CN118334870A - Vehicle auxiliary scheduling method, system, device and medium based on radar - Google Patents
Vehicle auxiliary scheduling method, system, device and medium based on radar Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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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
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
- G08G1/096827—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route where the route is computed onboard
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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
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
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Abstract
The invention discloses a radar-based vehicle auxiliary dispatching method, a system, a device and a medium, wherein the method comprises the following steps: acquiring and counting lane information of a radar monitoring target road; based on the change of the traffic flow corresponding to each lane in the lane information along with the time sequence, the historical road condition information corresponding to each lane in each time period is counted and determined; acquiring real-time road condition information of the radar at the current moment of the target road, and predicting the road condition of each current lane by combining the historical road condition information of each lane in each time period according to historical statistics. The invention can solve the technical problems that a road section without a traffic light in the prior art has a certain traffic safety risk or is crowded when vehicles are more and the real-time effective vehicle dispatching cannot be carried out.
Description
Technical Field
The invention relates to the technical field of radars, in particular to a radar-based vehicle auxiliary scheduling method, a radar-based vehicle auxiliary scheduling system, a radar-based vehicle auxiliary scheduling device and a radar-based vehicle auxiliary scheduling medium.
Background
In city planning, some road sections are not provided with traffic lights, and the road sections have certain traffic safety risks or cause congestion when vehicles are more and cannot be effectively scheduled in real time.
Aiming at the scene, in the current scheme, the vehicle statistics is carried out by relying on GPS, but the GPS can provide limited traffic information, and has no better effect on real-time auxiliary vehicle dispatching of the lane level of the road section without traffic lights. In the prior art, in the response scheme partially depending on the intersection cameras, the effect is often greatly influenced by the visibility of weather, and particularly the performance of the intersection cameras is seriously degraded in the weather such as fog, haze, rain, snow and the like.
Therefore, how to reduce traffic safety risk in a road section without traffic lights, or to enable real-time and efficient vehicle dispatching when more vehicles are crowded, is a problem to be solved.
Disclosure of Invention
The invention provides a radar-based vehicle auxiliary dispatching method, system, device and medium, which aim to effectively solve the technical problems that a road section without traffic lights in the prior art has a certain traffic safety risk, or the vehicles are crowded when more vehicles are involved and the real-time effective vehicle dispatching cannot be carried out.
According to a first aspect of the present invention, the present invention provides a radar-based vehicle assisted scheduling method, comprising: acquiring and counting lane information of a radar monitoring target road; based on the change of the traffic flow corresponding to each lane in the lane information along with the time sequence, the historical road condition information corresponding to each lane in each time period is counted and determined; acquiring real-time road condition information of the radar at the current moment of the target road, and predicting the road condition of each current lane by combining the traffic flow information of each lane in each time period according to history statistics; and sending the predicted result to the target vehicle through the radar so as to enable the owner of the target vehicle to make decisions to select different lanes, control the vehicle speed or plan a new driving route.
Further, the step of the statistical radar monitoring the lane information of the target road includes: step one, acquiring the position, the speed and the signal-to-noise ratio of a monitoring target in radar data obtained in a radar monitoring range; screening a vehicle target point by using the speed of the monitoring target and the position of the monitoring target, and filtering by using the signal-to-noise ratio of the monitoring target to obtain the vehicle target point with the confidence degree exceeding a preset value; and thirdly, fitting and defining the target points of the vehicles by using a preset number of the target points of the vehicles to obtain the lane information of each lane on the target road.
Further, the step of the statistical radar monitoring the lane information of the target road further comprises: and screening a vehicle target point by using the speed of the monitoring target and the position of the monitoring target, filtering by using the signal-to-noise ratio of the monitoring target, judging whether the vehicle target point is accumulated with the preset quantity after obtaining the vehicle target point with the confidence exceeding the preset value, if not, continuing the step of obtaining the position, the speed and the signal-to-noise ratio of the monitoring target in the radar data obtained in the radar monitoring range, and returning to execute the step II, if so, executing the step III.
Further, the step of counting the historical road condition information includes: continuously monitoring the traffic flow of all lanes in each time period; defining the congestion state of a target road according to the traffic flow; and solving the average speed of vehicles in each lane in different congestion states at different moments, and counting the congestion duration to obtain the historical road condition information of each lane.
