CN114791564A - Vehicle networking based mileage calibration method, device, equipment and storage medium - Google Patents
Vehicle networking based mileage calibration method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a mileage calibration method, a device, equipment and a storage medium based on Internet of vehicles, wherein the method comprises the following steps: when receiving parking information uploaded by a target vehicle, determining whether the target vehicle is in a red light waiting state of a traffic light according to a real-time position and a driving path uploaded by the target vehicle; if the target vehicle is in a red light waiting state, calling current video data of a road section controlled by a traffic light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data; and sending the waiting time to the target vehicle so that the target vehicle calibrates the remaining mileage according to the waiting time. The embodiment of the invention can improve the accuracy of the prediction of the remaining mileage of the vehicle, and solves the technical problem of low accuracy of the prediction of the remaining mileage caused by the fact that the influence of the waiting time on the power consumption of the battery during the red light is not considered in the prior art.
Description
Technical Field
The embodiment of the application relates to the field of Internet of vehicles, in particular to a mileage calibration method, device, equipment and storage medium based on the Internet of vehicles.
Background
Currently, when predicting the mileage that a new energy automobile can travel, the remaining mileage is generally estimated by predicting the power consumption of a battery. However, because the number of traffic lights in a city is large, when a new energy automobile runs in the city, the new energy automobile needs to stop frequently to wait for the red light, and the electricity consumption of the battery is low in the process. When the remaining mileage is predicted, the influence of the waiting time at the time of red light on the battery power consumption is not generally considered, so that the accuracy in predicting the remaining mileage is low.
Disclosure of Invention
The embodiment of the invention provides a mileage calibration method, device, equipment and storage medium based on an internet of vehicles, which solve the technical problem of low accuracy in predicting the remaining mileage of a new energy automobile in the prior art.
In a first aspect, an embodiment of the present invention provides a mileage calibration method based on an internet of vehicles, including:
when parking information uploaded by a target vehicle is received, determining whether the target vehicle is in a red light waiting state according to a real-time position and a driving path uploaded by the target vehicle;
if the target vehicle is in a state of waiting for the red light, calling current video data of a road section controlled by the traffic light, and predicting waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data;
and sending the waiting time to the target vehicle so that the target vehicle calibrates the remaining mileage according to the waiting time.
Preferably, the determining whether the target vehicle is in a state of waiting for a red light of a traffic light according to the real-time position uploaded by the target vehicle and the driving path includes:
according to the driving path and the real-time position uploaded by the target vehicle, the traffic light closest to the target vehicle on the driving path is confirmed;
judging whether the current state of the traffic light is a red light or not;
if the traffic light is the red light, judging whether the distance between the real-time position and a stop line corresponding to the traffic light is smaller than a preset distance or not;
if the distance is smaller than the preset distance, determining that the target vehicle is in a red light waiting state;
and if the distance is larger than or equal to the preset distance, acquiring the congestion condition of the road between the real-time position and the stop line corresponding to the traffic light, and determining whether the target vehicle is in a red light waiting state of the traffic light according to the congestion condition.
Preferably, the determining whether the target vehicle is in a state of waiting for a red light of the traffic light according to the congestion condition includes:
and when the current road congestion state is determined according to the road congestion condition, determining that the target vehicle is in a state of waiting for the red light of the traffic light.
Preferably, predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data comprises:
determining the number of vehicles in front of the target vehicle according to the real-time position and the video data;
and predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the number of the vehicles.
Preferably, the determining the number of vehicles ahead of the target vehicle according to the real-time position and the video data includes:
generating 3D data of a road section controlled by the traffic light according to the video data;
determining a 3D position of the target vehicle in the 3D data according to the real-time position;
determining the number of vehicles ahead of the target vehicle according to the 3D position.
Preferably, predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the number of the vehicles includes:
determining the number of first vehicles which can pass through the stop line when the traffic light is a green light;
determining whether the target vehicle can pass through a stop line or not at the next green light according to the number of vehicles and the first number of vehicles;
if so, calculating the starting waiting time of the target vehicle at the next green light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the starting waiting time and the countdown time of the red light when the stop information is received;
if not, calculating the number of red lights required to be waited for the target vehicle subsequently and the starting waiting time of the target vehicle when the red lights are switched to the green lights each time, and predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the countdown time of the red lights when the stop information is received, the number of the red lights required to be waited and the starting waiting time each time.
Preferably, the calculating the starting waiting time of the target vehicle at the next green light comprises:
and inputting the number of the vehicles into a starting time prediction model so that the starting time prediction model outputs the starting waiting time of the target vehicle.
