CN115331458A - Signal lamp control method, device, equipment and storage medium - Google Patents
Signal lamp control method, device, equipment and storage medium Download PDFInfo
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- CN115331458A CN115331458A CN202110987584.6A CN202110987584A CN115331458A CN 115331458 A CN115331458 A CN 115331458A CN 202110987584 A CN202110987584 A CN 202110987584A CN 115331458 A CN115331458 A CN 115331458A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/085—Controlling traffic signals using a free-running cyclic timer
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/095—Traffic lights
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Abstract
The method, the device, the equipment and the storage medium are suitable for the technical field of computers, the driving direction and the distance of a target can be accurately and quickly obtained by utilizing the characteristic that point cloud data contain depth information, namely the vehicle queuing length of each lane of a target intersection in each direction at the current time is obtained according to the point cloud data, the vehicle queuing length at the current time of each lane is obtained according to the current time, the lamp time configuration at the current time is adjusted according to the vehicle queuing length at the current time and a preset lamp time optimization model, the adjusted lamp time configuration is obtained, then the signal lamps of the target intersection are configured and controlled by adopting the adjusted lamp time, fine control at the lane level can be realized, the problem of inaccurate time distribution caused by signal lamp time distribution depending on the traffic flow in a single direction is avoided, the problem that the fluctuation of the traffic flow in different time periods cannot be adapted when fixed lamps are adopted is also avoided, and the problem of traffic jam caused by the fluctuation of the traffic flow is further avoided.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for controlling a signal lamp.
Background
With the annual increase of the vehicle holding amount, the urban road congestion is more and more severe, and the traveling efficiency of people is seriously influenced. If the lamp of the traffic intersection signal lamp is not properly arranged, the passing of the traffic intersection is seriously influenced.
The existing traffic intersection signal lamp usually displays according to preset fixed lamp duration, however, the number of vehicles passing through the same traffic intersection at different time intervals has large fluctuation, and the preset fixed lamp cannot adapt to the fluctuation of vehicle flow at different time intervals, so that traffic intersection congestion is easily caused.
Disclosure of Invention
The application provides a signal lamp control method, a signal lamp control device, signal lamp control equipment and a storage medium, which can adapt to the fluctuation of traffic flow in different time periods and avoid traffic intersection congestion caused by the fluctuation of the traffic flow.
In a first aspect, an embodiment of the present application provides a method for controlling a signal lamp, including:
acquiring point cloud data of a target intersection, and acquiring vehicle queuing lengths of the target intersection at the current time of each lane in each direction according to the point cloud data;
adjusting the light time configuration at the current moment according to the vehicle queuing length at the current moment and a preset light time optimization model to obtain the adjusted light time configuration; the lamp time configuration is used for indicating the lamp time length of signal lamps of each lane in each direction of the target intersection;
and configuring signal lamps of the control target intersection when the adjusted lamps are adopted.
In an embodiment, the obtaining of the vehicle queuing length of the target intersection at the current time of each lane in each direction includes:
acquiring video data and point cloud data of each lane of a target intersection in each direction in real time according to a preset sampling frequency;
and acquiring the vehicle queuing length of the target intersection at the current moment of each lane in each direction according to the video data and the point cloud data.
In an embodiment, the obtaining the vehicle queue length of the target intersection at the current time of each lane in each direction according to the video data and the point cloud data includes:
performing first target detection processing on video data at the current moment to obtain a video detection result; the video detection result comprises first position information of the target object and a target type;
performing second target detection processing on the point cloud data at the current moment to obtain a point cloud detection result; the point cloud detection result comprises driving state information and second position information of the target object;
and obtaining the vehicle queuing length of each lane at the current moment of each direction of the target intersection according to the video detection result and the point cloud detection result.
In an embodiment, the obtaining the vehicle queue length of each lane of the target intersection at the current time in each direction according to the video detection result at the current time and the point cloud detection result at the current time includes:
registering the video detection result and the point cloud detection result based on the first position information and the second position information, and performing fusion processing on the video detection result and the point cloud detection result by adopting a fusion algorithm to obtain a fusion result of the target intersection at the current moment of each lane in each direction, wherein the fusion result comprises the number of vehicles and the length of the vehicles of each lane in each direction of the target intersection;
and carrying out statistical calculation on the number of vehicles and the length of the vehicles in the fusion result to obtain the vehicle queuing length of the target intersection at the current time of each lane in each direction.
