CN114677848A - 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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Abstract
The disclosure provides a perception early warning system, a perception early warning method, a perception early warning device, electronic equipment, a storage medium and a computer program product, relates to the technical field of computers, particularly relates 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 or not according to the perception information to obtain a perception result; and the target platform is used for responding to the sensing result to represent that the target object exists in the preset area, and sending early warning information to the target object based on the target vehicle about to enter the preset area. The present disclosure improves traffic efficiency and vehicle driving safety.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to intelligent transportation and automatic driving technologies, and in particular, to a perception early warning system, method, apparatus, electronic device, storage medium, and computer program product, which may be used in an intelligent transportation scenario.
Background
A station, such as a bus station, is easily an area with dense traffic and pedestrian flow. Passengers and vehicles near the platform need to pay attention to whether any vehicle enters the platform at all times so as to lead the vehicle to the station, thereby avoiding traffic jam and even traffic accidents. At present, passengers and vehicles near a platform cannot sense the vehicles about to enter the station, and people waiting for the passengers and the vehicles parked near the platform need to pay attention to the vehicles entering the station.
Disclosure of Invention
The present disclosure provides a perception early warning system, a method, an apparatus, an electronic device, a storage medium and a computer program product.
According to a first aspect, there is provided a perceptual pre-warning system comprising: the system comprises sensing equipment, information processing equipment and a target station, wherein 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 or not according to the perception information to obtain a perception result; and the target platform is used for responding to the sensing result to represent that the target object exists in the preset area, and sending early warning information to the target object based on the target vehicle about to enter the preset area.
According to a second aspect, there is provided a perceptual pre-warning method comprising: acquiring perception information of perception equipment on a preset area of a target platform; determining whether a target object exists in a preset area or not 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 about to enter the preset area.
According to a third aspect, there is provided a perception alert device comprising: a sensing unit configured to acquire sensing information of a sensing device for a preset area of a 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; 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 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, the instructions being executable by the at least one processor to enable the at least one processor to perform the 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 having stored thereon computer instructions for causing a computer to perform the method as described in any one of the implementations 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 implementation 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 can avoid traffic jam, and the traffic efficiency and the vehicle traveling safety are improved.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The drawings are included to provide 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 in which one embodiment according to the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a perceptual alert method in accordance with the present disclosure;
fig. 3 is a schematic diagram of an application scenario of the perceptual warning method according to the present embodiment;
FIG. 4 is a flow diagram of yet another embodiment of a perceptual alert method in accordance with the present disclosure;
FIG. 5 is a block diagram of one embodiment of a perception alert device according to the present disclosure;
FIG. 6 is a schematic block 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 with reference to the accompanying drawings, in which various details of embodiments of the present disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those 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 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 collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
Fig. 1 illustrates an exemplary architecture 100 to which the perceptual alert method and apparatus of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include sensing devices 101, 102, a network 103, an information processing device 104, a network 105, and a target station 106. The communication connections between the terminal devices 101, 102 constitute a topological network, and the networks 103, 105 serve to provide a medium for communication links between the terminal devices 101, 102 and the information processing device 104, and between the information processing device 104 and the target station 106. The networks 103, 105 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The perceiving device 101, 102 may be a hardware device or software that supports information collection, network connectivity for data transfer. When the sensing devices 101 and 102 are hardware, they may be various electronic devices supporting functions of network connection, information collection, interaction, display, processing, etc., including but not limited to an image collection device, a voice collection device, a point cloud collection device, a pressure sensor, a thermal infrared sensor, geomagnetism, a gate, etc. When the perceiving device 101, 102 is software, it can be installed in the electronic devices listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
Specifically, the sensing devices 101 and 102 are configured to generate sensing information for a predetermined area of the target station 106. The preset area can be any area in the target platform, such as a parking area when the bus arrives at the station, and a blind sight area of a driver when the bus arrives at the station.
The information processing apparatus 104 may be an information processing apparatus that provides various services, such as a server. Specifically, the information processing device 104 is configured to determine whether a target object exists in the preset area according to the sensing information, and obtain a sensing result.
As an example, the information processing apparatus 104 may determine a perception result corresponding to the perception information through a pre-trained perception model. The perception model is used for representing the corresponding relation between perception information and a perception result. The perception model can be various neural network models such as a convolutional neural network, a residual error network and a cyclic neural network. Specifically, the target object may be a movable object such as a person or a vehicle.