In a second aspect, an embodiment of the present invention provides a mileage calibration device based on an internet of vehicles, including a state judgment module, a waiting time prediction module, and a mileage calibration module;
the state judgment module is used for determining whether the target vehicle is in a red light state waiting for a traffic light according to a real-time position and a driving path uploaded by the target vehicle when the parking information uploaded by the target vehicle is received;
the waiting time prediction module is used for calling current video data of a road section controlled by the traffic light if the target vehicle is in a state of waiting for the red light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data;
the mileage calibration module is used for sending the waiting time to the target vehicle so that the target vehicle calibrates the remaining mileage according to the waiting time.
In a third aspect, an embodiment of the present invention provides a mileage calibration device based on a vehicle networking, where the mileage calibration device based on a vehicle networking includes a processor and a memory;
the memory is used for storing a computer program and transmitting the computer program to the processor;
the processor is configured to execute a method for internet of vehicles based mileage calibration according to instructions in the computer program.
In a fourth aspect, embodiments of the present invention provide a storage medium storing computer-executable instructions for performing a network of vehicles-based mileage calibration method as described in the first aspect when executed by a computer processor.
In the foregoing, an embodiment of the present invention provides a method, an apparatus, a device, and a storage medium for mileage calibration based on an internet of vehicles, where the method includes: when receiving parking information uploaded by a target vehicle, determining whether the target vehicle is in a red light waiting state of a traffic light according to a real-time position and a driving path uploaded by the target vehicle; if the target vehicle is in a red light waiting state, calling current video data of a road section controlled by a traffic light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data; and sending the waiting time to the target vehicle so that the target vehicle calibrates the remaining mileage according to the waiting time.
In the embodiment of the invention, when the parking information of the target vehicle is received, after the target vehicle is confirmed to be in the state of waiting for the red light of the traffic light, the waiting time of the target vehicle passing through the stop line corresponding to the traffic light is predicted according to the real-time position of the target vehicle and the video data shot by the camera at the current intersection, and then the waiting time is sent to the target vehicle, so that the target vehicle can correct the future power consumption according to the waiting time, and the remaining mileage of the target vehicle is calibrated according to the future power consumption, thereby improving the accuracy of predicting the remaining mileage of the vehicle, and solving the technical problem that the influence of the waiting time at the red light on the power consumption of the battery is not considered in the prior art, so that the accuracy is lower when the remaining mileage is predicted.
Drawings
Fig. 1 is a flowchart of a mileage calibration method based on an internet of vehicles according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a system of a vehicle networking system provided in an embodiment of the present invention.
Fig. 3 is a flowchart of another mileage calibration method based on the internet of vehicles according to the embodiment of the present invention.
Fig. 4 is a schematic diagram of an intersection controlled by a traffic light.
Fig. 5 is a schematic structural diagram of a mileage calibration device based on an internet of vehicles according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of a mileage calibration device based on an internet of vehicles according to an embodiment of the present invention.
Detailed Description
The following description and the annexed drawings set forth in detail certain illustrative embodiments of the application so as to enable those skilled in the art to practice them. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the embodiments of the present application includes the full ambit of the claims, as well as all available equivalents of the claims. Embodiments may be referred to herein, individually or collectively, by the term "invention" merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the structures, products and the like disclosed in the embodiments, the description is simple because the structures, the products and the like correspond to the parts disclosed in the embodiments, and the relevant parts can be referred to the description of the method part.
As shown in fig. 1, fig. 1 is a flowchart of a mileage calibration method based on an internet of vehicles according to an embodiment of the present invention. The mileage calibration method based on the internet of vehicles provided by the embodiment of the invention can be executed by mileage calibration equipment based on the internet of vehicles, the mileage calibration equipment based on the internet of vehicles can be realized in a software and/or hardware mode, and the mileage calibration equipment based on the internet of vehicles can be composed of two or more physical entities or can be composed of one physical entity. For example, the mileage calibration device based on the internet of vehicles can be a computer, an upper computer or a server and other devices. In this embodiment, a server is taken as an example for explanation, as shown in fig. 2, fig. 2 is a schematic structural diagram of an internet of vehicles system, a vehicle-mounted terminal is installed on an automobile, the vehicle-mounted terminal is connected with electronic devices such as a sensor, a GPS positioning system, and a vehicle-mounted interaction device on the automobile, and the vehicle-mounted terminal is connected with the server in the cloud through a wireless communication network, and data transmission with the server is realized by using the wireless communication network.
The embodiment of the invention provides a mileage calibration method based on Internet of vehicles, which comprises the following steps:
In this embodiment, the target vehicle is a new energy vehicle, and the target vehicle is provided with a vehicle-mounted terminal, and during a running process of the target vehicle, the vehicle-mounted terminal can acquire a real-time position of the target vehicle from a GPS positioning system in real time and upload the real-time position of the target vehicle to a server in real time. Meanwhile, the vehicle-mounted terminal is also connected with a speed sensor, the speed sensor is used for detecting the running speed of the target vehicle, when the vehicle-mounted terminal determines that the speed of the target vehicle is 0 according to the running speed detected by the speed sensor, the target vehicle is determined to be parked at the moment, and the vehicle-mounted terminal generates parking information and uploads the parking information to the server. And after the server receives the parking information uploaded by the vehicle-mounted terminal, determining whether the target vehicle is in a red light waiting state according to the real-time position and the driving path of the target vehicle uploaded by the vehicle-mounted terminal in real time.
