CN111930117A

CN111930117A - Transverse control method, device, equipment and storage medium based on steering

Info

Publication number: CN111930117A
Application number: CN202010763285.XA
Authority: CN
Inventors: 孙子文; 李斌; 霍达; 韩旭
Original assignee: Guangzhou Jingqi Technology Co ltd
Current assignee: Guangzhou Jingqi Technology Co ltd
Priority date: 2020-07-31
Filing date: 2020-07-31
Publication date: 2020-11-13
Anticipated expiration: 2040-07-31
Also published as: CN111930117B

Abstract

The embodiment of the invention provides a transverse control method, a device, equipment and a storage medium based on steering, wherein the method comprises the following steps: when it is detected that the vehicle is turning around a curve, the type and the deviation value of the curve are calculated, the partial deviation value is divided into a first target deviation value representing urgent steering and a second target deviation value representing non-urgent steering with the lane line of the curve as a reference, updating the event recognition module matched with the type of the curve by taking the first target deviation value and the second target deviation value as classified samples to obtain a target event recognition model, inputting the partial deviation values into the target event recognition model for classification so as to recognize the operation representing the emergency steering, according to the emergency steering operation, the steering of the vehicle in the curve is transversely controlled, the deviation value of the vehicle driven by the user is acquired in real time, the training is continued on the basis of the prior event recognition model, the training amount is small, the real-time requirement is met, and the event recognition model which accords with the driving style of the user is gradually learned.

Description

Transverse control method, device, equipment and storage medium based on steering

Technical Field

The embodiment of the invention relates to the technical field of automatic control, in particular to a transverse control method, a transverse control device, transverse control equipment and a storage medium based on steering.

Background

When a user drives a vehicle, steering along with changes of roads is a common operation, which is also called turning, and in some cases, the user can turn greatly, which is also called sharp turning, possibly causes the vehicle to be out of the controllable range of the user, causing drift, not only reducing the comfort of passengers, but also possibly causing safety risks.

Therefore, the automatic driving system can detect large-amplitude steering and intervene the large-amplitude steering, so that the comfort of passengers is improved, and the safety risk is reduced.

Currently, to detect a large magnitude of steering, it is common to detect the heading of the vehicle and set a corresponding static threshold, above or below which a change in heading is considered a large magnitude of steering.

However, the threshold is an empirical value, and needs to be adjusted continuously according to the situation of different users, which is cumbersome to operate.

Disclosure of Invention

The embodiment of the invention provides a transverse control method, a device, equipment and a storage medium based on steering, which aim to solve the problem that the operation for detecting large-amplitude steering by a user is complicated.

In a first aspect, an embodiment of the present invention provides a steering-based lateral control method, including:

when it is detected that the vehicle turns around a curve, calculating the type of the curve and a deviation value, wherein the deviation value represents the degree of deviation of the vehicle from a standard direction;

dividing part of the deviation value into a first target deviation value representing emergency steering and a second target deviation value representing non-emergency steering by taking a lane line of the curve as a reference;

taking the first target deviation value and the second target deviation value as classified samples, updating an event recognition module matched with the type of the curve, and obtaining a target event recognition model;

inputting a portion of the deviation values into the target event recognition model for classification to recognize an operation indicative of an urgent steering;

and performing transverse control on the steering of the vehicle at the curve according to the emergency steering operation.

In a second aspect, an embodiment of the present invention further provides a steering-based lateral control device, including:

the deviation value calculating module is used for calculating the type and the deviation value of the curve when the vehicle is detected to turn around the curve, and the deviation value represents the degree of deviation of the vehicle from the standard direction;

the deviation value dividing module is used for dividing part of the deviation values into a first target deviation value representing emergency steering and a second target deviation value representing non-emergency steering by taking a lane line of the curve as a reference;

the target event recognition model training module is used for updating the event recognition module matched with the type of the curve by taking the first target deviation value and the second target deviation value as classified samples to obtain a target event recognition model;

an offset value classification module for inputting a portion of the offset values into the target event recognition model for classification to identify an operation indicative of an urgent steering;

and the transverse control module is used for transversely controlling the steering of the vehicle at the curve according to the emergency steering operation.

In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:

one or more processors;

a memory for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the steering-based lateral control method of the first aspect.

In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steering-based lateral control method according to the first aspect.

In this embodiment, when it is detected that the vehicle turns around a curve, the type and the offset value of the curve are calculated, the lane line of the curve is used as a reference, a partial offset value is divided into a first target offset value representing urgent steering and a second target offset value representing non-urgent steering, the first target offset value and the second target offset value are used as classified samples, an event recognition module matching the type of the curve is updated to obtain a target event recognition model, the partial offset value is input into the target event recognition model for classification to recognize an operation representing urgent steering, the turning of the vehicle around the curve is controlled laterally according to the urgent steering operation, the type of the curve is used as a condition for training the event recognition model and recognizing the operation representing urgent acceleration/deceleration, not only the calculation amount can be reduced, but also the facing range of the event recognition model can be reduced, thereby ensuring the accuracy of the event recognition model, the deviation value of a vehicle driven by a user is collected in real time, individuation and authenticity of the deviation value can be guaranteed, training is continued on the basis of a prior event recognition model, training amount is small, real-time requirements are met, the event recognition model which accords with driving style of the user can be gradually learned, and accordingly individualized emergency steering operation of the user is recognized, operation is simple and convenient, basis is provided for follow-up decision-making for assisting transverse control, driving of the user is assisted, and comfort and safety of driving are improved.

Drawings

FIG. 1 is a schematic structural diagram of an unmanned vehicle according to an embodiment of the present invention;

FIG. 2 is a flowchart of a lateral control method based on steering according to an embodiment of the present invention;

FIG. 3 is an exemplary diagram of an emergency steering system according to an embodiment of the present invention;

FIG. 4 is a flowchart of a steering-based lateral control method according to a second embodiment of the present invention;

fig. 5 is a schematic structural diagram of an event recognition model according to a second embodiment of the present invention;

FIG. 6 is a diagram illustrating a relationship between event recognition models according to a second embodiment of the present invention;

fig. 7 is a schematic structural diagram of a lateral control device based on steering according to a third embodiment of the present invention;

fig. 8 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Referring to fig. 1, an unmanned vehicle 100 to which an embodiment of a steering-based lateral control method, a steering-based lateral control apparatus, according to an embodiment of the present invention, may be applied is shown.

