used as the target time interval threshold, and the candidate lane change probability generation factor in the target parameter combination is used as the target lane change probability generation factor.

Specifically, the candidate parameter combination may further include a candidate lane change probability threshold. The server may input the third sample motion feature in the third sample motion feature set into the trained lane change prediction regression model, obtain the predicted lane change probability of the third sample motion feature output by the lane change prediction regression model, and convert the predicted lane change The lane change probability is compared with the candidate lane change probability threshold. When the predicted lane change probability is greater than the candidate lane change probability threshold, the predicted lane change result of the motion feature of the third sample is determined as a lane change, and when the predicted lane change probability is less than or equal to the candidate lane change When the probability threshold is set, it is determined that the predicted lane change result of the motion feature of the third sample is an unchanged lane. The predicted lane change result is the predicted lane change result. The server can obtain the standard lane change result of the motion feature of the third sample, compare the predicted lane change result with the standard lane change result, and when the comparison is consistent, determine the prediction result of the trained lane change prediction regression model on the motion feature of the third sample is correct, otherwise the prediction result is wrong. The standard lane change result is the real lane change result of the motion feature of the third sample. The server can determine the model accuracy of the trained lane change prediction regression model according to the prediction results corresponding to the motion characteristics of each third sample. For example, the model accuracy can be obtained by dividing the number of correct prediction results by the number of all prediction results. . The accuracy condition may include at least one of accuracy greater than an accuracy threshold or model accuracy being at a maximum. The accuracy threshold can be set as required, or can be preset, for example, it can be 95%. The target parameter combination is a candidate parameter combination that satisfies the accuracy condition among the obtained at least two candidate parameter combinations.

In some embodiments, the server may determine the respective accuracies of the trained lane change prediction regression models obtained from different second sample motion feature sets under the same candidate parameter combination, and calculate the average value of the respective accuracies as the candidate parameter combination. The model accuracy of the corresponding trained lane change prediction regression model. The server may determine the model accuracy corresponding to each candidate parameter combination in the at least two candidate parameter combinations, and select a candidate parameter combination that satisfies the accuracy condition from the model accuracy corresponding to each candidate parameter combination as the target parameter combination, for example The candidate parameter combination corresponding to the maximum model accuracy is selected as the target parameter combination. The server may use the candidate lane change probability threshold in the target parameter combination as the target lane change probability threshold.

In some embodiments, the server may combine the target parameter combination with the corresponding trained lane change prediction regression model as the lane change prediction regression model to be trained.

In the above embodiment, the model accuracy of the trained lane change prediction regression model corresponding to the candidate parameter combination is determined, and the target parameter combination that satisfies the accuracy condition is selected from at least two candidate parameter combinations based on the model accuracy, and the target parameter The candidate time interval threshold in the combination is used as the target time interval threshold, and the candidate lane change probability generation factor in the target parameter combination is used as the target lane change probability generation factor, so as to obtain a target time interval that can make the lane change prediction regression model with high accuracy Threshold and target lane change probability generation factor.

In some embodiments, the first sample motion feature includes a distance change feature, and the step of determining the distance change feature includes: acquiring a target distance sequence corresponding to the sample moving object during the sample collection time, and the target distance sequence is sorted according to the motion time; The magnitude relationship of the distances in the distance sequence determines a distance change trend; and, according to the distance change trend, a distance change feature corresponding to the sample moving object is determined.

