CN113947947A - Vehicle collision early warning method and device, electronic equipment and storage medium - Google Patents
Vehicle collision early warning method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a vehicle collision early warning method, a vehicle collision early warning device, electronic equipment and a storage medium, wherein the vehicle collision early warning method comprises the following steps: receiving vehicle information sent by a positioning device of a vehicle, and determining natural environment information where the vehicle is currently located, wherein the vehicle comprises a target vehicle and associated vehicles associated with the target vehicle, and the associated vehicles comprise vehicles with a distance smaller than a set distance from the target vehicle; generating spatial information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the associated vehicle information of the associated vehicle according to the time sequence; inputting the spatial information and the natural environment information into a pre-established space-time prediction network to generate predicted vehicle information at a predicted time; and determining whether the predicted vehicle information meets a preset early warning condition, and if so, generating early warning information to prompt the target vehicle to collide at the predicted moment. According to the embodiment of the invention, the vehicle information is acquired through the positioning equipment, so that the effectiveness of the vehicle collision prediction result is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of vehicle control, in particular to a vehicle collision early warning method and device, electronic equipment and a storage medium.
Background
With the improvement of the living standard of people, the number of automobiles kept by people increases year by year, and the frequent occurrence of traffic accidents follows, wherein the collision between the vehicles is generally more serious, so the realization of the vehicle collision early warning function is particularly urgent.
In the prior art, vehicle collision early warning is usually realized based on a large number of visual perception sensors, and the position of an automobile is judged to brake through image information acquired by the visual perception sensors, but the method has serious dependence on the visual perception sensors and high cost; and the change of weather and environment has great influence on the acquisition result of the visual perception sensor, thereby influencing the accuracy of the vehicle collision prediction result.
Disclosure of Invention
The embodiment of the invention provides a vehicle collision early warning method and device, electronic equipment and a storage medium, which are used for accurately acquiring vehicle information and improving the accuracy and effectiveness of a vehicle collision prediction result.
In a first aspect, the embodiment of the invention provides a vehicle collision early warning method, which receives vehicle information sent by a positioning device of a vehicle, and determines natural environment information where the vehicle is currently located, wherein the vehicle comprises a target vehicle and an associated vehicle associated with the target vehicle, and the associated vehicle comprises a vehicle whose distance from the target vehicle is less than a set distance;
generating spatial information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the related vehicle information of the related vehicle according to a time sequence;
inputting the spatial information and the natural environment information into a pre-established space-time prediction network to generate predicted vehicle information at a predicted moment;
and determining whether the predicted vehicle information meets a preset early warning condition, and if so, generating early warning information to prompt the target vehicle to collide at the predicted time.
In a second aspect, an embodiment of the present invention further provides a vehicle collision warning apparatus, where the apparatus includes: the receiving vehicle information module is used for receiving vehicle information sent by a positioning device of a vehicle and determining the information of the natural environment where the vehicle is currently located, wherein the vehicle comprises a target vehicle and associated vehicles associated with the target vehicle, and the associated vehicles comprise vehicles with the distance to the target vehicle smaller than a set distance;
the generating space information module is used for generating space information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the related vehicle information of the related vehicle according to a time sequence;
the generation and prediction vehicle information module is used for inputting the spatial information and the natural environment information into a pre-established space-time prediction network and generating prediction vehicle information at a prediction moment;
and the collision predicting module is used for determining whether the predicted vehicle information meets a preset early warning condition, and if so, generating early warning information to prompt the target vehicle to collide at the predicted time.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle collision warning method provided by any embodiment of the invention.
In a fourth aspect, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the vehicle collision warning method provided in any embodiment of the present invention.
The embodiment of the invention provides a vehicle collision early warning method, which comprises the steps of receiving vehicle information sent by positioning equipment of a vehicle, wherein the vehicle comprises a target vehicle and a related vehicle related to the target vehicle; generating spatial information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the associated vehicle information of the associated vehicle according to the time sequence; generating predicted vehicle information at a predicted time by inputting spatial information and natural environment information into a pre-established spatio-temporal prediction network; and determining whether the target vehicle has a collision condition at the prediction moment by determining whether the predicted vehicle information meets a preset early warning condition. According to the embodiment of the invention, the vehicle information is acquired through the positioning equipment to complete the prediction of the vehicle collision, and the positioning equipment is slightly influenced by the weather environment, so that the accuracy of the acquired vehicle information is high, and the accuracy and the effectiveness of the vehicle collision prediction result are favorably improved.
