CN109785611B - Unmanned vehicle control method, device, server and storage medium - Google Patents
Unmanned vehicle control method, device, server and storage medium Download PDFInfo
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Abstract
A method of controlling an unmanned vehicle, comprising: when a taking request is received, acquiring a first geographic position and a destination where a passenger is located; judging whether a target unmanned vehicle exists or not according to a pre-trained electric quantity distance model, wherein the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination; and when the target unmanned vehicle is determined to exist, returning a message of successful request to the terminal equipment of the passenger and controlling the target unmanned vehicle to move to the first geographic position where the passenger is located. The invention also provides a control device of the unmanned vehicle, a server and a storage medium. The invention can fully consider the road quality and the traffic congestion degree, calculate the distance that the residual electric quantity can travel, solve the problem that whether the unmanned vehicle can deliver the passenger to the destination or not can not be accurately estimated, and improve the experience of the passenger taking the unmanned vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to a method and a device for controlling an unmanned vehicle, a server and a storage medium.
Background
The unmanned vehicle is a novel intelligent vehicle, also called as a wheel type mobile robot, and the full-automatic running of the vehicle is realized by carrying out accurate Control and calculation analysis on each part in the vehicle mainly through an Electronic Control Unit (ECU), namely vehicle-mounted terminal equipment, so that the purpose of unmanned running of the vehicle is achieved.
The unmanned vehicle endurance technology detects whether the current residual electric quantity of the unmanned vehicle is lower than a preset threshold electric quantity; if so, acquiring current illumination intensity information, and further detecting whether the illumination intensity information meets a preset condition; and responding to the detected light intensity information to meet the preset condition, and opening a solar cell panel of the unmanned vehicle for charging.
Although the above scheme can solve the problem of low electric quantity of the unmanned running vehicle to a certain extent, the problem of low or insufficient electric quantity cannot be solved if the illumination is not satisfactory to the preset condition.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a server and a storage medium for controlling an unmanned vehicle, which can calculate a distance that can be traveled by a current remaining power amount in consideration of road quality and a traffic congestion degree, solve a problem that whether the unmanned vehicle can deliver a passenger to a destination geographic location cannot be accurately estimated, and improve the experience of the passenger in taking the unmanned vehicle.
A first aspect of the present invention provides an unmanned vehicle control method applied to a server, the method including:
when a taking request is received, acquiring a first geographic position and a destination where a passenger is located;
judging whether a target unmanned vehicle exists or not according to a pre-trained electric quantity distance model, wherein the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination;
and when the target unmanned vehicle is determined to exist, returning a message of successful request to the terminal equipment of the passenger and controlling the target unmanned vehicle to move to the first geographic position where the passenger is located.
Preferably, the determining whether a target unmanned vehicle exists according to a pre-trained electric quantity distance model, wherein the step of enabling the remaining electric quantity of the target unmanned vehicle to load the passenger to the destination includes:
inputting the remaining power of the unmanned vehicle and road condition information between the second geographic position where the unmanned vehicle is located and the first geographic position where the passenger is located into the pre-trained power distance model to obtain a first distance where the remaining power of the unmanned vehicle can travel;
calculating a first sub-distance between the second geographic position and the first geographic position and a second sub-distance between the first geographic position and the destination, and summing the first sub-distance and the second sub-distance to obtain a second distance;
when the first distance is judged to be greater than or equal to the second distance, determining the unmanned vehicle located at the second geographic position as the target unmanned vehicle;
and when the first distance is judged to be smaller than the second distance, determining that the target unmanned vehicle does not exist.
Preferably, the training process of the electric quantity distance model includes:
collecting different residual electric quantity and road condition information of the unmanned vehicle and driving distances corresponding to the road condition information and the residual electric quantity as sample data;
randomly dividing the sample data into a training set with a first preset proportion and a verification set with a second preset proportion, training a neural network by using the training set to obtain an electric quantity distance model, and verifying the accuracy of the electric quantity distance model obtained by training by using the verification set;
if the accuracy is greater than or equal to a preset accuracy threshold, ending the training;
if the accuracy is smaller than the preset accuracy threshold, increasing the number of samples in the training set and retraining the electric quantity distance model until the accuracy is larger than or equal to the preset accuracy threshold.