In an embodiment, the adjusting the light time configuration at the current time according to the vehicle queuing length at the current time and the preset light time optimization model to obtain the adjusted light time configuration includes:
acquiring the traffic capacity of each lane in each direction of a target intersection; the capacity is used to indicate the maximum number of vehicles passing the stop line on the lane within one signal cycle of the signal light;
determining the total passing time of all vehicles on each lane in each direction according to the passing capacity of each lane in each direction and the vehicle queuing length at the current moment on the corresponding lane;
and adjusting the lamp time configuration at the current moment according to the total passing time and a preset lamp time optimization model to obtain the adjusted lamp time configuration.
In an embodiment, the adjusting the lamp timing configuration at the current time according to the total transit time and the preset lamp timing optimization model to obtain the adjusted lamp timing configuration includes:
receiving the passing state of an adjacent intersection of the target intersection, wherein the passing state is used for indicating that the passing is smooth or the passing is not smooth;
and inputting the traffic state, the total traffic time and the light time configuration at the current moment into a preset light time optimization model to obtain the adjusted light time configuration.
In one embodiment, the adjacent intersections include a previous intersection and a next intersection of the target intersection in each direction.
In a second aspect, an embodiment of the present application provides a control apparatus for a signal lamp, where the apparatus includes:
the acquisition module is used for acquiring point cloud data of the target intersection and acquiring the vehicle queuing length of each lane of the target intersection at the current moment in each direction according to the point cloud data;
the adjusting module is used for adjusting the light time configuration at the current moment according to the vehicle queuing length at the current moment and a preset light time optimization model to obtain the adjusted light time configuration; the time-of-light configuration is used for indicating the time-of-light length of signal lights of each lane in each direction of the target intersection;
and the control module is used for configuring signal lamps of the control target intersection when the adjusted lamps are adopted.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the method according to the first aspect.
According to the control method, the device, the equipment and the storage medium of the signal lamp, the characteristic that the point cloud data contains depth information is utilized, the driving direction and the distance of a target can be accurately and quickly acquired based on the characteristic, namely the vehicle queuing length of each lane of the target intersection in each direction at the current time is acquired according to the point cloud data, the lamp time configuration at the current time is adjusted according to the vehicle queuing length at the current time and a preset lamp time optimization model, the adjusted lamp time configuration is obtained, and then the signal lamp for controlling the target intersection is configured by adopting the adjusted lamp time, wherein the lamp time configuration is used for indicating the lamp time length of each lane of the signal lamp at each direction of the target intersection, so that the lamp time length of each lane of the target intersection in each direction is adjusted in real time according to the vehicle queuing length at the current time, fine control at a lane level can be realized, the problem of inaccurate time distribution caused by signal lamp time distribution depending on the vehicle flow in a single direction is solved, and the problem that the fluctuation of the vehicle flow in different time intervals cannot be adapted when the preset fixed lamp time distribution is avoided, and the traffic jam problem of the traffic flow is further avoided. The scheme of the application can adaptively and real-timely dynamically adjust the signal lamp of the intersection when a single lane is jammed in a certain direction, and improves the traffic dispatching capacity of the traffic intersection.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an application environment of a control method of a signal lamp according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a method for controlling a traffic light according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of a signal lamp control method according to another embodiment of the present application;
fig. 4 is a schematic flowchart of a signal lamp control method according to another embodiment of the present application;
FIG. 5 is a schematic flow chart illustrating a method for controlling a traffic light according to another embodiment of the present disclosure;
fig. 6 is a schematic flowchart of a signal lamp control method according to another embodiment of the present application;
fig. 7 is a schematic flowchart of a signal lamp control method according to another embodiment of the present application;
fig. 8 is a schematic structural diagram of a control device of a signal lamp provided in an embodiment of the present application;
fig. 9 is an internal structural diagram of an electronic device in 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 is described in further detail 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.
