CN113793518A - Vehicle passing processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to a vehicle passing processing method, a vehicle passing processing device, an electronic device and a storage medium, wherein the vehicle passing processing method comprises the following steps: acquiring a road section image of a first road section; identifying a target vehicle with a priority right of way from the road section image; in a case where it is determined that the target vehicle is performing a task, a traffic light at a next intersection is scheduled such that the traffic light is in a passable state when the target vehicle reaches the next intersection. The application provides various embodiments to at least solve the problem that the identification cost of the vehicle with the priority right of way is high in the related art.
Description
Technical Field
The present application relates to the field of intelligent transportation technologies, and in particular, to a vehicle passing processing method and apparatus, an electronic device, and a storage medium.
Background
In transportation scenarios, some vehicles often require the right to pass first in order to perform emergency tasks. Typically, these vehicles include, for example, ambulances, fire trucks, police cars, construction wreckers, military organizers, and the like. Identifying vehicles with priority right of passage and efficiently scheduling the switching of intersection traffic lights are the key to ensure the smooth passage of the vehicles with priority right of passage.
In the related art, it is common to mount an electronic tag on a vehicle having a priority right of passage. When the vehicle passes through the electronic tag reading device, the electronic tag reading device can read the information of the vehicle with the priority right of passage and inform the traffic light dispatching system to dispatch the traffic light at the intersection where the vehicle is about to pass, so that the vehicle can pass quickly. It can be seen that, in the related art, an electronic tag needs to be installed on each vehicle with priority right of way, and since the number of vehicles with priority right of way is large, the method needs to consume a large amount of time cost and material cost.
Therefore, there is a need in the art for an efficient and low-cost vehicle passing processing method.
Disclosure of Invention
The embodiment of the application provides a vehicle passing processing method and device, electronic equipment and a storage medium, and aims to at least solve the problem that the identification cost of a vehicle with a priority right of passage is high in the related art.
In a first aspect, an embodiment of the present application provides a vehicle passage processing method, including:
acquiring a road section image of a first road section;
identifying a target vehicle with a priority right of way from the road section image;
in a case where it is determined that the target vehicle is performing a task, a traffic light at a next intersection is scheduled such that the traffic light is in a passable state when the target vehicle reaches the next intersection.
According to the vehicle passing processing method provided by the embodiment of the application, the target vehicle with the priority passing right can be identified according to the road section image, devices such as an electronic tag and the like do not need to be additionally installed, and the identification cost is saved. In addition, in the case where it is determined that the target vehicle is performing a task, a traffic light at the next intersection is rescheduled. The target vehicle with the priority right of way does not need to run the priority right of way under the condition that the task is not executed, so that the scheduling resource can be saved.
Optionally, in an embodiment of the present application, the determining that the target vehicle is performing a task includes:
acquiring audio data at the target vehicle;
and under the condition that the target vehicle is determined to emit the preset sound signal according to the audio data, determining that the target vehicle executes the task.
Optionally, in an embodiment of the present application, the identifying a target vehicle with a priority right of way from the road segment image includes:
inputting the road section image into a machine learning model, and outputting the identification result of whether the target vehicle with the priority right of passage exists in the road section image or not through the machine learning model;
the machine learning model is obtained by training a plurality of road section image samples, wherein the plurality of road section image samples comprise image samples of vehicles with priority right of passage in a road section.
Optionally, in an embodiment of the present application, the scheduling a traffic light at a next intersection includes:
acquiring the distance between the target vehicle and the next intersection and the driving speed;
determining the time length of the target vehicle to travel to the next intersection according to the distance and the travel speed;
after the period of time, switching the traffic light to a passable state.
Optionally, in an embodiment of the present application, the scheduling a traffic light at a next intersection includes:
determining a congestion level of a road segment between the location of the target vehicle and a next intersection;
and under the condition that the congestion degree is determined to be larger than a preset threshold value, switching the traffic light of the next intersection to a passable state until the target vehicle drives away from the next intersection.