In addition, the vehicle collision early warning device, the electronic equipment and the storage medium provided by the invention correspond to the method, and have the same beneficial effects.
Drawings
In order to illustrate the embodiments of the present invention more clearly, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a vehicle collision warning method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a vehicle collision prediction system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a generation process of spatial information according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a roadside receiving unit receiving data according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a roadside server prediction provided by an embodiment of the present invention;
FIG. 6 is a schematic illustration of prediction provided by an embodiment of the present invention;
FIG. 7 is a flow chart of prediction provided by an embodiment of the present invention;
FIG. 8 is a schematic diagram of the operation of a two-dimensional causal convolution prediction network provided by an embodiment of the present invention;
fig. 9 is a structural diagram of a vehicle collision warning apparatus according to an embodiment of the present invention;
fig. 10 is a block diagram of an electronic device according to an 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.
It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Example one
Fig. 1 is a flowchart of a vehicle collision warning method according to an embodiment of the present invention. The method can be executed by a vehicle collision early warning device, the device can be realized by software and/or hardware, and the device can be configured in a terminal and/or a server to realize the vehicle collision early warning method in the embodiment of the invention.
As shown in fig. 1, the method of the embodiment may specifically include:
s101, receiving vehicle information sent by positioning equipment of a vehicle, and determining the information of the current natural environment of the vehicle.
It should be noted that, the embodiment of the present invention provides a vehicle collision prediction system, which includes a vehicle end device and a roadside device. FIG. 2 is a schematic diagram of a vehicle collision prediction system according to an embodiment of the present invention; as shown in fig. 2, the vehicle-end device includes a vehicle-end transmitting unit, a vehicle-end early warning unit, and a vehicle-end receiving unit. The road side equipment comprises a road side receiving unit, a road side server and a road side transmitting unit, and the vehicle collision prediction method provided by the embodiment of the invention can be applied to the road side server.
In specific implementation, the positioning device sends vehicle information of a vehicle through the vehicle-end sending unit, and acquires the vehicle information through the roadside receiving unit. Illustratively, the vehicle information includes at least one of position information, speed information, and acceleration information of the vehicle; the positioning device comprises a global satellite positioning navigation system.
Further, the vehicle includes a target vehicle and a related vehicle related to the target vehicle, and the related vehicle includes a vehicle whose distance from the target vehicle is less than a set distance. Those skilled in the art can determine the value of the set distance according to the actual application, which is not limited in the embodiment of the present invention. The number of the associated vehicles may be 0, 1, or a plurality of vehicles. When the number of the associated vehicles is 0, it is indicated that there is no vehicle having a distance to the target vehicle smaller than the preset distance, and it can be directly determined that the target vehicle has no collision risk for the moment.
Specifically, the natural environment information where the vehicle is currently located is determined, where the natural environment information includes at least one of temperature, humidity, time, and weather of the current environment, and the weather includes rain, snow, haze, sand storm, and the like.
Optionally, the receiving vehicle information sent by the positioning device of the vehicle includes:
receiving current frame vehicle data sent by a vehicle end sending unit by positioning equipment of a vehicle, and determining whether the current frame vehicle data has data mutation relative to the previous frame vehicle data; if yes, discarding the vehicle data of the current frame, and counting the total number of the current continuous discarded frames; if the total number of the continuously discarded frames reaches the preset number of frames, discarding each frame of vehicle data, and restarting to receive the first frame of data; and if the total number of the continuously discarded frames does not reach the preset frame number, receiving the vehicle data of the next frame, and generating the vehicle information based on the vehicle data of each frame.
Specifically, the vehicle-end sending unit can send vehicle data frame by frame, and after receiving the current frame of vehicle data sent by the vehicle-end sending unit, can judge whether the current frame of vehicle data is the first frame of data, if so, store the frame of data, and continue the next frame of data; if not, in order to ensure the integrity of the received data, the integrity of the current frame vehicle data can be checked, and if the integrity of the received data is not complete, the data is received again.