It is to be understood that the terms "first," "second," "third," "fourth," and the like (if any) in the embodiments of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method for controlling the signal lamp provided by the embodiment can be applied to the application environment shown in fig. 1. The intelligent base station 100 (also called road side fusion sensing system or road side base station) arranged on the road side of the target intersection is in communication connection with the traffic signal lamp 200 of the target intersection, the intelligent base station 100 is integrated with the acquisition device 110 and the processor 120, the acquisition device 110 acquires the environmental information of the target intersection, and the processor 120 processes the environmental information to obtain the vehicle queuing lengths of all lanes of the target intersection in all directions, so that the processor 120 can adjust the current lamp time configuration of the traffic signal lamp 200 in real time according to the vehicle queuing lengths of all lanes of the target intersection in all directions.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments.
It should be noted that the execution subject of the following method embodiments may also be a control device of a signal lamp, and the device may be implemented by software, hardware, or a combination of software and hardware to become part or all of the electronic device (smart base station, also called roadside fusion sensing system or roadside base station). The following method embodiments take an execution subject as an example of an electronic device.
Fig. 2 is a schematic flowchart of a signal lamp control method according to an embodiment of the present application. The present embodiment relates to a specific procedure of how to improve the accuracy of the control result of the signal lamp. As shown in fig. 2, the method comprises the steps of:
s101, point cloud data of a target intersection are obtained; and obtaining the vehicle queuing length of each lane of the target intersection in each direction at the current moment according to the point cloud data.
The target intersection may be an intersection or a t-intersection, which is not limited in the embodiment of the present application. In one possible scenario, when the target intersection is an intersection, the target intersection may include a direction from east to west, a direction from west to east, a direction from south to north, and a direction from north to south. Of course, each direction of the target intersection may not be a direction of a true south and a true north, which is not limited in the embodiment of the present application. There may be multiple lanes in each direction of the target intersection, for example, the same direction road of the target intersection may include a left-turn lane, a straight-through lane, and a right-turn lane. In a possible case, the same-direction road at the target intersection may only include a straight lane, which is not limited by the embodiment of the present application.
The intelligent base station can acquire point cloud data of a target intersection through radar acquisition, and acquire the vehicle queuing length of each lane at the current time in each direction through the point cloud data. In a possible case, the vehicle queue length of each lane at the current moment in each direction can be obtained through the video data and the point cloud data.
S102, adjusting the light time configuration at the current moment according to the vehicle queuing length at the current moment and a preset light time optimization model to obtain the adjusted light time configuration; the light time configuration is used for indicating the light time length of the signal lights of each lane in each direction of the target intersection.
The light time configuration may be used to indicate the light time lengths of the signal lights of the lanes of the target intersection in various directions, for example, the target intersection includes a direction from east to west, a direction from west to east, a direction from south to north, and a direction from north to south, and the roads in various directions include a left-turn lane, a straight-going lane, and a right-turn lane, that is, there are 12 lanes on the target intersection, and correspondingly, the signal lights have light time lengths corresponding to the 12 lanes. After the vehicle queuing lengths of the target intersection at the current time of each lane in each direction are obtained, the light time length of the corresponding lane can be adjusted according to the vehicle queuing lengths of the target intersection at the current time of each lane in each direction and the preset light time optimization model, and the adjusted light time configuration is obtained. For example, when the vehicle queue length on the straight lane in the east-west direction is long, the adjusted light time configuration may be obtained by increasing the light time length of the signal light indicating the passage of the straight lane in the east-west direction by the preset light time optimization model. The preset lamp-time optimization model may be a model for adjusting the lamp-time configuration at the current time according to the vehicle queuing length at the current time to obtain the adjusted lamp-time configuration, and may be a mathematical model or a neural network model, which is not limited in the embodiment of the present application.
It should be noted that the intelligent base station can adjust the light time configuration at the current moment according to the vehicle queuing length at the current moment and the preset light time optimization model to obtain the adjusted light time configuration; or the intelligent base station sends the vehicle queuing length at the current moment to the signal lamp, and the processor in the signal lamp adjusts the light time configuration at the current moment according to the vehicle queuing length at the current moment and a preset light time optimization model to obtain the adjusted light time configuration; the embodiments of the present application do not limit this.
And S103, configuring signal lamps of the control target intersection when the adjusted lamps are adopted.