Further, the real-time road condition information comprises traffic flow, congestion state and average speed of all lanes at the current moment.
Further, the predicting step includes: using the congestion state and the congestion duration of each lane in each time of the history statistics and at the corresponding time of the current time, and taking the congestion duration as the predicted congestion duration to be sustained at the current time; and calculating a recommended speed by using the average speed of each lane at the historical moment and the average speed of each lane at the current moment at the corresponding moment to obtain a prediction result, wherein the historical road condition information of each time period of the historical statistics is at least the historical road condition information in one time period, the real-time road condition information is the road condition information in the other time period, and the cycle lengths of the two time periods are the same.
Further, the method for calculating the recommended vehicle speed includes: calculating the sum of the average speed of the corresponding lane at the corresponding moment of the history statistics and the average speed of the corresponding lane at the current moment; dividing the sum by two to obtain the recommended vehicle speed.
Further, the step of transmitting the predicted result to the target vehicle through the radar includes: and sending the traffic flow, the congestion state, the average speed, the predicted congestion duration and the recommended speed of each lane to the target vehicle.
According to a second aspect of the present invention, there is also provided a radar-based vehicle assisted dispatch system comprising: the radar module is used for acquiring and counting radar data of the target road; the historical data calculation module is used for counting and determining historical road condition information of each corresponding lane in each time period based on the change of the vehicle flow corresponding to each lane in the lane information along with the time sequence; the prediction module is used for acquiring real-time road condition information of the radar at the current moment of the target road, and predicting the road condition of each current lane by combining the historical road condition information of each lane in each time period according to the historical statistics;
and the data transmission module is used for transmitting the predicted result to the target vehicle through the radar so as to enable the owner of the target vehicle to make decisions to select different lanes, control the speed of the vehicle or plan a new driving route.
According to a third aspect of the present invention, there is also provided an electronic device comprising: the radar-based vehicle assisted scheduling method according to any one of the above, a memory, a processor and a computer program stored on the memory and executable on the processor, when the processor executes the computer program.
According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the radar-based vehicle assisted scheduling method of any one of the above.
Through one or more of the above embodiments of the present invention, at least the following technical effects can be achieved:
According to the technical scheme disclosed by the invention, the vehicle condition of each lane at the current time in the second time period can be predicted according to the historical road condition information of each lane in the first time period, and the prediction result can be sent to the target vehicle on the target road, so that owners of the target vehicle can select different lane control speeds or plan new driving routes and the like, auxiliary scheduling of the vehicle is realized, the crowded state of the target road can be relieved to a great extent, and the traffic safety risk is reduced.
Drawings
The technical solution and other advantageous effects of the present invention will be made apparent by the following detailed description of the specific embodiments of the present invention with reference to the accompanying drawings.
FIG. 1 is a flow chart of a radar-based vehicle assisted dispatch method provided by an embodiment of the present invention;
FIG. 2 is a two-lane road imaging obtained by the radar-based vehicle assisted dispatch method according to an embodiment of the present invention;
FIG. 3 is a flowchart of a two-lane road information acquired by a vehicle assisted scheduling method for radar according to an embodiment of the present invention;
FIG. 4 is a block diagram of a radar-based vehicle assisted dispatch system provided by an embodiment of the present invention;
fig. 5 is a schematic block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
In the description of the present invention, it should be noted that, unless explicitly specified and defined otherwise, the term "and/or" herein is merely an association relationship describing associated objects, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. The character "/" herein generally indicates that the associated object is an "or" relationship unless otherwise specified.
Road sections without traffic lights in urban roads often present certain traffic safety risks or cause congestion when there are more vehicles and do not allow for efficient vehicle dispatching in real time. In the current scheme of the scene, the vehicle statistics is carried out by relying on GPS, but the traffic information is limited, and the method has no good effect on real-time auxiliary vehicle dispatching of the lane level of the road section without traffic lights. In the response scheme partially depending on the intersection cameras, the effect is often greatly influenced by the visibility of weather, and particularly the performance of the intersection cameras is seriously degraded in the weather such as fog, haze, rain, snow and the like.
