CN114694371A - Estimation method, vehicle, server and road side equipment - Google Patents
Estimation method, vehicle, server and road side equipment Download PDFInfo
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- CN114694371A CN114694371A CN202011614780.0A CN202011614780A CN114694371A CN 114694371 A CN114694371 A CN 114694371A CN 202011614780 A CN202011614780 A CN 202011614780A CN 114694371 A CN114694371 A CN 114694371A
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- 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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
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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
- G08G1/0133—Traffic data processing for classifying traffic situation
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Abstract
The application discloses a road congestion estimation method, which comprises the following steps: the method comprises the steps of receiving current road information sent by the road side equipment, responding to a traffic route selected by a driver according to the current road information to generate feedback information, sending the feedback information to the road side equipment, and receiving congestion information fed back by the road side equipment, wherein the congestion information is a communication route estimated by a server communicated with the road side equipment according to the feedback information and the current road information. According to the estimation method, the road side equipment acquires the current road information in real time, and the congestion model is used for estimating the congestion information, so that the real-time performance and the accuracy of the congestion information are guaranteed to a certain extent. The application also discloses a vehicle, a server and road side equipment.
Description
Technical Field
The present application relates to the field of vehicle technologies, and in particular, to an estimation method for road congestion, a vehicle, a server, and a roadside device.
Background
With the complication of the road traffic jam condition, in the related art, a driver usually checks the road jam condition by positioning the current road section through map software in combination with a vehicle or a terminal GPS, however, this method requires that all vehicle owners on the road are networked to collect data, so that the number of running vehicles on the current route is collected to judge the jam condition, and the result is not accurate enough. At present, only the running time and the approximate predicted time of the congested road section exist, so that a driver is difficult to obtain more detailed running time or congestion information, and real-time judgment such as optimization of a running route is not facilitated for the driver in the running process.
Disclosure of Invention
In view of the above, embodiments of the present application provide an estimation method for road congestion, a vehicle, a server, and a roadside apparatus.
The application provides a road congestion estimation method, which comprises the following steps:
receiving current road information sent by road side equipment;
responding to a passing route selected by a driver according to the current road information to generate feedback information;
sending the feedback information to the road side equipment;
receiving congestion information fed back by the road side equipment, wherein the congestion information is the communication route estimated by a server communicated with the road side equipment according to the feedback information and the current road information.
In some implementations, the current road information includes: at least one of each traffic light period, the average running speed of vehicles, the distance between the vehicle heads, the distance between the traffic lights of two adjacent road sections, the number of the vehicles actually passing in each traffic light period and the selectable passing routes.
In some implementations, the estimation method further includes:
and estimating the passing time of the vehicle to the next roadside device according to the current road information and the running information of the vehicle.
In some implementations, the driving information of the vehicle includes the headway and/or a current speed of the vehicle.
In some implementations, the estimation method further includes:
and carrying out early warning prompt according to the congestion information and/or the communication time.
The application also provides a method for estimating road congestion, which comprises the following steps:
receiving traffic information of a current road sent by road side equipment;
receiving a passing route of a driver sent by the road side equipment;
estimating congestion information of the traffic route according to the traffic information and the traffic route;
transmitting the congestion information to a vehicle in communication with the roadside device via the roadside device.
The application also provides a method for estimating road congestion, which comprises the following steps:
receiving feedback information generated by a passing route selected according to the traffic information of the current road and sent by a vehicle;
sending the traffic information of the current road and the feedback information to a server;
receiving congestion information of the traffic route estimated by a server according to the traffic information and the feedback information;
and sending the congestion information to the vehicle.
The present application further provides a vehicle, comprising:
the communication module is used for receiving current road information sent by the road side equipment;
the control module is used for responding to a passing route selected by a driver according to the current road information to generate feedback information;
the communication module is further used for sending the feedback information to the road side equipment; and
receiving congestion information fed back by the roadside device, wherein the congestion information is the communication route estimated by a server communicated with the roadside device according to the feedback information and the current road information.
The present application further provides a server, including:
the communication module is used for receiving the traffic information of the current road sent by the road side equipment; and
receiving a passing route of a driver sent by the road side equipment;
the calculation module is used for estimating congestion information of the traffic route according to the traffic information and the traffic route;
the communication module is further configured to send the congestion information to a vehicle in communication with the roadside device via the roadside device.