In the above description of S102, it can be known that the intelligent base station may adjust the light time configuration at the current time according to the vehicle queuing length at the current time and the preset light time optimization model, so as to obtain the adjusted light time configuration; or the intelligent base station sends the vehicle queuing length at the current moment to the signal lamp, and the processor in the signal lamp adjusts the light time configuration at the current moment according to the vehicle queuing length at the current moment and a preset light time optimization model to obtain the adjusted light time configuration; correspondingly, when the adjusted lamp time configuration obtained through the intelligent base station is configured, the adjusted lamp time configuration can be sent to the signal lamp through the intelligent base station, and the signal lamp executes the adjusted lamp time configuration to control the signal lamp of the target intersection; when the adjusted lamp time configuration is obtained by the processor in the signal lamp, the adjusted lamp time configuration can be directly executed after the adjusted lamp time configuration is obtained, so as to control the signal lamp of the target intersection.
According to the control method of the signal lamp, the characteristic that the point cloud data contains depth information is utilized, the driving direction and the distance of the target can be accurately and quickly obtained on the basis of the characteristic, namely the vehicle queuing length of each lane (lane level) of the target intersection at each direction at the current moment is obtained according to the point cloud data, the lamp time configuration at the current moment is adjusted according to the vehicle queuing length at the current moment and the preset lamp time optimization model at the current moment, the adjusted lamp time configuration is obtained, and then the signal lamp for controlling the target intersection is configured when the adjusted lamp time is adopted, wherein the lamp time configuration is used for indicating the lamp time length of each lane signal lamp at each direction of the target intersection, so that the lamp time length of each lane signal lamp at each direction of the target intersection is adjusted in real time according to the vehicle queuing length at the current moment, fine control at the lane level can be realized, the problem of inaccurate timing caused by signal lamp timing through single-direction traffic flow is avoided, and the problem of traffic jam caused by the fact that the preset fixed lamp flow cannot adapt to the fluctuation of different time periods is also avoided. The scheme of the application can adaptively and real-timely dynamically adjust the signal lamp of the intersection when a single lane is jammed in a certain direction, and improves the traffic dispatching capacity of the traffic intersection.
The following describes in detail a specific process of how to obtain the vehicle queue length of the target intersection at the current time of each lane in each direction by using the embodiments shown in fig. 3-5.
Fig. 3 is a schematic flowchart of a control method of a signal light in another embodiment of the present application, which relates to a specific process of how to obtain a vehicle queue length of a target intersection at a current time of each lane in each direction, and as shown in fig. 3, the above S101 "obtaining a vehicle queue length of a target intersection at a current time of each lane in each direction" includes:
s201, video data and point cloud data of each lane of the target intersection in each direction are obtained in real time according to a preset sampling frequency.
The video data can be collected through a camera arranged on the intelligent base station, and the point cloud data can be collected through a radar arranged on the intelligent base station. The preset sampling frequency may include a frequency at which the camera acquires video data and a frequency at which the radar acquires point cloud data. The frequency of the camera for collecting the video data and the frequency of the radar for collecting the point cloud data can be the same or different, and the embodiment of the application does not limit the frequency.
S202, obtaining the vehicle queuing length of the target intersection at the current time of each lane in each direction according to the video data and the point cloud data.
After the video data and the point cloud data are obtained, the target type of the target object in the video data at the same time can be fused with the driving state information of the target object in the point cloud data, and the vehicle queuing length of each lane at the current time of each lane at the target intersection in each direction can be obtained based on the fused information.
In a possible case, in the above S202, "obtaining the vehicle queue length of the target intersection at the current time of each lane in each direction according to the video data and the point cloud data," a possible implementation method may be as shown in fig. 4, and includes:
s301, performing first target detection processing on video data at the current moment to obtain a video detection result; the video detection result comprises first position information of the target object and a target type.
The first target detection process may be a processing method for extracting first position information and a target type of a target from the video data, for example, the first target detection process may include a target detection algorithm based on deep learning, such as SSD (Single Shot multi box Detector), fast RCNN, YOLO (young Only Look on), and the like, and the position information and the target type of the target in the video data at the current time are obtained by the first target detection process, for example, the first target detection process is performed on the video data at the current time to obtain target frame coordinate information including the target, and the target type of the target in the target frame, such as category information of a vehicle, a pedestrian, and the like, is identified.
S302, carrying out second target detection processing on the point cloud data at the current moment to obtain a point cloud detection result; the point cloud detection result comprises driving state information and second position information of the target object.