Further, if the current frame vehicle data is complete, whether data mutation occurs in the current frame vehicle data compared with the previous frame vehicle data is determined, so that the validity of the current frame vehicle data is ensured. If the data mutation of the current frame vehicle data is determined, the current frame vehicle data is detected wrongly, and the current frame vehicle data can be discarded. In order to ensure the validity of vehicle information formed by all the finally obtained frame vehicle data, the total number of the current continuous discarded frames needs to be counted, when the total number of the continuous discarded frames reaches a preset number of frames, the received frame vehicle data can be discarded, and the first frame data is restarted to be received; when the total number of the continuously discarded frames does not reach the preset frame number, the discarded number of the current frame data does not influence the effective vehicle information, and the vehicle data of the next frame can be continuously received.
And if the current frame vehicle data has no data mutation, storing the current frame vehicle data and receiving the next frame vehicle data. And completing the reception of all frame data until receiving the end signal, and generating the vehicle information based on each received frame of vehicle data.
And S102, generating space information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the related vehicle information of the related vehicle according to the time sequence.
Specifically, a two-dimensional matrix including the target vehicle information and the related vehicle information may be generated based on the positional relationship between the target vehicle and the related vehicle, and the three-dimensional spatial information corresponding to the target vehicle may be generated in accordance with the two-dimensional matrix corresponding to each time in time order.
Optionally, according to the time sequence, generating spatial information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the associated vehicle information of the associated vehicle includes:
establishing a space rectangular coordinate system with the target vehicle position of the target vehicle as an origin; determining the associated coordinate position of each associated vehicle in a plane formed by an x axis and a y axis in a space rectangular coordinate system based on the associated vehicle position of each associated vehicle; storing target vehicle information at an origin position of a rectangular spatial coordinate system, and storing associated vehicle information at a corresponding associated coordinate position; and determining the z-axis of the space rectangular coordinate system as a time axis, and generating space information consisting of the target vehicle information and the associated vehicle information according to the time sequence.
Fig. 3 is a schematic diagram of a generation process of spatial information according to an embodiment of the present invention; as shown in fig. 3, the target vehicle information and the associated vehicle information at each time point form a two-dimensional matrix, and the target vehicle position is set as a central point of the two-dimensional matrix, or may be an origin in a spatial rectangular coordinate system. The position relationship between the associated vehicle and the target vehicle is determined, that is, the position relationship between the coordinates of the associated vehicle and the origin in the two-dimensional matrix can be determined, so that the x-axis coordinates and the y-axis coordinates of the associated vehicle are determined, and the storage position of the associated vehicle in the two-dimensional matrix is the determined coordinates of the associated vehicle. For example, the two-dimensional matrix may be a 3 × 3 matrix, and the two-dimensional matrix at different time instants constitutes spatial information including associated vehicle information and target vehicle information.
Information such as the speed and acceleration of the vehicle is stored in the storage location of each piece of vehicle information. For example, the speed of the vehicle can be divided into an x-axis speed and a y-axis speed according to the directions of the x-axis and the y-axis for storage; similarly, the acceleration of the vehicle may be divided into an x-axis acceleration and a y-axis acceleration, and the current position may be divided into an x-axis position and a y-axis position.
And S103, inputting the spatial information and the natural environment information into a pre-established space-time prediction network to generate predicted vehicle information at a predicted time.
Optionally, before inputting the spatial information and the natural environment information into the pre-established spatio-temporal prediction network, the method further includes: obtaining sample vehicle information, sample collision information and sample natural environment information of a sample vehicle; and training a pre-established causal convolutional network in a minimum mean square error training mode based on the sample vehicle information, the sample collision information and the sample natural environment information to generate a space-time prediction network.
Specifically, sample vehicle information, sample collision information, and sample natural environment information of the sample vehicle may be acquired. Illustratively, the sample vehicle information is spatial information. Taking the sample vehicle information and the sample natural environment information as input information, and performing feature fusion operation on the sample vehicle information and the sample natural environment information to realize feature superposition of the sample vehicle information and the sample natural environment information; inputting the superposed information into a pre-established cause and effect convolution network, and taking sample collision information as output information; and training the causal convolutional network by adopting a minimum mean square error training mode to generate a space-time prediction network.
Further, whether the predicted vehicle information changes suddenly relative to the vehicle information at the current moment is determined, if yes, the predicted vehicle information is not in accordance with an actual physical model of the vehicle running process, and the predicted vehicle information can be discarded; and re-determining the predicted vehicle information until the predicted vehicle information which has no mutation and meets the actual physical model is obtained. If not, the predicted vehicle information may be stored for use in determining whether the target vehicle has collided at the predicted time.
