CN114677848B - Perception early warning system, method, device and computer program product - Google Patents
Perception early warning system, method, device and computer program product Download PDFInfo
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/00—Traffic control systems for road vehicles
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Abstract
The disclosure provides a perception early warning system, a perception early warning method, a perception early warning device, an electronic device, a storage medium and a computer program product, relates to the technical field of computers, in particular to an intelligent traffic technology, and can be used in an intelligent traffic scene. The specific implementation scheme is as follows: the sensing equipment is used for generating sensing information of a preset area of the target station; the information processing equipment is used for determining whether a target object exists in a preset area according to the perception information to obtain a perception result; and the target platform is used for responding to the determined perception result to represent the existence of a target object in the preset area and sending early warning information to the target object based on the target vehicle which is about to enter the preset area. The present disclosure improves traffic efficiency and vehicle travel safety.
Description
Technical Field
The disclosure relates to the field of computer technology, in particular to intelligent traffic and automatic driving technology, and especially relates to a perception early warning system, a perception early warning method, a perception early warning device, an electronic device, a storage medium and a computer program product, which can be used in an intelligent traffic scene.
Background
Stations, such as bus stops, are prone to becoming areas of relatively dense traffic. Passengers and vehicles near the platform need to pay attention to whether vehicles enter the platform at any time so as to make the vehicles travel to avoid causing traffic jams and even traffic accidents. Currently, passengers and vehicles near a platform have no perception of the vehicle about to get in, and it is necessary to wait for the passengers and persons in the vehicle parked near the platform to pay attention to the vehicle about to get in.
Disclosure of Invention
The present disclosure provides a perception early warning system, method, apparatus, electronic device, storage medium, and computer program product.
According to a first aspect, there is provided a perception early warning system comprising: the system comprises a sensing device, an information processing device and a target station, wherein the sensing device is used for generating sensing information of a preset area of the target station; the information processing equipment is used for determining whether a target object exists in a preset area according to the perception information to obtain a perception result; and the target platform is used for responding to the determined perception result to represent the existence of a target object in the preset area and sending early warning information to the target object based on the target vehicle which is about to enter the preset area.
According to a second aspect, there is provided a perception early warning method, comprising: acquiring perception information of a perception device on a preset area of a target station; determining whether a target object exists in a preset area according to the perception information to obtain a perception result; and responding to the fact that the sensing result represents that the target object exists in the preset area, and sending early warning information to the target object based on the target vehicle which is about to enter the preset area.
According to a third aspect, there is provided a perception early warning device comprising: the sensing unit is configured to acquire sensing information of the sensing equipment on a preset area of the target station; the determining unit is configured to determine whether a target object exists in a preset area according to the perception information to obtain a perception result; and the early warning unit is configured to respond to the fact that the sensing result represents that the target object exists in the preset area, and send early warning information to the target object based on the target vehicle which is about to enter the preset area.
According to a fourth aspect, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as described in any one of the implementations of the second aspect.
According to a fifth aspect, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method as described in any implementation of the second aspect.
According to a sixth aspect, there is provided a computer program product comprising: a computer program which, when executed by a processor, implements a method as described in any of the implementations of the second aspect.
According to the technology disclosed by the invention, a perception early warning system is provided, when a target vehicle is about to enter a target station, early warning information is sent to a target object in a preset area of the target station, so that the target vehicle of the traveling station is prevented from causing traffic jam, and the traffic efficiency and the vehicle traveling safety are improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is an exemplary system architecture diagram to which an embodiment according to the present disclosure may be applied;
FIG. 2 is a flow chart of one embodiment of a perception early warning method according to the present disclosure;
fig. 3 is a schematic diagram of an application scenario of the perception early-warning method according to the present embodiment;
FIG. 4 is a flow chart of yet another embodiment of a perception early warning method according to the present disclosure;
FIG. 5 is a block diagram of one embodiment of a perception early-warning device according to the present disclosure;
FIG. 6 is a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.
FIG. 1 illustrates an exemplary architecture 100 in which the perception early-warning methods and apparatus of the present disclosure may be applied.