The point cloud data is data obtained by reflecting a radar signal against an obstacle, and is generally accumulated based on data obtained by reflecting a plurality of radar signals which have been transmitted at different times, and may indicate a motion state of a target object, such as driving state information. The second target detection processing may be a processing method for extracting a point cloud detection result from the point cloud data. For example, the second target detection process may include a deep learning-based target detection algorithm such as second and PointRCNN, and the second target detection process obtains coordinate frame information including the target object from the point cloud data at the current time, and identifies the driving state information of the target object in the target frame.
And S303, acquiring the vehicle queuing length of the target intersection at the current time of each lane in each direction according to the video detection result and the point cloud detection result.
Generally, video data may not include all objects within a preset range due to mutual occlusion between multiple objects. The point cloud data is signal data obtained by reflecting a radar signal by a target object, and the target object cannot be displayed visually in general. The video data is subjected to first target detection processing to obtain a video detection result, and the point cloud data is subjected to second target detection processing to obtain a point cloud detection result, and the point cloud detection result is fused to obtain a fused detection result, so that the problem that all target objects in a preset range cannot be included in the video detection result obtained by singly using the video data or the target objects cannot be visually displayed in the point cloud detection result obtained by singly using the point cloud data can be solved.
In a possible case, the step S303 "obtaining the vehicle queue length of the target intersection at the current time of each lane in each direction according to the video detection result and the point cloud detection result" may be implemented as shown in fig. 5, and includes:
s401, registering the video detection result and the point cloud detection result based on the first position information and the second position information, and performing fusion processing on the video detection result and the point cloud detection result by adopting a fusion algorithm to obtain a fusion result of the target intersection at the current time of each lane in each direction, wherein the fusion result comprises the number of vehicles and the length of the vehicles of each lane of the target intersection in each direction.
After the video data is subjected to first target detection processing to obtain a video detection result, and the point cloud data is subjected to second target detection processing to obtain a point cloud detection result, the video detection result and the point cloud detection result can be subjected to rotation and translation matrix registration based on first position information indicating a target object in the video detection result and second position information indicating the same target object in the point cloud detection result, and the video detection result and the point cloud detection result are unified into the same coordinate system. And then fusing the target type of each target object in the video detection result and the driving state information of each target object in the point cloud detection result to obtain a fusion result comprising the number of vehicles and the length of the vehicles of each lane of the target intersection in each direction.
S402, counting the number of vehicles and the length of the vehicles in the fusion result to obtain the vehicle queuing length of the target intersection at the current time of each lane in each direction.
The fusion result includes the number of vehicles and the length of the vehicles in each lane of the target intersection in each direction, and the number of vehicles and the length of the vehicles in the fusion result can be statistically calculated, for example, the lengths of the vehicles in the same lane are added to obtain the vehicle queue length at the current time on the lane.
According to the control method of the signal lamp, the video data and the point cloud data in the preset range are obtained according to the preset sampling frequency, and then the vehicle queuing length of each lane of the target intersection at the current time in each direction is obtained according to the video data and the point cloud data, so that the obtained vehicle queuing length of each lane of the target intersection at the current time in each direction is obtained jointly according to the video data and the point cloud data, the condition that the obtained vehicle queuing length at the current time is inaccurate due to a single data type when the vehicle queuing length of each lane of the target intersection at the current time in each direction is obtained only according to the video data or only according to the point cloud data is avoided, and the condition that the configuration cannot be matched with the vehicle flow of the target intersection when the lamp of the signal lamp is adjusted according to the vehicle queuing length at the inaccurate current time is further avoided.
On the basis of the above embodiments, the following detailed description is given by using the embodiments shown in fig. 6 and fig. 7 to adjust the light time configuration at the current time according to the vehicle queuing length at the current time and the preset light time optimization model, so as to obtain the specific process of the adjusted light time configuration.
Fig. 6 is a schematic flow chart of a signal lamp control method according to another embodiment of the present application, and as shown in fig. 6, the step S102 "of adjusting the lamp time configuration at the current time according to the vehicle queuing length at the current time and the preset lamp time optimization model to obtain an adjusted lamp time configuration" is a possible implementation method, including:
s501, acquiring the traffic capacity of each lane in each direction of a target intersection; the traffic capacity is used to indicate the maximum number of vehicles passing the stop line on the lane during one signal cycle of the signal light.