The information processing device 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 may be implemented as a single information processing apparatus. When the information processing apparatus is software, it may be implemented as a plurality of pieces of software or software modules (for example, software or software modules for providing distributed services), or as a single piece of software or software module. And 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 the 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 a target object that a target vehicle is about to enter a preset area where the target object is located, and remind a user of keeping away from the preset area of the target platform, so that the target vehicle of a traveling station can be avoided from causing traffic jam.
In order to further improve the pertinence and the effectiveness of the early warning information, in the process of obtaining the characterization perception result, the symbolic feature information (for example, the symbolic appearance feature) of the target object may be further determined. For example, for a person in the preset area, the landmark feature may be, for example, a sex, a dress, etc. feature; for vehicles in the preset area, the characteristic features may be, for example, license plate number, vehicle type, vehicle color, and the like. In this example, the neural network model that obtains the sensing result may further determine landmark characteristic information of the target object, that is, the neural network model represents a correspondence between the sensing information and the sensing result, as well as between the landmark characteristics of the target object; in this example, in response to the first neural network model obtaining the sensing result determining that the target object exists in the preset region, the landmark feature information of the target object may also be determined by a second neural network model independent of the first neural network model. Wherein the second neural network model is used for characterizing the correspondence between the target object and the characteristic features.
Furthermore, when the target station sends out the early warning information, the early warning information can be further sent out in a targeted mode according to the symbolic characteristics of the target objects in the preset area. For example, the warning message is "101 buses are about to drive into the station, please drive vehicles with license plate XXX away from the station".
It should be understood that the number of sensing devices, information processing devices, networks, and target stations in fig. 1 is merely illustrative. There may be any number of sensing devices, information processing devices, networks, and target stations, as desired for an implementation. When the electronic device on which the early warning perception method is operated does not need to perform data transmission with other electronic devices, the system architecture may only include the electronic device (e.g., the perception device, the information processing device, or the target station) on which the early warning method is operated.
In this embodiment, 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 located in a preset area of the target station, so that the target vehicle at the target station avoids causing traffic congestion, and traffic efficiency and vehicle driving safety are improved.
In some optional implementations of this embodiment, the target station is further configured to: obtaining location information of a target vehicle associated with a target platform; determining whether the target vehicle is about to enter a preset area or not according to the position information; and responding to the fact that the target vehicle is about to enter the preset area, and sending early warning information to the target object.
The target vehicle may be any type of vehicle that may enter the target platform. For example, the target vehicle is a bus, a private car, or the like. Taking a bus as an example, when the bus passes through a target platform in the operation process and enters the station at the target platform, the bus is considered to be associated with the target platform. The association relationship between the target station and the bus is generally predetermined, so that the bus needing to enter the station at the target station can be predetermined. The target platform can communicate with the associated bus through the cloud platform, the real-time position of the bus is determined based on a Global Positioning System (GPS), and then whether the bus is about to enter a preset area of the target platform is determined according to the real-time position of the bus and the position of the target platform.
As an example, in response to a bus leaving a station immediately above a current target station, the bus is driven to the current target station, and it is determined that the bus is about to enter a preset area of the current target station. As yet another example, in response to determining that the distance between the bus and the target platform is within a preset distance threshold, it is determined that the bus is about to enter a preset area of the current target platform. The preset distance threshold value can be specifically set according to actual conditions. For example, the preset distance is 100 meters.
Taking a private car as an example, since it is unpredictable whether the private car will stop at the target platform, that is, the association relationship between the private car and the target platform cannot be predetermined, a data processing device capable of performing target tracking on the target vehicle may be disposed near the target platform, so as to track the target vehicle through the data processing device and determine the running track of the target vehicle. Specifically, the data processing device can collect video data of vehicles on a route where the target platform is located, track the target vehicles through a neural network model with a target tracking function according to each video frame in the video data, determine the running tracks of the target vehicles based on the positions of the target vehicles among different frames, and predict the running dynamics of the target vehicles so as to determine whether private vehicles enter a preset area of the target platform.