It should be noted that the driving route is a route from the starting point to the destination of the target vehicle. In one embodiment, the vehicle-mounted terminal is connected with the vehicle-mounted interaction device of the target vehicle, when a user starts the target vehicle, a destination is input into the vehicle-mounted interaction device of the target vehicle, and the vehicle-mounted interaction device generates a driving path according to the position and the destination of the target vehicle when the target vehicle is started and sends the driving path to the vehicle-mounted terminal. In another embodiment, the vehicle-mounted terminal can be connected with a mobile phone of a user, and when the user inputs a destination in map software on the mobile phone, the map software generates a driving path and sends the driving path to the vehicle-mounted terminal through Bluetooth.
And 102, if the target vehicle is in a red light waiting state, calling current video data of a road section controlled by a traffic light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data.
When the server determines that the target vehicle is in a red light waiting state, the server inquires a camera for shooting a road section controlled by the traffic light, and video data shot by the camera in real time are obtained. And then, predicting the waiting time of the target vehicle when the target vehicle passes through the traffic light according to the real-time position of the target vehicle and the video data. For example, in this embodiment, when the waiting time of the vehicle passing through the stop line corresponding to the traffic light is predicted, the number of vehicles located in front of the target vehicle may be determined according to the real-time position and the video data, and then the waiting time of the target vehicle passing through the stop line corresponding to the traffic light may be predicted according to the number of vehicles. The waiting time refers to the time for the target vehicle to stop and wait in the period from the time when the server receives the parking information uploaded by the vehicle-mounted terminal to the time when the target vehicle passes through the parking line corresponding to the traffic light. It can be understood that the stop line corresponding to the traffic light is a stop line at an intersection controlled by the traffic light, and the vehicle body cannot exceed the stop line when the vehicle waits for the traffic light.
And 103, sending the waiting time to the target vehicle so that the target vehicle can calibrate the remaining mileage according to the waiting time.
And then the server sends the waiting time to the vehicle-mounted terminal of the target vehicle, and because the electric energy consumed by the target vehicle is far less than the electric energy consumed by the target vehicle when the target vehicle drives during parking, the target vehicle can calculate the electric energy consumed by the vehicle during the waiting time, correct the future electric consumption of the vehicle according to the electric energy consumed by the vehicle during the waiting time, and calibrate the remaining mileage of the target vehicle according to the future electric consumption.
In the embodiment of the invention, when the parking information of the target vehicle is received, after the target vehicle is confirmed to be in the state of waiting for the red light of the traffic light, the waiting time of the target vehicle passing through the stop line corresponding to the traffic light is predicted according to the real-time position of the target vehicle and the video data shot by the camera at the current intersection, and then the waiting time is sent to the target vehicle, so that the target vehicle can correct the future power consumption according to the waiting time, and the remaining mileage of the target vehicle is calibrated according to the future power consumption, thereby improving the accuracy of predicting the remaining mileage of the vehicle.
As shown in fig. 3, fig. 3 is another method for calibrating mileage based on internet of vehicles according to an embodiment of the present invention, and fig. 3 is a concrete example of the method for calibrating mileage based on internet of vehicles, including the following steps:
In this embodiment, when the server receives the parking information uploaded by the vehicle-mounted terminal of the target vehicle, the server confirms the traffic light which is closest to the target vehicle at present on the driving path according to the real-time position uploaded by the vehicle-mounted terminal in real time and the driving path uploaded by the vehicle-mounted terminal in advance. It is understood that the traffic light closest to the target vehicle refers to the traffic light in the forward direction of the target vehicle.
After the traffic light closest to the target vehicle on the driving path is confirmed, whether the state of the traffic light is the red light or not is judged. It can be understood that, in the present embodiment, it is determined whether the traffic light of the lane in the direction of the driving path where the traffic light is located is a red light. For example, for a stop line corresponding to a traffic light, the stop line generally includes a plurality of lanes, and the passing direction of each lane is different, for example, for an intersection controlled by the red light as shown in fig. 4, the lane of the leftmost vehicle only allows left turning, the lane in the middle only allows straight going, the lane at the rightmost side only allows right turning, and different lanes correspond to different signal lights in the traffic light. According to the running path of the target vehicle, the lane of the target vehicle at the traffic light can be determined, and therefore whether the signal light of the lane is the red light or not is determined in the traffic light.