The application also provides roadside equipment, including:
the communication module is used for receiving feedback information generated by a passing route selected according to the traffic information of the current road and sent by a vehicle; and
sending the traffic information of the current road and the feedback information to a server; and
receiving congestion information of the traffic route estimated by a server according to the traffic information and the feedback information; and
and sending the congestion information to the vehicle.
According to the estimation method for road congestion, the vehicle, the server and the road side equipment, the vehicle is communicated with the road side equipment, and the road side equipment acquires current road information in real time, estimates the current road information to obtain congestion information and then sends the congestion information to the vehicle on the current road. Therefore, the road congestion is estimated according to the real-time road information, and the accuracy of the data source is guaranteed to a certain extent. Meanwhile, the road side equipment sends real-time road information to a server communicated with the road side equipment, and the server estimates congestion through a congestion model constructed by big data, so that the accuracy of congestion information is guaranteed to a certain extent. The vehicle and the road side equipment can perform communication interaction in real time and acquire real-time road information, so that the real-time performance of the congestion information is effectively improved.
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The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings.
FIG. 1 is a schematic flow chart of a method for estimating road congestion according to some embodiments of the present disclosure;
FIG. 2 is a schematic flow chart of a method for estimating road congestion according to some embodiments of the present disclosure;
FIG. 3 is a flow chart illustrating a method for estimating road congestion in accordance with certain embodiments of the present disclosure;
FIG. 4 is a block diagram of an apparatus for a method of estimating road congestion according to some embodiments of the present disclosure;
FIG. 5 is a block diagram of an apparatus for a method of estimating road congestion according to some embodiments of the present disclosure;
FIG. 6 is a block diagram of an apparatus for a method of estimating road congestion according to some embodiments of the present disclosure;
FIG. 7 is a network architecture diagram of a method for estimating road congestion according to some embodiments of the present application;
fig. 8 is a flowchart illustrating a method for estimating road congestion according to some embodiments of the present disclosure.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
Referring to fig. 1, the present application provides a method for estimating road congestion, in this embodiment, taking a vehicle as an execution object as an example, the method includes:
s10: receiving current road information sent by road side equipment;
s20: generating feedback information in response to a traffic route selected by a driver according to the current road information;
s30: sending feedback information to the road side equipment;
s40: and receiving congestion information fed back by the road side equipment, wherein the congestion information is a communication route estimated by a server communicated with the road side equipment according to the feedback information and the current road information.
Similarly, referring to fig. 2, in some embodiments, taking roadside devices as an example of the execution object, the estimation method includes:
s50: receiving feedback information generated by a passing route selected according to the traffic information of the current road and sent by a vehicle;
s60: sending the traffic information and the feedback information of the current road to a server;
s70: receiving congestion information of a traffic route estimated by a server according to the traffic information and the feedback information;
s80: the congestion information is sent to the vehicle.
Similarly, referring to fig. 3, in some embodiments, taking a server as an execution object as an example, the estimation method includes:
s90: receiving traffic information of a current road sent by road side equipment;
s100: receiving a passing route of a driver sent by the road side equipment;
s110: estimating congestion information of the traffic route according to the traffic information and the traffic route;
s120: the congestion information is transmitted via the roadside device to a vehicle in communication with the roadside device.
Referring to fig. 4, the embodiment of the present application further provides a vehicle 1000, and the estimation method of the embodiment of the present application can be implemented by the vehicle 1000. The vehicle 1000 includes a communication module 1100 and a control module 1200. S10, S30, and S40 may be implemented by the communication module 1100, and S20 may be implemented by the control module 1200. Or, the communication module 1100 is configured to receive current road information sent by the roadside device, send feedback information to the roadside device, and receive congestion information fed back by the roadside device, where the congestion information is a communication route estimated by a server in communication with the roadside device according to the feedback information and the current road information. The control module 1200 is configured to generate feedback information in response to a transit route selected by a driver in accordance with current road information.
Referring to fig. 5, the present embodiment further provides a roadside apparatus 2000 connected to the vehicle 1000 through wireless communication. The estimation method using the roadside apparatus as the execution object according to the embodiment of the present application may be implemented by the server 2000. The roadside apparatus 2000 includes a communication module 2100. S50-S80 may be implemented with the communication module 2100. In other words, the communication module 2100 is configured to receive feedback information generated by a traffic route selected according to traffic information of a current road and sent by the vehicle 1000, and send the traffic information of the current road and the feedback information to the server, and is further configured to receive congestion information of the traffic route estimated by the server according to the traffic information and the feedback information, and send the congestion information to the vehicle 1000.