The millimeter wave radar has the advantage of being capable of working all-weather, has good multi-target detection capability, and can sense road condition information in real time. Millimeter wave radars are distributed at intersections of road sections without traffic lights, and current time information and prediction information of the monitored road sections are sent to vehicles through the data transmission module, so that the vehicles can be effectively assisted in dispatching, road safety is guaranteed, and road congestion is relieved.
Therefore, the application provides a radar-based vehicle auxiliary dispatching method, a radar-based vehicle auxiliary dispatching system, a radar-based vehicle auxiliary dispatching device and a radar-based vehicle auxiliary dispatching medium, which can reduce traffic safety risks of road sections without traffic lights or can effectively dispatch vehicles in real time when more vehicles cause congestion.
Fig. 1 shows a radar-based vehicle auxiliary dispatching method provided by an embodiment of the invention, which includes:
s101, acquiring and counting lane information of a radar monitoring target road;
S102, based on the change of the traffic flow corresponding to each lane in the lane information along with the time sequence, statistics and determination are carried out on the historical road condition information corresponding to each lane in each time period;
s103, acquiring real-time road condition information of the radar monitoring target road at the current moment, and predicting the road condition of each current lane by combining the historical road condition information of each lane in each time period according to the historical statistics;
And S104, sending the predicted result to the target vehicle through a radar so as to enable a vehicle owner of the target vehicle to make decisions to select different lanes, control the vehicle speed or plan a new driving route.
In step S101, radar data is acquired by millimeter wave radar on a target road, so it can be understood that the radar-based vehicle assisted scheduling method provided in the present embodiment further includes: before step S101, at least one millimeter wave radar is deployed and installed at an intersection of a target road. In this embodiment, the radar data acquired by the millimeter wave radar can acquire the lane information of the target road, so that the number of lanes of the target road can be obtained.
In step S102, based on the number of lanes acquired in step S101, the historical road condition information of each lane in each time period may be counted in a time period, in this embodiment, the time period may be 1 day, so that the historical road condition information of each time period in1 day may be collected, and 1 day of historical data is provided for subsequent prediction. In other embodiments, the time period may be 1 week, 1 month, etc., and the same time is provided every week/month, so that the historical data of every week/month may be used to provide guidance for the target road traffic at any time of another week/month, and it is understood that the longer the time period of the collected historical data, the higher the accuracy of guidance provided by the historical data for prediction, that is, the higher the accuracy of prediction. In other embodiments, the time period may be further divided into two parts, one part is a working day, and the other part is a holiday, and since the traffic probabilities of the working day and the holiday are different, the historical road condition information of the two parts is collected, so that more accurate historical data can be provided for prediction of the working day/the holiday, and the accuracy of the prediction can be improved.
In step S103, real-time data of the current time of each lane in the target road is obtained, and an up-to-date data is provided for prediction. Because the historical data of one time period is collected, the current time can be regarded as the data in the other time period, the time periods of the two time periods are identical, and for convenience of description, the time period of the historical data is the first time period, the time period of the current time is the second time period, and when the prediction is carried out, because the historical road condition information of each time in the first time period is the historical data, even if the time does not reach some time in the second time period, the historical road condition information of the corresponding time in the first time period and the second time period can be utilized to carry out prediction guidance on the time which does not arrive in the second time period, so that the traffic flow information of the time which does not arrive in the second time period can be predicted. For example, the first time period and the second time period are both 1 day, and the current time of the second time period is 11 am, then the first method uses the historical road condition information after 11 am in the first time to conduct prediction guidance for the traffic information after 11 am in the second time period, so as to predict the traffic information after 11 am in the second time period.
In step S104, by sending the prediction result to the target vehicle, the target vehicle may determine, according to the prediction result, traffic flow information of each lane of the current target road in a period of time in the future, thereby selecting a different lane, controlling the vehicle speed, or planning a new driving route.
Therefore, the radar-based vehicle auxiliary dispatching method provided by the embodiment can predict the vehicle condition of each lane at the current moment in the second time period according to the historical road condition information of each lane in the first time period, and can send the prediction result to the target vehicle on the target road, so that owners of the target vehicle can select different lane control speeds or plan new driving routes and the like, the auxiliary dispatching of the vehicles is realized, the crowded state of the target road can be relieved to a great extent, and the traffic safety risk is reduced.