Specifically, the distance sequence refers to a sequence composed of relative distances, and each relative distance in the distance sequence is sorted by movement time. In the distance sequence, the earlier the movement time is, the earlier the relative distance is sorted. For example, it can be (s1, s2, s3, s4, s5, s6), where s1 to S6 respectively represent a relative distance. The movement time refers to the time during the movement of the sample moving object, which can be a time point or a time period. The sample moving object may correspond to multiple distance sequences, for example, may correspond to at least one of a left border distance sequence or a right border distance sequence. The left border distance sequence refers to a sequence obtained by sorting the distances of the left border of the road in chronological order, and the right border distance sequence refers to a sequence obtained by sorting the distances of the right border of the road in chronological order. Distance sequences can also be represented by vectors. The sample moving objects can correspond to multiple distance sequences. For example, the server can obtain multiple distance sequences corresponding to the sample moving objects according to the motion data collected at the sample collection time, and the target distance sequence can be from each distance sequence corresponding to the sample moving objects. The selected distance sequence. It can be selected by random selection, or can be selected by a preset selection method. For example, it can be selected according to the distance change trend corresponding to the distance sequence, and the distance sequence satisfying the distance change trend condition is used as the target distance sequence. Certainly, each distance sequence of the sample moving object can be respectively used as the target distance sequence. There can be multiple target distance sequences. The distance change trend refers to the law of the distance in the distance series changing with time, which can include either a gradually decreasing change trend or a gradually increasing change trend. The distance change trend condition may be, for example, a gradually decreasing change trend. For example, when the distance change trend of the left border distance sequence is gradually decreasing, the left border distance sequence is used as the target distance sequence. When the distance change trend of the left border distance sequence is gradually increasing, the right The boundary distance sequence is used as the target distance sequence.

The distance change feature can be the feature of the distance change trend, and can be calculated according to the distance change trend. For example, the speed of the distance change can be determined according to the distance change trend to obtain the distance change feature. The speed of the distance change may include at least one of the speed of the distance becoming larger or the speed of the distance becoming smaller. The distance change feature can also be determined according to the average distance change degree, which refers to the size of the distance change per unit time, and can be calculated according to the target distance sequence and the time series corresponding to the target distance sequence. The time sequence corresponding to the target distance sequence includes the time corresponding to each distance in the target distance sequence, and the time in the time sequence is sorted in order from front to back. The higher the time, the higher the sorting. The time series may be (t1, t2, t3, t4, t5, t6), for example. Among them, t1 occurs before t6. For example, the distance difference can be obtained by the difference between the distance of the starting position and the distance of the ending position in the target distance sequence, and the time difference can be obtained according to the difference between the time of the starting position and the ending position in the time series. The ratio to the time difference obtains the average distance change degree, and the average distance change degree can be, for example, (s6-s1)÷(t6-t1). The distance change feature can be represented by a vector, for example, it can be called a distance feature vector. The distance change feature can also be a target distance sequence, that is, the target distance sequence can be used as the distance change feature.

In some embodiments, the server may calculate the target distance sequence and the corresponding time sequence by using the change feature calculation method to obtain the distance transformation feature. The variation characteristic calculation method may be, for example, formulas (2) and (3). Among them, x represents the distance in the target distance sequence, and y represents the time corresponding to the distance in the target distance sequence. a represents the distance change feature, which can also be understood as the change rate (slope) of the relative distance with time.

represents the mean of y,

represents the mean of x.

In the above embodiment, in the sample collection time, the target distance sequence corresponding to the sample moving object is obtained, the target distance sequence is sorted according to the movement time, the distance change trend is determined according to the size relationship of the distances in the target distance sequence, and the sample movement is determined according to the distance change trend. The distance change feature corresponding to the object improves the accuracy of the motion feature of the first sample.

In some embodiments, as shown in FIG. 4 , a method for predicting a lane change is provided, which is described by taking the method applied to the terminal 102 in FIG. 1 as an example, including the following steps:

S402, acquiring target motion features corresponding to the target motion object.

Specifically, the target moving object may be a moving object existing in the surrounding environment of the terminal. The surrounding environment of the terminal may be, for example, an area surrounded by a circle with the terminal as the center and a fixed radius as the radius. The fixed radius can be set as needed or preset. The target motion feature refers to the motion feature of the current time corresponding to the target motion object. The current time is the time period, the ending time of the current time is the current time, and the starting time of the current time is the historical time whose time interval from the current time is the first time interval. The first time interval may be preset or determined as required.

S404, input the target motion feature into the trained lane change prediction regression model to obtain the target lane change probability corresponding to the target moving object; the trained lane change prediction regression model is based on the first sample motion feature and the first sample motion The standard lane change probability corresponding to the feature is obtained by training, the first sample motion feature is obtained according to the motion data of the sample moving object collected at the sample collection time, and the standard lane change probability is obtained according to the object lane change corresponding to the first sample motion feature The lane change time interval between the time and the sample collection time is determined, and the standard lane change probability is negatively correlated with the lane change time interval.