S502, determining the total passing time of all vehicles on each lane in each direction according to the passing capacity of each lane in each direction and the vehicle queuing length at the current moment on the corresponding lane.
After the traffic capacity of each lane in each direction of the target intersection is obtained, the total traffic time of all vehicles in each lane in each direction can be obtained by dividing the vehicle queuing length of each lane at the current time in each direction by the traffic capacity of the corresponding lane.
S503, adjusting the light time configuration at the current moment according to the total passing time and the preset light time optimization model to obtain the adjusted light time configuration.
The optimization model in the light presetting process can be a model obtained by optimizing the maximum traffic capacity of each lane of the target intersection in each direction. For example, the objective function in the optimization model when the lamp is preset may be: max (Y) = max (T × num _ car), where Y represents the traffic capacity of each lane at the target intersection in each direction, T represents the light time conversion cycle of the lane corresponding to the signal lamp, num _ car represents the vehicle queue length of the corresponding lane, and the light time configuration at the current time is adjusted by using the total traffic time T = (T1, T2.. Tn) of each lane in each direction and the green light time H = (H1, H2.. Hn) of each lane in each direction as constraint conditions, so as to obtain the adjusted light time configuration, where tn represents the traffic time of the nth lane, and Hn represents the green light time of the nth lane.
In a possible case, the passing status of the adjacent intersection may also be used as a parameter for adjusting the lamp time configuration at the current time, which is described in detail below with an embodiment shown in fig. 7, and as shown in fig. 7, the step S503 "adjusts the lamp time configuration at the current time according to the total passing time and the preset lamp time optimization model to obtain the adjusted lamp time configuration" is a possible implementation method including:
s601, receiving the passing state of an adjacent intersection of the target intersection, wherein the passing state is used for indicating passing smoothness or passing incompleteness.
The smart base station can receive the passing state of the adjacent intersection collected by other smart base stations arranged on the roadside of the adjacent intersection, wherein the passing state is used for indicating passing smoothness or passing unsmooth. The traffic state may be indicated by a parameter, for example, the traffic state may indicate clear traffic by a parameter 0 and not clear traffic by a parameter 1.
Optionally, the adjacent intersections include an upper intersection and a lower intersection of the target intersection in all directions. Vehicles can travel from an upper intersection to a target intersection and through the target intersection to a next intersection. It should be noted that, since the vehicle driving directions of the lanes in the respective directions are different, the previous gates of the different lanes are different, and similarly, the next gates of the different lanes are also different.
S602, inputting the traffic state, the total traffic time and the light time configuration at the current moment into a preset light time optimization model to obtain the adjusted light time configuration.
As can be seen from the above description of S503, the optimization model in preset light time may use max (Y) = max (t × num _ car) as an objective function, where Y represents the traffic capacity of each lane at the target intersection in each direction, t represents the light time conversion period of the corresponding lane on the traffic light, and num _ car represents the vehicle queue length of the corresponding lane; and adjusting the lamp time configuration at the current moment by taking the total passing time T = (T1, T2,. Tn) of each lane in each direction and the green lamp time length H = (H1, H2,. Hn) of each lane in each direction as constraint conditions to obtain the adjusted lamp time configuration, wherein tn represents the passing time of the nth lane. In a possible case, the traffic state K = (K1, k2... Kn) of the adjacent intersection can be further used as a constraint condition, and the light time configuration at the current time is adjusted to obtain the adjusted light time configuration, wherein kn represents the vehicle traffic state of the nth adjacent intersection, and can indicate the traffic state of the adjacent intersection by using a parameter 0 or a parameter 1.
According to the control method of the signal lamp, the passing state of the adjacent intersection of the target intersection is received, the passing state, the total passing time and the light time configuration at the current moment are input into the preset light time optimization model, and the adjusted light time configuration is obtained, wherein the passing state is used for indicating that the passing is smooth or the passing is not smooth. The adjusted lamp time configuration obtained by adjusting the current lamp time configuration can be matched with the vehicle queuing length of the target intersection, and meanwhile, the traffic state of the adjacent intersection can be combined, so that the adjusted lamp time configuration obtained by adjusting the traffic state of the adjacent intersection can be matched with the vehicle flow change of the target intersection, and the traffic capacity of the target intersection is further improved.