Referring to fig. 6, the present embodiment further provides a server 3000, which is in communication connection with the roadside apparatus 2000. The server-based estimation method according to the embodiment of the present application may be implemented by the server 3000. The server 3000 includes a communication module 3100 and a computing module 3200. S90, S100 and S120 may be implemented by the communication module 3100, and S110 may be implemented by the computing module 3200. Alternatively, the communication module 3100 is configured to receive the traffic information of the current road transmitted by the roadside apparatus 2000 and the transit route of the driver transmitted by the roadside apparatus 2000, and is further configured to transmit the congestion information to the vehicle 1000 in communication with the roadside apparatus 2000 via the roadside apparatus 2000. The calculation module 3200 is used for estimating congestion information of the traffic route according to the traffic information and the traffic route.
Referring to fig. 7, the vehicle 1000 is connected to the roadside apparatus 2000 through wireless communication, and may send or receive communication information in real time, and meanwhile, the roadside apparatus 2000 is connected to the server 3000 of the roadside system through communication, where the server 3000 may be a cloud server.
Specifically, with the development of vehicle-road coordination and vehicle networking intelligence, the roadside device 2000 has various built-in communication modes, and provides various sensor interfaces such as a signal machine and a detector, and the built-in detector of the roadside device 2000 can transmit real-time traffic information to the running vehicle 1000. An intelligent communication device such as an OBU is provided at the vehicle end to be connected to the roadside device 2000 for wireless communication, or communication is performed by an Electronic Control Unit (ECU) of the vehicle, such as an ECU of a car navigation system. Further, a server connected to the roadside device 2000, such as a cloud server, may perform real-time communication with the roadside device 2000, process the communication request sent by the roadside device 2000, and return the communication request.
It is understood that the roadside apparatus 2000 may communicate with a range of vehicles 1000. The roadside apparatus 2000 includes, among other things, a traffic light system built-in detector that can communicate with the vehicle 1000. The traffic light built-in detector may be configured to communicate with a communication vehicle within a range from a current traffic light to a next traffic light, and to transmit current road information within the range to the vehicle within the range 1000 in real time. The current road information is a parameter related to traffic flow, such as congestion of each lane.
Further, the vehicle 1000 receives the current road information of the roadside apparatus 2000 in real time through the communication apparatus. When the vehicle 1000 enters the communication range of the road side device such as the traffic light a, the real-time current road information sent by the current traffic light a is received until the vehicle exits the communication range. It is understood that the communication range is a settable range, for example, the communication range of the traffic light a is a road range from the previous traffic light to the traffic light a, wherein the front-back direction is based on the driving direction of the vehicle.
The vehicle 1000 receives the current road information transmitted by the roadside apparatus 2000. The traffic light related information, the average speed of the current road section, the congestion condition of each passing route of the current road section and the like are included. The driver may select a traffic route according to the current road information, and the vehicle communication device transmits the selected traffic route as feedback information to the roadside device 2000 to acquire more information. It is understood that the current road includes a plurality of traffic routes.
Further, the roadside device 2000 receives the feedback information, i.e., the selected passing route, extracts the real-time traffic information of the passing route and transmits the information to the vehicle 1000. The real-time traffic information is the current road information of the traffic route, and comprises the average running speed of vehicles, traffic light related information and the like.
Meanwhile, the roadside device 2000 receives the feedback information, i.e., the selected traffic route, and then transmits the feedback information and the traffic information of the current road to the server 3000 for congestion estimation.
The server 3000 is in communication with the roadside device 2000, and may be a cloud server. After receiving the traffic route and the real-time traffic information of the route, the server 3000 performs congestion estimation according to a preset congestion model, thereby obtaining congestion information of the current traffic route, including congestion time passing through the current congested road section, congestion time of all traffic routes on the current road, and the like. And transmits the congestion information to the roadside apparatus 2000.
The congestion model is trained by the server 3000 according to big data in an early stage, and can be periodically trained and updated according to real-time data. The congestion model may include a prediction model based on intelligent theory or a prediction model based on non-linear theory, etc. The prediction model based on the intelligent theory can comprise a neural network model or a deep learning model and the like. The neural network model adopts a typical black box type learning mode, rules can be automatically summarized from existing data without an empirical formula, and the internal rules of the data are obtained. Even if the internal mechanism of the prediction problem is not clear, a good input and output mapping model can be established as long as a large number of input and output samples are automatically adjusted through the interior of a neural network black box. The server 3000 may acquire a large amount of real-time traffic information in real time through the connected roadside apparatus 2000 for training the model and for the current road. Due to big data and real-time of traffic flow, the neural network model can guarantee the accuracy of congestion estimation to a certain extent.