As shown in fig. 1, the unmanned vehicle 100 may include a driving Control device 101, a vehicle body bus 102, an ECU (Electronic Control Unit) 103, an ECU 104, an ECU 105, a sensor 106, a sensor 107, a sensor 108, and an actuator 109, an actuator 110, and an actuator 111.

A driving control device (also referred to as an in-vehicle brain) 101 is responsible for overall intelligent control of the entire unmanned vehicle 100. The driving control device 101 may be a controller that is separately provided, such as a Programmable Logic Controller (PLC), a single chip microcomputer, an industrial controller, and the like; or the equipment consists of other electronic devices which have input/output ports and have the operation control function; but also a computer device installed with a vehicle driving control type application. The driving control device can analyze and process the data sent by each ECU and/or the data sent by each sensor received from the vehicle body bus 102, make a corresponding decision, and send an instruction corresponding to the decision to the vehicle body bus.

The body bus 102 may be a bus for connecting the driving control apparatus 101, the ECU 103, the ECU 104, the ECU 105, the sensor 106, the sensor 107, the sensor 108, and other devices of the unmanned vehicle 100, which are not shown. Since the high performance and reliability of a CAN (Controller area network) bus are widely accepted, a vehicle body bus commonly used in a motor vehicle is a CAN bus. Of course, it is understood that the body bus may be other types of buses.

The vehicle body bus 102 may transmit the instruction sent by the driving control device 101 to the ECU 103, the ECU 104, and the ECU 105, and the ECU 103, the ECU 104, and the ECU 105 analyze and process the instruction and send the instruction to the corresponding execution device for execution.

Sensors

106, 107, 108 include, but are not limited to, laser radar, cameras, acceleration sensors, angle sensors, and the like.

It should be noted that the steering-based lateral control method provided by the embodiment of the present invention may be executed by the driving control apparatus 101, and accordingly, the steering-based lateral control device is generally provided in the driving control apparatus 101.

It should be understood that the numbers of unmanned vehicles, driving control devices, body buses, ECUs, actuators, and sensors in fig. 1 are merely illustrative. There may be any number of unmanned vehicles, driving control devices, body buses, ECUs, and sensors, as desired for implementation.

Example one

Fig. 2 is a flowchart of a steering-based lateral control method according to an embodiment of the present invention, where the present embodiment is applicable to a case where an operation of an adaptive user identifies an operation of an emergency steering, and the method may be executed by a steering-based lateral control device, and the steering-based lateral control device may be implemented by software and/or hardware, and may be configured in a computer device, for example, a driving control device, and the like, and specifically includes the following steps:

step 201, when the vehicle is detected to turn around the curve, calculating the type and deviation value of the curve.

In this embodiment, when the user drives the vehicle, an automatic driving mode may be initiated, which may refer to a mode in which the vehicle itself has environmental awareness, path planning, and autonomously implements vehicle control, that is, humanoid driving by electronically controlling the vehicle.

Depending on the degree of grasp of the vehicle handling task, the driving modes may be classified into L0 non-Automation (No Automation), L1 Driver Assistance (Driver Assistance), L2 Partial Automation (Partial Automation), L3 Conditional Automation (Conditional Automation), L4 High Automation (High Automation), and L5 Full Automation (Full Automation).

The automatic driving mode in the present embodiment may be a driving mode in L1-L3, and serves as an assist function for the user in driving the vehicle.

In the concrete implementation, the external environment and/or the internal environment of the vehicle can be detected, whether the vehicle turns in a curve or not is judged, if yes, an angle sensor arranged in the vehicle can be called in real time and continuously to collect angles, the frequency of collecting the angles is generally more than 10Hz, the angles are sorted according to time, a deviation value is calculated, the deviation value represents the degree of deviation of the vehicle from a standard direction, the deviation values can form a data sequence, and therefore the deviation values are used for identifying the operation of emergency turning, namely the operation of large-amplitude turning relative to a user, and the emergency turning generally has certain risk.

For example, as shown in fig. 3, when the vehicle 301 travels on a road in the arrow direction, if the speed of the vehicle 301 is high, the lateral movement is obvious during steering, and the vehicle is difficult to control and is likely to enter an adjacent lane, at this time, the vehicle 301 may detect an urgent steering event, and the steering progress of the vehicle 321 may be adjusted by assisting other measures, so as to avoid the risks of scratching, colliding and the like between the vehicle entering the adjacent lane and the vehicle in the adjacent lane.

It should be noted that the condition for detecting curve steering may be set by a person skilled in the art according to actual business requirements, which is not limited in this embodiment, and the curve steering is used as the condition for training the event recognition model and recognizing the operation of emergency steering, so that not only the calculation amount may be reduced, but also the range of the event recognition model may be reduced, thereby ensuring the accuracy of the event recognition model.

In one way of detecting curve steering, video data may be captured to the outside of the vehicle, a lane line is targeted for detection, a lane line of a lane in which the vehicle is located is detected in the video data, and a curvature of the lane line is calculated.

In one example, original image data in video data is converted into image data in HSL (hue H, saturation S, brightness L) format, yellow and white are separated from the image data in HSL format, and the image data in HSL format after the separation of yellow and white is combined with the original image data to obtain target image data.

Converting the target image data into gray level image data, smoothing edges by applying Gaussian blur, and applying Canny edge detection on the smoothed gray level image data to obtain edge information.

Tracking the region of interest edge information and eliminating information of other regions, performing Hough transform on the region of interest edge information to obtain lane lines in the region of interest, tracking the lane lines with a specific color (such as red) to separate the left lane from the right lane, and then inserting a straight gradient to create two complete and smooth lane lines.

If the curvature is larger than the preset curvature threshold, the curve degree of the lane is larger, and the lane can be determined to be a curve and the vehicle can turn at the curve.

At this time, a curvature range in which the curvature is located is searched for in a plurality of preset curvature ranges as a target range, wherein each curvature range is associated with a type, such as a small curve, a medium curve, a large curve, and the like.