In one embodiment, the step of the statistical radar monitoring lane information of the target road includes:
Step one, acquiring the position, the speed and the signal-to-noise ratio of a monitoring target in radar data obtained in a radar monitoring range;
Screening a vehicle target point by using the speed of the monitoring target and the position of the monitoring target, and filtering by using the signal-to-noise ratio of the monitoring target to obtain the vehicle target point with the confidence degree exceeding a preset value;
Fitting and defining by utilizing a preset number of vehicle target points to obtain lane information of each lane on the target road.
In this embodiment, the position, speed and signal-to-noise ratio of the target are output in real time through the radar, the target point of the vehicle is screened according to the speed, a proper signal-to-noise ratio threshold is set to filter out the impurity point, the target point with higher reliability is reserved, the target data for a period of time is monitored and collected, and the collection can be stopped after N frames are accumulated, namely, after a preset number of frames are accumulated. The lane of the monitored road section can be fitted and defined by combining the target points of the N frames, as shown in fig. 2, which is an automatic acquisition lane diagram, and a simpler two-vehicle target road section can be subjected to lane acquisition through 500-frame accumulation. It should be noted that in practical application, the constraint N is generally not less than 3000, and N is not less than 5000 when the lanes are more complex or the vehicles are fewer.
For example, the fitting and defining in the third step of the present embodiment may be to fit the boundary of a certain lane according to the coverage area of a plurality of frames of target points in the certain lane in a period of time, and correct the lane boundary as the number of accumulated target points in the lane increases, so as to obtain the lane information of the lane, and so on.
In one embodiment, the step of the statistical radar monitoring the lane information of the target road further comprises: and screening a vehicle target point by using the speed of the monitoring target and the position of the monitoring target, filtering by using the signal-to-noise ratio of the monitoring target, judging whether the vehicle target point is accumulated with the preset quantity after obtaining the vehicle target point with the confidence exceeding the preset value, if not, continuing the step of obtaining the position, the speed and the signal-to-noise ratio of the monitoring target in the radar data obtained in the radar monitoring range, and returning to execute the step II, if so, executing the step III.
Referring to fig. 3, in this embodiment, after each frame of image is acquired, step two is executed, and after step two is executed, it is determined whether the acquired image is accumulated to reach N frames, if yes, no image is required to be acquired, and if no, image is required to be continuously acquired. The technical scheme that sets up like this for can all process in the very first time after every frame radar image gathers, can guarantee like this that every frame image gathers the back, all can the fastest processing, prevent to gather the problem emergence that the work load that can increase image processing and inefficiency of processing is unified again to radar image of a certain quantity.
In one embodiment, the step of counting the historical road condition information includes:
Continuously monitoring the traffic flow of all lanes in each time period;
Defining the congestion state of a target road according to the traffic flow;
and solving the average speed of vehicles in each lane in different congestion states at different moments, and counting the congestion duration to obtain the historical road condition information of each lane.
In this embodiment, taking the first time period as 1 day as an example, after lane information is obtained, the traffic flow of the road section within 24 hours needs to be continuously monitored and counted, and the traffic flow is defined as a congestion-free state, a general congestion state and a serious congestion state according to the traffic flow. And solving the average speed of each lane corresponding to different moments and different congestion states, and counting the congestion duration, wherein if the congestion duration is the congestion-free state, the congestion duration is 0.
The definition of the congestion state is defined as serious congestion according to the traffic flow, for example, the number of vehicles passing through the predetermined area per minute is smaller than or equal to 10, the number of vehicles passing through the predetermined area per minute is smaller than or equal to 20 and larger than 10, the definition is general congestion, the number of vehicles passing through the predetermined area per minute is larger than 20 and is no congestion, and different road sections have different division modes, for example, if the number of vehicles passing through the road is larger than the number of vehicles passing through the predetermined area per minute is smaller than or equal to 20, the definition is serious congestion, the number of vehicles passing through the predetermined area per minute is smaller than or equal to 30 and is larger than 20, the definition is general congestion, and the number of vehicles passing through the predetermined area per minute is larger than 30 and is no congestion.
In one embodiment, the real-time traffic information includes traffic flow, congestion status and average speed of all lanes at the current time.