Illustratively, the vehicle information includes at least one of position information, speed information, and acceleration information of the vehicle; generating predicted vehicle information at a predicted time, comprising: a target predicted position of the target vehicle and a related predicted position of the related vehicle at the predicted time are generated.
Correspondingly, the predicted vehicle information includes a target predicted position of the target vehicle and an associated predicted position of the associated vehicle; after the predicted vehicle information at the predicted time is generated, the method further includes: determining whether the target predicted position and the associated predicted position have position mutation; if yes, discarding the predicted vehicle information, and inputting the spatial information and the natural environment information into a pre-established space-time prediction network again to read the predicted vehicle information.
And S104, determining whether the predicted vehicle information meets a preset early warning condition, and if so, generating early warning information to prompt the target vehicle to collide at the predicted time.
In a specific implementation, when the predicted vehicle information is a predicted position of the vehicle, the preset early warning condition may be whether a predicted distance between a target predicted position of the target vehicle and an associated predicted position of the associated vehicle is smaller than a preset threshold.
Specifically, determining whether the predicted vehicle information meets a preset early warning condition includes: determining a predicted distance between the target vehicle and the associated vehicle at the predicted time based on the target predicted position and the associated predicted position; determining whether the predicted distance is smaller than a preset distance threshold; if yes, determining that the predicted vehicle information meets the preset early warning condition.
When the predicted distance is smaller than the preset distance threshold value, the fact that the target predicted position and the associated predicted position are close to each other is indicated, the risk of collision exists, the preset early warning condition is met, and collision early warning needs to be carried out on the current situation so as to play a role in prompting the target vehicle. When the predicted distance is larger than or equal to the preset distance threshold, the distance between the target predicted position and the associated predicted position is a safe distance, and collision early warning is not needed.
According to the vehicle collision early warning method provided by the embodiment of the invention, the vehicle information is acquired through the positioning equipment to complete the prediction of the vehicle collision, and the positioning equipment is slightly influenced by the weather environment, so that the accuracy of the acquired vehicle information is high, and the accuracy of the vehicle collision prediction result is favorably improved; and the validity of the acquired information is ensured by determining whether the vehicle information and the predicted vehicle information are mutated.
Example two
The embodiment corresponding to the vehicle collision warning method is described in detail above, and specific application scenarios are given below in order to make the technical solutions of the method further clear to those skilled in the art.
The embodiment of the invention provides a vehicle collision prediction system which comprises vehicle-end equipment and road-side equipment. The vehicle-end equipment comprises a vehicle-end sending unit, a vehicle-end early warning unit and a vehicle-end receiving unit. The roadside apparatus includes a roadside receiving unit, a roadside server, and a roadside transmitting unit.
In specific implementation, the vehicle end sending unit sends the vehicle information collected by the positioning device to the roadside receiving unit. For example, the positioning device of the target vehicle transmits the target vehicle information to the roadside receiving unit through the target vehicle end transmitting unit, and the positioning device of the associated vehicle transmits the associated vehicle information to the roadside receiving unit through the associated vehicle end transmitting unit.
Fig. 4 is a flowchart of the roadside receiving unit receiving data according to the embodiment of the present invention, and as shown in fig. 4, specifically, after the roadside receiving unit starts receiving current frame vehicle data, it may be determined whether the current frame vehicle data is first frame data, if so, the current frame data is stored, and the next frame data is continued; if not, in order to ensure the integrity of the received data, the integrity of the current frame vehicle data can be checked, and if the data is incomplete, the data is received again;
further, if the current frame vehicle data is complete, it is determined whether a data mutation occurs in the current frame vehicle data compared with the previous frame vehicle data, and if it is determined that a data mutation occurs in the current frame vehicle data, the current frame vehicle data may be discarded. In order to ensure the validity of the vehicle information formed by all the final frame vehicle data, the total number of the current continuous discarded frames needs to be counted, when the total number of the continuous discarded frames reaches a preset number of frames, the received frame vehicle data can be discarded, and the first frame data is restarted to be received; when the total number of the continuously discarded frames does not reach the preset frame number, the discarded number of the current frame data does not influence the effective vehicle information, and the vehicle data of the next frame can be continuously received. For example, the preset frame number may be 10.
And if the current frame vehicle data has no data mutation, storing the current frame vehicle data, and receiving the next frame vehicle data until receiving the end signal.