The type of the target range association is set as the type of the curve in which the vehicle is currently located.

And calling an angle sensor to acquire the angle of the vehicle on the curve, namely the orientation of the vehicle.

And calculating the difference value between the angle and the standard direction as a deviation value by taking the specified direction as the standard direction.

In addition, after the angle is collected, the angle may be preprocessed to facilitate subsequent calculation of the deviation value, for example, data that the difference between the current angle and the angle at the previous time and the next time exceeds the angle threshold is eliminated, and the present embodiment does not limit this.

And step 202, dividing the partial deviation value into a first target deviation value representing urgent steering and a second target deviation value representing non-urgent steering by taking the lane line of the curve as a reference.

In a specific implementation, a user usually drives a vehicle within the capability range of the user, the emergency steering is less in the case of emergency steering, and the control degree of the vehicle is reduced during the emergency steering, so that the emergency steering can be reflected in the relationship between the vehicle and a lane line.

In one example, a target lane line is determined, which is in the opposite direction of the direction the vehicle is turning, i.e., if the vehicle is turning left, the target lane line is the lane line on the right side of the vehicle, and if the vehicle is turning right, the target lane line is the lane line on the left side of the vehicle.

The distance between the vehicle and the target lane line is measured by using a geometrical relation in the video data, the distance is a series of data, and the difference between every two adjacent distances can be calculated, so that the change trend of the distance can be determined.

And if the variation trend of the distance is monotonously reduced, determining the deviation value as a first target deviation value representing urgent steering.

And if the variation trend of the distance is oscillation, namely the distance is maintained within a preset safety range, determining the deviation value as a second target deviation value representing non-emergency steering.

Of course, the above-mentioned manner of dividing the first target deviation value and the second target deviation value is only an example, and other manners of dividing the first target deviation value and the second target deviation value may be set according to actual situations when implementing the embodiment of the present invention, for example, the n deviation values with the highest kurtosis value, deviation value, etc. are set as the first target deviation value, and other deviation values are set as the second target deviation value, and so on, which is not limited in the embodiment of the present invention. In addition, besides the above-mentioned manner of dividing the first target deviation value and the second target deviation value, those skilled in the art may also adopt other manners of dividing the first target deviation value and the second target deviation value according to actual needs, which is not limited in the embodiment of the present invention.

And step 203, updating the event recognition module matched with the type of the curve by taking the first target deviation value and the second target deviation value as classified samples, and obtaining a target event recognition model.

In a specific implementation, deviation values occurring in different types of curves are collected at a server, and emergency acceleration and non-emergency acceleration are marked, so that the deviation values are used as classified samples, and an event recognition model which is universal in the type of curves is trained, namely the event recognition model can be used for recognizing deviation values of emergency steering and deviation values of non-emergency steering.

The event recognition model is a two-class model, which may be a mechanical learning model, such as an SVM (support vector Machine), a Logistic (regression model), etc., or a neural network, and this embodiment is not limited thereto.

Upon completion of training, the server may distribute the event recognition model to the vehicle.

In this embodiment, based on the initial event recognition model, the event recognition model may be continuously trained according to the driving styles of different users, that is, the event recognition model is trained by using a previously collected partial deviation value as a sample to obtain a target event recognition model, and the target event recognition model is stored in the vehicle as the event recognition model, and the continuous training is waited for based on this, so the event recognition model matched with the type of the curve may be the initial general event recognition model or the continuously trained event recognition model, which is not limited in this embodiment.

And step 204, inputting the partial deviation value into a target event recognition model for classification so as to recognize operation representing emergency steering.

In this embodiment, for the same driving operation triggered by the same user, the partial deviation values collected later may be input into the target event recognition model, so as to classify the deviation values to recognize an operation representing urgent steering and an operation representing non-urgent steering.

For identifying the identity of the user, the identity of the user may be identified through information (such as a user account) that the user directly logs in the vehicle or logs in the associated device when the vehicle is started, or the identity of the user may be determined by acquiring image data facing a driving seat by calling a camera in the vehicle and performing face recognition on the image data, and the like, which is not limited in this embodiment.

After confirming the identity of the user, the driving maneuver triggered by the user between the start and the shut down of the vehicle may be considered the same driving maneuver triggered by the same user.

In addition, if the identity of the user is not recognized, the driving operation of the side door of the driving position between two opening and closing operations can be regarded as the same driving operation triggered by the same user.

And step 205, performing lateral control on the steering of the vehicle in the curve according to the emergency steering operation.

If the user is detected to trigger the emergency steering operation, the vehicle can be controlled transversely by taking the lane line as a reference and referring to the situation of the lane line, namely, the transverse movement is controlled.

In the concrete implementation, on one hand, the braking force of the steering is increased, namely the steering angle is increased, the deviation value of the steering is increased, the vehicle is prevented from deviating from the current lane and entering the adjacent lane, and on the other hand, the speed (namely the vehicle speed) can be reduced, the control degree of the user on the vehicle is increased, and the distance between the vehicle and the lane line is kept within the preset safety range.

Example two

Fig. 4 is a flowchart of a lateral control method based on steering according to a second embodiment of the present invention, where the present embodiment further refines and refines operations of searching for an event recognition model, training a target event recognition model, and recognizing emergency steering based on the foregoing embodiment, and the method specifically includes the following steps:

step 401, when it is detected that the vehicle turns around a curve, calculating the type and deviation value of the curve.

Wherein the deviation value indicates a degree to which the vehicle deviates from the standard direction.

And 402, dividing the partial deviation value into a first target deviation value representing urgent steering and a second target deviation value representing non-urgent steering by taking the lane line of the curve as a reference.

Step 403, finding an event recognition model trained for the type of the curve.

In the present embodiment, the event recognition model trained for the type of the current curve, which is distributed by the current vehicle local extraction server, is associated with a standard deviation value representing a characteristic of a deviation value (i.e., a second target deviation value) used for training the event recognition model to identify non-urgent steering.

Step 404, calculating a correlation between the second target deviation value and the standard deviation value.

After the event recognition model is determined, the second target deviation value may be compared with the standard deviation value of the event recognition model, and the correlation between the two may be calculated, thereby measuring the closeness between the two.