As shown in fig. 1, a system architecture 100 may include awareness devices 101, 102, a network 103, an information processing device 104, a network 105, and a target station 106. The communication connection between the terminal devices 101, 102 constitutes a topology network, and the networks 103, 105 are media for providing communication links between the terminal devices 101, 102 and the information processing device 104, and between the information processing device 104 and the destination station 106. The networks 103, 105 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The sensing devices 101, 102 may be hardware devices or software supporting information gathering, network connection for data transmission. When the sensing devices 101, 102 are hardware, they may be various electronic devices supporting network connection, information acquisition, interaction, display, processing, etc., including but not limited to image acquisition devices, voice acquisition devices, point cloud acquisition devices, pressure sensors, thermal infrared sensors, geomagnetism, gates, etc. When the sensing devices 101, 102 are software, they may be installed in the electronic devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.
Specifically, the sensing devices 101, 102 are configured to generate sensing information for a preset area of the target station 106. The preset area may be any area in the destination station, such as a parking area when a bus is in a stop, and a blind area of a driver when the bus is in the stop.
The information processing apparatus 104 may be an information processing apparatus that provides various services, such as a server. Specifically, the information processing apparatus 104 is configured to determine, according to the sensing information, whether a target object exists in the preset area, and obtain a sensing result.
As an example, the information processing apparatus 104 may determine the perception result corresponding to the perception information through a pre-trained perception model. The perception model is used for representing the corresponding relation between the perception information and the perception result. The perception model can be various neural network models such as a convolutional neural network, a residual network, a cyclic neural network and the like. Specifically, the target object may be a movable object such as a person, a car, or the like.
The information processing apparatus 104 may be hardware or software. When the information processing apparatus 104 is hardware, it may be implemented as a distributed server cluster composed of a plurality of information processing apparatuses, or as a single information processing apparatus. When the information processing apparatus is software, it may be implemented as a plurality of software or software modules (for example, software or software modules for providing distributed services), or as a single software or software module. The present invention is not particularly limited herein.
The target station 106 may be a smart station with information interaction, processing, presentation, etc. Specifically, the target station 106 is configured to, in response to determining that the sensing result indicates that the target object exists in the preset area, send early warning information to the target object based on the target vehicle that is about to enter the preset area.
The early warning information can prompt the target object that the target vehicle is about to enter the preset area where the target vehicle is located, and remind the user to keep away from the preset area of the target station so as to enable the target vehicle at the destination station to travel, and traffic jam is avoided.
In order to further improve pertinence and effectiveness of the early warning information, in the process of obtaining the characterization sensing result, the landmark feature information (for example, the landmark appearance feature) of the target object may be further determined. For example, for a person in a predetermined area, the characteristic features may be, for example, gender, wear, etc.; for vehicles in the preset area, the characteristic features may be, for example, license plate numbers, vehicle types, vehicle colors, and the like. In this example, the neural network model that obtains the sensing result may further determine the landmark feature information of the target object, that is, the neural network model characterizes the corresponding relationship between the sensing information and the sensing result, and the landmark feature of the target object; in this example, in response to the first neural network model that obtains the sensing result, it is determined that the target object exists in the preset area, and the landmark feature information of the target object may also be determined by the second neural network model that is independent of the first neural network model. Wherein the second neural network model is used for representing the corresponding relation between the target object and the marking characteristic.
Furthermore, the target station can further pertinently send out the early warning information according to the characteristic features of the target object in the preset area when sending out the early warning information. For example, the warning information is "101 buses are about to drive into a station, and please drive a vehicle with a license plate number XXX out of the station".
It should be understood that the number of sensing devices, information processing devices, networks, and destination stations in fig. 1 is merely illustrative. There may be any number of sensing devices, information processing devices, networks, and destination stations, as desired for implementation. When the electronic device on which the early warning sensing method is operated does not need to perform data transmission with other electronic devices, the system architecture may include only the electronic device (e.g., the sensing device, the information processing device, or the target station) on which the early warning method is operated.
In this embodiment, a sensing and early warning system is provided, when a target vehicle is about to enter a target station, early warning information is sent to a target object in a preset area of the target station, so that the target vehicle traveling in the station is prevented from causing traffic jam, and traffic efficiency and vehicle traveling safety are improved.
In some alternative implementations of the present embodiment, the target station is further configured to: acquiring position information of a target vehicle associated with a target station; determining whether the target vehicle is about to enter a preset area according to the position information; and sending early warning information to the target object in response to determining that the target vehicle is about to enter the preset area.