The method for acquiring the real-time information at the current time in this embodiment may refer to the step of counting the historical road condition information of each lane at each time in the first time period in the above embodiment, which is not described in detail in this embodiment.
In one embodiment, the predicting step includes: using the congestion state and the congestion duration of each lane in each time of the history statistics and at the corresponding time of the current time, and taking the congestion duration as the predicted congestion duration to be sustained at the current time; and calculating a recommended speed by using the average speed of each lane at the historical moment and the average speed of each lane at the current moment at the corresponding moment to obtain a prediction result, wherein the historical road condition information of each time period of the historical statistics is at least traffic flow information in one time period, the real-time road condition information is road condition information in the other time period, and the cycle lengths of the two time periods are the same.
In this embodiment, since the time periods of the first time period and the second time period are the same in length, the time points of the first time period and the second time period are identical, so that when prediction is performed, the historical data of each time point, that is, the congestion state and the congestion duration of each lane, the average speed of each lane, can be used to predict the congestion state and the congestion duration of each lane at the current time, and the average speed of each lane, and the recommended speed of each lane is calculated on the basis of the historical data, so that the prediction result in this embodiment is based on the historical data that has already occurred, so that the prediction result is more fit to the actual situation, and the congestion state, the congestion duration, the average speed, and the like of each lane at the time point when the second time period has not occurred can be more accurately obtained.
In one embodiment, the method for calculating the recommended vehicle speed includes:
Calculating the sum of the average speed of the corresponding lane at the corresponding moment of the history statistics and the average speed of the corresponding lane at the current moment;
Dividing the sum by two to obtain the recommended vehicle speed.
In the embodiment, the vehicle owners of each target vehicle on the lane can be assisted and scheduled by calculating the recommended vehicle speed, if the vehicle owners of most target vehicles accept the recommended vehicle speed, the congestion state can be relieved to a great extent, and the recommended vehicle speed calculated by the embodiment is based on the average vehicle speed of the corresponding lane in the first time period and the average vehicle speed of the corresponding lane at the current moment, and the vehicle speed in the current congestion state is matched, so that the congestion and scheduling of the target road can be assisted more accurately.
In one embodiment, the step of transmitting the predicted result to the target vehicle by radar includes: and sending the traffic flow, the congestion state, the average speed, the predicted congestion duration and the recommended speed of each lane to the target vehicle.
In this embodiment, by sending the traffic flow, the congestion status, the average speed, the estimated congestion duration and the recommended speed of each lane to the target vehicle, after receiving these information, the owner of the target vehicle generally considers the information contained in the prediction result in order to better pass through the road section, so as to make a decision of lane replacement, speed control or new driving route planning, thus being capable of greatly relieving the congestion status and reducing the traffic safety risk.
Referring to fig. 4, an embodiment of the present application further provides a radar-based vehicle auxiliary dispatching system, including: the system comprises a radar module 1, a historical data calculation module 2, a prediction module 3 and a data transmission module 4; the radar module 1 is used for acquiring and counting radar data of a target road; the historical data calculation module 2 is used for counting and determining historical road condition information of each corresponding lane in each time period based on the change of the vehicle flow corresponding to each lane in the lane information along with the time sequence; the prediction module 3 is used for acquiring real-time road condition information of the radar at the current moment of the target road, and predicting the road condition of each current lane by combining the historical road condition information of each lane in each time period according to the historical statistics; the data transmission module 4 is used for sending the predicted result to the target vehicle through the radar so as to enable the owner of the target vehicle to make decisions to select different lanes, control the speed of the vehicle or plan a new driving route.
The radar-based vehicle auxiliary dispatching system provided by the embodiment can predict the vehicle condition of each lane at the current moment in the second time period according to the historical road condition information of each lane in the first time period, and can send the prediction result to the target vehicle on the target road, so that owners of the target vehicle can select different lane control speeds or plan new driving routes and the like, auxiliary dispatching of the vehicle is realized, the congestion state of the target road can be relieved to a great extent, and traffic safety risks are reduced.