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 correlation between the target vehicle and the target platform, the real-time position of the target vehicle and the position of the target platform, 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 that deploys the target application, a display device, and a voice broadcast device. Wherein the target station is further configured to: the method comprises the steps of obtaining a sensing result from information processing equipment through a communication device, responding to the fact that a target object exists in a sensing result representation preset area determined through target application, displaying early warning information to the target object through a display device, and broadcasting the early warning information to the target object through a voice broadcasting device.
The communication means may acquire the sensing result from the information processing apparatus by wired connection or wireless connection communication. The target application may be an application program specially developed for the smart station, and is configured to determine a sensing result and send an early warning instruction to the display device and the voice broadcasting device based on the sensing result.
In the implementation mode, the specific structure of the target platform is provided, and the early warning information is sent out based on multiple 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 perceptual image. In this implementation, the preset area adjacent to the target platform may be provided with an image acquisition device to acquire the perception image of the preset area in real time, and the information processing device is further configured to: and carrying out image recognition on the perception image, and determining whether a target object exists in a preset area to obtain a perception result.
As an example, the information processing apparatus may identify a target object in the detection image by an 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 a landmark feature corresponding to 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, a point cloud collecting device may be disposed in a preset area adjacent to the target station to collect the point cloud data of the preset area in real time, and the information processing apparatus is further configured to: and analyzing the point cloud data to determine whether a target object exists in a preset area or not, and obtaining a perception result.
As an example, when a target object enters a predetermined area of a target platform, the collected point cloud data may change, and the changed portion may be identified as a new object. If the objects do not exist in the background point cloud data corresponding to the target station, the target object in the predetermined 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 for better processing of the information. This border helps to roughly classify the target. For example, whether the detected object is a car or a pedestrian can be determined from this frame.
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 implementation manners 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, the corresponding sensing result can be obtained according to the sensing information of each sensing device based on the high-performance data processing capability of the cloud. As an example, for each target station, its corresponding station identification is set. The identity of the sensing device corresponding to each target station corresponds to the station identity. And sending the processed sensing result to a target platform corresponding to the identification based on the platform identification corresponding to the received sensing information.
In this implementation, when the information processing device is a cloud information processing device, based on the cloud information processing device, the target station may locally adopt a simpler device structure, which improves the simplicity of deployment of the target station; and moreover, based on the high-performance information processing capability of the cloud, the accuracy of the obtained notification result is improved.
When the information processing equipment is roadside information processing equipment of the target station, each target station, the corresponding local sensing equipment and the corresponding information processing equipment form relatively independent units, sensing of the target object can be achieved inside the units, and processes such as early warning based on sensing results are achieved, information transmission links are shortened, response time is shortened, early warning information is timely sent out, and traffic safety is improved.
In the implementation mode, when the information processing equipment is the roadside information processing equipment of the target platform, the timeliness of early warning is improved, traffic accidents can be further avoided, and traffic safety is guaranteed.
In some optional implementations of this embodiment, the sensing device and the information processing device are integrated in the target station.
In the implementation mode, based on an integrated mode, the communication link of the whole information processing process of sensing of the target object and early warning based on a sensing result is further shortened, the link is optimal, the integration level is highest, and the quick response of platform services can be completed.
Referring to fig. 2, fig. 2 is a flowchart of a perception early warning method according to an embodiment of the disclosure, where the process 200 includes the following steps:
In this embodiment, the main body (e.g., the sensing device, the information processing device, or the target station in fig. 1) of the execution of the sensing and early warning method may obtain the sensing information of the sensing device for the preset area of the target station from a remote location or a local location based on a wired network connection manner or a wireless network connection manner.
The preset area can be any area in the target platform, such as a parking area when the bus arrives at the station, and a blind sight area of a driver when the bus arrives at the station. The sensing device may be an information collecting device supporting information collection and network connection for data transmission, for example, an image collecting device, a voice collecting device, a point cloud collecting device, a pressure sensor, a thermal infrared sensor, geomagnetism, a gate machine, and the like.
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.
In this embodiment, the execution main 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 entity may determine a sensing result corresponding to the sensing information through a pre-trained sensing model. The perception model is used for representing the corresponding relation between perception information and a perception result. The perception model can be various neural network models such as a convolutional neural network, a residual error network and a cyclic neural network. Specifically, the target object may be a movable object such as a person or a vehicle.
As yet another example, the perception result includes a plurality of perception data collected by different perception devices. And for various perception data, processing the perception data through the neural network model corresponding to the perception data to obtain a perception result corresponding to the perception data. And determining a final sensing result by adopting a weighting summation mode and other modes according to the multiple sensing results.