The standard deviation value has two forms, one of which is a data point representing the average value of the samples (second target deviation values) of the previously trained event recognition model, and the other of which is a data range representing the magnitude of the samples (second target deviation values) of the previously trained event recognition model (i.e., the range of the same-position data point between the maximum value and the minimum value).

If the standard deviation value is a data point, the similarity between the second target deviation value and the standard deviation value can be calculated as a correlation through algorithms such as EDR, LCSS, DTW, and the like.

And if the standard deviation value is the data range, determining data points falling into the data range in the second target deviation value as target points, and counting the proportion of the target points in the second target deviation value as correlation.

Of course, the above-mentioned manner for calculating the correlation is only an example, and when implementing the embodiment of the present invention, other manners for calculating the correlation may be set according to actual situations, which is not limited in this embodiment of the present invention. In addition, besides the above-mentioned way of calculating the correlation, a person skilled in the art may also adopt other ways of calculating the correlation according to actual needs, and the embodiment of the present invention is not limited to this.

Step 405, select a raw event recognition model from the event recognition models based on the correlation.

In this embodiment, the operation of non-urgent steering belongs to a relatively stable operation, and may represent the driving style of the user, that is, the second target deviation value for identifying non-urgent steering may represent the driving style of the user, so that an event recognition model suitable for processing the second target deviation value (that is, matching with the driving style of the user) may be searched in the event recognition model trained for the business scenario as the original event recognition model.

In general, the higher the correlation between the second target deviation value and the standard deviation value of the event recognition model, the higher the degree of adaptation between the event recognition model and the driving style of the current user, whereas the lower the correlation between the second target deviation value and the standard deviation value of the event recognition model, the lower the degree of adaptation between the event recognition model and the driving style of the current user, therefore, in this embodiment, the correlation between different second target deviation values and the standard deviation values of the event recognition model may be referred to select suitable candidate event recognition models as the original event recognition models.

In one approach, an average of the correlations may be calculated and compared to a preset correlation threshold.

If the average value of the correlation is greater than or equal to a preset correlation threshold value, calculating a discrete value of the correlation, wherein the discrete value represents the discrete degree of the correlation, such as variance, standard deviation and the like.

And selecting the event identification model with the minimum discrete value as the original event identification model, thereby keeping the stable performance of the original event identification model and improving the robustness of the original event identification model.

And if the average value of the correlation is smaller than a preset correlation threshold value, selecting the event identification model with the minimum average value of the correlation as the original event identification model, namely selecting the original event identification model closest to the sample, and ensuring the accuracy of the original event identification model.

Of course, the above-mentioned manner of selecting the original event recognition model is only used as an example, and when the embodiment of the present invention is implemented, other manners of selecting the original event recognition model may be set according to actual situations, for example, a sum of all correlations is calculated to be used as a total correlation, an event recognition model with a highest total correlation is selected to be used as the original event recognition model, and the like, which is not limited in this embodiment of the present invention. In addition, besides the above-mentioned manner of selecting the original event recognition model, a person skilled in the art may also adopt other manners of selecting the original event recognition model according to actual needs, and the embodiment of the present invention is not limited to this.

And step 406, updating the original event recognition module by taking the first target deviation value and the second target deviation value as classified samples, so as to obtain a target event recognition model.

In this embodiment, for the first target deviation value, an urgent steering operation may be identified, for the second target deviation value, a non-urgent steering operation may be identified, and the original event recognition model continues to be trained with the first target deviation value and the second target deviation value as classified samples, so as to obtain the target event recognition model, thereby further improving the degree of adaptation between the target event recognition model and the driving style of the user.

It should be noted that the original event recognition model can ensure a certain accuracy, so on one hand, before the training of the target event recognition model is completed, the operation of recognizing the emergency steering from the deviation value by the original event recognition model can be called under the same type of curve, and when the training of the target event recognition model is completed, the operation of recognizing the emergency acceleration and deceleration from the deviation value by the target event recognition model is switched from the original event recognition model, so that under the same type of curve, the operation of recognizing the emergency acceleration and deceleration from the deviation value by the target event recognition model is called, on the other hand, the training of the target event recognition model is considered to be completed when the number of iterations is used as a condition for stopping the training, that is, when the iteration training reaches the preset number, so as to.

In one embodiment of the present invention, step 406 may include the steps of:

step 4061, obtain an offset value identifying the emergency steering as a new first target offset value.

In this embodiment, the difference between the first target deviation value and the second target deviation value may be relatively small, and in order to prevent overfitting during training, some operation indicating typical urgent steering, that is, deviation values indicating urgent steering, may be set in advance for event recognition models corresponding to different types of curves and distributed to the respective vehicles.

After the event identification model is determined, the deviation value for emergency steering may then be extracted locally from the current vehicle as a new first target deviation value, combined with the original first target deviation value.

Step 4062, extract a first sample feature from all the first target bias values.

In this embodiment, for each first target deviation value (including the original first target deviation value and the new first target deviation value), the features of the dimension such as the degree of association, the waveform, the statistics, etc. may be extracted therefrom as the first sample feature, and the urgency is marked as the Tag (Tag).

In one example, the first sample feature includes at least one of a first sample residual, a first sample statistical feature, a second sample statistical feature, and a second sample residual, and in this example, a standard deviation value associated with the original event recognition model may be found, and a difference value at the same position between the first target deviation value and the standard deviation value may be calculated as the first sample residual.

It should be noted that if the standard deviation value is a data point, the difference value at the same position as the first target deviation value can be directly calculated, and if the standard deviation value is a data range, the median of the data range is calculated, so that the difference value at the same position as the first target deviation value is calculated.

And calculating data such as an average value, a maximum value, a minimum value, a variance, a deviation value, a kurtosis value and the like of the first residual as statistical characteristics of the first sample.

And calculating data such as an average value, a maximum value, a minimum value, a variance, a deviation value, a kurtosis value and the like of the first target deviation value as statistical characteristics of the second sample.

And calculating the difference value of the second sample statistical characteristic and the standard statistical characteristic (such as data of average value, maximum value, minimum value, variance, deviation value, kurtosis value and the like) of the standard deviation value at the same position to be used as the second sample residual error.