And 203, if the traffic light is the red light, judging whether the distance between the real-time position and the stop line corresponding to the traffic light is smaller than a preset distance.
And if the current traffic light is in the red light state, further inquiring the position of a stop line corresponding to the traffic light, and judging whether the distance between the real-time position of the target vehicle and the stop line is smaller than the preset distance. It is understood that the preset distance can be set according to actual needs. In one embodiment, the preset distance may be set to 50 meters. If the light is not red, the flow is ended.
And step 204, if the distance is smaller than the preset distance, determining that the target vehicle is in a red light waiting state of the traffic lights.
If the distance between the target vehicle and the stop line corresponding to the traffic light is smaller than the preset distance, the target vehicle can be determined to be in a red light waiting state at the moment because the target vehicle is not generally stopped when approaching the stop line corresponding to the traffic light. In one embodiment, in order to further accurately judge whether the target vehicle is in a state of waiting for a traffic light, the server may further obtain a congestion condition between the target vehicle and a stop line corresponding to the traffic light, and determine whether the target vehicle is in the state of waiting for the red light of the traffic light according to the congestion condition.
And step 205, if the distance is larger than or equal to the preset distance, acquiring the congestion condition of the road between the real-time position and the stop line corresponding to the traffic light, and determining whether the target vehicle is in a red light waiting state of the traffic light according to the congestion condition.
If the preset distance between the target vehicle and the stop line is larger than or equal to the preset distance, further inquiring the congestion condition in the road between the current real-time position of the target vehicle and the stop line, and determining whether the target vehicle is in a red light state waiting for traffic according to the congestion condition of the road. In one embodiment, determining whether the target vehicle is in a red light waiting traffic light state based on the congestion condition comprises:
and when the current road congestion state is determined according to the road congestion condition, determining that the target vehicle is in a red light waiting state of the traffic light.
In one embodiment, after the congestion condition of the road is obtained, if it is determined that the current real-time position and the stop line in the road are all in the congestion state, the road is in the congestion state because other vehicles in the road before the target vehicle are waiting for the red light, and it may be determined that the target vehicle is currently in the red light waiting state. In one embodiment, for more accurate judgment, current video data of a road section managed and controlled by a traffic light can be called, and whether the road section between the current real-time position and a stop line is in a congestion state or not can be determined according to the video data.
And step 206, if the target vehicle is in a red light waiting state, calling current video data of a road section controlled by the traffic light, and determining the number of vehicles in front of the target vehicle according to the real-time position and the video data.
When the target vehicle is in a red light waiting state, video data of a road section controlled by a traffic light is called, and then the number of vehicles in front of the target vehicle is determined according to the real-time position of the vehicle and the video data, wherein the number of vehicles in front of the target vehicle is the number of vehicles between the real-time position of the target vehicle and a stop line of the traffic light. It can be understood that, in this embodiment, when the real-time position of the target vehicle is very close to the distance between the stop lines, for example, within 1 meter, it may be considered that no other vehicle exists between the target vehicle and the stop lines, and the server may predict the waiting time for the target vehicle to pass through the stop line corresponding to the traffic light by determining the countdown time of the red light when receiving the stop information sent by the vehicle-mounted terminal, without performing subsequent steps.
On the basis of the above embodiment, the step 206 determines the number of vehicles ahead of the target vehicle according to the real-time position and the video data, and is specifically executed by the steps 2061 to 2063, including:
step 2061, generating 3D data of the road section controlled by the traffic light according to the video data.
Firstly, generating 3D data of a road section controlled by a traffic light according to current video data of the road section controlled by the traffic light. The road section controlled by the traffic light is a road section between a stop line corresponding to the traffic light and a stop line corresponding to the previous traffic light. In one embodiment, a plurality of cameras for shooting the road section controlled by the traffic light are symmetrically arranged at two ends of the road section controlled by the traffic light, and after video data shot by each camera at present is obtained, the video data shot by the cameras can be spliced to synthesize 3D data of the road section controlled by the traffic light at present. The process of generating 3D data according to video data may refer to a process of generating 3D data in the prior art, which is not described in detail in this embodiment. It can be understood that, in order to reduce the calculation amount of the server too much, the video data captured by the camera behind the real-time position of the target vehicle may not be acquired, and the local 3D data of the road section controlled by the traffic light is generated only according to the video data captured by the camera ahead of the real-time position.
Step 2062, determining the 3D position of the target vehicle in the 3D data according to the real-time position.
And determining the 3D position of the target vehicle in the 3D data according to the real-time position of the target vehicle when the 3D data in the road section controlled by the traffic light is obtained.
Step 2063, determining the number of vehicles in front of the target vehicle according to the 3D position.