Optionally, in an embodiment of the application, in a case that the number of the target vehicles includes a plurality of vehicles, the scheduling the traffic light at the next intersection includes:
acquiring a road section image of a second road section, wherein the second road section comprises a road section which is passed by the target vehicle after passing through the next intersection from the first road section;
and scheduling the traffic light at the next intersection so that the traffic light is in a passable state when the first target vehicle reaches the next intersection until the last target vehicle drives away from the next intersection, wherein the last target vehicle is determined by comparing the road section image of the first road section with the road section image of the second road section.
Optionally, in an embodiment of the present application, the last target vehicle is determined by:
determining the vehicle identifier of the last target vehicle according to the road section image of the first road section;
determining the vehicle identification of the passing vehicle according to the road section image of the second road section;
and determining that the passing vehicle is the last target vehicle under the condition that the vehicle identification of the passing vehicle is matched with the vehicle identification of the last target vehicle.
Optionally, in an embodiment of the present application, after the scheduling the traffic light at the next intersection, the method further includes:
and under the condition that the target vehicle is determined to drive away from the next intersection, restoring the traffic light at the next intersection to the state before dispatching.
In a second aspect, a vehicle passage processing apparatus includes:
the image acquisition module is used for acquiring a road section image of the first road section;
the vehicle identification module is used for identifying a target vehicle with a priority right of way from the road section image;
and the traffic light scheduling module is used for scheduling the traffic light at the next intersection under the condition that the target vehicle is determined to execute the task, so that the traffic light is in a passable state when the target vehicle reaches the next intersection.
In a third aspect, an electronic device includes a memory and a processor, the memory stores a computer program, and the processor is configured to run the computer program to execute the vehicle passing processing method.
Optionally, in an embodiment of the present application, the method further includes:
and the acquisition device is used for acquiring the road section image of the first road section.
In a fourth aspect, a non-transitory computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the vehicle transit processing method.
In a fifth aspect, a computer program product includes computer readable codes or a non-volatile computer readable storage medium carrying computer readable codes, when the computer readable codes are run in a processor of an electronic device, the processor in the electronic device executes the vehicle passing processing method.
In a sixth aspect, a chip comprises at least one processor, which is configured to execute a computer program or computer instructions stored in a memory to perform the vehicle passing processing method.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic structural diagram of a vehicle communication processing system according to an embodiment of the present application.
Fig. 2 is a method flowchart of a vehicle passage processing method provided by an embodiment of the application.
Fig. 3 is a schematic block structure diagram of a vehicle passage processing device provided in an embodiment of the present application.
Fig. 4 is a schematic block structure diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference herein to "a plurality" means greater than or equal to two. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present application. It will be understood by those skilled in the art that the present application may be practiced without some of these specific details. In some instances, devices, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present application.
In order to clearly show the technical solutions of the embodiments of the present application, an exemplary scenario of the embodiments of the present application is described below with reference to fig. 1.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a vehicle passage processing system provided in an embodiment of the present application, the system includes a collecting device 101 and a vehicle passage processing device 103, where the collecting device 101 and the vehicle passage processing device 103 may communicate through a network to send collected road segment images to the vehicle passage processing device 103, and the vehicle passage processing device 103 completes processing of lane passage.
The acquisition device 101 may be an electronic device having data acquisition capabilities and data transceiving capabilities. For example, the acquisition device 101 may be a road side unit equipped with one or more sensors such as a lidar, a camera, and the like. The roadside unit may be installed at the roadside and acquire road segment images within a coverage area. The laser radar is mainly used for collecting point cloud data, and because the laser radar can accurately reflect position information, the speed information of vehicles in a road can be obtained through the laser radar; the camera is mainly used for collecting information such as marks and lane lines on the road surface. It should be noted that one road side unit may acquire the intersection information, or a plurality of road side units may cooperate to acquire the intersection information, so as to acquire the information of the complete road segment. The road side unit can be composed of a high-gain directional beam control read-write antenna and a radio frequency controller. The high-gain directional beam control read-write antenna is a microwave transceiver module and is responsible for transmitting/receiving, modulating/demodulating, coding/decoding, encrypting/decrypting signals and data; the radio frequency controller is a module for controlling data transmission and reception and processing information transmission and reception to an upper computer. In other embodiments, the capturing device 101 may further include a capturing vehicle, which is not limited herein.