Specifically, the trained lane change prediction regression model may be trained by using the above proposed lane change prediction regression model training method. The first time interval may be consistent with the duration of the sample collection time.

S406: Determine a target lane change result corresponding to the target moving object according to the target lane change probability.

Specifically, the target lane change result may include any one of lane change or unchanged lane, and may also include predicted lane change time information. The predicted lane change time information is used to predict the time when the lane change occurs, for example, it may be within 5 seconds in the future. If the target lane change result is a lane change, it means that the target moving object is about to change lanes, for example, a lane change may occur in the next 5 seconds. The lane change can be represented by 1, and the unchanged lane can be represented by 0.

In some embodiments, the terminal may control the movement of the terminal according to the target lane change result, so as to avoid collision with the target moving object. For example, when the target lane change result of the moving object on the road in front of the terminal is a lane change, the terminal may decelerate and drive.

In the above lane change prediction method, the target motion feature corresponding to the target moving object is obtained, and the target motion feature is input into the trained lane change prediction regression model to obtain the target lane change probability corresponding to the target moving object, which is determined according to the target lane change probability. The target lane change result corresponding to the target moving object is obtained by training the trained lane change prediction regression model according to the first sample motion feature and the standard lane change probability corresponding to the first sample motion feature. The standard lane change probability is determined according to the lane change time interval between the object lane change time corresponding to the first sample movement feature and the sample collection time, and the standard lane change probability is obtained from the motion data of the sample moving object collected at the sample collection time. There is a negative correlation with the lane change time interval, thereby improving the accuracy of the trained lane change prediction regression model and improving the lane change prediction accuracy.

In some embodiments, determining the target lane change result corresponding to the target moving object according to the target lane change probability includes: comparing the target lane change probability with the target lane change probability threshold to obtain a comparison result; and determining the target movement according to the comparison result The target lane change result corresponding to the object.

Specifically, the target lane change probability threshold may be preset or determined by training a lane change prediction regression model, for example, it may be 0.9. The comparison result may be any one of the target lane change probability is greater than the target lane change probability threshold, and the target lane change probability is less than or equal to the target lane change probability threshold. When the comparison result is that the target lane change probability is greater than the target lane change probability threshold, the terminal may determine that the target lane change result is a lane change; otherwise, the terminal may determine that the target lane change result is an unchanged lane.

In the above embodiment, the target lane change probability is compared with the target lane change probability threshold to obtain a comparison result, and the target lane change result corresponding to the target moving object is determined according to the comparison result, which improves the accuracy of the target lane change result.

It should be understood that although the steps in the flowcharts of FIGS. 2-4 are shown in sequence according to the arrows, these steps are not necessarily executed in the sequence shown by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in FIGS. 2-4 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. These sub-steps or stages are not necessarily completed at the same time. The order of execution of the steps is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of sub-steps or stages of other steps.

In some embodiments, as shown in FIG. 5, a lane change prediction regression model training device is provided, including: a first sample motion feature acquisition module 502, a standard lane change probability acquisition module 504, and a predicted lane change probability acquisition module 506. The model loss value obtaining module 508 and the trained lane change prediction regression model obtaining module 510, wherein:

The first sample motion feature acquisition module 502 is configured to acquire the first sample motion feature, and the first sample motion feature is obtained according to the motion data of the sample motion object collected at the sample collection time.

The standard lane change probability acquisition module 504 is used to obtain the standard lane change probability corresponding to the motion feature of the first sample, and the standard lane change probability is based on the lane change between the object lane change time corresponding to the first sample motion feature and the sample acquisition time The time interval is determined, and the standard lane change probability is negatively correlated with the lane change time interval.

The predicted lane change probability obtaining module 506 is configured to input the motion feature of the first sample into the lane change prediction regression model to be trained to obtain the predicted lane change probability corresponding to the motion feature of the first sample.

The model loss value obtaining module 508 is configured to obtain the model loss value according to the probability difference between the predicted lane change probability and the standard lane change probability.

The trained lane change prediction regression model obtaining module 510 is used to adjust the model parameters in the lane change prediction regression model by using the model loss value to obtain the trained lane change prediction regression model, so as to perform the lane change prediction regression model according to the trained lane change prediction regression model. Lane change prediction.