Preferably, after the message that the request is successful is returned to the terminal device of the passenger and the target unmanned vehicle is controlled to drive to the first geographical location where the passenger is located, the method further comprises:
sending a preset message to other unmanned vehicles except the target unmanned vehicle to inform the other unmanned vehicles that the target unmanned vehicle provides the passenger with boarding service.
Preferably, after determining that the target unmanned vehicle is not present, the method further comprises:
obtaining a first unmanned vehicle closest to the first geographic location;
acquiring a fourth geographical position of a chargeable electric pile or a second unmanned vehicle between the first geographical position and the destination;
judging whether the residual electric quantity of the first unmanned vehicle can drive to the fourth geographic position or not according to the pre-trained electric quantity distance model;
controlling the first unmanned vehicle to drive to the first geographic location where the passenger is located when it is determined that the remaining capacity of the first unmanned vehicle can drive to the fourth geographic location.
Preferably, the determining whether the remaining power of the first unmanned vehicle can travel to the fourth geographic location according to the pre-trained power distance model includes:
inputting the remaining power of the first unmanned vehicle and the road condition information between the third geographic position and the fourth geographic position into the pre-trained power distance model to obtain a third distance that the remaining power of the first unmanned vehicle can travel;
calculating a third sub-distance between the third geographic position and the first geographic position and a fourth sub-distance between the first geographic position and the fourth geographic position, and summing the third sub-distance and the fourth sub-distance to obtain a fourth distance;
determining that the remaining capacity of the first unmanned vehicle can travel to the fourth geographic location when it is determined that the third distance is greater than or equal to the fourth distance;
and when the third distance is judged to be smaller than the fourth distance, determining that the remaining capacity of the first unmanned vehicle cannot travel to the fourth geographic position.
Preferably, after determining that the remaining amount of power of the first unmanned vehicle is capable of traveling to the fourth geographic location, before controlling the first unmanned vehicle to travel to the first geographic location where the passenger is located, the method further comprises:
sending an inquiry message of transfer required in the middle to a terminal device of the passenger so that the passenger can select whether to board the first unmanned vehicle;
said controlling said first unmanned vehicle to travel to a first geographic location where said passenger is located, comprising: controlling the first unmanned vehicle to drive toward the first geographic location where the passenger is located when the passenger's confirmation selection is received;
the method further comprises the following steps:
controlling the second unmanned vehicle to lock a door when the passenger's confirmation selection is received.
A second aspect of the present invention provides an unmanned vehicle control apparatus, operating in a server, the apparatus comprising:
the obtaining module is used for obtaining a first geographic position and a destination of a passenger when the taking request is received;
the judging module is used for judging whether a target unmanned vehicle exists or not according to a pre-trained electric quantity distance model, and the remaining electric quantity of the target unmanned vehicle can load the passenger to the destination;
and the control module is used for returning a message of successful request to the terminal equipment of the passenger and controlling the target unmanned vehicle to drive to the first geographical position of the passenger when the judging module determines that the target unmanned vehicle exists.
A third aspect of the invention provides a server comprising a processor and a memory, the processor being configured to implement the unmanned vehicle control method when executing a computer program stored in the memory.
A fourth aspect of the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the unmanned vehicle control method.
According to the unmanned vehicle control method, the unmanned vehicle control device, the unmanned vehicle control server and the storage medium, when a boarding request is received at the beginning, the current geographic position and the target geographic position of a passenger, the current residual electric quantity and the current geographic position of a plurality of unmanned vehicles are obtained; and judging that the target unmanned vehicle can carry the passenger to the target geographic position under the current residual electric quantity according to a pre-trained electric quantity distance model, returning a message of successful request to the terminal equipment of the passenger, and controlling the target unmanned vehicle to drive to the current geographic position of the passenger. According to the pre-trained electric quantity distance model, the distance which can be traveled by the current residual electric quantity is calculated under the condition that the road quality and the traffic congestion degree are fully considered, the actual situation is met, the problem that whether the unmanned vehicle can deliver the passenger to the target geographic position or not can not be accurately estimated is solved, and the passenger experience of taking the unmanned vehicle is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic application environment diagram of a control method for an unmanned vehicle according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for controlling an unmanned vehicle according to an embodiment of the present invention.