Of course, the first sample feature is only used as an example, and when the embodiment of the present invention is implemented, other first sample features may be set according to practical situations, and the embodiment of the present invention is not limited to this. In addition, besides the first sample feature, other first sample features may also be adopted by those skilled in the art according to actual needs, and the embodiment of the present invention is not limited to this.

Step 4063, extracting a second sample feature from the second target bias value.

In this embodiment, for each second target deviation value, dimensional features such as correlation, waveform, statistics, and the like may be extracted therefrom as the first sample feature, and urgency may be marked as a Tag (Tag).

In one example, the second sample characteristic includes at least one of a third sample residual, a third sample statistical characteristic, a fourth sample statistical characteristic, and a fourth sample residual, and in this example, a standard deviation value associated with the original event recognition model may be found, and a difference value between the second target deviation value and the standard deviation value at the same position may be calculated as the third sample residual.

It should be noted that if the standard deviation value is a data point, the difference value at the same position as the second target deviation value can be directly calculated, and if the standard deviation value is a data range, the median of the data range is calculated, so that the difference value at the same position as the first target deviation value is calculated.

And calculating data such as an average value, a maximum value, a minimum value, a variance, a deviation value and a kurtosis value of the second residual as statistical characteristics of a third sample.

And calculating data such as an average value, a maximum value, a minimum value, a variance, a deviation value, a kurtosis value and the like of the second target deviation value as statistical characteristics of a fourth sample.

And calculating the difference value of the second sample statistical characteristic and the standard statistical characteristic (such as data of average value, maximum value, minimum value, variance, deviation value, kurtosis value and the like) of the standard deviation value at the same position to serve as a fourth sample residual error.

Of course, the second sample characteristics are only examples, and when implementing the embodiment of the present invention, other second sample characteristics may be set according to practical situations, and the embodiment of the present invention is not limited to this. In addition, in addition to the second sample characteristics, those skilled in the art may also adopt other second sample characteristics according to actual needs, and the embodiment of the present invention is not limited thereto.

Step 4064, taking the first sample feature and the second sample feature as samples, and taking emergency steering and non-emergency steering as classification targets, and performing transfer learning on the original event recognition model to obtain a target event recognition model.

In this embodiment, the first sample feature and the second sample feature may be used as classified samples, urgent steering and non-urgent steering are used as classified targets, and the first sample feature and the second sample feature are used as samples to perform transfer learning on the original event recognition model to obtain a target event recognition model.

The transfer learning refers to transferring the parameters of the trained original event recognition model to a new target event recognition model to help the training of the target event recognition model, and considering that most data or tasks have correlation, the learned parameters can be shared with the new target event recognition model through the transfer learning in a certain mode, so that the learning efficiency of the target event recognition model is accelerated and optimized, and the instantaneity is ensured.

In a specific implementation, the migration learning of the original event recognition model can be performed by applying one of the following manners:

(1) transfer Learning: all convolutional layers of the pre-trained model (original event recognition model) are frozen, and only the custom fully-connected layer is trained.

(2) Extract Feature Vector: calculating the feature vectors (first sample feature and second sample feature) of the convolution layer of the pre-training model (original event recognition model) to all training and testing data, then discarding the pre-training model (original event recognition model), and only training the customized simple configuration version full-connection network.

(3) Fine-tune: freezing part of the convolutional layers (usually most convolutional layers near the input) of the pre-trained model (original event recognition model), training the remaining convolutional layers (usually part of the convolutional layers near the output) and fully-connected layers.

In the process of transfer learning, the classification predicted for the sample (urgent, non-urgent) and the actual classification (urgent, non-urgent) can be compared, so as to calculate the loss value in each iteration training, and the parameters in the original event recognition model can be updated based on the loss value, by using gradient descent, random gradient descent and the like.

In addition, when the training of the target event recognition model is completed, the standard deviation value is generated based on the second target deviation value, so that the association relationship between the target event recognition model and the second target deviation value is established and stored locally in the current vehicle.

In one example, the average of the data points at the same position in the second target offset value may be calculated as the data point of the standard deviation value.

In another example, the magnitude of the data point at the same position in the second target deviation value (i.e., the range between the maximum value and the minimum value) may be counted as the data range of the standard deviation value.

Of course, the above manner of calculating the standard deviation value is only an example, and when implementing the embodiment of the present invention, other manners of calculating the standard deviation value may be set according to actual situations, which is not limited in the embodiment of the present invention. In addition, besides the above-mentioned manner for calculating the standard deviation value, a person skilled in the art may also adopt other manners for calculating the standard deviation value according to actual needs, and the embodiment of the present invention is not limited to this.

And step 407, extracting target features from the partial deviation values.

In this embodiment, deviation values may be collected in curves of the same type, and dimensional features such as relevance, waveform, statistics, and the like may be extracted therefrom as target features.

In one example, the target feature includes at least one of a first target residual, a first target statistical feature, a second target statistical feature, and a second target residual, and in this example, a standard deviation value associated with the target event identification model may be found, and a difference between the deviation value and the standard deviation value may be calculated as the first target residual.

Calculating data such as an average value, a maximum value, a minimum value, a variance, a deviation value and a kurtosis value of the first target residual error to be used as first target statistical characteristics;

calculating data such as an average value, a maximum value, a minimum value, a variance, a deviation value and a kurtosis value of the deviation value as second target statistical characteristics;

and calculating a difference value between the second target statistical characteristic and a standard statistical characteristic (such as data of an average value, a maximum value, a minimum value, a variance, a deviation value, a kurtosis value and the like) of the standard deviation value to serve as a second target residual error.

Of course, the above target features are only examples, and when implementing the embodiment of the present invention, other target features may be set according to practical situations, and the embodiment of the present invention is not limited thereto. In addition, besides the above target features, other target features may be adopted by those skilled in the art according to actual needs, and the embodiment of the present invention is not limited thereto.

Step 408, performing convolution processing on the target feature in the convolutional neural network of the target event recognition model to output a candidate feature.

And 409, calculating residual error characteristics of the candidate characteristics in a residual error network of the target event identification model.

And step 410, performing feature mapping on the residual error features in the long-term and short-term memory network of the target event recognition model to output the category of the deviation value.