Further, when the server 3000 estimates the congestion information, the information is returned to the roadside device 2000, and then the roadside device 2000 forwards to the vehicle 1000.
In this manner, a congestion model based on big data is created to estimate real-time traffic information to obtain congestion information, and then the congestion information is transmitted to the vehicle 1000, so that the vehicle 1000 can acquire the congestion information in real time. The congestion information estimated from the real-time traffic information of the roadside apparatus 2000 can ensure the accuracy of the data source to a certain extent. Meanwhile, the road side equipment sends real-time road information to the server 3000 communicated with the road side equipment, and the server 3000 estimates congestion through a congestion model, so that the accuracy of congestion information is guaranteed to a certain extent. The vehicle 1000 and the roadside device 2000 can perform communication interaction and acquire real-time road information, so that the real-time performance of congestion information is effectively improved.
In some implementations, the current road information includes: at least one of each traffic light period, the average running speed of the vehicles, the distance between the vehicle heads, the distance between the traffic lights of two adjacent road sections, the number of the vehicles actually passing in each traffic light period and the selectable passing routes.
The current road information is traffic information acquired by the roadside device 2000 in real time, and is mainly traffic flow parameters. The traffic flow parameters mainly comprise average traffic flow, average speed, average occupancy, traffic flow density, headway time, headway distance and the like. The acquisition may be specifically performed according to characteristics required by the congestion model.
Referring to fig. 8, in some embodiments, the estimation method further includes:
s130: and estimating the passing time of the vehicle to the next roadside device according to the current road information and the running information of the vehicle.
In some implementations, the travel information of the vehicle includes headway and/or a current speed of the vehicle.
In some implementations, S130 may be implemented by the control module 1200. Alternatively, the control module 1200 is configured to estimate the transit time for the vehicle 1000 to travel to the next roadside apparatus 2000 based on the current road information and the travel information of the vehicle 1000.
Specifically, after the vehicle 1000 receives the current road information returned by the roadside device 2000, the time of passing to the next roadside device 2000, such as a traffic light, may be estimated in combination with the current driving information including the headway and/or the current speed of the vehicle. The current traffic light period, the average running speed of the current road vehicle, the distance between the vehicle heads, the actual passing vehicle number of the current road and the distance from the current traffic light to the next traffic light can be extracted from the current road information.
Thus, the real-time detailed traffic information of the roadside apparatus 2000 and the travel information of the vehicle 1000 itself can allow the vehicle 1000 to estimate a more accurate travel time such as a transit time to the next roadside apparatus 2000. It is understood that the vehicle 1000 may estimate more information such as travel time to the destination, etc. based on such information.
Referring again to fig. 8, in some embodiments, the estimation method further includes:
s140: and carrying out early warning prompt according to the congestion information and/or the communication time.
In some implementations, S140 may be implemented by the control module 1200. Or the control module 1200 is configured to perform early warning according to the congestion information and/or the communication time.
Specifically, as described above, the server 3000 may estimate the congestion time of the current road, and transmit the congestion time to the roadside device 2000 for further forwarding to the vehicle 1000. Meanwhile, the communication device of the vehicle 1000 such as the ECU may estimate a travel time such as a transit time to the next roadside device 2000 in combination with its own travel information. After the vehicle 1000 obtains the congestion time and the time to reach the next roadside device 2000, an early warning prompt may be performed according to the information, such as displaying on a vehicle-mounted central control large screen or other display screen devices, or prompting through a voice system, displaying or prompting information such as "the current traffic light passing time is predicted to be 5 minutes, and the time to reach the next traffic light is predicted to be 8 minutes".
Therefore, the driver can clearly learn the passing time of each road section which is more accurate than the running time of the whole road section by carrying out early warning prompt on the driver according to the congestion information and/or the communication time, so that better judgment such as re-selection of the passing road section can be made, and the user experience is improved to a certain extent.