Fig. 3 is a functional block diagram of a control apparatus for an unmanned vehicle according to a second embodiment of the present invention.
Fig. 4 is a schematic diagram of a server provided in the third embodiment of the present invention.
The following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Fig. 1 is a schematic view of an application environment of the unmanned vehicle control method according to the present invention.
The unmanned vehicle control method can be applied to an application environment consisting of the unmanned vehicle 1, the network 2, the remote server 3 and the terminal device 4.
The unmanned vehicle 1 may be various types of unmanned vehicles such as an unmanned bus, an unmanned sedan, and the like. In this embodiment, a solar cell panel is installed in the unmanned vehicle 1, and electric energy is stored in the solar cell panel to provide electric energy for the unmanned vehicle during operation. The unmanned vehicle 1 may also send a prompt to the terminal device 4 via the network 2.
The network 2 is the medium used to provide a communication connection between the unmanned vehicle 1 and the remote server 3. The network 2 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The remote server 3 may be a remote server providing various services, and the remote server may receive preset warning information of the unmanned vehicle, learn accurate geographical location information of the chargeable electric pile or the unmanned vehicle by combining a high-definition map, and provide geographical locations of the chargeable electric pile or other available unmanned vehicles to the plurality of unmanned vehicles 1 through the network 2. The remote server 3 may also send a prompt to the terminal device 4 via the network 2.
Various communication client applications, such as social applications, may be installed on the terminal device 4. The terminal device 4 may be a terminal device held by an unmanned vehicle. Terminal device 4 may be a variety of unmanned vehicles having a display screen and supporting wireless communications, including but not limited to smart phones, tablets, laptop portable computers, and the like.
It should be noted that the unmanned vehicle control method provided by the embodiment of the present invention may be executed by the remote server 3, and accordingly, the unmanned vehicle-based anti-abduction device is generally disposed in the remote server 3.
It should be understood that the number of unmanned vehicles, networks, remote servers, and terminal devices in fig. 1 is illustrative only. There may be any number of mobile terminals, networks, remote servers and terminal devices, as desired for implementation. In other embodiments, the terminal device may not be included in the application environment of the method.
Example one
Referring to fig. 2, a flowchart of a method for controlling an unmanned vehicle according to an embodiment of the present invention is shown. The execution sequence in the flowchart may be changed and some steps may be omitted according to different requirements.
S21: when a boarding request is received, a first geographic position and a destination where a passenger is located are obtained.
The passenger can send a boarding request to a plurality of unmanned vehicles through a network by using the portable terminal device.
In some embodiments, an application corresponding to the unmanned vehicle may be downloaded and installed in the passenger's terminal device from a remote server. The passenger sends a ride request to a plurality of unmanned vehicles via the application. The taking request carries the first geographic position and the destination of the passenger.
Specifically, an input interface may be displayed in the terminal device through the application program. And displaying a destination input box on the input interface. The destination input box is used for receiving the destination input by the passenger. And after detecting that the destination is input in the destination input box, the terminal equipment sends the first geographic position of the passenger and the destination to a remote server. The remote server receives a boarding request of the terminal equipment and acquires a first geographic position where a passenger is located and a destination of the passenger according to the boarding request.
In this embodiment, when a boarding request of a passenger is received, a first geographic location where the passenger is located and a destination of the passenger are obtained according to the boarding request, and meanwhile, the remaining capacity of the unmanned vehicle is also obtained.
In the embodiment, the remaining capacity of the solar battery of the unmanned vehicle can be monitored in a timed polling mode. Polling is a way for a Central Processing Unit (CPU) to make decisions on how to provide services to peripheral devices.