And 411, if the type is the emergency steering, determining that the deviation value represents the operation of the emergency steering.

In order to ensure real-time performance, the structure of an event recognition model (including a current target event recognition model) is designed to be simpler, and the target event recognition model belongs to a model under a specified curve type, so that the oriented scenes are more concentrated, and the simple structure can still keep higher accuracy.

In this embodiment, as shown in fig. 5, the event recognition model has three layers, which are:

1. convolutional Neural Network (CNN) 510

CNNs are a class of feed forward Neural Networks (fed Neural Networks) that contain convolution calculations and have a deep structure, and are one of the algorithms for deep learning (deep learning). CNNs have a feature learning (representation learning) capability, and can perform Shift-Invariant classification (Shift-Invariant classification) on input information according to their hierarchical structure, and are therefore also called "Shift-Invariant Artificial Neural Networks (SIANN)".

The CNN is constructed by imitating a visual perception (visual perception) mechanism of a living being, can perform supervised learning and unsupervised learning, and has the advantages that the parameter sharing of convolution kernels in an implicit layer and the sparsity of connection among layers enable a convolution neural network to be capable of carrying out grid-like topology (grid-like topology) characteristics with small calculation amount.

2. Residual network 520

Generally, each layer of the network corresponds to extracting feature information of different layers, including a low layer, a middle layer and a high layer, and when the network is deeper, the extracted information of different layers is more, and the combination of layer information among different layers is more, so that the "grade" of the feature is higher as the depth of the network is increased, and the depth of the network is an important factor for realizing good effect, however, gradient dispersion/explosion becomes an obstacle for training the network of the deep layer, and convergence cannot be realized.

The method has the advantages that the residual error network is introduced into the event recognition model, when the input signal is transmitted in the forward direction, the input signal can be directly transmitted to the high layer from any low layer, the network degradation problem can be solved to a certain extent due to the fact that the input signal comprises an identity mapping, the error signal can be directly transmitted to the low layer without any intermediate weight matrix transformation, the gradient dispersion problem can be relieved to a certain extent, forward and backward information transmission is smooth and visible, the problems of gradient extinction and gradient explosion in the training process of the event recognition model can be effectively solved, the number of layers of the network does not need to be increased, and accurate training results can be obtained.

3. Long Short-Term Memory network (LSTM) 530

The LSTM is a time-cycle neural network, and is designed to solve the long-term dependence problem of the general RNN (cyclic neural network).

LSTM is a neural network of the type that contains LSTM blocks (blocks) or other types of blocks, which may be described as intelligent network elements, because it can remember values of varying lengths of time, with a gate in a block that can determine whether an input is important enough to be remembered and cannot be output.

The LSTM has four S function units, the leftmost function may be input of a block as the case may be, the right three will determine whether the input can be transferred into the block through the gate, the second on the left is input gate, if the output is close to zero, the value will be blocked and will not go to the next layer. The third on the left is the forget gate, which will forget the memorized value in the block when this yields a value close to zero. The fourth, rightmost input is output gate, which determines whether the input in the block memory can be output.

In this embodiment, in the target event recognition model, the target feature is input into CNN, CNN performs convolution processing on the target feature, the candidate feature is output to the residual error network, the residual error network calculates the residual error feature for the candidate feature and outputs the residual error feature to LSTM, and LSTM performs feature mapping on the residual error feature and outputs the category of the deviation value.

If the category of the output deviation value is non-urgent steering, it is determined that the deviation value represents an operation of the non-urgent steering.

If the category of the output offset value is urgent steering, it is determined that the offset value represents an urgent acceleration and deceleration operation.

By applying the embodiment of the invention, the event recognition model can be used as a node, the training dependency relationship is used as a directional side, the tree structure is generated, and the iterative training is continuously carried out along with the accumulation of the deviation value of the driving of the user, so that the event recognition model with high adaptation degree to the driving style of the user is generated, and the operation of personalized and high-precision recognition acceleration and deceleration is realized.

The tree structure comprises a Root node Root and leaf nodes, a path between the Root node Root and the leaf nodes is traversed to serve as a model link, the model link represents the direction of iterative training, reasonable iterative training can be screened out through judging the effectiveness of the iterative training, and a final event recognition model is generated according to the iterative training, namely, a plurality of event recognition models are arranged in the model link, a parent-child relationship exists among the event recognition models, the event recognition model serving as a child node depends on the event recognition model training serving as a parent node, namely, the event recognition model serving as the parent node is an original event recognition model, and the event recognition model serving as the child node is a target event recognition model.

The Root node Root is a general event recognition model trained by the server, the molecular nodes are started along the Root node Root, and the leaf nodes are obtained when the Root node Root is continuously subdivided until no child nodes exist.

It should be noted that one event identification model may have a plurality of parent-child relationships, in a certain parent-child relationship, a certain event identification model may serve as a child node, and in other parent-child relationships, the event identification model may serve as a parent node, which is not limited in this embodiment.

For example, for the tree structure shown in fig. 6, the following model links may be divided:

1、Root→A1→A2→A3→A4→A5→A6

2、Root→A1→A2→A3→A4→A41

3、Root→B1→B2→B3→B4

4、Root→B1→B2→B21

5、Root→B1→B2→B3→B31

6、Root→C1→C2→C3

7、Root→C1→C21→C22

for model 1 link, for parent-child relationships between a1, a2, a1 is the parent node, a2 is the child node, for parent-child relationships between a2, A3, a2 is the parent node, A3 is the child node, and so on.

It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.

EXAMPLE III

Fig. 7 is a block diagram of a structure of a lateral control device based on steering according to a third embodiment of the present invention, which may specifically include the following modules:

an offset value calculation module 701, configured to calculate a type and an offset value of a curve when it is detected that a vehicle turns around the curve, where the offset value represents a degree of deviation of the vehicle from a standard direction;

an offset value dividing module 702, configured to divide a part of the offset values into a first target offset value representing urgent steering and a second target offset value representing non-urgent steering with a lane line of the curve as a reference;

a target event recognition model training module 703, configured to update the event recognition module matched with the type of the curve by using the first target deviation value and the second target deviation value as classified samples, so as to obtain a target event recognition model;

an offset value classification module 704 for inputting a portion of the offset values into the target event identification model for classification to identify an operation indicative of an urgent steering;

a lateral control module 705 for controlling the steering of the vehicle in the curve in a lateral direction according to the emergency steering operation.