Optionally, the roadside receiving unit is physically isolated from the connection internet module. And after receiving all the vehicle data, the road side receiving unit encrypts the vehicle data frame by frame and transmits the encrypted vehicle data to the road side server.
Fig. 5 is a schematic diagram illustrating a roadside server prediction according to an embodiment of the present invention, and as shown in fig. 5, the roadside server decrypts vehicle data, determines whether a time interval between the decrypted current frame of vehicle data and a previous frame of vehicle data is a preset time interval, and discards the current frame of vehicle data if the time interval is not the preset time interval; if so, recombining the vehicle data of each frame according to the time sequence, and correspondingly generating the vehicle information based on the vehicle data of each frame. Inputting vehicle information into a pre-trained space-time prediction network to obtain a prediction result; when an end signal is received, the prediction process is ended.
Specifically, spatial information is generated based on the target vehicle information and the associated vehicle information, and the spatial information is input to a pre-trained spatio-temporal prediction network. Illustratively, a 3 × 3 two-dimensional matrix is established based on a position relationship between the associated vehicle position of the associated vehicle and the target vehicle position with the target vehicle position of the target vehicle information as an origin, and the associated vehicle coordinates and the target vehicle coordinates in the two-dimensional matrix satisfy a relative distance between the associated vehicle and the target vehicle, and a relative distance calculation formula is as follows:
wherein d isiIs a relative distance, xiTo correlate the x-axis coordinate, y, of vehicle iiIs the y-axis coordinate of the associated vehicle i; x is the number of0Is the x-axis coordinate, y, of the target vehicle i0Is the y-axis coordinate of the target vehicle i. For example, the relative distance may also be calculated by a euclidean distance, which is not limited in the embodiment of the present invention.
Further, decomposing the speed of each vehicle into a space rectangular coordinate system, and respectively splitting the speed into an x-axis speed and a y-axis speed for storage; similarly, the acceleration of the vehicle can be divided into an x-axis acceleration and a y-axis acceleration. And recombining the two-dimensional matrix into a three-dimensional matrix, namely the space information of the vehicle according to the time sequence and preset time intervals. The skilled person can set the preset time interval according to the practical application, and the set time interval is different, and the characteristics of different time granularities can be extracted.
FIG. 6 is a schematic illustration of prediction provided by an embodiment of the present invention; as shown in fig. 6, the three-dimensional matrix and the external natural environment information are input to the spatio-temporal prediction network to obtain an output prediction result. The external natural environment information includes information that may affect the running of the vehicle, such as temperature, humidity, weather, or time. Illustratively, feature extraction is respectively carried out on the basis of vehicle position information and vehicle speed information, namely natural environment information, after feature fusion is carried out on the position features and the vehicle speed features to obtain fusion information, the fusion information and the extracted natural environment features are input into a space-time prediction network to obtain a prediction result.
Fig. 7 is a prediction flowchart provided in the embodiment of the present invention, and as shown in fig. 7, the prediction result output by the spatio-temporal prediction network is read, and whether there is a sudden change between the prediction result and the actual situation is determined. The sudden change comprises vehicle position sudden change, vehicle speed sudden change or vehicle acceleration sudden change, and whether a prediction result meets a vehicle running state model or not is judged; and if the vehicle driving state model is suddenly changed or is not accordant with the vehicle driving state model, discarding the prediction result and reading the prediction result again. If the space-time prediction grids are all satisfied, storing the information of the input space-time prediction grids; checking whether the distance between the predicted position of the target vehicle and the predicted position of the associated vehicle in the prediction result is smaller than a preset distance threshold value, if so, indicating that the distance between the vehicles is too close and the collision risk is high, and generating an early warning instruction to feed back the early warning instruction to the target vehicle terminal; if the current collision risk of the target vehicle is greater than the preset collision risk, the target vehicle can be detected continuously until the judgment process is ended after the ending signal is received. After the target vehicle terminal receives the early warning instruction, the driver can be prompted that the current vehicle has a collision risk through a voice broadcasting or display screen displaying mode.