The vehicle passing processing device 103 may be an electronic device with data processing capability and data transceiving capability, and may be a physical device such as a host, a rack server, a blade server, or the like, or may be a virtual device such as a virtual machine, a container, or the like. The vehicle passage processing device 103 may identify the target vehicle 105 having the priority passage from the link image. In the case where it is determined that the target vehicle 105 is performing a task, a traffic light 107 at the next intersection is scheduled so that the traffic light 107 is in a passable state when the target vehicle 105 reaches the next intersection.
It should be noted that the vehicle passing processing device 103 may also be integrated in the acquisition device 101, for example, the road side unit completes the work flow of acquiring the road section image and the vehicle passing processing, and this embodiment of the present application is not limited in any way.
The following describes the vehicle passing processing method in detail with reference to the drawings. Fig. 2 is a schematic flow chart of an embodiment of a vehicle passage processing method provided by the present application. Although the present application provides method steps as shown in the following examples or figures, more or fewer steps may be included in the method based on conventional or non-inventive efforts. In the case of steps where no necessary causal relationship exists logically, the order of execution of the steps is not limited to that provided by the embodiments of the present application. The method can be executed sequentially or in parallel (for example, in the context of a parallel processor or a multi-thread process) in the method according to the embodiments or the figures during actual vehicle passing process or when the method is executed.
Specifically, an embodiment of the vehicle passage processing method provided by the present application is shown in fig. 2, and the method may include:
s201: a road segment image of a first road segment is acquired.
In the embodiment of the present application, the first road segment may include any road segment in a road. Optionally, in an embodiment of the present application, the first segment may include a segment whose distance to the next intersection is less than a preset distance threshold. The smaller the distance between the first road section and the next intersection is, the more accurately the time length for the target vehicle with the priority right of way to travel to the next intersection can be determined subsequently. In one example, the first road segment may comprise a road segment 50-100 meters from the next intersection.
In the embodiment of the present application, the road section image of the first road section may be acquired by the acquisition device 101. In some embodiments, the acquisition device 101 may include an electronic device including at least one sensor such as a camera, a laser radar, or the like. The collection device 101 may include a road side unit, and may also include a collection vehicle, which is not limited herein. The camera device can acquire image data or video data of the first road section. In the case where the video data of the first link is captured by the camera, the link image may include a video frame in the video data. In the case of collecting data using the laser radar, the road section image may include a three-dimensional point cloud image of the first road section. It should be noted that one acquisition device 101 may acquire the road segment image of the first road segment, or a plurality of acquisition devices 101 may cooperate to acquire the road segment image, which is not limited herein.
S203: and identifying the target vehicle with the priority right of way from the road section image.
In the embodiment of the application, the image of the road section may include an image of a vehicle running on the road. Therefore, the target vehicle with the priority right of way can be identified from the road section image through some technical means. The vehicle with priority right of way may include a special vehicle (an ambulance, a fire truck, a police car, an engineering rescue vehicle, a military surveillance vehicle), or other vehicle (such as a part of a bus) with priority right of way, and the like, and the application is not limited herein.
In one embodiment of the present application, the target vehicle may be identified from the road segment image by using a machine learning method. In practical applications, vehicles with priority traffic often have significant vehicle characteristics, such as a normal police vehicle with a blue-white color, a fire truck with a red color, an ambulance with a white color, and fire and ambulance vehicles with fixed vehicle types. Based on this, in an embodiment of the present application, the identifying the target vehicle with the priority right of way from the road section image may include:
s301: inputting the road section image into a machine learning model, and outputting the identification result of whether the target vehicle with the priority right of passage exists in the road section image or not through the machine learning model;
the machine learning model is obtained by training a plurality of road section image samples, wherein the plurality of road section image samples comprise image samples of vehicles with priority right of passage in a road section.