In some embodiments, the standard lane change probability acquisition module 504 includes:

The target lane change probability generation factor obtaining unit is configured to obtain the target lane change probability generation factor when the lane change time interval is less than or equal to the target time interval threshold.

The standard lane change probability obtaining unit is used for calculating according to the lane change time interval and the target lane change probability generation factor to obtain the standard lane change probability corresponding to the motion feature of the first sample.

In some embodiments, the standard lane change probability obtaining unit is further configured to perform a square operation on the lane change time interval to obtain the interval square value; calculate the ratio of the interval square value to the target lane change probability generation factor to obtain the target ratio value; and, The first value is used as the base, and the negative number of the target ratio is used as the index to perform exponential calculation to obtain the standard lane change probability corresponding to the motion feature of the first sample.

In some embodiments, the lane change prediction regression model training device further includes a target time interval threshold obtaining module, and the target time interval threshold obtaining module includes:

The candidate parameter combination determination unit is configured to determine at least two candidate parameter combinations, where the candidate parameter combinations include a candidate time interval threshold and a candidate lane change probability generation factor.

The probability label obtaining unit is configured to obtain the motion feature of the second sample, and determine the lane change probability corresponding to the motion feature of the second sample according to the candidate parameter combination, as the probability label corresponding to the motion feature of the second sample.

The trained lane change prediction regression model obtaining unit is used to train the lane change prediction regression model based on the motion feature of the second sample and the probability label corresponding to the motion feature of the second sample, and obtain the trained lane change corresponding to the candidate parameter combination Predictive regression models.

The unit for obtaining the target time interval threshold is used to determine the model accuracy of the trained lane change prediction regression model corresponding to the candidate parameter combination, and based on the model accuracy, select the target parameter combination that satisfies the accuracy condition from at least two candidate parameter combinations , the candidate time interval threshold in the target parameter combination is used as the target time interval threshold, and the candidate lane change probability generation factor in the target parameter combination is used as the target lane change probability generation factor.

In some embodiments, the first sample motion feature includes a distance change feature, the lane change prediction regression model training device further includes a distance change feature determination module, and the distance change feature determination module includes:

The target distance sequence acquisition unit is used for acquiring the target distance sequence corresponding to the sample moving object in the sample collection time, and the target distance sequence is sorted according to the movement time.

The distance change trend determination unit is used for determining the distance change trend according to the magnitude relationship of the distances in the target distance sequence.

The distance change feature determination unit is used for determining the distance change feature corresponding to the sample moving object according to the distance change trend.

For the specific limitation of the lane change prediction regression model training device, please refer to the limitation on the lane change prediction regression model training method above, which will not be repeated here. Each module in the above-mentioned lane change prediction regression model training device may be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In some embodiments, as shown in FIG. 6, a lane change prediction apparatus is provided, including: a target motion feature acquisition module 602, a target lane change probability acquisition module 604, and a target lane change result determination module 606, wherein:

The target motion feature acquisition module 602 is configured to acquire target motion features corresponding to the target motion object.

The target lane change probability obtaining module 604 is used to input the target motion feature into the trained lane change prediction regression model to obtain the target lane change probability corresponding to the target moving object; the trained lane change prediction regression model is based on the first sample The motion feature and the standard lane change probability corresponding to the motion feature of the first sample are obtained by training, the first sample motion feature is obtained according to the motion data of the sample moving object collected at the sample collection time, and the standard lane change probability is obtained according to the first sample The lane-change time interval between the object's lane-change time corresponding to this motion feature and the sample collection time is determined, and the standard lane-change probability has a negative correlation with the lane-change time interval.

The target lane change result determination module 606 is configured to determine the target lane change result corresponding to the target moving object according to the target lane change probability.

In some embodiments, the target lane change result determination module 606 includes:

The comparison result obtaining unit is used for comparing the target lane change probability with the target lane change probability threshold to obtain the comparison result.

The target lane change result obtaining unit is used for determining the target lane change result corresponding to the target moving object according to the comparison result.