In this embodiment, the execution subject may send the warning information to the target object based on the target vehicle about to enter the preset area in response to determining that the sensing result represents that the target object exists in the preset area.
The early warning information can prompt a target object that a target vehicle is about to enter a preset area where the target object is located, and remind a user of keeping away from the preset area of the target platform, so that the target vehicle of a traveling station can be avoided from causing traffic jam.
In order to further improve the pertinence and the effectiveness of the early warning information, in the process of obtaining the characterization perception result, the symbolic feature information (for example, the symbolic appearance feature) of the target object may be further determined. For example, for a person in the preset area, the landmark feature may be, for example, a sex, a dress, etc. feature; for vehicles in the preset area, the characteristic of the logo may be, for example, the license plate number, the vehicle type, the vehicle color, and the like. In this example, the neural network model that obtains the sensing result may further determine symbolic feature information of the target object, that is, the neural network model represents a corresponding relationship between the sensing information and the sensing result, and the symbolic feature of the target object; in this example, in response to the first neural network model obtaining the sensing result determining that the target object exists in the preset region, the landmark feature information of the target object may also be determined by a second neural network model independent of the first neural network model. Wherein the second neural network model is used for characterizing the correspondence between the target object and the characteristic features.
Furthermore, when sending out the early warning information, the target station can further send out the early warning information in a targeted manner according to the symbolic characteristics of the target object in the preset area. For example, the warning message is "101 buses are about to drive into the station, please drive vehicles with license plate XXX away from the station".
With continued reference to fig. 3, fig. 3 is a schematic diagram 300 of an application scenario of the perceptual warning method according to the present embodiment. In the application scenario of fig. 3, there is a suspended private car in the bus parking area of the target station 301. The sensing device 302 near the target platform collects sensing information of the bus parking area of the target platform in real time and sends the sensing information to the information processing device 303 corresponding to the target platform. The information processing device 303 determines that the target object, including the private car 304, exists in the bus parking area according to the sensing information, and obtains a sensing result. Further, the target station 301, in response to determining that the sensing result indicates that the target object exists in the bus parking area, sends an early warning message 306 to the target object 304 based on the bus 305 that is about to enter the bus parking area.
In the embodiment, a perception 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 at the target station avoids traffic jam, and the traffic efficiency and the vehicle driving safety are improved.
In some optional implementations of the embodiment, the executing body issues the warning information to the target object based on the target vehicle about to enter the preset area by:
firstly, acquiring position information of a target vehicle associated with a target platform; then, determining whether the target vehicle is about to enter a preset area or not according to the position information; and finally, responding to the fact that the target vehicle is about to enter the preset area, and sending early warning information to the target object.
The target vehicle may be any type of vehicle that may enter the target platform. For example, the target vehicle is a bus, a private car, or the like. Taking a bus as an example, when the bus passes through a target platform in the operation process and enters the station at the target platform, the bus is considered to be associated with the target platform. The association relationship between the target station and the bus is generally predetermined, so that the bus needing to enter the station at the target station can be predetermined. The target platform can communicate with the associated bus through the cloud platform, the real-time position of the bus is determined based on a Global Positioning System (GPS), and then whether the bus is about to enter a preset area of the target platform is determined according to the real-time position of the bus and the position of the target platform.
As an example, in response to a bus leaving a station immediately above a current target station, the bus is driven to the current target station, and it is determined that the bus is about to enter a preset area of the current target station. As yet another example, in response to determining that the distance between the bus and the target platform is within a preset distance threshold, it is determined that the bus is about to enter a preset area of the current target platform. The preset distance threshold value can be specifically set according to actual conditions. For example, the preset distance is 100 meters.
Taking a private car as an example, since it is unpredictable whether the private car will stop at the target platform, that is, the association relationship between the private car and the target platform cannot be predetermined, a data processing device capable of performing target tracking on the target vehicle may be disposed near the target platform, so as to track the target vehicle through the data processing device and determine the running track of the target vehicle. Specifically, the data processing device can collect video data of vehicles on a route where the target platform is located, track the target vehicles through a neural network model with a target tracking function according to each video frame in the video data, determine the running tracks of the target vehicles based on the positions of the target vehicles among different frames, and predict the running dynamics of the target vehicles so as to determine whether private vehicles enter a preset area of the target platform.