The inquiry may be periodically issued by the CPU of the remote server to sequentially inquire whether each unmanned vehicle needs service, for example, whether to monitor the remaining charge of the solar cell. When the corresponding result of the inquiry is determination, corresponding service is given, for example, when the remaining capacity of the solar battery needs to be monitored, the remaining capacity of the solar battery is monitored. After the service is finished, the next peripheral device is asked, and the process is repeated continuously.
In this embodiment, the remaining power of the unmanned vehicle may be monitored in a periodic polling manner, wherein the polling period may be set by the passenger or may be set by default (for example, the period is 5 seconds).
S22: and judging whether a target unmanned vehicle exists or not according to a pre-trained electric quantity distance model, wherein the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination.
In this embodiment, an electric quantity distance model may be trained in advance, and it is determined through the electric quantity distance model whether the remaining electric quantity of each unmanned vehicle can carry the passenger from the first geographic location to the destination. The electricity distance model may be pre-trained by a remote server.
Preferably, the determining whether a target unmanned vehicle exists according to a pre-trained electric quantity distance model, where the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination, includes:
inputting the remaining power of the unmanned vehicle and road condition information between the second geographic position where the unmanned vehicle is located and the first geographic position where the passenger is located into the pre-trained power distance model to obtain a first distance where the remaining power of the unmanned vehicle can travel;
calculating a first sub-distance between the second geographic position and the first geographic position and a second sub-distance between the first geographic position and the destination, and summing the first sub-distance and the second sub-distance to obtain a second distance;
judging whether the first distance is greater than the second distance;
when the first distance is judged to be greater than or equal to the second distance, determining the unmanned vehicle located at the second geographic position as the target unmanned vehicle;
and when the first distance is judged to be smaller than the second distance, determining that the target unmanned vehicle does not exist.
In this embodiment, a first distance that can be traveled by the remaining power of each unmanned vehicle is calculated through a pre-trained power distance model, a first sub-distance between a second geographic location where each unmanned vehicle is located and the first geographic location where the passenger is located is calculated, a second sub-distance between the first geographic location where the passenger is located and the destination is calculated, and a sum of the first sub-distance and the second sub-distance is calculated to obtain a second distance. And determining whether a target unmanned vehicle exists according to a magnitude relation between the first distance and the second distance, the passenger being able to be loaded to the destination. When the remote server determines that the remaining capacity of any unmanned vehicle can load the passenger from the first geographical position to the destination, the target unmanned vehicle can be considered to exist. When the remote server determines that the remaining charge of any unmanned vehicle can be used for carrying the passenger to the destination from the first geographical position, the destination unmanned vehicle can be considered to be absent.
Preferably, the training process of the electric quantity distance model includes:
1) collecting different residual electric quantity and road condition information of the unmanned vehicle, and driving distances corresponding to the road condition information and the residual electric quantity as sample data;
the same residual electric quantity and the driving distance under different road condition information can be collected;
different residual electric quantities and driving distances under the same road condition information can be collected;
and the running distances under different residual electric quantity and different road condition information can be collected.
The traffic information may include, but is not limited to: road quality, degree of traffic congestion, etc. Therefore, different traffic information may indicate that the quality of the roads is the same and the degree of traffic congestion is different, or that the quality of the roads is different and the degree of traffic congestion is the same, or that the quality of the roads is different and the degree of traffic congestion is different.
2) Randomly dividing the sample data into a training set with a first preset proportion and a verification set with a second preset proportion, training a neural network by using the training set to obtain an electric quantity distance model, and verifying the accuracy of the electric quantity distance model obtained by training by using the verification set.
3) If the accuracy is greater than or equal to a preset accuracy threshold, ending the training; otherwise, if the accuracy is smaller than the preset accuracy threshold, increasing the number of the training sets and retraining the electric quantity distance model until the accuracy is larger than or equal to the preset accuracy threshold.
As an example, assume that 1 ten thousand pieces of travel distances including different road qualities, traffic congestion degrees, remaining power amounts, and corresponding to the remaining power amounts are acquired as sample data. Extracting sample data of a first preset proportion as a training set, extracting sample data of a second preset proportion of the remaining sample data in the sample data as a verification set, wherein the number of the sample data in the training set is greater than that of the sample data in the verification set, for example, 80% of the sample data in the sample data is used as the training set, and 15% of the sample data in the remaining 20% of the sample data is used as the verification set.