After the 3D position of the target vehicle is confirmed, the number of vehicles between the 3D position and the stop line may be further calculated, thereby calculating the number of vehicles ahead of the target vehicle. It is understood that, in calculating the number of vehicles, only the number of vehicles ahead of the lane where the target vehicle is currently located is calculated.
And step 207, predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the number of the vehicles.
After the number of vehicles which are positioned in front of the target vehicle at present is determined, the waiting time of the target vehicle passing through a stop line corresponding to the traffic light can be predicted according to the number of vehicles. Specifically, in one embodiment, the server may query a first number of vehicles that can pass through a parking line after the traffic light is switched to the green light, determine the number of red lights that the target vehicle needs to wait according to the first number of vehicles and the number of vehicles ahead of the target vehicle, determine the starting waiting time of the target vehicle according to the number of vehicles ahead of the target vehicle after the red light is switched to the green light each time, and predict the waiting time of the target vehicle passing through the parking line corresponding to the traffic light according to the countdown time of the red light when the parking information is received, the number of red lights that the target vehicle needs to wait, and the starting waiting time of the target vehicle when the target vehicle is at the green light each time. The starting waiting time of the target vehicle is the waiting time required by the target vehicle when the target vehicle starts after the traffic light is switched to the green light. It can be understood that a vehicle needs several seconds of starting time when starting, and a subsequent vehicle can move forward only after waiting for the starting of the previous vehicle, so that the target vehicle cannot start immediately after the traffic light is switched to the green light, the target vehicle can move forward only after the previous vehicle needs to be started and moved forward, and the time required by the target vehicle in the period is the starting waiting time.
On the basis of the foregoing embodiment, in step 207, the waiting time of the target vehicle passing through the stop line corresponding to the traffic light is predicted according to the number of vehicles, and specifically executed by steps 2071 to 2074, the method includes:
step 2071, determining the first number of vehicles that the stop line can pass through when the traffic light is green.
Firstly, when the traffic light is a green light, the server inquires the number of the first vehicles which can pass through the lane where the target vehicle is located and the parking line corresponding to the traffic light at most. In the present embodiment, the number of the first vehicles which can pass the stop line at most on different lanes of each traffic light in different duration of the green light is stored in the big data system in advance, for example, when the duration of the green light is 30S, the number of the first vehicles which can pass the stop line at most is 10, and when the duration of the green light is 60S, the number of the vehicles which can pass the stop line at most is 20. The server can inquire the duration of the green light of the traffic light and inquire the number of the first vehicles which can pass through the parking line at most in the big data system when the red light and the green light are the green light according to the duration of the green light.
Step 2072, determining whether the target vehicle can pass the stop line when the next green light is determined according to the number of vehicles and the first number of vehicles.
After the current number of vehicles of the target vehicle and the first number of vehicles which can pass through the stop line after the traffic light is switched to the green light are obtained, the server further judges whether the target vehicle can pass through the stop line when the next green light is obtained. Specifically, it may be determined whether the first number of vehicles is greater than the number of vehicles, if so, the target vehicle may pass through the stop line at the next green light, and if not, the target vehicle may still not pass through the stop line at the next green light.
And 2073, if yes, calculating the starting waiting time of the target vehicle at the next green light, and predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the starting waiting time and the countdown time of the red light when the stop information is received.
If the target vehicle can pass through the stop line at the next green light, the starting waiting time of the target vehicle is further calculated according to the number of vehicles before the target vehicle, then the server further determines the countdown time of the red light when the stop information is received, and the countdown time plus the starting waiting time is the waiting time required by the target vehicle to pass through the stop line of the traffic light. In one embodiment, in the process of determining the countdown time of the red light when the parking information is received, the server may determine the countdown time of the red light when the parking information is received by simultaneously calculating the time difference between the time when the parking information is received and the current time according to the countdown time when the current red light is obtained and adding the time difference to the countdown time of the current red light.
In one embodiment, calculating the starting waiting time of the target vehicle at the next green light comprises:
the number of vehicles is input to the starting time prediction model so that the starting time prediction model outputs the starting waiting time of the target vehicle.
In one embodiment, the number of vehicles may be input into a preset starting time prediction model, and the starting time prediction model may be used to predict the starting waiting time of the target vehicle. In one embodiment, the run away time prediction model may be derived by training a convolutional neural network. Specifically, when the convolutional neural network is trained, vehicles with different numbers in front of the vehicles are obtained as training data, the starting waiting time of the corresponding vehicle is marked in the training data, and partial data are obtained from the marked training data as a verification set. Inputting training data into a convolutional neural network for training, adjusting weight parameters of the convolutional neural network in the training process, inputting a verification set into the convolutional neural network after the training is finished, verifying whether the error between the starting waiting time output by the convolutional neural network and the marked starting waiting time is within a preset range, if so, finishing the training of the convolutional neural network to obtain a starting time prediction model, and if not, re-training the convolutional neural network. And after the number of vehicles before the target vehicle is obtained in real time, inputting the number of vehicles into the trained starting time prediction model so that the starting time prediction model outputs the starting waiting time of the target vehicle. In one embodiment, to improve the accuracy of the calculation, the training data further includes the model of each vehicle in front of the vehicle, such as cars, SUVs, and vans, etc., in the training of the convolutional neural network, since the departure times of different vehicle models are different. And after the number of vehicles in front of the target vehicle is determined according to the 3D data, the vehicle type of each vehicle in front is obtained from the 3D data, and the number of vehicles and the vehicle type are input into a starting time prediction model together to obtain more accurate starting waiting time.