In the embodiment of the application, a machine learning model for identifying whether the image has the target vehicle or not can be trained. In particular, the machine learning model may be arranged to be trained in the following manner:
s401: acquiring a plurality of road section image samples, wherein the road section image samples comprise image samples of vehicles with priority right of passage in a road section;
s403: constructing a machine learning model, wherein training parameters are set in the machine learning model;
s405: respectively inputting the road image samples into the machine learning model to generate a prediction result;
s407: and iteratively adjusting the training parameters based on the difference between the prediction result and the result of whether the vehicle with the prior traffic exists in the road section image sample until the difference meets the preset requirement.
In the embodiment of the application, a plurality of road segment image samples may be acquired, and among the plurality of road segment image samples, a plurality of image samples of vehicles having priority right of passage running in a road segment may be included. Furthermore, the road section image sample can also be marked with a vehicle type of a vehicle with priority right of way, for example, whether the vehicle in the road section image sample is a fire engine or an ambulance. Then, a machine learning model is constructed for learning the road segment image samples. Specifically, the plurality of road segment image samples may be input into the machine learning model, respectively, and prediction results may be generated. In one embodiment of the present application, the prediction result may include a probability that a vehicle with priority right of way exists in the road section image sample, and in the case that a plurality of vehicle types are included, the prediction result may include a probability that different types of vehicles with priority right of way exist in the road section image sample. Finally, the training parameters may be iteratively adjusted based on a difference between the prediction result and a result of whether a vehicle having a priority pass exists in the road segment image sample until the difference meets a preset requirement.
In this embodiment, the machine learning model may include a model obtained by training in a machine learning manner. The machine learning mode can also comprise a K nearest neighbor algorithm, a perception machine algorithm, a decision tree, a support vector machine, a logistic background regression, a maximum entropy and the like, and correspondingly, the generated model is naive Bayes, hidden Markov and the like. Of course, in other embodiments, the machine learning manner may further include a deep learning manner, a reinforcement learning manner, and the like, and the generated model may include a Convolutional Neural Network model (CNN), a Recurrent Neural Network model (RNN), LeNet, ResNet, a Long Short Term Memory Network model (Long Short-Term Memory, LSTM), a bidirectional Long Short Term Memory Network model (Bi-LSTM), and the like, which is not limited herein.
In the embodiment of the application, the target vehicle is identified from the road section image in a machine learning mode, the vehicle with the priority right of passage can be identified through the appearance characteristics of the vehicle, and the method has high accuracy and high identification efficiency.
Of course, in other embodiments, the target vehicle with the priority right of passage in the road segment image may also be identified by using a license plate recognition method, and the like, which is not limited herein.
S205: in a case where it is determined that the target vehicle is performing a task, a traffic light at a next intersection is scheduled such that the traffic light is in a passable state when the target vehicle reaches the next intersection.
In practical applications, some target vehicles with priority right of way can normally pass through the road whether or not performing tasks, such as police cars. However, if the target vehicle having the priority right of passage is not performing the emergency task, it is not necessary to exercise the right of priority passage. Based on this, in one embodiment of the application, in the case where it is determined that the target vehicle is performing a task, the target vehicle is caused to exercise the right of priority traffic by adjusting the traffic light. Based on this, in one embodiment of the present application, it may be determined that the target vehicle is performing a task by:
s501: acquiring audio data at the target vehicle;
s503: and under the condition that the target vehicle is determined to emit the preset sound signal according to the audio data, determining that the target vehicle executes the task.
