CN115583237A

CN115583237A - Method, system, device, medium, and program product for predicting vehicle congestion behavior

Info

Publication number: CN115583237A
Application number: CN202211144752.6A
Authority: CN
Inventors: 魏建; 程建磊; 赵锋; 盛辉
Original assignee: Aokenuo Shanghai Automotive Technology Co ltd
Current assignee: Aokenuo Shanghai Automotive Technology Co ltd
Priority date: 2022-09-20
Filing date: 2022-09-20
Publication date: 2023-01-10

Abstract

The disclosed embodiments provide a method, system, device, medium and program product for predicting a vehicle congestion behavior, the method comprising: acquiring plugging characteristic data of a vehicle in a preset range in real time through a vehicle-mounted detection device, wherein the plugging characteristic data comprises at least one of a relative transverse distance, a relative transverse speed, a relative transverse acceleration, a relative longitudinal distance, a relative longitudinal speed, a relative longitudinal acceleration and a course angle of the vehicle in the preset range between the vehicle and a workshop in the preset range; constructing an SVM model, and training the SVM model based on the selected jamming feature data training set and the jamming behavior judgment result corresponding to the training set; and inputting the real-time acquired jamming feature data into the trained SVM model, and predicting the jamming behavior of the vehicle within the preset range. Through the processing scheme disclosed by the invention, the safety and the comfort of driving are improved.

Description

Method, system, device, medium and program product for predicting vehicle congestion behavior

Technical Field

The present invention relates to the field of intelligent driving technologies, and in particular, to a method, system, device, medium, and program product for predicting driving congestion behavior.

Background

With the continuous and rapid development of national economy in China, the automobile holding amount shows a continuously increasing trend. In the process of continuous change and rapid development of cities, urban land for continuous widening is increasingly scarce, urban traffic jam conditions are increasingly severe, and the number of traffic accidents caused by illegal traffic jams of vehicles during the rush hours in the morning and evening is also increasingly large. Therefore, how to better suppress the high incidence of road traffic accidents and reduce the loss caused by traffic accidents is an important problem to be solved urgently.

In the prior art, congestion is not quantified, but research is focused on vehicle lane change, and lane change affects the following condition of vehicle Adaptive Cruise Control (ACC) but does not affect the safety of the vehicle.

Unlike lane change behavior, the stoppages behavior will introduce great uncertainty and insecurity. When a vehicle is subjected to a jamming behavior, an automatic emergency braking system (AEB) is caused to suddenly brake emergently, and the driving safety and comfort are influenced.

Accordingly, there is a need for a method and system that can efficiently predict the behavior of a vehicle jam.

Disclosure of Invention

In view of the above, embodiments of the present disclosure provide a method, system, device, medium, and program product for predicting vehicle congestion behavior, which at least partially solve the problems in the prior art.

In a first aspect, an embodiment of the present disclosure provides a method for predicting a vehicle jam behavior, including the following steps:

acquiring the jamming feature data of the vehicle in a preset range in real time through a vehicle-mounted detection device, wherein the jamming feature data comprise at least one of the relative transverse distance, the relative transverse speed, the relative transverse acceleration, the relative longitudinal distance, the relative longitudinal speed, the relative longitudinal acceleration and the course angle of the vehicle in the preset range between the vehicle and the workshop in the preset range;

constructing an SVM model, and training the SVM model based on the selected jamming characteristic data training set and the jamming behavior judgment result corresponding to the training set; and

inputting the real-time collected jamming feature data into the trained SVM model, and predicting the jamming behavior of the vehicle within the preset range.

According to a specific implementation manner of the embodiment of the disclosure, the method further comprises the step of warning and prompting a braking measure when the vehicle congestion behavior in the preset range is predicted.

According to a specific implementation manner of the embodiment of the present disclosure, the method further includes performing at least one of feature screening, data cleaning, and labeling on the plugged feature data.

According to a specific implementation manner of the embodiment of the disclosure, the vehicle-mounted detection device comprises at least one of a radar and a camera.

According to a specific implementation manner of the embodiment of the disclosure, the jam feature data includes a relative lateral distance, a relative lateral speed, a relative lateral acceleration and a heading angle of the vehicle within the preset range.

In a second aspect, an embodiment of the present disclosure provides a system for predicting traffic congestion behavior, where the system includes:

the vehicle-mounted detection device comprises a data acquisition module, a data processing module and a control module, wherein the data acquisition module is configured for acquiring the jamming feature data of the vehicle in a preset range through the vehicle-mounted detection device, and the jamming feature data comprise the relative transverse distance, the relative transverse speed, the relative transverse acceleration, the relative longitudinal distance, the relative longitudinal speed, the relative longitudinal acceleration and the course angle of the vehicle in the preset range between the vehicle and the vehicle in the preset range;

the model processing module is configured for constructing an SVM model, and training the SVM model based on the selected jamming feature data training set and the jamming behavior judgment result corresponding to the training set to obtain a trained SVM model; and

and the prediction module is used for inputting the clogging characteristic data acquired in real time into the trained SVM model and predicting the clogging behavior of the vehicle in the preset range.