In an embodiment of the present invention, the deviation value calculating module 701 includes:

the lane line detection submodule is used for detecting a lane line of a lane where a vehicle is located and calculating the curvature of the lane line;

the curve steering determining submodule is used for determining that the lane is a curve and the vehicle steers at the curve if the curvature is larger than a preset curvature threshold;

the curvature range searching submodule is used for searching a curvature range where the curvature is located in a plurality of preset curvature ranges to serve as a target range, and each curvature range is associated with a type;

a curve type setting submodule for setting a type associated with the target range as a type of the curve;

the angle acquisition submodule is used for acquiring the angle of the vehicle on the curve;

and the angle difference value calculation submodule is used for calculating the difference value between the angle and the standard direction as a deviation value by taking the specified direction as the standard direction.

In an embodiment of the present invention, the deviation value dividing module 702 includes:

the target lane line determining submodule is used for determining a target lane line, and the direction of the target lane line is opposite to the steering direction of the vehicle;

the distance measuring submodule is used for measuring the distance between the vehicle and the target lane line;

a first target deviation value determination submodule for determining the deviation value as a first target deviation value representing an urgent steering if the distance monotonically decreases;

and the second target deviation value determining submodule is used for determining the deviation value as a second target deviation value representing non-emergency steering if the distance is within a preset safety range.

In an embodiment of the present invention, the target event recognition model training module 703 includes:

an event recognition model searching submodule for searching an event recognition model trained for the type of the curve, the event recognition model being associated with a standard deviation value;

a correlation calculation sub-module for calculating a correlation between the second target deviation value and the standard deviation value;

a primitive event recognition model selection sub-module for selecting a primitive event recognition model from the event recognition models based on the correlation;

and the original event recognition module updating submodule is used for updating the original event recognition module by taking the first target deviation value and the second target deviation value as classified samples to obtain a target event recognition model.

In one embodiment of the present invention, the correlation calculation sub-module includes:

a similarity calculation unit configured to calculate a similarity between the second target deviation value and the standard deviation value as a correlation if the standard deviation value is a data point;

alternatively, the first and second electrodes may be,

a target point determining unit, configured to determine, as a target point, a data point falling within the data range in the second target deviation value if the standard deviation value is the data range;

and the proportion counting unit is used for counting the proportion of the target point in the second target deviation value as correlation.

In one embodiment of the present invention, the primitive event recognition model selection sub-module includes:

a correlation average value calculation unit for calculating an average value of the correlation;

the discrete value calculating unit is used for calculating the discrete value of the correlation if the average value of the correlation is greater than or equal to a preset correlation threshold value;

a discrete value selection unit, configured to select the event identification model with the smallest discrete value as an original event identification model;

and the correlation selection unit is used for selecting the event identification model with the minimum average value of the correlation as the original event identification model if the average value of the correlation is smaller than a preset correlation threshold value.

In one embodiment of the present invention, the primitive event identifying module updating sub-module includes:

a new deviation value acquisition unit for acquiring a deviation value identifying an urgent steering as a new first target deviation value;

a first sample feature extraction unit configured to extract a first sample feature from the first target bias value;

a second sample feature extraction unit configured to extract a second sample feature from the second target deviation value;

and the model transfer learning unit is used for performing transfer learning on the original event identification model by taking the first sample characteristic and the second sample characteristic as samples and taking the emergency steering and the non-emergency steering as classified targets to obtain a target event identification model.

In an example of the embodiment of the present invention, the first sample feature includes at least one of a first sample residual, a first sample statistical feature, a second sample statistical feature, and a second sample residual, and the first sample feature extraction unit is further configured to:

searching a standard deviation value associated with the original event recognition model;

calculating a difference between the first target deviation value and the standard deviation value as a first sample residual;

calculating a first sample statistical characteristic for the first residual;

calculating a second sample statistical characteristic for the first target deviation value;

and calculating a difference value between the second sample statistical characteristic and the standard statistical characteristic of the standard deviation value to serve as a second sample residual error.

In an example of the embodiment of the present invention, the second sample feature includes at least one of a third sample residual, a third sample statistical feature, a fourth sample statistical feature, and a fourth sample residual, and the second sample feature extraction unit is further configured to:

calculating a difference between the second target deviation value and the standard deviation value as a third sample residual;

calculating a third sample statistical feature for the second residual;

calculating a fourth sample statistical characteristic for the second target deviation value;

and calculating a difference value between the second sample statistical characteristic and the standard statistical characteristic of the standard deviation value to serve as a fourth sample residual.

In an embodiment of the present invention, the target event recognition model training module 703 further includes:

the standard deviation value generation sub-module is used for generating a standard deviation value based on the second target deviation value when the training of the target event recognition model is finished;

and the incidence relation establishing submodule is used for establishing the incidence relation between the target event recognition model and the second target deviation value.

In one embodiment of the present invention, the standard deviation value generation submodule includes:

the data point setting unit is used for calculating the average value of the data points at the same position in the second target deviation value as the data point of the standard deviation value;

alternatively, the first and second electrodes may be,

and the data range setting unit is used for counting the amplitude of the data points at the same position in the second target deviation value to be used as the data range of the standard deviation value.

In one embodiment of the present invention, the deviation value classification module 704 includes:

the target feature extraction submodule is used for extracting target features from part of the deviation values;

a candidate feature output sub-module, configured to perform convolution processing on the target feature in a convolutional neural network of the target event recognition model to output a candidate feature;

a residual error feature calculation sub-module, configured to calculate a residual error feature for the candidate feature in a residual error network of the target event recognition model;

the category output submodule is used for performing feature mapping on the residual error features in a long-term and short-term memory network of the target event recognition model so as to output the category of the deviation value;

and the emergency acceleration and deceleration operation determining submodule is used for determining that the deviation value represents the operation of emergency steering if the type is emergency steering.