In the embodiment of the application, whether the target vehicle executes the task can be judged according to the audio data based on the phenomenon that the vehicle with the priority right of passage makes a sound when executing the task. In some examples, police, fire and ambulance sirens sound at a particular frequency while performing a task, for example, police sirens sound at 2-3Hz, ambulance sirens sound at 1Hz, and fire fighters sound at 0.3 Hz. Therefore, by analyzing the characteristic information of the sound signals in the audio data, it can be identified whether the target vehicle emits preset sound signals, and the preset sound signals can comprise sound signals emitted by vehicles with priority right of way when performing tasks. For example, the collected audio data may be analyzed for the inclusion of an ambulance siren at a frequency of 1 Hz. In some embodiments, whether the target vehicle emits the preset sound signal may be determined by a machine learning manner, for example, the audio data is input into a sound processing machine learning model, and a result of whether the audio data includes the preset sound signal is output through the sound processing machine learning model, which is not limited in the present application. It should be noted that, in the embodiment of the present application, the audio data at the target vehicle may be collected by a microphone. The microphone may be separately installed at the roadside, or may be coupled to the acquisition device 101, which is not limited herein.
Of course, in other embodiments, it may also be determined whether the target vehicle is performing a task by means of image recognition or the like. For example, during the execution of a task, a vehicle with a priority right of way may turn on a warning light, and then it may be recognized whether the warning light on the target vehicle is in an on state by means of image recognition. Further, by combining the above-mentioned various manners of determining whether the target vehicle is performing the task, for example, in a case that it is determined that the target vehicle emits the preset sound signal and the warning light is in the on state, it is determined that the target vehicle is performing the task, and the application is not limited herein.
In the embodiment of the application, under the condition that the target vehicle is determined to execute the task, the traffic light at the next intersection can be scheduled. The next intersection may include an intersection closest to the target vehicle on the travel route of the target vehicle, and the intersection may be provided with a traffic light. And scheduling the traffic light of the next intersection so that the traffic light is in a passable state when the target vehicle reaches the next intersection. Specifically, in an embodiment of the present application, the scheduling the traffic light at the next intersection may include:
s601: acquiring the distance between the target vehicle and the next intersection and the driving speed;
s603: determining the time length of the target vehicle to travel to the next intersection according to the distance and the travel speed;
s605: after the period of time, switching the traffic light to a passable state.
In the embodiment of the present application, first, the distance between the target vehicle and the next intersection and the traveling speed may be determined. In one example, the distance of the target vehicle from the next intersection may be determined based on the position of the acquisition device 101. Specifically, from the road segment image acquired by the acquisition device 101, the distance S1 between the target vehicle and the acquisition device 101 can be determined, and the distance S2 between the acquisition device 101 and the next intersection is also known, so that the distance S between the target vehicle and the next intersection can be determined to be S1+ S2. The traveling speed of the target vehicle may also be acquired from the link image. In one example, the sampling interval of the two adjacent frames of road segment images may be determined according to the acquisition frequency of the acquisition device 101, for example, the acquisition frequency of the prime number acquisition device 101 is 100Hz, and then the sampling interval of the two adjacent frames of road segment images is 10 ms. In addition, according to the displacement s of the target vehicle in the two adjacent frame road section images, the running speed of the target vehicle can be determined to be s/10 ms. It should be noted that, in the case where the acquisition device 101 includes a radar, the road section image includes a three-dimensional point cloud image, and since the three-dimensional point cloud image is generated from radar echo data reflected from the target vehicle, the traveling speed of the target vehicle can be determined more accurately.
When the distance and the traveling speed between the target vehicle and the next intersection are acquired, the time length for the target vehicle to travel to the next intersection can be determined. Based on this, the traffic light may be scheduled such that after the duration, the status of the traffic light is a passable status. In some examples, the traffic light may be adjusted to a passable state if the state of the traffic light is an impassable state (e.g., a red light or yellow light state) after the period of time. Of course, if the traffic light status is a passable status (e.g., a green status) after the period of time, the traffic light status may be maintained.