For the specific limitation of the lane change prediction apparatus, reference may be made to the limitation of the lane change prediction method above, which will not be repeated here. Each module in the above-mentioned lane change prediction apparatus may be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In some embodiments, a computer device is provided, and the computer device may be a server, and its internal structure diagram may be as shown in FIG. 7 . The computer device includes a processor, memory, a network interface, and a database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, computer readable instructions and a database. The internal memory provides an environment for the execution of the operating system and computer-readable instructions in the non-volatile storage medium. The database of the computer equipment is used to store data such as sample motion characteristics, standard lane change probability, predicted lane change probability, model loss value, and lane change prediction regression model. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer readable instructions, when executed by a processor, implement a method for training a lane change prediction regression model.

In some embodiments, a computer device is provided, and the computer device may be a terminal, and its internal structure diagram may be as shown in FIG. 8 . The computer equipment includes a processor, memory, a communication interface, a display screen, and an input device connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The nonvolatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with an external terminal, and the wireless communication can be realized by WIFI, operator network, NFC (Near Field Communication) or other technologies. The computer program, when executed by a processor, implements a network communication method. The display screen of the computer equipment may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment may be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the computer equipment , or an external keyboard, trackpad, or mouse.

Those skilled in the art can understand that the structures shown in FIGS. 7 and 8 are only block diagrams of partial structures related to the solution of the present application, and do not constitute a limitation on the computer equipment to which the solution of the present application is applied. A device may include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.

A computer device, comprising a memory and one or more processors, the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, one or more processors execute the above-mentioned lane change prediction regression model training method A step of.

A computer device includes a memory and one or more processors, where computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processors, cause the one or more processors to perform the steps of the above-mentioned lane change prediction method.

One or more computer storage media storing computer-readable instructions, when executed by one or more processors, cause the one or more processors to perform the steps of the above-mentioned method for training a regression model for lane change prediction.

One or more computer storage media storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the above-mentioned lane change prediction method.

Wherein, the computer storage medium is a readable storage medium, and the readable storage medium may be non-volatile or volatile.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile computer. In the readable storage medium, the computer-readable instructions, when executed, may include the processes of the foregoing method embodiments. Wherein, any reference to memory, storage, database or other medium used in the various embodiments provided in this application may include non-volatile and/or volatile memory. Nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, all It is considered to be the range described in this specification.

The above-mentioned embodiments only represent several embodiments of the present application, and the descriptions thereof are specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the scope of protection of the patent of the present application shall be subject to the appended claims.

Claims

A lane change prediction regression model training method, comprising:

acquiring a first sample motion feature, the first sample motion feature is obtained according to the motion data of the sample motion object collected at the sample collection time;

Obtain a standard lane change probability corresponding to the motion feature of the first sample, and the standard lane change probability is based on the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time It is determined that the standard lane change probability is negatively correlated with the lane change time interval;

Inputting the first sample motion feature into the lane change prediction regression model to be trained to obtain the predicted lane change probability corresponding to the first sample motion feature;

obtaining a model loss value based on the probability difference between the predicted lane change probability and the standard lane change probability; and

The model parameters in the lane change prediction regression model are adjusted by using the model loss value to obtain the trained lane change prediction regression model, so as to perform lane change prediction according to the trained lane change prediction regression model.
The method according to claim 1, wherein the obtaining a standard lane change probability corresponding to the motion feature of the first sample comprises:

obtaining a target lane change probability generation factor when the lane change time interval is less than or equal to a target time interval threshold; and

According to the lane change time interval and the target lane change probability generation factor, the standard lane change probability corresponding to the motion feature of the first sample is obtained.
The method according to claim 2, wherein the calculating according to the lane change time interval and the target lane change probability generation factor to obtain the standard lane change probability corresponding to the motion feature of the first sample comprises:

performing a square operation on the lane change time interval to obtain a squared value of the interval;

calculating the ratio of the interval squared value to the target lane change probability generation factor to obtain a target ratio; and

The first numerical value is used as the base, and the negative number of the target ratio is used as the index to perform exponential calculation to obtain the standard lane change probability corresponding to the motion feature of the first sample.
The method according to claim 3, wherein the step of obtaining the target time interval threshold and the target lane change probability generation factor comprises:

determining at least two candidate parameter combinations, the candidate parameter combinations comprising a candidate time interval threshold and a candidate lane change probability generation factor;