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 correlation between the target vehicle and the target platform, the real-time position of the target vehicle and the position of the target platform, whether the target vehicle is about to enter the preset area can be accurately determined.
In some optional implementations of the embodiment, the executing entity may issue the warning information to the target object by executing the following steps: and displaying and voice broadcasting the early warning information to the target object.
Specifically, the target station is further configured to: the method comprises the steps of obtaining a sensing result from information processing equipment through a communication device, responding to the situation that a target object exists in a preset area of the sensing result representation determined through target application, displaying early warning information to the target object through a display device, and broadcasting the early warning information to the target object through a voice broadcasting device.
The communication means may acquire the sensing result from the information processing apparatus by wired connection or wireless connection communication. The target application may be an application program specially developed for the smart station, and is configured to determine a sensing result and send an early warning instruction to the display device and the voice broadcasting device based on the sensing result.
In this implementation mode, the concrete structure of target platform is provided to based on display device and the multiple mode of voice broadcast device send early warning information, improved early warning information's validity.
In some optional implementations of this embodiment, the perceptual information is a perceptual image. In this implementation, an image acquisition device may be disposed in the preset area adjacent to the target platform to acquire the sensing image of the preset area in real time.
In this implementation, the executing body may execute the step 202 as follows: and carrying out image recognition on the perception image, and determining whether a target object exists in a preset area or not to obtain a perception result.
As an example, the information processing apparatus may identify a target object in the detection image through an image perception model, and obtain a perception result. Specifically, when the target object exists in the preset area, the sensing result may include each target object and a landmark feature corresponding to 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 the embodiment, the sensing information is point cloud data, and a point cloud collecting device may be disposed in a preset area adjacent to the target platform to collect the point cloud data of the preset area in real time. In this implementation, the executing entity may execute the step 202 as follows: and analyzing the point cloud data to determine whether a target object exists in a preset area or not, and obtaining a perception result.
As an example, when a target object enters a predetermined area of a target station, the acquired point cloud data may be changed, 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, it is recognized that the target object is present in the preset area. According to the convex hull principle, a so-called bounding box is drawn around the target object. The convex hull describes the smallest circumference of the object by connecting points of the outermost layer of the object. The bounding box encloses the object in a cuboid as small as possible for better processing of the information. This border helps to roughly classify the target. For example, whether the detected object is a car or a pedestrian can be determined from this frame.
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, an exemplary flow 400 of one method embodiment of perceptual positioning according to the methods of the present disclosure is shown, comprising the steps of:
In response to determining that the sensing result indicates that the target object exists in the preset area, position information of the target vehicle associated with the target platform is obtained in step 403.
And step 404, determining whether the target vehicle is about to enter a preset area according to the position information.
And step 405, responding to the fact 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 perception early warning method in this embodiment specifically illustrates a determination process that a target vehicle is about to enter a preset area, and an early warning process, which further improves the accuracy of the determined perception early warning, and improves traffic efficiency and vehicle driving safety.
With continued reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides an embodiment of a perception early warning apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 5, the perception early warning apparatus 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, and obtain a sensing result; an early warning unit 503 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 a target vehicle about to enter the preset area.
In some optional implementations of this embodiment, the early warning unit 503 is further configured to: obtaining location information of a target vehicle associated with a target platform; determining whether the target vehicle is about to enter a preset area or not according to the position information; and responding to the fact that the target vehicle is about to enter the preset area, and sending early warning information to the target object.
In some optional implementations of this embodiment, the early 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 perception information is a perception image, and the notice 502 is further configured to: and carrying out image recognition on the perception image, and determining whether a target object exists in a preset area to obtain a perception result.
In some optional implementations of this embodiment, the sensing information is point cloud data, and the sensing unit 502 is further configured to: and analyzing the point cloud data to determine whether a target object exists in a preset area or not, and obtaining a perception result.
In this embodiment, a perception 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 located in a preset area of the target station, so that the target vehicle at the target station avoids causing traffic congestion, and traffic efficiency and vehicle driving safety are improved.
According to an embodiment of the present disclosure, the present disclosure also provides an electronic device including: 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, the instructions being executable by the at least one processor to enable the at least one processor to implement the perceptual alert method as described in any of the embodiments above.