In practical application, a certain congestion degree exists in a road section between the target vehicle and the next intersection, and in this case, the traffic light can be quickly responded and scheduled, so that the state of the traffic light is switched to a passable state. Based on this, in an embodiment of the present application, the scheduling the traffic light at the next intersection may include:
s701: determining a congestion level of a road segment between the location of the target vehicle and a next intersection;
s703: and under the condition that the congestion degree is determined to be larger than a preset threshold value, switching the traffic light of the next intersection to a passable state until the target vehicle drives away from the next intersection.
In the embodiment of the present application, the congestion degree of the road segment between the position of the target vehicle and the next intersection may be quantified, for example, the congestion degree may be determined according to the length of the road segment and the number of vehicles on the road segment. The greater the number of vehicles per unit length of road section, the more severe the congestion. In a case where it is determined that the congestion degree is greater than the preset threshold value, the traffic light of the next intersection may be switched to a passable state until the target vehicle drives away from the next intersection.
Through the embodiment, a solution can be provided for a special application scene of road congestion, so that the target vehicle can smoothly pass under the condition of road congestion.
In practical applications, a vehicle with priority right of way often acts together with multiple vehicles when performing tasks, such as a fire truck, a police truck, and the like. In this case, in the process of scheduling the traffic lights, it is possible to ensure that the target vehicles in all of the tasks are performed to pass. Based on this, in an embodiment of the present application, in a case where the number of the target vehicles includes a plurality, the scheduling the traffic light at the next intersection may include:
s801: acquiring a road section image of a second road section, wherein the second road section comprises a road section which is passed by the target vehicle after passing through the next intersection from the first road section;
s803: and scheduling the traffic light at the next intersection so that the traffic light is in a passable state when the first target vehicle reaches the next intersection until the last target vehicle drives away from the next intersection, wherein the last target vehicle is determined by comparing the road section image of the first road section with the road section image of the second road section.
In this embodiment, the second road segment may include a road segment that the target vehicle passes after passing the next intersection from the first road segment. The obtaining manner of the road section image of the second road section may refer to the obtaining manner of the road section image of the first road section, and is not described herein again. Since the first road segment and the second road segment are road segments located on both sides of the next intersection, respectively, it can be determined whether the last one of the plurality of target vehicles drives away from the next intersection by comparing the road segment images of the first road segment and the second road segment. In one embodiment of the present application, in determining the second road segment, the second road segment may be determined according to a driving direction of the target vehicle. Specifically, the driving direction of the target vehicle may be determined according to the link image of the first link. For example, the target vehicle may be determined to be traveling straight if the target vehicle is traveling in a straight lane, and may be determined to be turning left if the target vehicle is traveling in a left turn lane. After determining the driving direction, the second road segment may be determined according to the driving direction. Of course, in other embodiments, the road segment images of the road segments in the three directions of straight movement, right turning and left turning may be acquired, and in the case that the target vehicle is identified from the road segment images, the corresponding road segment is determined as the second road segment.
The embodiment provides a vehicle passing processing mode when a plurality of target vehicles simultaneously execute tasks, so that all the target vehicles can pass through the next intersection, and the situation that part of the vehicles cannot be detained at the intersection is prevented.
In one embodiment of the present application, the last of the target vehicles may be determined by:
s901: determining the vehicle identifier of the last target vehicle according to the road section image of the first road section;
s903: determining the vehicle identification of the passing vehicle according to the road section image of the second road section;
s905: and determining that the passing vehicle is the last target vehicle under the condition that the vehicle identification of the passing vehicle is matched with the vehicle identification of the last target vehicle.
In the embodiment of the application, whether a passing vehicle in the second road segment is the last target vehicle or not can be determined by comparing vehicle identifications of vehicles in the road segment images of the first road segment and the second road segment. The vehicle identification may include information such as a license plate number and an electronic tag of the vehicle. In one example, the vehicle number of the last target vehicle is determined to be "Zhe A12345" according to the road section image of the first road section, and then whether the passing vehicle in the second road section is the last target vehicle can be determined by reading the license plate number of the passing vehicle in the second road section and comparing the license plate number "Zhe A12345".