When a neural network model is trained for the first time, parameters of the neural network model are trained by adopting default parameters, the parameters are continuously adjusted in the training process, after the neural network model is generated by training, the generated neural network model is verified by using sample data to be verified, if the verification passing rate is greater than or equal to a preset threshold value, for example, the accuracy rate is greater than or equal to 98%, the training is finished, and the neural network model obtained by training is used as an electric quantity distance model; if the accuracy is smaller than the preset threshold, for example, smaller than 98%, the number of sample data participating in training is increased, and the above steps are executed again until the verification pass rate is greater than or equal to the preset accuracy threshold.
In this embodiment, because road quality and traffic jam degree are different, the distance that can travel when the residual capacity is the same must be different, and the distance that different residual capacities can travel is more inequality, therefore with residual capacity, road quality, traffic jam degree, the distance of traveling as sample data jointly train the electric quantity distance model that obtains, the model robustness is strong, the accuracy is high, more fits actual conditions. The driving distance corresponding to the residual electric quantity calculated through the distance electric quantity model is more accurate.
When it is determined that the target unmanned vehicle exists, S23 is executed; otherwise, when it is determined that the target unmanned vehicle does not exist, S24 is executed.
S23: and returning a message of successful request to the terminal equipment of the passenger and controlling the target unmanned vehicle to drive to the first geographic position of the passenger.
And when the target unmanned vehicle is determined to exist, the target unmanned vehicle is considered to provide the service corresponding to the boarding request, and a message of successful request is returned to the terminal equipment of the passenger.
The message that the request is successful may include: and providing a device number of a target unmanned vehicle carrying the service, a verification code for correspondingly starting the target unmanned vehicle and the like. The device number of the target unmanned vehicle is used for indicating the identification of the unmanned vehicle capable of providing the embarkation service, and the passenger can search the corresponding target unmanned vehicle according to the device number of the unmanned vehicle. The verification code is used to verify a correspondence between a passenger who sends a boarding request and a target unmanned vehicle that can provide a boarding service.
Preferably, after the message that the request is successful is returned to the terminal device of the passenger and the target unmanned vehicle is controlled to drive to the first geographical location where the passenger is located, the method further comprises:
sending a preset message to other unmanned vehicles except the target unmanned vehicle to inform the other unmanned vehicles that the target unmanned vehicle provides the passenger with boarding service.
The preset message may be "this pickup request has been responded to".
S24: returning a message to the passenger that the request failed.
When it is determined that the target unmanned vehicle does not exist, it is considered that no target unmanned vehicle can provide the service corresponding to the boarding request, and a message of request failure is returned to the terminal device of the passenger.
Preferably, after determining that the target unmanned vehicle is not present, the method further comprises:
obtaining a first unmanned vehicle closest to the first geographic location;
acquiring a fourth geographical position of a chargeable electric pile or a second unmanned vehicle between the first geographical position and the destination;
judging whether the residual electric quantity of the first unmanned vehicle can be driven to the fourth geographic position or not according to the pre-trained electric quantity distance model;
controlling the first unmanned vehicle to drive to the first geographic location where the passenger is located when it is determined that the remaining capacity of the first unmanned vehicle can drive to the fourth geographic location.
In this embodiment, the server queries a traffic condition between a geographic position where the unmanned vehicle is located and a passenger destination through a high-definition three-dimensional map, acquires a first unmanned vehicle closest to a first geographic position where the passenger is located, acquires a fourth geographic position where a rechargeable electric pile or a second unmanned vehicle between the first geographic position where the passenger is located and the destination is located, and determines whether the remaining electric quantity of the first unmanned vehicle can be driven to the fourth geographic position where the rechargeable electric pile or the second unmanned vehicle is located.