In an example of the embodiment of the present invention, the target feature includes at least one of a first target residual, a first target statistical feature, a second target statistical feature, and a second target residual, and the target feature extraction sub-module is further configured to:

searching a standard deviation value associated with the target event recognition model;

calculating a difference between a portion of the deviation values and the standard deviation values as a first target residual;

calculating a first target statistical characteristic for the first target residual;

calculating a second target statistical characteristic for a portion of the deviation values;

and calculating a difference value between the second target statistical characteristic and the standard statistical characteristic of the standard deviation value to serve as a second target residual error.

In one embodiment of the present invention, the lateral control module 705 comprises:

and the range holding submodule is used for increasing the braking force of steering and reducing the speed so as to keep the distance between the vehicle and the lane line within a preset safety range.

The steering-based transverse control device provided by the embodiment of the invention can execute the steering-based transverse control method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

Example four

Fig. 8 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 8 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in fig. 8 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present invention.

As shown in FIG. 8, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 8, and commonly referred to as a "hard drive"). Although not shown in FIG. 8, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.

Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, computer device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing, such as implementing a steering-based lateral control method provided by an embodiment of the present invention, by running a program stored in the system memory 28.

EXAMPLE five

An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the steering-based lateral control method, and can achieve the same technical effect, and in order to avoid repetition, the detailed description is omitted here.

A computer readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A steering-based lateral control method, comprising:

2. The method of claim 1, wherein calculating the type of curve and the bias value when the vehicle is detected turning around the curve comprises:

detecting a lane line of a lane where a vehicle is located, and calculating the curvature of the lane line;

if the curvature is larger than a preset curvature threshold value, determining that the lane is a curve and the vehicle turns at the curve;

searching a curvature range in which the curvature is located in a plurality of preset curvature ranges as a target range, wherein each curvature range is associated with a type;

setting the type of the target range association as the type of the curve;

collecting an angle of the vehicle on the curve;

and calculating the difference value between the angle and the standard direction by taking the specified direction as the standard direction to serve as a deviation value.

3. The method according to claim 1, wherein dividing a portion of the deviation values into a first target deviation value representing urgent steering and a second target deviation value representing non-urgent steering with a lane line of the curve as a reference comprises:

determining a target lane line, the direction of the target lane line being opposite to the direction in which the vehicle is turning;

measuring a distance between the vehicle and the target lane line;

if the distance monotonically decreases, determining the deviation value as a first target deviation value representing emergency steering;

and if the distance is within a preset safety range, determining the deviation value as a second target deviation value representing non-emergency steering.

4. The method of claim 1, wherein updating the event recognition module matching the type of the curve with the first target deviation value and the second target deviation value as classified samples to obtain a target event recognition model comprises:

searching an event recognition model trained for the type of the curve, wherein the event recognition model is associated with a standard deviation value;

calculating a correlation between the second target deviation value and the standard deviation value;

selecting an original event recognition model from the event recognition models based on the correlation;

and updating the original event recognition module by taking the first target deviation value and the second target deviation value as classified samples to obtain a target event recognition model.

5. The method of claim 4, wherein calculating the correlation between the second target deviation value and the standard deviation value comprises:

if the standard deviation value is a data point, calculating the similarity between the second target deviation value and the standard deviation value as correlation;

alternatively, the first and second electrodes may be,

if the standard deviation value is a data range, determining a data point falling into the data range in the second target deviation value as a target point;

and counting the proportion of the target point in the second target deviation value as correlation.

6. The method of claim 4, wherein selecting an original event recognition model from the event recognition models based on the correlation comprises:

calculating an average of the correlations;

if the average value of the correlation is larger than or equal to a preset correlation threshold value, calculating a discrete value of the correlation;

selecting the event identification model with the minimum discrete value as an original event identification model;

and if the average value of the correlation is smaller than a preset correlation threshold value, selecting the event identification model with the minimum average value of the correlation as the original event identification model.

7. The method of claim 4, wherein updating the primitive event recognition module with the first target bias value and the second target bias value as classified samples to obtain a target event recognition model comprises:

acquiring a deviation value for marking emergency steering as a new first target deviation value;

extracting a first sample feature from all the first target deviation values;

extracting a second sample feature from the second target deviation value;

and taking the first sample characteristic and the second sample characteristic as samples, and taking the emergency steering and the non-emergency steering as classified targets, and performing transfer learning on the original event recognition model to obtain a target event recognition model.

8. The method of claim 7,

the extracting the first sample feature from the first target bias value includes:

calculating a first sample statistical characteristic for the first residual;

calculating a difference value between the second sample statistical characteristic and a standard statistical characteristic of the standard deviation value as a second sample residual error;

the second sample feature comprises at least one of a third sample residual, a third sample statistical feature, a fourth sample statistical feature, and a fourth sample residual, and the extracting the second sample feature from the second target deviation value comprises:

calculating a third sample statistical feature for the second residual;

9. The method of claim 4, wherein the updating the event recognition module matching the type of the curve with the first target deviation value and the second target deviation value as classified samples to obtain a target event recognition model, further comprises:

when the training of the target event recognition model is finished, generating a standard deviation value based on the second target deviation value;

and establishing an incidence relation between the target event recognition model and the second target deviation value.

10. The method of any of claims 1-9, wherein said inputting a portion of said bias values into said target event identification model for classification to identify an action indicative of an imminent turn comprises:

extracting target features from part of the deviation values;

performing convolution processing on the target feature in a convolution neural network of the target event recognition model to output a candidate feature;

calculating residual features for the candidate features in a residual network of the target event recognition model;

performing feature mapping on the residual error features in a long-term and short-term memory network of the target event recognition model to output the category of the deviation value;

and if the type is emergency steering, determining that the deviation value represents the operation of emergency steering.

11. The method according to any one of claims 1-9, wherein said steering of said vehicle in said curve in accordance with said emergency steering operation is controlled laterally, comprising:

and increasing the braking force of the steering and reducing the speed so as to keep the distance between the vehicle and the lane line within a preset safety range.

12. A steering-based lateral control apparatus, comprising:

13. A computer device, characterized in that the computer device comprises:

one or more processors;

a memory for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the steering-based lateral control method of any of claims 1-11.

14. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steering-based lateral control method according to any one of claims 1-11.