In the above embodiment, by comparing the vehicle identifiers of the vehicles in the two road sections in the passing direction, it can be quickly determined whether the passing vehicle in the second road section is the last target vehicle.
It should be noted that, when it is determined that the target vehicle has left the next intersection, the scheduling of the traffic light at the next intersection may be ended, and the traffic light may be returned to the state before the scheduling.
In another aspect of the present application, there is provided a vehicle passage processing device, as shown in fig. 3, the vehicle passage processing device 300 may include:
an image obtaining module 301, configured to obtain a road segment image of a first road segment;
a vehicle identification module 303, configured to identify a target vehicle with a priority right of way from the road section image;
a traffic light scheduling module 305 for scheduling a traffic light at a next intersection if it is determined that the target vehicle is performing a task such that the traffic light is in a passable state when the target vehicle reaches the next intersection.
Optionally, in an embodiment of the present application, the traffic light scheduling module is specifically configured to:
acquiring audio data at the target vehicle;
and under the condition that the target vehicle is determined to emit the preset sound signal according to the audio data, determining that the target vehicle executes the task.
Optionally, in an embodiment of the present application, the vehicle identification module is specifically configured to:
inputting the road section image into a machine learning model, and outputting the identification result of whether the target vehicle with the priority right of passage exists in the road section image or not through the machine learning model;
the machine learning model is obtained by training a plurality of road section image samples, wherein the plurality of road section image samples comprise image samples of vehicles with priority right of passage in a road section.
Optionally, in an embodiment of the present application, the traffic light scheduling module is specifically configured to:
acquiring the distance between the target vehicle and the next intersection and the driving speed;
determining the time length of the target vehicle to travel to the next intersection according to the distance and the travel speed;
after the period of time, switching the traffic light to a passable state.
Optionally, in an embodiment of the present application, the traffic light scheduling module is specifically configured to:
determining a congestion level of a road segment between the location of the target vehicle and a next intersection;
and under the condition that the congestion degree is determined to be larger than a preset threshold value, switching the traffic light of the next intersection to a passable state until the target vehicle drives away from the next intersection.
Optionally, in an embodiment of the application, in a case that the number of the target vehicles includes a plurality of target vehicles, the traffic light scheduling module is specifically configured to:
acquiring a road section image of a second road section, wherein the second road section comprises a road section which is passed by the target vehicle after passing through the next intersection from the first road section;
and scheduling the traffic light at the next intersection so that the traffic light is in a passable state when the first target vehicle reaches the next intersection until the last target vehicle drives away from the next intersection, wherein the last target vehicle is determined by comparing the road section image of the first road section with the road section image of the second road section.
Optionally, in an embodiment of the present application, the last target vehicle is determined by:
determining the vehicle identifier of the last target vehicle according to the road section image of the first road section;
determining the vehicle identification of the passing vehicle according to the road section image of the second road section;
and determining that the passing vehicle is the last target vehicle under the condition that the vehicle identification of the passing vehicle is matched with the vehicle identification of the last target vehicle.
Optionally, in an embodiment of the present application, the apparatus further includes:
and the recovery module is used for recovering the traffic light at the next intersection to the state before dispatching under the condition that the target vehicle is determined to drive away from the next intersection.
An embodiment of the present application provides an electronic device, as shown in fig. 4, the apparatus includes: a processor and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions.
Optionally, in an embodiment of the present application, the electronic device further includes:
and the acquisition device is used for acquiring the road section image of the first road section. Embodiments of the present application provide a non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
Embodiments of the present application provide a computer program product comprising computer readable code, or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable Compact Disc Read-Only Memory (CD-ROM), a Digital Versatile Disc (DVD), a Memory stick, a floppy disk, a mechanical coding device, a punch card or an in-groove protrusion structure, for example, having instructions stored thereon, and any suitable combination of the foregoing.