Preferably, the determining whether the remaining power of the first unmanned vehicle can travel to the fourth geographic location according to the pre-trained power distance model includes:
inputting the remaining power of the first unmanned vehicle and the road condition information between the third geographic position and the fourth geographic position into the pre-trained power distance model to obtain a third distance that the remaining power of the first unmanned vehicle can travel;
calculating a third sub-distance between the third geographic position and the first geographic position and a fourth sub-distance between the first geographic position and the fourth geographic position, and summing the third sub-distance and the fourth sub-distance to obtain a fourth distance;
judging whether the third distance is greater than the fourth distance;
determining that the remaining capacity of the first unmanned vehicle can travel to the fourth geographic location of the rechargeable electric pile or the second unmanned vehicle when it is determined that the third distance is greater than or equal to the fourth distance;
determining that the remaining power of the first unmanned vehicle cannot travel to the fourth geographic location of the rechargeable electric pile or the second unmanned vehicle when it is determined that the third distance is less than the fourth distance.
In this embodiment, the remote server calculates a third distance that the remaining charge of the first unmanned vehicle can travel, and at the same time, calculates a fourth distance between the schematic geographic location of the first unmanned vehicle and a fourth geographic location of the rechargeable electric pile or the second unmanned vehicle.
When the third distance is greater than or equal to the fourth distance, it is indicated that the residual electric quantity of the first unmanned vehicle closest to the first geographic position of the passenger can travel to the rechargeable electric pile or the fourth geographic position of the second unmanned vehicle, the first unmanned vehicle is controlled to drive to the passenger, meanwhile, the second unmanned vehicle is controlled to lock the door, the boarding request of other passengers is not received, and the passenger is waited to board the first unmanned vehicle and automatically start after arriving. Or after the passenger boards the first unmanned vehicle, the first unmanned vehicle is controlled to run to the chargeable electric pile, and charging is suspended.
Preferably, after determining that the remaining amount of power of the first unmanned vehicle is capable of traveling to the fourth geographic location, before controlling the first unmanned vehicle to travel to the first geographic location where the passenger is located, the method further comprises:
and sending an inquiry message of transfer required in the middle to the terminal equipment of the passenger so that the passenger can select whether to board the first unmanned vehicle.
Said controlling said first unmanned vehicle to travel to a first geographic location where said passenger is located, comprising:
controlling the first unmanned vehicle to drive toward the first geographic location where the passenger is located when a confirmation selection of the passenger is received.
The method further comprises the following steps:
controlling the second unmanned vehicle to lock a door when the passenger's confirmation selection is received.
In this embodiment, the passenger is allowed to autonomously select whether he or she wishes to suspend charging or transfer by sending a query message to the passenger. When the passenger wants to suspend charging or change the passenger, the first unmanned vehicle is controlled to drive to the passenger, and meanwhile the second unmanned vehicle is controlled to lock the door to wait for the first unmanned vehicle, so that the passenger is given multiple choices, and the passenger riding experience is improved.
In summary, in the unmanned vehicle control method provided in the embodiment of the present invention, when the pickup request is initially received, the geographic location and the destination where the passenger is located, and the remaining electric energy and the geographic location where the plurality of unmanned vehicles are located are obtained; and judging that a target unmanned vehicle exists according to a pre-trained electric quantity distance model, wherein the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination, returning a message of successful request to the terminal equipment of the passenger, and controlling the target unmanned vehicle to drive to the geographic position of the passenger. According to the pre-trained electric quantity distance model, the distance which can be traveled by the residual electric quantity is calculated under the condition that the road quality and the traffic congestion degree are fully considered, the practical situation is met, the problem that whether the unmanned vehicle can deliver the passenger to the destination or not can not be accurately estimated is solved, and the passenger experience of taking the unmanned vehicle is improved.
The above description is only a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and it will be apparent to those skilled in the art that modifications may be made without departing from the inventive concept of the present invention, and these modifications are within the scope of the present invention.
The functional modules and hardware structures of the unmanned vehicle for implementing the above-mentioned unmanned vehicle control method will be described below with reference to fig. 3 to 4.
Example two
Fig. 3 is a functional block diagram of a control apparatus for an unmanned vehicle according to a second embodiment of the present invention.