Specifically, a space-time prediction network can be pre-established, the space-time prediction network is specifically a two-dimensional causal convolution prediction network, fig. 8 is a working schematic diagram of the two-dimensional causal convolution prediction network provided by the embodiment of the present invention, as shown in fig. 8, vehicle position information and vehicle speed information extract position characteristics and vehicle speed characteristics through a residual error structure, external natural environment information extracts environment characteristics through a full-connection network manner, and the position characteristics of multiple vehicles are subjected to characteristic fusion to obtain position fusion characteristics; the method comprises the steps of carrying out feature fusion on vehicle speed features of a plurality of vehicles to obtain vehicle speed fusion features, carrying out feature superposition processing on the position fusion features and the vehicle speed fusion features, inputting the position fusion features and the vehicle speed fusion features into a cause and effect convolution prediction module, carrying out feature superposition on output features of the cause and effect convolution prediction module and environment features, and obtaining a final prediction result through activating a function.
According to the vehicle collision early warning method provided by the embodiment of the invention, the vehicle information is acquired through the positioning equipment to complete the prediction of vehicle collision, so that the accuracy of acquiring the vehicle information is improved; the vehicle collision condition is predicted through a space-time prediction network, so that the accuracy of a prediction result is improved; and the validity of the acquired information is ensured by determining whether the vehicle information and the predicted vehicle information are mutated.
EXAMPLE III
Fig. 9 is a structural diagram of a vehicle collision warning apparatus according to an embodiment of the present invention, which is configured to execute the vehicle collision warning method according to any of the embodiments. The device and the vehicle collision early warning method of each embodiment belong to the same inventive concept, and details which are not described in detail in the embodiment of the vehicle collision early warning device can refer to the embodiment of the vehicle collision early warning method. The device may specifically comprise:
the receiving vehicle information module 10 is configured to receive vehicle information sent by a positioning device of a vehicle, and determine natural environment information where the vehicle is currently located, where the vehicle includes a target vehicle and an associated vehicle associated with the target vehicle, and the associated vehicle includes a vehicle whose distance from the target vehicle is less than a set distance;
the space information generating module 11 is configured to generate space information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the associated vehicle information of the associated vehicle in time sequence;
the generation predicted vehicle information module 12 is used for inputting the spatial information and the natural environment information into a pre-established space-time prediction network and generating predicted vehicle information at a predicted time;
and the collision predicting module 13 is used for determining whether the predicted vehicle information meets the preset early warning condition, and if so, generating early warning information to prompt the target vehicle of the collision condition at the predicted moment.
On the basis of any optional technical scheme in the embodiment of the invention, optionally, the vehicle information includes at least one of position information, speed information and acceleration information of the vehicle; the generate predicted vehicle information module 12 includes:
and a generation position unit configured to generate a target predicted position of the target vehicle and a related predicted position of the related vehicle at the predicted time.
On the basis of any optional technical solution in the embodiment of the present invention, optionally, the spatial information generating module 11 includes:
a coordinate system establishing unit for establishing a spatial rectangular coordinate system with the target vehicle position of the target vehicle as an origin; determining the associated coordinate position of each associated vehicle in a plane formed by an x axis and a y axis in a space rectangular coordinate system based on the associated vehicle position of each associated vehicle; storing target vehicle information at an origin position of a rectangular spatial coordinate system, and storing associated vehicle information at a corresponding associated coordinate position; and determining the z-axis of the space rectangular coordinate system as a time axis, and generating space information consisting of the target vehicle information and the associated vehicle information according to the time sequence.
On the basis of any optional technical solution in the embodiment of the present invention, optionally, the apparatus further includes:
the system comprises a space-time prediction generation network module, a time-space prediction generation module and a time-space prediction generation module, wherein the space-time prediction generation network module is used for acquiring sample vehicle information, sample collision information and sample natural environment information of a sample vehicle before inputting the space information and the natural environment information into a pre-established space-time prediction network; and training a pre-established causal convolutional network in a minimum mean square error training mode based on the sample vehicle information, the sample collision information and the sample natural environment information to generate a space-time prediction network.
On the basis of any optional technical scheme in the embodiment of the invention, optionally, the predicted vehicle information includes a target predicted position of the target vehicle and an associated predicted position of the associated vehicle; the device also includes:
a confirmed position mutation module for determining whether the target predicted position and the associated predicted position have position mutation after generating the predicted vehicle information at the predicted time; if yes, discarding the predicted vehicle information, and inputting the spatial information and the natural environment information into a pre-established space-time prediction network again to read the predicted vehicle information.
On the basis of any optional technical solution in the embodiment of the present invention, optionally, the predicting whether to collide the module 13 includes:
a predicted distance determining unit configured to determine a predicted distance between the target vehicle and the associated vehicle at the prediction time based on the target predicted position and the associated predicted position; determining whether the predicted distance is smaller than a preset distance threshold; if yes, determining that the predicted vehicle information meets the preset early warning condition.