According to an embodiment of the present disclosure, a readable storage medium is further provided, where the readable storage medium stores computer instructions, and the computer instructions are configured to enable a computer to implement the perception early warning method described in any of the above embodiments when executed.
The embodiments of the present disclosure provide a computer program product, which when executed by a processor can implement the perception early warning method described in any of the embodiments above.
Fig. 6 illustrates a schematic block diagram of an example electronic device 800 that can 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the device 600 comprises a computing unit 601, which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, and the like; 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 the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the various methods and processes described above, such as the perceptual pre-warning method. For example, in some embodiments, the perceptual alert method may be implemented as a computer software program tangibly embodied in 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 the 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 by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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 clients and servers. A client and server are generally 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 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 existing in the traditional physical host and Virtual Private Server (VPS) service; it may also 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 traveling station can avoid traffic jam, and the traffic efficiency and the vehicle traveling safety are improved.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in this disclosure may be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions provided by this disclosure can be achieved, which are not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (16)
1. A perceptual-early warning system comprising: 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 the preset area or not according to the perception information to obtain a perception result;
the target platform is used for responding to the sensing result representing that a target object exists in the preset area, and sending early warning information to the target object based on a target vehicle about to enter the preset area.
2. The system of claim 1, wherein the target station is further to:
obtaining location information of a target vehicle associated with the target platform;
determining whether the target vehicle is about to enter the preset area or not according to the position information;
and responding to the fact that the target vehicle is about to enter the preset area, and sending early warning information to the target object.
3. The system according to claim 1 or 2, wherein the target station comprises a communication device, a processing device deploying a target application, a display device and a voice announcement device;
the target station is further to: the sensing result is obtained from the information processing equipment through the communication device, the target object exists in the preset area in response to the sensing result representation determined through the target application, the early warning information is displayed to the target object through the display device, and the early warning information is broadcasted to the target object through the voice broadcasting device.
4. The system of claim 1, wherein the perceptual information is a perceptual image, and
the information processing apparatus is further configured to: and carrying out image recognition on the perception image, and determining whether a target object exists in the preset area to obtain the perception result.
5. The system of claim 1, wherein the perceptual information is point cloud data, an
The information processing apparatus is further configured to: and analyzing the point cloud data to determine whether a target object exists in the preset area or not, and obtaining the perception result.
6. The system of claim 1, wherein the information processing device is a cloud information processing device or a roadside information processing device of the target station.
7. The system of claim 1, wherein the sensing device and the information processing device are integrated in the target station.
8. A perceptual-forewarning method comprising:
acquiring perception information of perception equipment on a preset area of a target platform;
determining whether a target object exists in the preset area or not according to the perception information to obtain a perception result;
responding to the fact that the perception result represents that a target object exists in the preset area, and sending early warning information to the target object based on a target vehicle about to enter the preset area.
9. The method of claim 8, wherein the issuing warning information to the target object based on the target vehicle about to enter the preset area comprises:
obtaining location information of a target vehicle associated with the target platform;
determining whether the target vehicle is about to enter the preset area or not according to the position information;
and responding to the fact that the target vehicle is about to enter the preset area, and sending early warning information to the target object.
10. The method of claim 8 or 9, wherein the issuing of the warning information to the target object comprises:
and displaying and voice broadcasting the early warning information to the target object.
11. The method of claim 8, 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 step of determining whether the target object exists in the preset area comprises the following steps:
and carrying out image recognition on the perception image, and determining whether a target object exists in the preset area to obtain the perception result.
12. The method of claim 8, wherein the perceptual information is point cloud data, an
Determining whether a target object exists in the preset area according to the perception information to obtain a perception result, wherein the step of determining whether the target object exists in the preset area comprises the following steps:
and analyzing the point cloud data to determine whether a target object exists in the preset area or not, and obtaining the perception result.
13. A perception alert device comprising:
a sensing unit configured to acquire sensing information of a sensing device for a preset area of a target station;
the determining unit is configured to determine whether a target object exists in the preset area according to the perception information to obtain a perception result;
an early warning unit configured to, in response to determining that the perception result indicates that a target object exists in the preset area, send early warning information to the target object based on a target vehicle that is about to enter the preset area.
14. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 8-12.
15. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 8-12.
16. A computer program product, comprising: computer program which, when being executed by a processor, carries out the method according to any one of claims 8-12.
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