In some embodiments, the unmanned vehicle control device 30 operates in an unmanned vehicle. The unmanned vehicle control device 30 may include a plurality of functional modules comprised of program code segments. Program codes of respective program segments in the unmanned vehicle control device 30 may be stored in the memory and executed by the at least one processor to perform (see fig. 2 and its associated description for details) the unmanned vehicle control method.
In the present embodiment, the unmanned vehicle control device 30 may be divided into a plurality of functional modules according to the functions it performs. The functional module may include: a first obtaining module 301, a second obtaining module 302, a first determining module 303, a first control module 304, a first sending module 305, a second sending module 306, a third obtaining module 307, a fourth obtaining module 308, a second determining module 309, and a second control module 310. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In some embodiments, the functionality of the modules will be described in greater detail in subsequent embodiments.
The first obtaining module 301 is configured to obtain a first geographic location and a destination where a passenger is located when the passenger receives the boarding request.
The passenger can send a boarding request to a plurality of unmanned vehicles through a network by using the portable terminal device.
In some embodiments, an application corresponding to the unmanned vehicle may be downloaded and installed in the passenger's terminal device from a remote server. The passenger sends a ride request to a plurality of unmanned vehicles via the application. The taking request carries the first geographic position and the destination of the passenger.
Specifically, an input interface may be displayed in the terminal device through the application program. And displaying a destination input box on the input interface. The destination input box is used for receiving the destination input by the passenger. And after detecting that the destination is input in the destination input box, the terminal equipment sends the first geographic position of the passenger and the destination to a remote server. The remote server receives a boarding request of the terminal device through the first obtaining module 301 and obtains a first geographic position where a passenger is located and a destination of the passenger according to the boarding request.
And a second obtaining module 302, configured to obtain a remaining power of the unmanned vehicle.
In this embodiment, when a boarding request of a passenger is received, a first geographic location where the passenger is located and a destination of the passenger are obtained according to the boarding request, and meanwhile, the remaining capacity of the unmanned vehicle is also obtained.
In the embodiment, the remaining capacity of the solar battery of the unmanned vehicle can be monitored in a timed polling mode. Polling is a way for a Central Processing Unit (CPU) to make decisions on how to provide services to peripheral devices.
The inquiry may be periodically issued by the CPU of the remote server to sequentially inquire whether each unmanned vehicle needs service, for example, whether to monitor the remaining charge of the solar cell. When the corresponding result of the inquiry is determination, corresponding service is given, for example, when the remaining capacity of the solar battery needs to be monitored, the remaining capacity of the solar battery is monitored. After the service is finished, the next peripheral device is asked, and the process is repeated continuously.
In this embodiment, the remaining power of the unmanned vehicle may be monitored in a periodic polling manner, wherein the polling period may be set by the passenger or may be set by default (for example, the period is 5 seconds).
The first judging module 303 is configured to judge whether a target unmanned vehicle exists according to a pre-trained electric quantity distance model, where the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination.
In this embodiment, an electric quantity distance model may be trained in advance, and it is determined through the electric quantity distance model whether the remaining electric quantity of each unmanned vehicle can carry the passenger from the first geographic location to the destination. The electricity distance model may be pre-trained by a remote server.
Preferably, the determining module 303 determines whether a target unmanned vehicle exists according to a pre-trained electric quantity distance model, where the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination, and the determining includes:
inputting the remaining power of the unmanned vehicle and road condition information between the second geographic position where the unmanned vehicle is located and the first geographic position where the passenger is located into the pre-trained power distance model to obtain a first distance where the remaining power of the unmanned vehicle can travel;
calculating a first sub-distance between the second geographic position and the first geographic position and a second sub-distance between the first geographic position and the destination, and summing the first sub-distance and the second sub-distance to obtain a second distance;
judging whether the first distance is greater than the second distance;
when the first distance is judged to be greater than or equal to the second distance, determining the unmanned vehicle located at the second geographic position as the target unmanned vehicle;
and when the first distance is judged to be smaller than the second distance, determining that the target unmanned vehicle does not exist.