On the basis of any optional technical solution in the embodiment of the present invention, optionally, the vehicle information receiving module 10 includes:
the frame data discarding unit is used for receiving the current frame vehicle data sent by the vehicle-end sending unit through the positioning equipment of the vehicle and determining whether the current frame vehicle data has data mutation relative to the previous frame vehicle data; if yes, discarding the vehicle data of the current frame, and counting the total number of the current continuous discarded frames; if the total number of the continuously discarded frames reaches the preset number of frames, discarding each frame of vehicle data, and restarting to receive the first frame of data; and if the total number of the continuously discarded frames does not reach the preset frame number, receiving the vehicle data of the next frame, and generating the vehicle information based on the vehicle data of each frame.
The vehicle collision early warning device provided by the embodiment of the invention can execute the vehicle collision early warning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, in the embodiment of the vehicle collision warning device, the included units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example four
Fig. 10 is a block diagram of an electronic device according to an embodiment of the present invention. FIG. 10 illustrates a block diagram of an exemplary electronic device 20 suitable for use in implementing embodiments of the present invention. The illustrated electronic device 20 is merely an example and should not be used to limit the functionality or scope of embodiments of the present invention.
As shown in fig. 10, the electronic device 20 is embodied in the form of a general purpose computing device. The components of the electronic device 20 may include, but are not limited to: one or more processors or processing units 201, a system memory 202, and a bus 203 that couples the various system components (including the system memory 202 and the processing unit 201).
The system memory 202 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)204 and/or cache memory 205. The electronic device 20 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system 206 may be used to read from and write to non-removable, nonvolatile magnetic media. 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 203 by one or more data media interfaces. Memory 202 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 208 having a set (at least one) of program modules 207 may be stored, for example, in memory 202, such program modules 207 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 207 generally perform the functions and/or methodologies of embodiments of the present invention as described herein.
The electronic device 20 may also communicate with one or more external devices 209 (e.g., keyboard, pointing device, display 210, etc.), with one or more devices that enable a user to interact with the electronic device 20, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 20 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 211. Also, the electronic device 20 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 the network adapter 212. As shown, the network adapter 212 communicates with other modules of the electronic device 20 over the bus 203. It should be understood that other hardware and/or software modules may be used in conjunction with electronic device 20, 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 201 executes various functional applications and data processing by running a program stored in the system memory 202.
The electronic equipment provided by the invention can realize the following method: receiving vehicle information sent by a positioning device of a vehicle, wherein the vehicle comprises a target vehicle and an associated vehicle associated with the target vehicle; generating spatial information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the associated vehicle information of the associated vehicle according to the time sequence; generating predicted vehicle information at a predicted time by inputting spatial information and natural environment information into a pre-established spatio-temporal prediction network; and determining whether the target vehicle has a collision condition at the prediction moment by determining whether the predicted vehicle information meets a preset early warning condition. According to the embodiment of the invention, the vehicle information is acquired through the positioning equipment to complete the prediction of the vehicle collision, and the positioning equipment is slightly influenced by the weather environment, so that the accuracy of the acquired vehicle information is high, and the accuracy and the effectiveness of the vehicle collision prediction result are favorably improved.
EXAMPLE five
An embodiment of the present invention provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a vehicle collision warning method, the method including:
receiving vehicle information sent by a positioning device of a vehicle, wherein the vehicle comprises a target vehicle and an associated vehicle associated with the target vehicle; generating spatial information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the associated vehicle information of the associated vehicle according to the time sequence; generating predicted vehicle information at a predicted time by inputting spatial information and natural environment information into a pre-established spatio-temporal prediction network; and determining whether the target vehicle has a collision condition at the prediction moment by determining whether the predicted vehicle information meets a preset early warning condition. According to the embodiment of the invention, the vehicle information is acquired through the positioning equipment to complete the prediction of the vehicle collision, and the positioning equipment is slightly influenced by the weather environment, so that the accuracy of the acquired vehicle information is high, and the accuracy and the effectiveness of the vehicle collision prediction result are favorably improved.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the vehicle collision warning method provided by any embodiments of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
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 (10)
1. A vehicle collision warning method is characterized by comprising the following steps:
receiving vehicle information sent by a positioning device of a vehicle, and determining natural environment information where the vehicle is currently located, wherein the vehicle comprises a target vehicle and associated vehicles associated with the target vehicle, and the associated vehicles comprise vehicles with a distance smaller than a set distance from the target vehicle;
generating spatial information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the related vehicle information of the related vehicle according to a time sequence;
inputting the spatial information and the natural environment information into a pre-established space-time prediction network to generate predicted vehicle information at a predicted moment;
and determining whether the predicted vehicle information meets a preset early warning condition, and if so, generating early warning information to prompt the target vehicle to collide at the predicted time.