And 2074, if not, calculating the number of red lights required to wait subsequently by the target vehicle and the starting waiting time of the target vehicle when the red light is switched to the green light every time, and predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the countdown time of the red light when the stop information is received, the number of the red lights required to wait and the starting waiting time of each time.
If the vehicle cannot pass through the stop line corresponding to the traffic light when the next green light is displayed, the number of red lights required to be waited subsequently by the target vehicle after the countdown of the current red light is finished is calculated, and meanwhile, the starting waiting time required to be waited by the target vehicle when the red light is switched to the green light every time the signal light is switched is calculated. Specifically, in one embodiment, the quotient is obtained by dividing the number of vehicles ahead of the current target vehicle by the first number of vehicles, and the integer number of the quotient is the number of red lights that the target vehicle needs to wait for in the following process. For example, if the number of vehicles ahead of the current target vehicle is 15, and the number of vehicles that can pass through the parking line when the light is green is 6, 15/6=2.5 is rounded to 2, that is, the number of red lights that the target vehicle needs to wait subsequently after the countdown of the current red light is finished is 2. Then, the starting waiting time for the target vehicle to wait each time the red light is switched to the green light is also calculated, for example, when the number of vehicles before the current target vehicle is 15, the starting waiting time for the target vehicle when the next green light is calculated, and then, since the stop line passes through 6 vehicles when the next green light is, the number of vehicles before the target vehicle is 9, the starting waiting time for the target vehicle when the next green light is calculated according to 9, and when the last green light is, the number of vehicles before the target vehicle is 3, the starting waiting time for the target vehicle when the last green light is calculated according to 3. And finally, the server inquires the duration time of the traffic lights at the red lights, multiplies the duration time of the red lights by the number of the red lights to be waited to obtain the total waiting time of the red lights, and adds the countdown time of the red lights when the parking information is received, the total waiting time of the red lights and the starting waiting time of each time to calculate the waiting time when the target vehicle passes through the parking line of the traffic lights.
And step 208, sending the waiting time to the target vehicle so that the target vehicle can calibrate the remaining mileage according to the waiting time.
The number of vehicles before the target vehicle and the number of first vehicles which can pass through the parking line when the parking information is received are determined, the number of red lights which are required to wait subsequently by the target vehicle is determined according to the number of the first vehicles and the number of the vehicles, the starting waiting time of the target vehicle is determined according to the number of vehicles before the target vehicle after the red light is switched to the green light each time, and finally the waiting time of the target vehicle when the parking information is received and the waiting time of the red light which is required to wait by the target vehicle and the starting waiting time of the target vehicle when the parking information is received each time are predicted according to the counting-down time of the red light, the number of the red light which is required to wait by the target vehicle and the starting waiting time of the target vehicle when the parking information is switched to the green light each time. And then sending the waiting time to the target vehicle, so that the target vehicle can correct future power consumption according to the waiting time, calibrate the remaining mileage of the target vehicle according to the future power consumption, improve the accuracy in predicting the remaining mileage of the vehicle, and solve the technical problem of low accuracy in predicting the remaining mileage due to the fact that the influence of the waiting time on the power consumption of the battery is not considered in the prior art.
As shown in fig. 5, fig. 5 is a mileage calibration apparatus based on internet of vehicles according to an embodiment of the present invention, which includes a state determining module 301, a waiting time predicting module 302, and a mileage calibrating module 303;
the state judgment module 301 is configured to determine whether the target vehicle is in a red light waiting state for a traffic light according to a real-time position and a driving path uploaded by the target vehicle when the parking information uploaded by the target vehicle is received;
the waiting time prediction module 302 is configured to, if the target vehicle is in a red light waiting state, call current video data of a road section controlled by a traffic light, and predict waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data;
the mileage calibration module 303 is configured to send the waiting time to the target vehicle, so that the target vehicle calibrates the remaining mileage according to the waiting time.