In summary, in the estimation method for road congestion, the vehicle, the server and the roadside device according to the embodiment of the present application, a congestion model based on big data is created to calculate real-time traffic information to obtain congestion information, and then the congestion information is sent to the vehicle 1000, so that the vehicle 1000 can obtain the congestion information in real time. The congestion information estimated from the real-time traffic information of the roadside apparatus 2000 can ensure the accuracy of the data source to a certain extent. Meanwhile, the road side equipment sends real-time road information to the server 3000 communicated with the road side equipment, and the server 3000 estimates congestion through a congestion model, so that the accuracy of congestion information is guaranteed to a certain extent. The vehicle 1000 and the roadside device 2000 can perform communication interaction and acquire real-time road information, so that the real-time performance of congestion information is effectively improved. Further, the vehicle 1000 may estimate more travel time such as transit time to the next roadside device 2000 according to the current road information and the self travel information, and may prompt the driver with the congestion information and the travel time through early warning, thereby effectively enriching the travel related data that the driver can obtain, so that the driver may make a more optimal judgment according to the diversified congestion information and the travel time, and may alleviate traffic pressure to a certain extent, and improve user experience.
It will be understood by those skilled in the art that all or part of the processes of the method for implementing the above embodiments may be implemented by instructing relevant software through a computer program. The program may be stored in a non-volatile computer readable storage medium, which when executed, may include the flows of embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), or the like.
The above examples 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 present application. 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, which falls 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 estimating road congestion, comprising:
receiving current road information sent by road side equipment;
generating feedback information in response to a passing route selected by a driver according to the current road information;
sending the feedback information to the road side equipment;
receiving congestion information fed back by the roadside device, wherein the congestion information is the communication route estimated by a server communicated with the roadside device according to the feedback information and the current road information.
2. The estimation method according to claim 1, wherein the current road information includes: at least one of each traffic light period, the average running speed of vehicles, the distance between the vehicle heads, the distance between the traffic lights of two adjacent road sections, the number of the vehicles actually passing in each traffic light period and the selectable passing routes.
3. The estimation method according to claim 1, characterized in that the estimation method further comprises:
and estimating the passing time of the vehicle to the next roadside device according to the current road information and the running information of the vehicle.
4. The estimation method according to claim 3, characterized in that the running information of the vehicle includes the headway and/or a current vehicle speed of the vehicle.
5. The estimation method according to claim 3, characterized in that the estimation method further comprises:
and carrying out early warning prompt according to the congestion information and/or the communication time.
6. A method for estimating road congestion, comprising:
receiving traffic information of a current road sent by road side equipment;
receiving a passing route of a driver sent by the road side equipment;
estimating congestion information of the traffic route according to the traffic information and the traffic route;
transmitting the congestion information to a vehicle in communication with the roadside device via the roadside device.
7. A method for estimating road congestion, comprising:
receiving feedback information generated by a passing route selected according to the traffic information of the current road and sent by a vehicle;
sending the traffic information of the current road and the feedback information to a server;
receiving congestion information of the traffic route estimated by a server according to the traffic information and the feedback information;
and sending the congestion information to the vehicle.
8. A vehicle, characterized by comprising:
the communication module is used for receiving current road information sent by the road side equipment;
the control module is used for responding to a passing route selected by a driver according to the current road information to generate feedback information;
the communication module is further used for sending the feedback information to the road side equipment; and
receiving congestion information fed back by the roadside device, wherein the congestion information is the communication route estimated by a server communicated with the roadside device according to the feedback information and the current road information.
9. A server, comprising:
the communication module is used for receiving the traffic information of the current road sent by the road side equipment; and
receiving a passing route of a driver sent by the road side equipment;
the calculation module is used for estimating congestion information of the traffic route according to the traffic information and the traffic route;
the communication module is further configured to send the congestion information to a vehicle in communication with the roadside device via the roadside device.
10. A roadside apparatus characterized by comprising:
the communication module is used for receiving feedback information generated by a passing route selected according to the traffic information of the current road and sent by a vehicle;
sending the traffic information of the current road and the feedback information to a server;
receiving congestion information of the traffic route estimated by a server according to the traffic information and the feedback information; and
and sending the congestion information to the vehicle.
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| CN202011614780.0A CN114694371A (en) | 2020-12-30 | 2020-12-30 | Estimation method, vehicle, server and road side equipment |
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| CN202011614780.0A CN114694371A (en) | 2020-12-30 | 2020-12-30 | Estimation method, vehicle, server and road side equipment |
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Application publication date: 20220701 |