According to a specific implementation manner of the embodiment of the present disclosure, the system further includes:

the early warning module is configured to warn and prompt a braking measure when a vehicle jamming behavior within a preset range is predicted.

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

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of predicting congestion driving behavior of the first aspect or any implementation of the first aspect.

In a fourth aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method for predicting the traffic congestion behavior in the first aspect or any implementation manner of the first aspect.

In a fifth aspect, the disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the method for predicting the congestion behavior of a vehicle in the foregoing first aspect or any implementation manner of the first aspect.

According to the method and the system for predicting the traffic jam behavior in the embodiment of the disclosure, the traffic jam behavior is judged through real-time identification, so that preparation time is provided for safety braking measures, and meanwhile, the safety and the comfort of traffic are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required to be used in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic diagram of a method of predicting vehicle congestion behavior according to an embodiment of the present disclosure; and

fig. 2 is a schematic diagram of a system for predicting driving congestion behavior according to an embodiment of the present disclosure.

Detailed Description

The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. The disclosure may be carried into practice or applied to various other specific embodiments, and various modifications and changes may be made in the details within the description and the drawings without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without inventive step, are intended to be within the scope of the present disclosure.

It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.

It should be further noted that the drawings provided in the following embodiments are only schematic illustrations of the basic concepts of the present disclosure, and the drawings only show the components related to the present disclosure rather than the numbers, shapes and dimensions of the components in actual implementation, and the types, the numbers and the proportions of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

In addition, in the following description, specific details are provided to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.

The invention makes the following definition for the plugging behavior and the non-plugging behavior:

the blocking behavior is different from non-blocking behavior such as lane change.

Plugging behavior: 1. the running speed is lower and is below 60 km/h;2. the distance between the front vehicle and the rear vehicle is smaller; 3. in a scene of waiting for passage in a queue or slowly driving; 4. affecting the normal running of other vehicles.

Lane change behavior: 1. the running speed is 0-120 km/h in a full-speed section; 2. the distance between the front vehicle and the rear vehicle is larger; 3. not on congested roads; 4. the normal running of other vehicles is not influenced.

Fig. 1 shows a schematic diagram of a method of predicting a driving congestion behavior according to an embodiment of the present disclosure.

As shown in fig. 1, in step S110, the vehicle-mounted detection device collects the jamming feature data of the vehicle within the preset range in real time, and specifically, for example, in some aspects according to the embodiments of the present disclosure, the jamming feature data includes a relative lateral distance, a relative lateral speed, a relative lateral acceleration, a relative longitudinal distance, a relative longitudinal speed, a relative longitudinal acceleration, and a heading angle of the vehicle within the preset range between the vehicle and the vehicle in the preset range.

In the embodiment of the present invention, the vehicle-mounted detection device may include a radar and a camera, but the vehicle-mounted detection device is only an example and does not limit the present invention.

In the embodiment of the present invention, the accuracy of predicting the plugging behavior can be improved by performing feature screening, data cleaning and labeling on the plugging feature data, but the data processing method is only an example and does not limit the present invention.

More specifically, the lateral displacement and speed change more slowly, the heading angle change more significantly, and the plugging process duration is generally longer than the lane change, as compared to the lane change, and a gaming process exists. When only the dynamic jam process is considered, the motion change of the vehicle also affects the jam determination process, so the lateral relative displacement is the most important feature vector of the jam, and the lateral speed change is the next. In the game jamming process, the acceleration has an obvious positive process and a negative process, and the course angle and the three two-workshop transverse parameters influence each other, so that the four characteristic vectors which have the largest influence on jamming prediction are the relative transverse distance, the relative transverse speed, the relative transverse acceleration and the course angle of a jammed vehicle between the vehicle and the workshop in a preset range. Therefore, the preferred set of model training parameters may be the relative lateral distance between the vehicle and the vehicle, the relative lateral velocity, the relative lateral acceleration, and the heading angle of the jammed vehicle within a predetermined range.

It next goes to step S120.

In step S120, an SVM model is constructed, and the SVM model is trained based on the selected training set of the plugging feature data and the plugging behavior determination result corresponding to the training set.

As described above, the preferred training set of the jammed feature data may be the relative lateral distance between the vehicle and the vehicle, the relative lateral velocity, the relative lateral acceleration, and the heading angle of the jammed vehicle within a preset range.

In addition, more specifically, the reason for selecting the SVM model as the training model is that the SVM can solve the training problem of the small sample model, so that the establishment of the high-precision model can be realized under the condition of only a small batch of training data.

In the embodiment of the invention, the SVM model is a linear classifiable classifier, the classification problem of separating the hyperplane data classification is converted into a convex quadratic programming problem to be solved by maximizing the distance from the support vector to the separating hyperplane, namely, a hyperplane is found, and when the distance from the data to be classified to the hyperplane is longer, the SVM model classifies new data more accurately, namely, the classifier is more stable.