It should be understood that, although the respective steps in the flowcharts in the above-described embodiments are sequentially shown as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
Fig. 8 is a schematic structural diagram of a control device of a signal lamp in an embodiment of the present application, as shown in fig. 8, including: an obtaining module 10, an adjusting module 20 and a control module 30, wherein:
the acquisition module 10 is used for acquiring point cloud data of the target intersection and acquiring the vehicle queuing length of each lane of the target intersection at the current moment in each direction according to the point cloud data;
the adjusting module 20 is configured to adjust the light time configuration at the current time according to the vehicle queuing length at the current time and a preset light time optimization model to obtain an adjusted light time configuration; the lamp time configuration is used for indicating the lamp time length of signal lamps of each lane in each direction of the target intersection;
and the control module 30 is used for configuring signal lamps of the control target intersection when the adjusted lamps are adopted.
In an embodiment, the obtaining module 10 includes:
the sampling unit 101 is used for acquiring video data and point cloud data of each lane of the target intersection in each direction in real time according to a preset sampling frequency;
the first obtaining unit 102 is configured to obtain vehicle queue lengths of the target intersection at current time of each lane in each direction according to the video data and the point cloud data.
In an embodiment, the first obtaining unit 102 is specifically configured to perform a first target detection process on video data at a current time to obtain a video detection result; the video detection result comprises first position information of the target object and a target type; performing second target detection processing on the point cloud data at the current moment to obtain a point cloud detection result; the point cloud detection result comprises driving state information and second position information of the target object; and obtaining the vehicle queuing length of the target intersection at the current time of each lane in each direction according to the video detection result and the point cloud detection result.
In an embodiment, the first obtaining unit 102 is specifically configured to, based on the first position information and the second position information, register the video detection result and the point cloud detection result, and perform fusion processing on the video detection result and the point cloud detection result by using a fusion algorithm to obtain a fusion result of the target intersection at the current time of each lane in each direction, where the fusion result includes the number of vehicles and the length of the vehicles of each lane in each direction of the target intersection; and carrying out statistical calculation on the number of vehicles and the length of the vehicles in the fusion result to obtain the vehicle queuing length of the target intersection at the current time of each lane in each direction.
In one embodiment, the adjusting module 20 includes a second obtaining unit 201, a determining unit 202, and an adjusting unit 203, wherein:
the second obtaining unit 201 is used for obtaining the traffic capacity of each lane in each direction of the target intersection; the traffic capacity is used to indicate the maximum number of vehicles passing the stop line on the lane during one signal cycle of the signal light;
the determining unit 202 is configured to determine total passing time of all vehicles on each lane in each direction according to the passing capacity of each lane in each direction and the vehicle queuing length at the current time on the corresponding lane;
the adjusting unit 203 adjusts the lamp timing configuration at the current time according to the total transit time and the preset lamp timing optimization model, and obtains the adjusted lamp timing configuration.
In one embodiment, the adjusting unit 203 is specifically configured to receive a traffic state of an adjacent intersection of the target intersection, where the traffic state is used to indicate that the traffic is smooth or the traffic is not smooth; and inputting the traffic state, the total traffic time and the light time configuration at the current moment into a preset light time optimization model to obtain the adjusted light time configuration.
In one embodiment, the adjacent intersections include a previous intersection and a next intersection of the target intersection in each direction.
The control device for the signal lamp provided by the embodiment of the application can execute the method embodiment, the implementation principle and the technical effect are similar, and the details are not repeated herein.
For specific limitations of a control device for a signal lamp, reference may be made to the above limitations on the control method for the signal lamp, and details are not described here. All or part of each module in the control device of the signal lamp can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the electronic device, or can be stored in a memory in the electronic device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, an electronic device is provided, the internal structure of which may be as shown in fig. 9. The electronic device includes a processor, a memory, a network interface, and an input device connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the electronic device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method of controlling a signal lamp.
It will be understood by those skilled in the art that the structure shown in fig. 9 is a block diagram of only a portion of the structure related to the present application, and does not constitute a limitation on the electronic device to which the present application is applied, and a particular electronic device may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.
It should be clear that, in the embodiments of the present application, the process of executing the computer program by the processor is consistent with the process of executing the steps in the above method, and specific reference may be made to the description above.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, can implement the method for controlling a signal lamp provided in the above-mentioned method embodiments of the present application.