In one embodiment, the radar data includes a predetermined number of radar images over a predetermined time period; the history data calculation module 2 includes: the device comprises a data acquisition unit, a target point determination unit and a fitting definition unit; the data acquisition unit is used for acquiring the position, the speed and the signal-to-noise ratio of a monitoring target in the radar monitoring range; the target point determining unit is used for screening a vehicle target point by utilizing the speed of the monitoring target and the position of the monitoring target, and filtering noise points in the vehicle target point by utilizing the signal-to-noise ratio of the radar to obtain a target point of the target vehicle; the fitting definition unit is used for fitting and defining target points in a preset number of radar images to obtain lane information of each lane on the target road.
In one embodiment, the historical data calculation module 2 further includes: the judging unit is used for screening vehicle target points by using the speed of the monitoring target and the position of the monitoring target at the target point determining unit, filtering noise points in the vehicle target points by using the signal-to-noise ratio of the radar to obtain target points of the target vehicle, judging whether the radar images accumulate the preset quantity or not, if not, continuing to call the radar module 1 to acquire radar data of the target road, and continuing to call the target point determining unit, and if yes, calling the fitting defining unit.
In one embodiment, the historical data calculation module 2 further includes: the traffic flow monitoring unit is used for continuously monitoring the traffic flow of all lanes in the first time period; the congestion state definition unit is used for defining the congestion state of the target road according to the traffic flow; the traffic flow information calculation unit is used for solving the average speed of the vehicles in each lane in different congestion states at different moments, and counting the congestion duration to obtain the historical road condition information of each lane.
In one embodiment, the specific obtaining of the real-time traffic information at the current time by the prediction module 3 includes obtaining the traffic flow, the congestion state and the average speed of all lanes at the current time, and obtaining the real-time information at the current time.
In one embodiment, the prediction module includes: a congestion duration calculation unit and a prediction result calculation unit; the congestion duration calculation unit is used for calculating the congestion state and the congestion duration of each lane in each time according to the history statistics and at the corresponding time of the current time, and taking the congestion duration as the expected congestion duration to be sustained at the current time; the prediction result calculation unit is used for calculating a recommended speed by using the average speed of each lane at the historical moment and the average speed of each lane at the current moment at the corresponding moment to obtain a prediction result, wherein the historical road condition information of each time period of the historical statistics is at least the road condition information in one time period, the real-time road condition information is the road condition information in the other time period, and the cycle lengths of the two time periods are the same.
In one embodiment, the prediction result calculation unit includes: a summing subunit and a Shang Zi unit; the summation subunit is used for calculating the sum value of the average speed of the corresponding lane at the corresponding moment and the average speed of the corresponding lane at the current moment of the history statistics; and the quotient calculating subunit is used for dividing the sum value by two to obtain the recommended vehicle speed.
In one embodiment, the data transmission module 4 is specifically configured to send the traffic flow, the congestion status, the average speed, the predicted congestion duration, and the recommended speed of each lane to the target vehicle.
An embodiment of the present application further provides an electronic device, referring to fig. 5, including: the radar-based vehicle assisted scheduling method described in the foregoing is implemented by the memory 601, the processor 602, and a computer program stored on the memory 601 and executable on the processor 602, when the processor 602 executes the computer program.
Further, the electronic device further includes: at least one input device 603 and at least one output device 604.
The memory 601, the processor 602, the input device 603, and the output device 604 are connected via a bus 605.
The input device 603 may be a camera, a touch panel, a physical key, a mouse, or the like. The output device 604 may be, in particular, a display screen.
The memory 601 may be a high-speed random access memory (RAM, random Access Memory) memory or a non-volatile memory (non-volatile memory), such as a disk memory. The memory 601 is used for storing a set of executable program codes and the processor 602 is coupled to the memory 601.
Further, the embodiment of the present application also provides a computer readable storage medium, which may be provided in the electronic device in each of the above embodiments, and the computer readable storage medium may be the memory 601 in the above embodiments. The computer readable storage medium has stored thereon a computer program which, when executed by the processor 602, implements the radar-based vehicle assisted scheduling method described in the foregoing embodiments.
Further, the computer-readable medium may be any medium capable of storing a program code, such as a usb (universal serial bus), a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention.
It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present invention is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all required for the present invention.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
In summary, although the present invention has been described in terms of the preferred embodiments, the preferred embodiments are not limited to the above embodiments, and various modifications and changes can be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention is defined by the appended claims.