More specifically, the original data to be classified is (x) ₁ ,x ₂ ,y ₁ )、……、(x _m ,x _n ,y _m ) Wherein x is a feature of different dimensionality, and the value of y is 1 or-1 (binary classification).

(1) The target hyperplane is:

w ^T x + b =0 \ 8230 \ 8230;, equation 1;

the expansion is as follows:

w ₁ x ₁ +w ₂ x ₂ + b =0 \ 8230 \ 8230;, equation 2;

wherein w is a normal vector, determining the direction of the hyperplane, b is a displacement term, determining the distance between the hyperplane and the origin.

(2) The geometric distance between the data set and the target hyperplane in space is as follows:

wherein x _i And y _i The target values are respectively corresponding to the ith sample and the ith sample value.

(3) Since y takes the value of 1 or-1 (dichotomy), this ensures that if the sample classification is correct, this value is a positive number; this value is a negative number if the sample is classified incorrectly.

Namely the formula is as follows:

in order to improve fault tolerance, the following steps are introduced:

wherein the support vectors make the equal sign of the above formula hold, and the sum (interval) of the distances from the two heterogeneous support vectors to the hyperplane is:

(4) The hyperplane is divided for the maximum interval, and the corresponding w and b-y are solved, such as

Formula 7:

generally, an SVM model is applied to specific problems, and the model with higher accuracy is obtained by mainly carrying out early-stage data processing, feature screening and training on the influence weight of features on a result by using each feature respectively to obtain the classification accuracy, analyzing the weight of each feature influence, then carrying out certain adjustment and finally combining the weights together.

In the embodiment according to the disclosure, a high-precision prediction result of a small batch of training data is realized through an SVM model, so that a precise prediction result can be output even in the case of only a small number of parameters.

It next goes to step S130.

At step S130, the jamming feature data collected in real time is input into the trained SVM model to predict the jamming behavior of the vehicle within the preset range.

In the embodiment of the invention, when the vehicle jamming behavior in the preset range is predicted, the vehicle warns and prompts braking measures.

Based on the method provided by the embodiment of the invention, the accurate prediction of the vehicle jamming behavior in the preset range is realized. Compared with the prior art, the method based on the embodiment of the invention realizes automatic identification of the traffic jam behavior and the non-traffic jam behavior by selecting the key parameters to train the SVM model, thereby identifying and judging the traffic jam behavior in real time and providing preparation time for safety braking measures.

Fig. 2 illustrates a system 200 for predicting vehicle congestion behavior in accordance with an embodiment of the present disclosure. As shown in fig. 2, the system 200 includes a data collection module 210, a model processing module 220, a prediction module 230, and an early warning module 240.

The data acquisition module 210 acquires the jamming feature data of the vehicle within a preset range through the vehicle-mounted detection device, wherein the jamming feature data comprise the relative transverse distance, the relative transverse speed, the relative transverse acceleration, the relative longitudinal distance, the relative longitudinal speed, the relative longitudinal acceleration and the heading angle of the vehicle within the preset range between the vehicle and the vehicle within the preset range.

The model processing module 220 constructs an SVM model and trains the SVM model according to the selected training set of the jaming feature data and the jaming behavior determination result corresponding to the training set, thereby obtaining a trained SVM model.

The prediction module 230 inputs the jamming feature data collected in real time into the trained SVM model to predict the jamming behavior of the vehicle within the preset range.

When the vehicle jamming behavior within the preset range is predicted, the early warning module 240 warns and prompts braking measures.

The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A method of predicting vehicle congestion behavior, the method comprising the steps of:

acquiring plugging characteristic data of a vehicle in a preset range in real time through a vehicle-mounted detection device, wherein the plugging characteristic data comprises at least one of a relative transverse distance, a relative transverse speed, a relative transverse acceleration, a relative longitudinal distance, a relative longitudinal speed, a relative longitudinal acceleration and a course angle of the vehicle in the preset range between the vehicle and a workshop in the preset range;

2. The method according to claim 1, further comprising warning and prompting a braking measure to be taken by the host vehicle when congestion behavior of the vehicle within a preset range is predicted.

3. The method of claim 1, further comprising at least one of feature screening, data cleansing, and labeling the plugged feature data.

4. The method of claim 1, wherein the in-vehicle detection device comprises at least one of a radar and a camera.

5. The method according to claim 1, wherein the jam feature data is a relative lateral distance between the vehicle and the vehicle in the preset range, a relative lateral speed, a relative lateral acceleration and a heading angle of the vehicle in the preset range.

6. A system for predicting vehicle congestion behavior, the system comprising:

and the prediction module is used for inputting the plugging characteristic data acquired in real time into the trained SVM model and predicting the plugging behavior of the vehicle in the preset range.

7. The system of claim 6, further comprising:

8. An electronic device, comprising:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor, which when executed by the at least one processor, cause the at least one processor to perform a method of predicting vehicular congestion behavior as claimed in any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium storing computer instructions which, when executed by at least one processor, cause the at least one processor to perform the method of predicting vehicular congestion behavior of any of claims 1 to 5.

10. A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of predicting vehicular congestion behavior of any of claims 1 to 5.