The computer readable program instructions or code described herein may be downloaded to the respective computing/processing device from a computer readable storage medium, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present application may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present application by utilizing state information of the computer-readable program instructions to personalize custom electronic circuitry, such as Programmable Logic circuits, Field-Programmable Gate arrays (FPGAs), or Programmable Logic Arrays (PLAs).
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
It is also noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by hardware (e.g., a Circuit or an ASIC) for performing the corresponding function or action, or by combinations of hardware and software, such as firmware.
While the invention has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a review of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (14)
1. A vehicle passage processing method characterized by comprising:
acquiring a road section image of a first road section;
identifying a target vehicle with a priority right of way from the road section image;
in a case where it is determined that the target vehicle is performing a task, a traffic light at a next intersection is scheduled such that the traffic light is in a passable state when the target vehicle reaches the next intersection.
2. The method of claim 1, wherein the determining that the target vehicle is performing a task comprises:
acquiring audio data at the target vehicle;
and under the condition that the target vehicle is determined to emit the preset sound signal according to the audio data, determining that the target vehicle executes the task.
3. The method of claim 1, wherein the identifying the target vehicle with priority right of way from the road segment image comprises:
inputting the road section image into a machine learning model, and outputting the identification result of whether the target vehicle with the priority right of passage exists in the road section image or not through the machine learning model;
the machine learning model is obtained by training a plurality of road section image samples, wherein the plurality of road section image samples comprise image samples of vehicles with priority right of passage in a road section.
4. The method of claim 1, wherein the scheduling a traffic light at a next intersection comprises:
acquiring the distance between the target vehicle and the next intersection and the driving speed;
determining the time length of the target vehicle to travel to the next intersection according to the distance and the travel speed;
after the period of time, switching the traffic light to a passable state.
5. The method of claim 1, wherein the scheduling a traffic light at a next intersection comprises:
determining a congestion level of a road segment between the location of the target vehicle and a next intersection;
and under the condition that the congestion degree is determined to be larger than a preset threshold value, switching the traffic light of the next intersection to a passable state until the target vehicle drives away from the next intersection.
6. The method of claim 1, wherein the scheduling the traffic light at the next intersection in the case that the number of the target vehicles includes a plurality, comprises:
acquiring a road section image of a second road section, wherein the second road section comprises a road section which is passed by the target vehicle after passing through the next intersection from the first road section;
and scheduling the traffic light at the next intersection so that the traffic light is in a passable state when the first target vehicle reaches the next intersection until the last target vehicle drives away from the next intersection, wherein the last target vehicle is determined by comparing the road section image of the first road section with the road section image of the second road section.
7. The method of claim 6, wherein the last of the target vehicles is determined using:
determining the vehicle identifier of the last target vehicle according to the road section image of the first road section;
determining the vehicle identification of the passing vehicle according to the road section image of the second road section;
and determining that the passing vehicle is the last target vehicle under the condition that the vehicle identification of the passing vehicle is matched with the vehicle identification of the last target vehicle.
8. The method of claim 1, wherein after said scheduling a traffic light at a next intersection, the method further comprises:
and under the condition that the target vehicle is determined to drive away from the next intersection, restoring the traffic light at the next intersection to the state before dispatching.
9. A vehicle passage processing apparatus, characterized by comprising:
the image acquisition module is used for acquiring a road section image of the first road section;
the vehicle identification module is used for identifying a target vehicle with a priority right of way from the road section image;
and the traffic light scheduling module is used for scheduling the traffic light at the next intersection under the condition that the target vehicle is determined to execute the task, so that the traffic light is in a passable state when the target vehicle reaches the next intersection.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any one of claims 1-8.
11. The electronic device of claim 10, further comprising:
and the acquisition device is used for acquiring the road section image of the first road section.
12. A non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method of any of claims 1-8.
13. A computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code which, when run in a processor of an electronic device, the processor in the electronic device performs the method of any of claims 1-8.
14. A chip comprising at least one processor for executing a computer program or computer instructions stored in a memory for performing the method of any of the preceding claims 1-8.
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