On the basis of the above embodiment, the state determining module 301 is configured to determine whether the target vehicle is in a state of waiting for a red light of a traffic light according to the real-time position uploaded by the target vehicle and the driving route, and includes:
the traffic light is used for confirming the traffic light which is closest to the target vehicle at present on the driving path according to the driving path and the real-time position uploaded by the target vehicle; judging whether the current state of the traffic light is a red light or not; if the position is the red light, judging whether the distance between the real-time position and a stop line corresponding to the traffic light is smaller than a preset distance or not; if the distance is smaller than the preset distance, determining that the target vehicle is in a red light waiting state; and if the distance is greater than or equal to the preset distance, acquiring the congestion condition of the road between the real-time position and the stop line corresponding to the traffic light, and determining whether the target vehicle is in the red light waiting state of the traffic light according to the congestion condition.
On the basis of the above embodiment, the state determining module 301 is configured to determine whether the target vehicle is in a state of waiting for a red light of a traffic light according to the congestion condition, and includes:
and the traffic light control device is used for determining that the target vehicle is in a red light waiting state when the current road congestion state is determined according to the road congestion condition.
On the basis of the foregoing embodiment, the waiting time predicting module 302 is configured to predict the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the real-time position and the video data, and includes:
the system comprises a video data acquisition unit, a real-time position acquisition unit and a real-time position acquisition unit, wherein the video data acquisition unit is used for acquiring real-time position and video data of a target vehicle; and predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the number of the vehicles.
On the basis of the above embodiment, the waiting time prediction module 302 is configured to determine the number of vehicles ahead of the target vehicle according to the real-time position and the video data, and includes:
the system comprises a display unit, a display unit and a control unit, wherein the display unit is used for displaying the 3D data of the road section controlled by the traffic light according to the video data; determining the 3D position of the target vehicle in the 3D data according to the real-time position; the number of vehicles ahead of the target vehicle is determined from the 3D position.
On the basis of the above embodiment, the waiting time predicting module 302 is configured to predict the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the number of vehicles, and includes:
the first vehicle number which is used for determining that the stop line can pass when the traffic light is green; determining whether the target vehicle can pass through the stop line at the next green light according to the number of vehicles and the first number of vehicles; if yes, calculating the starting waiting time of the target vehicle at the next green light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the starting waiting time and the countdown time of the red light when the stop information is received; if not, calculating the number of red lights required to be waited for the target vehicle subsequently and the starting waiting time of the target vehicle when the red lights are switched to the green lights each time, and predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the countdown time of the red lights when the stop information is received, the number of the red lights required to be waited and the starting waiting time of each time.
On the basis of the above embodiment, the waiting time prediction module 302 is configured to calculate the starting waiting time of the target vehicle at the next green light, and includes:
the method is used for inputting the number of vehicles into the starting time prediction model so that the starting time prediction model outputs the starting waiting time of the target vehicle.
The embodiment also provides a device for internet of vehicles based mileage calibration, as shown in fig. 6, the device 40 for internet of vehicles based mileage calibration includes a processor 400 and a memory 401;
the memory 401 is configured to store a computer program 402 and to transmit the computer program 402 to the processor;
the processor 400 is configured to execute the steps of one of the embodiments of the internet of vehicles based mileage calibration method described above according to the instructions in the computer program 402.
Illustratively, the computer program 402 may be partitioned into one or more modules/units, which are stored in the memory 401 and executed by the processor 400 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions that describe the execution of the computer program 402 in the internet-of-vehicles based mileage calibration facility 40.
The internet of vehicles based mileage calibration device 40 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing device. The internet of vehicles based mileage calibration device 40 may include, but is not limited to, a processor 400, a memory 401. Those skilled in the art will appreciate that fig. 6 is merely an example of an internet of vehicles based mileage calibration device 40, and does not constitute a limitation of the internet of vehicles based mileage calibration device 40, and may include more or fewer components than shown, or some components in combination, or different components, e.g., the internet of vehicles based mileage calibration device 40 may also include input output devices, network access devices, buses, etc.
The Processor 400 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 401 may be an internal storage unit of the internet of vehicles based mileage calibrating device 40, such as a hard disk or a memory of the internet of vehicles based mileage calibrating device 40. The memory 401 may also be an external storage device of the network-based mileage calibration device 40, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the network-based mileage calibration device 40. Further, the memory 401 may also include both an internal storage unit and an external storage device of the internet of vehicles based mileage calibration device 40. The memory 401 is used to store the computer program and other programs and data required by the internet of vehicles based mileage calibrating device 40. The memory 401 may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit 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, which is substantially or partly contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing computer programs.
Embodiments of the present invention also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method for internet of vehicles based mileage calibration, the method comprising the steps of:
when parking information uploaded by a target vehicle is received, determining whether the target vehicle is in a red light waiting state according to a real-time position and a driving path uploaded by the target vehicle;
if the target vehicle is in a state of waiting for the red light, calling current video data of a road section controlled by the traffic light, and predicting waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data;
and sending the waiting time to the target vehicle so that the target vehicle calibrates the remaining mileage according to the waiting time.