The target vehicle may be any type of vehicle that may enter the target station. For example, the target vehicle is a bus, a private car, or the like. Taking a bus as an example, when a bus passes a destination station during operation and enters the destination station, the bus is considered to be associated with the destination station. The association between the destination station and the bus is generally predetermined, so that the bus to be taken at the destination station may be predetermined. The target station can communicate with the associated bus through the cloud platform, the real-time position of the bus is determined based on a GPS (Global Positioning System ), and whether the bus is about to enter a preset area of the target station or not is further determined according to the real-time position of the bus and the position of the target station.
As an example, in response to a bus starting from a last station at a current destination station, a determination is made that the bus is about to enter a preset area at the current destination station. As yet another example, in response to determining that the distance between the bus and the target station is within a preset distance threshold, it is determined that the bus is about to enter a preset area of the current target station. The preset distance threshold may be specifically set according to an actual situation. For example, the preset distance is 100 meters.
Taking private cars as an example, since it is not foreseen whether the private car will stop at the target station, that is, the association relationship between the private car and the target station cannot be predetermined, a data processing device capable of performing target tracking on the target vehicle may be provided near the target station, so that the target vehicle may be tracked by the data processing device, and the running track of the target vehicle may be determined. Specifically, the data processing device may collect video data of a vehicle on a route where the target station is located, track the target vehicle according to each video frame in the video data through a neural network model having a target tracking function, determine a moving track of the target vehicle based on a position of the target vehicle between different frames, and predict a moving dynamics of the target vehicle to determine whether a private car will enter a preset area of the target station.
In this implementation manner, a method for determining whether a target vehicle is about to enter a preset area is provided, and according to the association between the target vehicle and a target station, and the real-time position of the target vehicle and the position of the target station, whether the target vehicle is about to enter the preset area can be accurately determined.
In some optional implementations of this embodiment, the target station includes a communication device, a processing device for deploying the target application, a display device, and a voice broadcast device. Wherein the target station is further to: and obtaining a perception result from the information processing equipment through the communication device, responding to the fact that the perception result is confirmed to represent the existence of the target object in the preset area through the target application, displaying early warning information to the target object through the display device, and broadcasting the early warning information to the target object through the voice broadcasting device.
The communication means may acquire the sensing result from the information processing apparatus by a wired connection or a wireless connection communication manner. The target application may be an application program developed specifically for the intelligent platform, for determining the perception result, and sending an early warning instruction to the display device and the voice broadcast device based on the perception result.
In the implementation mode, the specific structure of the target station is provided, and early warning information is sent out based on various modes of the display device and the voice broadcasting device, so that the effectiveness of the early warning information is improved.
In some optional implementations of this embodiment, the perceptual information is a perceived image. In this implementation manner, an image acquisition device may be set adjacent to a preset area of the target station, so as to acquire a perceived image of the preset area in real time, where the information processing apparatus is further configured to: and carrying out image recognition on the perceived image, and determining whether a target object exists in a preset area to obtain a perceived result.
As an example, the information processing apparatus may recognize the target object in the detection image through the image perception model, resulting in a perception result. Specifically, when the target object exists in the preset area, the sensing result may include each target object and the corresponding landmark feature of each target object.
In the implementation mode, the sensing result is determined based on the image recognition mode, and the accuracy of the sensing result is improved.
In some optional implementations of this embodiment, the sensing information is point cloud data, and the point cloud collecting device may be set adjacent to a preset area of the target station to collect the point cloud data of the preset area in real time, where the information processing apparatus is further configured to: and analyzing the point cloud data, and determining whether a target object exists in a preset area to obtain a perception result.
As an example, when a target object is entered in a preset area of a target station, the collected point cloud data may change, and the changed portion may be identified as a new object. If the objects are not present in the background point cloud data corresponding to the target station, the existence of the target object in the preset area is identified. According to the convex hull principle, a so-called bounding box is drawn around the target object. Convex hulls describe the smallest perimeter of an object by connecting points that connect the outermost layers of the object. The bounding box encloses the object in a cuboid as small as possible in order to better process the information. This border helps to roughly classify the object. For example, it may be determined from this border whether the detected object is an automobile or a pedestrian.
In the implementation mode, the sensing result is determined based on the point cloud identification mode, and the accuracy of the sensing result is improved.
In some optional implementations of this embodiment, the information processing device is a cloud information processing device, or a roadside information processing device of the target station.