2. The method of claim 1, wherein the vehicle information includes at least one of position information, velocity information, and acceleration information of the vehicle;
the generating of the predicted vehicle information at the predicted time includes:
a target predicted position of the target vehicle and an associated predicted position of the associated vehicle at a predicted time are generated.
3. The method of claim 1, wherein the generating spatial information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the associated vehicle information of the associated vehicle in chronological order comprises:
establishing a space rectangular coordinate system with the target vehicle position of the target vehicle as an origin;
determining the associated coordinate position of each associated vehicle in a plane formed by an x axis and a y axis in the space rectangular coordinate system based on the associated vehicle position of each associated vehicle;
storing the target vehicle information in an origin position of the rectangular spatial coordinate system, and storing the associated vehicle information in the corresponding associated coordinate position;
and determining a z-axis of the rectangular spatial coordinate system as a time axis, and generating the spatial information consisting of the target vehicle information and the associated vehicle information according to a time sequence.
4. The method of claim 1, further comprising, prior to said inputting said spatial information and said natural environment information into a pre-established spatio-temporal prediction network:
obtaining sample vehicle information, sample collision information and sample natural environment information of a sample vehicle;
and training a pre-established causal convolutional network in a minimum mean square error training mode based on the sample vehicle information, the sample collision information and the sample natural environment information to generate the space-time prediction network.
5. The method of claim 1, wherein the predicted vehicle information includes a target predicted location of the target vehicle and an associated predicted location of the associated vehicle;
after the generating of the predicted vehicle information at the predicted time, the method further includes:
determining whether the target predicted position and the associated predicted position have a position mutation;
if yes, discarding the predicted vehicle information, and inputting the spatial information and the natural environment information into a pre-established space-time prediction network again to read the predicted vehicle information.
6. The method of claim 5, wherein the determining whether the predicted vehicle information satisfies a preset pre-warning condition comprises:
determining a predicted distance between the target vehicle and the associated vehicle at a predicted time based on the target predicted position and the associated predicted position;
determining whether the predicted distance is less than a preset distance threshold;
and if so, determining that the predicted vehicle information meets the preset early warning condition.
7. The method of claim 1, wherein the receiving vehicle information sent by a locating device of a vehicle comprises:
receiving current frame vehicle data sent by the positioning equipment of the vehicle through a vehicle end sending unit, and determining whether the current frame vehicle data has data mutation relative to the previous frame vehicle data;
if yes, discarding the current frame vehicle data, and counting the total number of the current continuous discarded frames;
if the total number of the continuously discarded frames reaches the preset number of frames, discarding each frame of vehicle data, and restarting to receive the first frame of data;
and if the total number of the continuously discarded frames does not reach the preset frame number, receiving the vehicle data of the next frame, and generating the vehicle information based on the vehicle data of each frame.
8. A vehicle collision warning apparatus, comprising:
the receiving vehicle information module is used for receiving vehicle information sent by a positioning device of a vehicle and determining the information of the natural environment where the vehicle is currently located, wherein the vehicle comprises a target vehicle and associated vehicles associated with the target vehicle, and the associated vehicles comprise vehicles with the distance to the target vehicle smaller than a set distance;
the generating space information module is used for generating space information corresponding to the target vehicle based on the target vehicle information of the target vehicle and the related vehicle information of the related vehicle according to a time sequence;
the generation and prediction vehicle information module is used for inputting the spatial information and the natural environment information into a pre-established space-time prediction network and generating prediction vehicle information at a prediction moment;
and the collision predicting module is used for determining whether the predicted vehicle information meets a preset early warning condition, and if so, generating early warning information to prompt the target vehicle to collide at the predicted time.
9. An electronic device, comprising:
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 vehicle collision warning method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a vehicle collision warning method according to any one of claims 1 to 7.
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