Obtaining a second sample motion feature, and determining a lane change probability corresponding to the second sample motion feature according to the candidate parameter combination, as a probability label corresponding to the second sample motion feature;

training the lane change prediction regression model based on the second sample motion feature and the probability label corresponding to the second sample motion feature to obtain a trained lane change prediction regression model corresponding to the candidate parameter combination; and

Determine the model accuracy of the trained lane change prediction regression model corresponding to the candidate parameter combination, select a target parameter combination that satisfies the accuracy condition from the at least two candidate parameter combinations based on the model accuracy, and use the The candidate time interval threshold in the target parameter combination is used as the target time interval threshold, and the candidate lane change probability generation factor in the target parameter combination is used as the target lane change probability generation factor.
The method of claim 1, wherein the first sample motion feature includes a distance change feature, and the step of determining the distance change feature comprises:

Acquiring the target distance sequence corresponding to the sample moving object in the sample collection time, and the target distance sequence is sorted according to the movement time;

Determine a distance change trend according to the magnitude relationship of the distances in the target distance sequence; and

The distance change feature corresponding to the sample moving object is determined according to the distance change trend.
A lane change prediction method, comprising:

Obtain the target motion feature corresponding to the target motion object;

The target motion feature is input into the trained lane change prediction regression model to obtain the target lane change probability corresponding to the target moving object; the trained lane change prediction regression model is based on the first sample motion feature and the The standard lane change probability corresponding to the first sample motion feature is obtained by training, the first sample motion feature is obtained according to the motion data of the sample moving object collected at the sample collection time, and the standard lane change probability is obtained according to the determining the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time, and the standard lane change probability and the lane change time interval are negatively correlated; and

A target lane change result corresponding to the target moving object is determined according to the target lane change probability.
The method according to claim 6, wherein the determining the target lane change result corresponding to the target moving object according to the target lane change probability comprises:

comparing the target lane change probability with the target lane change probability threshold to obtain a comparison result; and

A target lane change result corresponding to the target moving object is determined according to the comparison result.
A lane change prediction regression model training device, comprising:

a first sample motion feature acquisition module, configured to acquire a first sample motion feature, the first sample motion feature is obtained according to the motion data of the sample motion object collected at the sample collection time;

The standard lane change probability acquisition module is used to obtain the standard lane change probability corresponding to the motion feature of the first sample, and the standard lane change probability is based on the object lane change time corresponding to the first sample motion feature and the sample A lane change time interval between acquisition times is determined, and the standard lane change probability is negatively correlated with the lane change time interval;

A predicted lane change probability obtaining module, used for inputting the first sample motion feature into the lane change prediction regression model to be trained, to obtain the predicted lane change probability corresponding to the first sample motion feature;

a model loss value obtaining module for obtaining a model loss value according to the probability difference between the predicted lane change probability and the standard lane change probability; and

The trained lane change prediction regression model obtaining module is used to adjust the model parameters in the lane change prediction regression model by using the model loss value to obtain the trained lane change prediction regression model, so as to obtain the trained lane change prediction regression model according to the trained lane change prediction regression model. The lane change prediction regression model is used for lane change prediction.
A lane change prediction device, comprising:

The target motion feature acquisition module is used to acquire the target motion feature corresponding to the target motion object;

The target lane change probability obtaining module is used to input the target motion feature into the trained lane change prediction regression model to obtain the target lane change probability corresponding to the target moving object; the trained lane change prediction regression model Obtained by training according to the first sample motion feature and the standard lane change probability corresponding to the first sample motion feature, and the first sample motion feature is obtained according to the motion data of the sample moving object collected at the sample collection time , the standard lane change probability is determined according to the lane change time interval between the object lane change time corresponding to the first sample motion feature and the sample acquisition time, the standard lane change probability and the lane change time interval negatively correlated; and

A target lane change result determination module, configured to determine the target lane change result corresponding to the target moving object according to the target lane change probability.
A computer device comprising a memory and one or more processors, the memory having computer-readable instructions stored in the memory that, when executed by the one or more processors, cause the one or more processors to A processor performs the steps of the method of any one of claims 1 to 5 or 6 to 7.
A computer device comprising a memory and one or more processors, the memory having computer-readable instructions stored in the memory that, when executed by the one or more processors, cause the one or more processors to A processor performs the steps of the method of any one of claims 1 to 5 or 6 to 7.