When the information processing device is a cloud information processing device, corresponding sensing results can be obtained according to sensing information of each sensing device based on the cloud high-performance data processing capability. As an example, for each target station, its corresponding station identity is set. The identity of the perceived device to which each target station corresponds to the station identity. And then, based on the station identification corresponding to the received perception information, sending the perception result obtained after processing to the target station corresponding to the identification.
In the implementation manner, when the information processing equipment is cloud information processing equipment, based on the cloud information processing equipment, the target station can locally adopt a simpler device structure, so that the simplicity of the deployment of the target station is improved; and the accuracy of the obtained notification result is improved based on the high-performance information processing capability of the cloud.
When the information processing equipment is road side information processing equipment of the target stations, each target station, the corresponding local sensing equipment and the information processing equipment form a relatively independent unit, the sensing of the target object can be realized in the unit, and the early warning based on the sensing result and other processes are realized, so that the information transmission link is shortened, the response time is shortened, early warning information is timely sent out, and the traffic safety is improved.
In the implementation mode, when the information processing equipment is the road side information processing equipment of the target station, the timeliness of early warning is improved, traffic accidents can be further avoided, and traffic safety is guaranteed.
In some alternative implementations of the present embodiment, the awareness apparatus and the information processing apparatus are integrated in the target station.
In the implementation mode, based on an integration mode, the communication link of the whole information processing process of perception of a target object and early warning based on a perception result is further shortened, the link is optimal, the integration level is highest, and quick response of station service can be completed.
Referring to fig. 2, fig. 2 is a flowchart of a sensing and early warning method provided in an embodiment of the disclosure, where the flowchart 200 includes the following steps:
in step 201, sensing information of a sensing device for a preset area of a target station is obtained.
In this embodiment, the executing body of the sensing and early warning method (for example, the sensing device, the information processing device, or the target station in fig. 1) may acquire the sensing information of the sensing device for the preset area of the target station from a remote location or from a local location based on the wired network connection or the wireless network connection.
The preset area may be any area in the destination station, such as a parking area when a bus is in a stop, and a blind area of a driver when the bus is in the stop. The sensing device may be an information acquisition device supporting information acquisition, network connection for data transmission, for example, an image acquisition device, a voice acquisition device, a point cloud acquisition device, a pressure sensor, a thermal infrared sensor, geomagnetism, a gate, etc.
It should be noted that the sensing device may include various types of sensing devices. As an example, two sensing devices, namely an image acquisition device and a point cloud acquisition device, may be set, so as to obtain two sensing information, namely image data and point cloud data.
Step 202, determining whether a target object exists in a preset area according to the perception information to obtain a perception result.
In this embodiment, the executing body may determine whether a target object exists in the preset area according to the sensing information, so as to obtain a sensing result.
As an example, the executing body may determine the sensing result corresponding to the sensing information through a pre-trained sensing model. The perception model is used for representing the corresponding relation between the perception information and the perception result. The perception model can be various neural network models such as a convolutional neural network, a residual network, a cyclic neural network and the like. Specifically, the target object may be a movable object such as a person, a car, or the like.
As yet another example, the sensing results include a plurality of sensing data collected by different sensing devices. And for various perception data, processing the perception data through a neural network model corresponding to the perception data to obtain a perception result corresponding to the perception data. And further, determining a final perception result by adopting a weighted summation mode and the like according to various perception results.
In step 203, in response to determining that the sensing result represents that the target object exists in the preset area, early warning information is sent to the target object based on the target vehicle about to enter the preset area.
In this embodiment, the executing body may respond to the determination that the sensing result represents that the target object exists in the preset area, and send early warning information to the target object based on the target vehicle about to enter the preset area.
The early warning information can prompt the target object that the target vehicle is about to enter the preset area where the target vehicle is located, and remind the user to keep away from the preset area of the target station so as to enable the target vehicle at the destination station to travel, and traffic jam is avoided.
In order to further improve pertinence and effectiveness of the early warning information, in the process of obtaining the characterization sensing result, the landmark feature information (for example, the landmark appearance feature) of the target object may be further determined. For example, for a person in a predetermined area, the characteristic features may be, for example, gender, wear, etc.; for vehicles in the preset area, the characteristic features may be, for example, license plate numbers, vehicle types, vehicle colors, and the like. In this example, the neural network model that obtains the sensing result may further determine the landmark feature information of the target object, that is, the neural network model characterizes the corresponding relationship between the sensing information and the sensing result, and the landmark feature of the target object; in this example, in response to the first neural network model that obtains the sensing result, it is determined that the target object exists in the preset area, and the landmark feature information of the target object may also be determined by the second neural network model that is independent of the first neural network model. Wherein the second neural network model is used for representing the corresponding relation between the target object and the marking characteristic.