It should be noted that the foregoing is only a preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will appreciate that the embodiments of the present invention are not limited to the specific embodiments described herein, and that various obvious changes, adaptations, and substitutions are possible, without departing from the scope of the embodiments of the present invention. Therefore, although the embodiments of the present invention have been described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and many other equivalent embodiments can be included without departing from the concept of the embodiments of the present invention, and the scope of the embodiments of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A mileage calibration method based on Internet of vehicles is characterized by comprising the following steps:
when receiving parking information uploaded by a target vehicle, determining whether the target vehicle is in a red light waiting state according to a real-time position and a driving path uploaded by the target vehicle;
if the target vehicle is in a state of waiting for the red light, calling current video data of a road section controlled by the traffic light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data;
and sending the waiting time to the target vehicle so that the target vehicle calibrates the remaining mileage according to the waiting time.
2. The internet-of-vehicles based mileage calibration method as claimed in claim 1, wherein the determining whether the target vehicle is in a red light waiting state according to the real-time position uploaded by the target vehicle and the driving path comprises:
according to the driving path and the real-time position uploaded by the target vehicle, the traffic light closest to the target vehicle on the driving path at present is confirmed;
judging whether the current state of the traffic light is a red light or not;
if the position is the red light, judging whether the distance between the real-time position and a stop line corresponding to the traffic light is smaller than a preset distance or not;
if the distance is smaller than the preset distance, determining that the target vehicle is in a red light waiting state;
and if the distance is larger than or equal to the preset distance, acquiring the congestion condition of the road between the real-time position and the stop line corresponding to the traffic light, and determining whether the target vehicle is in a red light waiting state of the traffic light according to the congestion condition.
3. The internet-of-vehicles based mileage calibration method as claimed in claim 2, wherein said determining whether the target vehicle is in a state waiting for a red light of the traffic light according to the congestion condition comprises:
and when the current road congestion state is determined according to the road congestion condition, determining that the target vehicle is in a state of waiting for the red light of the traffic light.
4. The internet of vehicles based mileage calibration method as claimed in claim 1, wherein predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the real-time position and the video data comprises:
determining the number of vehicles in front of the target vehicle according to the real-time position and the video data;
and predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the number of the vehicles.
5. The Internet of vehicles based mileage calibration method of claim 4, wherein the determining the number of vehicles ahead of the target vehicle according to the real-time location and the video data comprises:
generating 3D data of a road section controlled by the traffic light according to the video data;
determining a 3D position of the target vehicle in the 3D data according to the real-time position;
determining the number of vehicles ahead of the target vehicle according to the 3D position.
6. The internet of vehicles-based mileage calibration method according to claim 4, wherein predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the number of vehicles comprises:
determining the first number of vehicles which can pass through the stop line when the traffic light is green;
determining whether the target vehicle can pass through a stop line at the next green light according to the number of the vehicles and the first number of the vehicles;
if so, calculating the starting waiting time of the target vehicle at the next green light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the starting waiting time and the countdown time of the red light when the stop information is received;
if not, calculating the number of red lights required to be waited for the target vehicle subsequently and the starting waiting time of the target vehicle when the red lights are switched to the green lights each time, and predicting the waiting time of the target vehicle passing through the stop line corresponding to the traffic light according to the countdown time of the red lights when the stop information is received, the number of the red lights required to be waited and the starting waiting time each time.
7. The internet of vehicles-based mileage calibration method of claim 6, wherein the calculating the target vehicle's green light pull-off waiting time comprises:
and inputting the vehicle number into a starting time prediction model so that the starting time prediction model outputs the starting waiting time of the target vehicle.
8. A mileage calibration device based on the Internet of vehicles is characterized by comprising a state judgment module, a waiting time prediction module and a mileage calibration module;
the state judgment module is used for determining whether the target vehicle is in a red light waiting state of a traffic light according to a real-time position and a driving path uploaded by the target vehicle when the parking information uploaded by the target vehicle is received;
the waiting time prediction module is used for calling current video data of a road section controlled by the traffic light if the target vehicle is in a state of waiting for the red light, and predicting the waiting time of the target vehicle passing through a stop line corresponding to the traffic light according to the real-time position and the video data;
the mileage calibration module is used for sending the waiting time to the target vehicle so that the target vehicle calibrates the remaining mileage according to the waiting time.
9. An internet of vehicles based mileage calibration device, comprising a processor and a memory;
the memory is used for storing a computer program and transmitting the computer program to the processor;
the processor is configured to execute a method of internet of vehicles based mileage calibration according to any one of claims 1-7, according to instructions in the computer program.
10. A storage medium storing computer-executable instructions for performing the internet of vehicles based mileage calibration method of any one of claims 1-7 when executed by a computer processor.
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