It should be clear that, in the embodiment of the present application, the process of executing the computer program by the processor is consistent with the execution process of each step in the method described above, and specific reference may be made to the description above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for controlling a signal lamp, comprising:
acquiring point cloud data of a target intersection, and acquiring vehicle queuing lengths of the target intersection at the current time of each lane in each direction according to the point cloud data;
adjusting the light time configuration at the current moment according to the vehicle queuing length at the current moment and a preset light time optimization model to obtain the adjusted light time configuration; the lamp hour configuration is used for indicating the lamp hour length of the signal lamp of each lane in each direction of the target intersection;
and configuring signal lamps for controlling the target intersection when the adjusted lamps are adopted.
2. The method of claim 1, wherein the obtaining point cloud data of the target intersection and the vehicle queue length of the target intersection at the current time of each lane in each direction according to the point cloud data comprises:
acquiring video data and point cloud data of each lane of the target intersection in each direction in real time according to a preset sampling frequency;
and acquiring the vehicle queuing length of the target intersection at the current moment of each lane in each direction according to the video data and the point cloud data.
3. The method of claim 2, wherein the obtaining the vehicle queue length of the target intersection at the current time of each lane in each direction according to the video data and the point cloud data comprises:
performing first target detection processing on the video data at the current moment to obtain a video detection result; the video detection result comprises first position information of a target object and a target type;
performing second target detection processing on the point cloud data at the current moment to obtain a point cloud detection result; the point cloud detection result comprises driving state information and second position information of the target object;
and acquiring the vehicle queuing length of the target intersection at the current moment of each lane in each direction according to the video detection result and the point cloud detection result.
4. The method according to claim 3, wherein the obtaining the vehicle queue length of the target intersection at the current time of each lane in each direction according to the video detection result at the current time and the point cloud detection result at the current time comprises:
registering the video detection result and the point cloud detection result based on the first position information and the second position information, and performing fusion processing on the video detection result and the point cloud detection result by adopting a fusion algorithm to obtain a fusion result of the target intersection at the current moment of each lane in each direction, wherein the fusion result comprises the number of vehicles and the length of the vehicles of each lane of the target intersection in each direction;
and carrying out statistical calculation on the number of vehicles and the length of the vehicles in the fusion result to obtain the vehicle queuing length of the target intersection at the current time of each lane in each direction.
5. The method according to any one of claims 1 to 4, wherein the adjusting the light time configuration at the current time according to the vehicle queue length at the current time and a preset light time optimization model to obtain an adjusted light time configuration comprises:
acquiring the traffic capacity of each lane in each direction of the target intersection; the traffic capacity is used for indicating the maximum number of vehicles passing through the stop line on the lane in one signal period of the signal lamp;
determining the total passing time of all vehicles on each lane in each direction according to the passing capacity of each lane in each direction and the vehicle queuing length at the current moment on the corresponding lane;
and adjusting the lamp time configuration at the current moment according to the total passing time and the preset lamp time optimization model to obtain the adjusted lamp time configuration.
6. The method of claim 5, wherein the adjusting the lamp time configuration at the current time according to the total transit time and the preset lamp time optimization model to obtain the adjusted lamp time configuration comprises:
receiving a traffic state of an adjacent intersection of the target intersection, wherein the traffic state is used for indicating that the traffic is smooth or not smooth;
and inputting the traffic state, the total traffic time and the current lamp time configuration into the preset lamp time optimization model to obtain the adjusted lamp time configuration.
7. The method of claim 6, wherein the adjacent intersections include a previous intersection and a next intersection in each direction of the target intersection.
8. A control apparatus of a signal lamp, comprising:
the acquisition module is used for acquiring point cloud data of a target intersection and acquiring the vehicle queuing length of each lane of the target intersection at the current moment in each direction according to the point cloud data;
the adjusting module is used for adjusting the light time configuration at the current moment according to the vehicle queuing length at the current moment and a preset light time optimization model to obtain the adjusted light time configuration; the lamp hour configuration is used for indicating the lamp hour length of the signal lamp of each lane in each direction of the target intersection;
and the control module is used for configuring and controlling the signal lamps of the target intersection when the adjusted lamps are adopted.
9. An electronic device, comprising a memory storing a computer program and a processor implementing the method according to any of claims 1-7 when the processor executes the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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