Furthermore, the target station can further pertinently send out the early warning information according to the characteristic features of the target object in the preset area when sending out the early warning information. For example, the warning information is "101 buses are about to drive into a station, and please drive a vehicle with a license plate number XXX out of the station".
With continued reference to fig. 3, fig. 3 is a schematic diagram 300 of an application scenario of the perception early warning method according to the present embodiment. In the application scenario of fig. 3, there is a suspended private car in the bus stop area of the destination station 301. The sensing device 302 near the destination station collects sensing information of the bus parking area of the destination station in real time, and sends the sensing information to the information processing device 303 corresponding to the destination station. The information processing apparatus 303 determines that a target object, including the private car 304, exists in the parking area of the bus based on the perception information, and obtains a perception result. Further, the target station 301 characterizes the existence of the target object in the bus parking area in response to the determination of the perception result, and sends pre-warning information 306 to the target object 304 based on the bus 305 that is about to enter the bus parking area.
In this embodiment, a sensing and early warning method is provided, when a target vehicle is about to enter a target station, early warning information is sent to a target object in a preset area of the target station, so that the target vehicle traveling in the station is prevented from causing traffic jam, and traffic efficiency and vehicle traveling safety are improved.
In some optional implementations of this embodiment, the executing body sends the early warning information to the target object based on the target vehicle that is about to enter the preset area by:
firstly, acquiring position information of a target vehicle associated with a target station; then, determining whether the target vehicle is about to enter a preset area according to the position information; and finally, in response to determining that the target vehicle is about to enter the preset area, sending out early warning information to the target object.
The target vehicle may be any type of vehicle that may enter the target station. For example, the target vehicle is a bus, a private car, or the like. Taking a bus as an example, when a bus passes through a destination station during operation and enters the destination station, the bus is considered to be associated with the destination station. The association between the destination station and the bus is generally predetermined, so that the bus to be taken at the destination station may be predetermined. The target station can communicate with the associated bus through the cloud platform, the real-time position of the bus is determined based on a GPS (Global Positioning System ), and whether the bus is about to enter a preset area of the target station or not is further determined according to the real-time position of the bus and the position of the target station.
As an example, in response to a bus starting from a last station at a current destination station, a determination is made that the bus is about to enter a preset area at the current destination station. As yet another example, in response to determining that the distance between the bus and the target station is within a preset distance threshold, it is determined that the bus is about to enter a preset area of the current target station. The preset distance threshold may be specifically set according to an actual situation. For example, the preset distance is 100 meters.
Taking private cars as an example, since it is not foreseen whether the private car will stop at the target station, that is, the association relationship between the private car and the target station cannot be predetermined, a data processing device capable of performing target tracking on the target vehicle may be provided near the target station, so that the target vehicle may be tracked by the data processing device, and the running track of the target vehicle may be determined. Specifically, the data processing device may collect video data of a vehicle on a route where the target station is located, track the target vehicle according to each video frame in the video data through a neural network model having a target tracking function, determine a moving track of the target vehicle based on a position of the target vehicle between different frames, and predict a moving dynamics of the target vehicle to determine whether a private car will enter a preset area of the target station.
In this implementation manner, a method for determining whether a target vehicle is about to enter a preset area is provided, and according to the association between the target vehicle and a target station, and the real-time position of the target vehicle and the position of the target station, whether the target vehicle is about to enter the preset area can be accurately determined.
In some optional implementations of this embodiment, the executing entity may send the early warning information to the target object by executing the following manner: and displaying and voice broadcasting the early warning information to the target object.
Specifically, the target station is further configured to: and obtaining a perception result from the information processing equipment through the communication device, responding to the fact that the perception result is confirmed to represent the existence of the target object in the preset area through the target application, displaying early warning information to the target object through the display device, and broadcasting the early warning information to the target object through the voice broadcasting device.
The communication means may acquire the sensing result from the information processing apparatus by a wired connection or a wireless connection communication manner. The target application may be an application program developed specifically for the intelligent platform, for determining the perception result, and sending an early warning instruction to the display device and the voice broadcast device based on the perception result.