Claims (11)
1. A radar-based vehicle assisted dispatch method, comprising:
Acquiring and counting lane information of a radar monitoring target road;
Based on the change of the traffic flow corresponding to each lane in the lane information along with the time sequence, the historical road condition information corresponding to each lane in each time period is counted and determined;
Acquiring real-time road condition information of the radar at the current moment of the target road, and predicting the road condition of each current lane by combining the historical road condition information of each lane in each time period according to historical statistics;
And sending the predicted result to the target vehicle through the radar so as to enable the owner of the target vehicle to make decisions to select different lanes, control the vehicle speed or plan a new driving route.
2. A radar-based vehicle assisted scheduling method according to claim 1, wherein,
The step of the statistical radar monitoring the lane information of the target road comprises the following steps:
Step one, acquiring the position, the speed and the signal-to-noise ratio of a monitoring target in radar data obtained in a radar monitoring range;
Screening a vehicle target point by using the speed of the monitoring target and the position of the monitoring target, and filtering by using the signal-to-noise ratio of the monitoring target to obtain the vehicle target point with the confidence degree exceeding a preset value;
and thirdly, fitting and defining the target points of the vehicles by using a preset number of the target points of the vehicles to obtain the lane information of each lane on the target road.
3. The radar-based vehicle assisted scheduling method of claim 2, wherein,
The step of the statistical radar monitoring the lane information of the target road further comprises the following steps:
And screening a vehicle target point by using the speed of the monitoring target and the position of the monitoring target, filtering by using the signal-to-noise ratio of the monitoring target, judging whether the vehicle target point is accumulated with the preset quantity after obtaining the vehicle target point with the confidence exceeding the preset value, if not, continuing the step of obtaining the position, the speed and the signal-to-noise ratio of the monitoring target in the radar data obtained in the radar monitoring range, and returning to execute the step II, if so, executing the step III.
4. A radar-based vehicle assisted scheduling method according to claim 1, wherein,
The step of statistics of the historical road condition information comprises the following steps:
Continuously monitoring the traffic flow of all lanes in each time period;
Defining the congestion state of a target road according to the traffic flow;
and solving the average speed of vehicles in each lane in different congestion states at different moments, and counting the congestion duration to obtain the historical road condition information of each lane.
5. A radar-based vehicle assisted scheduling method according to claim 1, wherein,
The real-time road condition information comprises traffic flow, congestion state and average speed of all lanes at the current moment.
6. The radar-based vehicle assisted scheduling method of claim 4, wherein,
The predicting step includes: using the congestion state and the congestion duration of each lane in each time of the history statistics and at the corresponding time of the current time, and taking the congestion duration as the predicted congestion duration to be sustained at the current time; and calculating a recommended speed by using the average speed of each lane at the historical moment and the average speed of each lane at the current moment at the corresponding moment to obtain a prediction result, wherein the historical road condition information of each time period is at least the historical road condition information in one time period, the real-time road condition information is the road condition information in the other time period, and the cycle lengths of the two time periods are the same.
7. The radar-based vehicle assisted scheduling method of claim 6, wherein,
The calculation method of the recommended vehicle speed comprises the following steps:
Calculating the sum of the average speed of the corresponding lane at the corresponding moment of the history statistics and the average speed of the corresponding lane at the current moment;
Dividing the sum by two to obtain the recommended vehicle speed.
8. The radar-based vehicle assisted scheduling method of claim 6, wherein,
The step of transmitting the predicted result to the target vehicle through the radar includes:
And sending the traffic flow, the congestion state, the average speed, the predicted congestion duration and the recommended speed of each lane to the target vehicle.
9. A radar-based vehicle assisted dispatch system, comprising:
the radar module is used for acquiring and counting radar data of the target road;
the historical data calculation module is used for counting and determining historical road condition information of each corresponding lane in each time period based on the change of the vehicle flow corresponding to each lane in the lane information along with the time sequence;
The prediction module is used for acquiring real-time road condition information of the radar at the current moment of the target road, and predicting the road condition of each current lane by combining the historical road condition information of each lane in each time period according to the historical statistics;
and the data transmission module is used for transmitting the predicted result to the target vehicle through the radar so as to enable the owner of the target vehicle to make decisions to select different lanes, control the speed of the vehicle or plan a new driving route.
10. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 8 when executing the computer program.
11. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1 to 8.
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