In the implementation mode, the specific structure of the target station is provided, and early warning information is sent out based on various modes of the display device and the voice broadcasting device, so that the effectiveness of the early warning information is improved.
In some optional implementations of this embodiment, the perceptual information is a perceived image. In this implementation manner, an image acquisition device may be disposed adjacent to a preset area of the target station, so as to acquire a perceived image of the preset area in real time.
In this implementation manner, the execution body may execute the step 202 as follows: and carrying out image recognition on the perceived image, and determining whether a target object exists in a preset area to obtain a perceived result.
As an example, the information processing apparatus may recognize the target object in the detection image through the image perception model, resulting in a perception result. Specifically, when the target object exists in the preset area, the sensing result may include each target object and the corresponding landmark feature of each target object.
In the implementation mode, the sensing result is determined based on the image recognition mode, and the accuracy of the sensing result is improved.
In some optional implementations of this embodiment, the sensing information is point cloud data, and the point cloud acquisition device may be set adjacent to a preset area of the target station, so as to acquire the point cloud data of the preset area in real time. In this implementation manner, the execution body may execute the step 202 as follows: and analyzing the point cloud data, and determining whether a target object exists in a preset area to obtain a perception result.
As an example, when a target object is entered in a preset area of a target station, the collected point cloud data may change, and the changed portion may be identified as a new object. If these objects are not present in the comparison with the original background image, the presence of the target object in the preset area is identified. According to the convex hull principle, a so-called bounding box is drawn around the target object. The convex hull describes the minimum circumference of the object by connecting the points of the outermost layer of the object. The bounding box encloses the object in a cuboid as small as possible in order to better process the information. This border helps to roughly classify the object. For example, it may be determined from this border whether the detected object is an automobile or a pedestrian.
In the implementation mode, the sensing result is determined based on the point cloud identification mode, and the accuracy of the sensing result is improved.
With continued reference to fig. 4, there is shown a schematic flow 400 of one embodiment of a method of perceived positioning according to the methods of the present disclosure, including the steps of:
in step 401, sensing information of a sensing device for a preset area of a target station is acquired.
Step 402, determining whether a target object exists in a preset area according to the perception information to obtain a perception result.
In step 403, in response to determining that the perception result characterizes that the target object exists in the preset area, position information of the target vehicle associated with the target platform is acquired.
Step 404, determining whether the target vehicle is about to enter the preset area according to the position information.
And step 405, in response to determining that the target vehicle is about to enter the preset area, displaying and voice broadcasting the early warning information to the target object.
As can be seen from this embodiment, compared with the embodiment corresponding to fig. 2, the flow 400 of the sensing and early warning method in this embodiment specifically illustrates a determining process of the target vehicle about to enter the preset area, and the early warning process further improves the accuracy of the determined sensing and early warning, and improves the traffic efficiency and the vehicle driving safety.
With continued reference to fig. 5, as an implementation of the method shown in the foregoing figures, the present disclosure provides an embodiment of a perception early-warning device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be specifically applied to various electronic devices.
As shown in fig. 5, the sensing and early warning device includes: a sensing unit 501 configured to acquire sensing information of a sensing device for a preset area of a target station; a determining unit 502 configured to determine whether a target object exists in a preset area according to the sensing information, so as to obtain a sensing result; the early warning unit 503 is configured to, in response to determining that the sensing result represents that the target object exists in the preset area, send early warning information to the target object based on the target vehicle about to enter the preset area.
In some optional implementations of the present embodiment, the pre-warning unit 503 is further configured to: acquiring position information of a target vehicle associated with a target station; determining whether the target vehicle is about to enter a preset area according to the position information; and sending early warning information to the target object in response to determining that the target vehicle is about to enter the preset area.
In some optional implementations of the present embodiment, the pre-warning unit 503 is further configured to: and displaying and voice broadcasting the early warning information to the target object.
In some optional implementations of the present embodiment, the perceptual information is a perceptual image, and the perceptual ticket 502 is further configured to: and carrying out image recognition on the perceived image, and determining whether a target object exists in a preset area to obtain a perceived result.
In some optional implementations of the present embodiment, the sensing information is point cloud data, and the sensing unit 502 is further configured to: and analyzing the point cloud data, and determining whether a target object exists in a preset area to obtain a perception result.
In this embodiment, a sensing and early warning device is provided, when a target vehicle is about to enter a target station, early warning information is sent to a target object in a preset area of the target station, so that the target vehicle traveling in the station is prevented from causing traffic jam, and traffic efficiency and vehicle traveling safety are improved.
According to an embodiment of the present disclosure, the present disclosure further provides an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to implement the perceptual pre-warning method described in any of the embodiments above when executed.
According to an embodiment of the disclosure, the disclosure further provides a readable storage medium storing computer instructions for enabling a computer to implement the perception early warning method described in any of the above embodiments when executed.
The disclosed embodiments provide a computer program product that, when executed by a processor, enables the perception pre-warning method described in any of the embodiments above.
Fig. 6 shows a schematic block diagram of an example electronic device 800 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the perceptual pre-warning method. For example, in some embodiments, the perceptual pre-warning method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM603 and executed by computing unit 601, one or more steps of the perceptual pre-warning method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the perceptual pre-warning method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called as a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the traditional physical host and virtual special server (VPS, virtual Private Server) service; or may be a server of a distributed system or a server incorporating a blockchain.
According to the technical scheme of the embodiment of the disclosure, a perception early warning system is provided, when a target vehicle is about to enter a target station, early warning information is sent to a target object in a preset area of the target station, so that the target vehicle of the destination station is prevented from causing traffic jam, and the traffic efficiency and the vehicle driving safety are improved.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions provided by the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (13)
1. A perception early warning system, comprising: a perception device, an information processing device and a target station, wherein,
The sensing device is used for generating various sensing information of a preset area of the target station, wherein the sensing device is provided with a device identifier, the target station is provided with a station identifier, and for each target station, the device identifier of the sensing device corresponding to the target station corresponds to the station identifier of the target station;
the information processing device is used for processing corresponding perception information according to the neural network models corresponding to the various perception information respectively, determining whether a target object exists in the preset area or not, and obtaining a plurality of sub-perception results, wherein the target object is a vehicle parked in the preset area; determining a sensing result according to the plurality of sub-sensing results; determining the significative characteristic information of a target object in response to determining that the perception result characterizes the target object exists in the preset area; according to the corresponding relation between the equipment identifier and the station identifier, the sensing result and the marking characteristic information are sent to a target station corresponding to the sensing equipment;
the target station is used for responding to the fact that the perception result represents that a target object exists in the preset area, and acquiring GPS position information of a target vehicle associated with the target station, wherein the target vehicle is a bus, the association relationship between the target station and the target vehicle is predetermined, and the association relationship represents that the target vehicle passes through the target station and enters the station at the target station; responding to the GPS position information to determine that the target vehicle is driven away from a last platform of the target platform, driving the target vehicle to the target platform, determining that the target vehicle is about to enter a preset area of the target platform, and sending early warning information to the vehicle parked in the preset area according to the characteristic feature information;
The target platform is further used for responding to the fact that the perception result represents that a target object exists in the preset area, and tracking a target vehicle according to video data through a neural network model with a target tracking function, wherein the target vehicle is a private car, and the video data are video data of vehicles on a route where the target platform is located; determining a moving track of the target vehicle based on the positions of the target vehicle in different video frames in the video data so as to predict the moving dynamic of the target vehicle; and responding to the running dynamic state, determining that the target vehicle is about to enter a preset area of the target station, and sending early warning information to the vehicle parked in the preset area according to the marking characteristic information.
2. The system of claim 1, wherein the target station comprises a communication device, a processing device for deploying a target application, a display device, and a voice broadcast device;
the target station is further to: and acquiring the perception result from the information processing equipment through the communication device, responding to the fact that the target application determines that the perception result represents that a target object exists in the preset area, displaying the early warning information to the target object through the display device, and broadcasting the early warning information to the target object through the voice broadcasting device.
3. The system of claim 1, wherein the perceptual information is a perceived image, and
the information processing apparatus is further configured to: and carrying out image recognition on the perceived image, and determining whether a target object exists in the preset area to obtain the perceived result.
4. The system of claim 1, wherein the perceived information is point cloud data, and
the information processing apparatus is further configured to: and analyzing the point cloud data, and determining whether a target object exists in the preset area to obtain the perception result.
5. The system of claim 1, wherein the information processing device is a roadside information processing device of the target station.
6. The system of claim 1, wherein the awareness device and the information processing device are integrated in the target station.
7. A perception early warning method, comprising:
acquiring various pieces of perception information of perception equipment for a preset area of a target station, wherein the perception equipment is provided with equipment identifiers, the target station is provided with station identifiers, and for each target station, the equipment identifier of the perception equipment corresponding to the target station corresponds to the station identifier of the target station;
Processing corresponding perception information according to the neural network model corresponding to each of the plurality of perception information, and determining whether a target object exists in the preset area to obtain a plurality of sub-perception results, wherein the target object is a vehicle parked in the preset area;
determining a sensing result according to the plurality of sub-sensing results;
determining the significative characteristic information of a target object in response to determining that the perception result characterizes the target object exists in the preset area;
according to the corresponding relation between the equipment identifier and the station identifier, the sensing result and the marking characteristic information are sent to a target station corresponding to the sensing equipment;
acquiring GPS position information of a target vehicle associated with the target platform, wherein the target vehicle is a bus, the association relationship between the target platform and the target vehicle is predetermined, and the association relationship represents that the target vehicle passes through the target platform and enters the station at the target platform;
responding to the GPS position information to determine that the target vehicle is driven away from a last platform of the target platform, driving the target vehicle to the target platform, determining that the target vehicle is about to enter a preset area of the target platform, and sending early warning information to the vehicle parked in the preset area according to the characteristic feature information;
In response to determining that the perception result represents that a target object exists in the preset area, tracking a target vehicle according to video data through a neural network model with a target tracking function, wherein the target vehicle is a private car, and the video data is video data of a vehicle on a route where the target platform is located;
determining a moving track of the target vehicle based on the positions of the target vehicle in different video frames in the video data so as to predict the moving dynamic of the target vehicle;
and responding to the running dynamic state, determining that the target vehicle is about to enter a preset area of the target station, and sending early warning information to the vehicle parked in the preset area according to the marking characteristic information.
8. The method of claim 7, wherein the issuing of the pre-warning information to the target object comprises:
and displaying and voice broadcasting the early warning information to the target object.
9. The method of claim 7, wherein the perceptual information is a perceptual image, and
determining whether a target object exists in the preset area according to the perception information to obtain a perception result, wherein the method comprises the following steps:
And carrying out image recognition on the perceived image, and determining whether a target object exists in the preset area to obtain the perceived result.
10. The method of claim 7, wherein the perceived information is point cloud data, and
determining whether a target object exists in the preset area according to the perception information to obtain a perception result, wherein the method comprises the following steps:
and analyzing the point cloud data, and determining whether a target object exists in the preset area to obtain the perception result.
11. A perception early warning device, comprising:
the sensing unit is configured to acquire various sensing information of sensing equipment for a preset area of a target station, wherein the sensing equipment is provided with equipment identifiers, the target station is provided with station identifiers, and for each target station, the equipment identifier of the sensing equipment corresponding to the target station corresponds to the station identifier of the target station;
the determining unit is configured to process corresponding perception information according to the neural network model corresponding to each of the plurality of perception information, determine whether a target object exists in the preset area or not, and obtain a plurality of sub-perception results, wherein the target object is a vehicle parked in the preset area; determining a sensing result according to the plurality of sub-sensing results;
An early warning unit configured to determine landmark feature information of a target object in response to determining that the perception result characterizes the existence of the target object in the preset area; according to the corresponding relation between the equipment identifier and the station identifier, the sensing result and the marking characteristic information are sent to a target station corresponding to the sensing equipment; acquiring GPS position information of a target vehicle associated with the target platform, wherein the target vehicle is a bus, the association relationship between the target platform and the target vehicle is predetermined, and the association relationship represents that the target vehicle passes through the target platform and enters the station at the target platform; responding to the GPS position information to determine that the target vehicle is driven away from a last platform of the target platform, driving the target vehicle to the target platform, determining that the target vehicle is about to enter a preset area of the target platform, and sending early warning information to the vehicle parked in the preset area according to the characteristic feature information;
the early warning unit is further configured to track a target vehicle according to video data through a neural network model with a target tracking function in response to determining that the perception result represents the existence of a target object in the preset area, wherein the target vehicle is a private vehicle, and the video data is video data of a vehicle on a route where the target station is located; determining a moving track of the target vehicle based on the positions of the target vehicle in different video frames in the video data so as to predict the moving dynamic of the target vehicle; and responding to the running dynamic state, determining that the target vehicle is about to enter a preset area of the target station, and sending early warning information to the vehicle parked in the preset area according to the marking characteristic information.
12. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 7-10.
13. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 7-10.
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