In this embodiment, a first distance that can be traveled by the remaining power of each unmanned vehicle is calculated through a pre-trained power distance model, a first sub-distance between a second geographic location where each unmanned vehicle is located and the first geographic location where the passenger is located is calculated, a second sub-distance between the first geographic location where the passenger is located and the destination is calculated, and a sum of the first sub-distance and the second sub-distance is calculated to obtain a second distance. And determining whether a target unmanned vehicle exists according to a magnitude relation between the first distance and the second distance, the passenger being able to be loaded to the destination. When the remote server determines that the remaining capacity of any unmanned vehicle can load the passenger from the first geographical position to the destination, the target unmanned vehicle can be considered to exist. When the remote server determines that the remaining charge of any unmanned vehicle can be used for carrying the passenger to the destination from the first geographical position, the destination unmanned vehicle can be considered to be absent.
Preferably, the training process of the electric quantity distance model includes:
1) collecting different residual electric quantity and road condition information of the unmanned vehicle, and driving distances corresponding to the road condition information and the residual electric quantity as sample data;
the same residual electric quantity and the driving distance under different road condition information can be collected;
different residual electric quantities and driving distances under the same road condition information can be collected;
and the running distances under different residual electric quantity and different road condition information can be collected.
The traffic information may include, but is not limited to: road quality, degree of traffic congestion, etc. Therefore, different traffic information may indicate that the quality of the roads is the same and the degree of traffic congestion is different, or that the quality of the roads is different and the degree of traffic congestion is the same, or that the quality of the roads is different and the degree of traffic congestion is different.
2) Randomly dividing the sample data into a training set with a first preset proportion and a verification set with a second preset proportion, training a neural network by using the training set to obtain an electric quantity distance model, and verifying the accuracy of the electric quantity distance model obtained by training by using the verification set.
3) If the accuracy is greater than or equal to a preset accuracy threshold, ending the training; otherwise, if the accuracy is smaller than the preset accuracy threshold, increasing the number of the training sets and retraining the electric quantity distance model until the accuracy is larger than or equal to the preset accuracy threshold.
As an example, assume that 1 ten thousand pieces of travel distances including different road qualities, traffic congestion degrees, remaining power amounts, and corresponding to the remaining power amounts are acquired as sample data. Extracting sample data of a first preset proportion as a training set, extracting sample data of a second preset proportion of the remaining sample data in the sample data as a verification set, wherein the number of the sample data in the training set is greater than that of the sample data in the verification set, for example, 80% of the sample data in the sample data is used as the training set, and 15% of the sample data in the remaining 20% of the sample data is used as the verification set.
When a neural network model is trained for the first time, parameters of the neural network model are trained by adopting default parameters, the parameters are continuously adjusted in the training process, after the neural network model is generated by training, the generated neural network model is verified by using sample data to be verified, if the verification passing rate is greater than or equal to a preset threshold value, for example, the accuracy rate is greater than or equal to 98%, the training is finished, and the neural network model obtained by training is used as an electric quantity distance model; if the accuracy is smaller than the preset threshold, for example, smaller than 98%, the number of sample data participating in training is increased, and the above steps are executed again until the verification pass rate is greater than or equal to the preset accuracy threshold.
In this embodiment, because road quality and traffic jam degree are different, the distance that can travel when the residual capacity is the same must be different, and the distance that different residual capacities can travel is more inequality, therefore with residual capacity, road quality, traffic jam degree, the distance of traveling as sample data jointly train the electric quantity distance model that obtains, the model robustness is strong, the accuracy is high, more fits actual conditions. The driving distance corresponding to the residual electric quantity calculated through the distance electric quantity model is more accurate.
A first control module 304, configured to, when the first determining module 303 determines that the target unmanned vehicle exists, return a message that the request is successful to the terminal device of the passenger, and control the target unmanned vehicle to move to the first geographic location where the passenger is located.
And when the target unmanned vehicle is determined to exist, the target unmanned vehicle is considered to provide the service corresponding to the boarding request, and a message of successful request is returned to the terminal equipment of the passenger.
The message that the request is successful may include: and providing a device number of a target unmanned vehicle carrying the service, a verification code for correspondingly starting the target unmanned vehicle and the like. The device number of the target unmanned vehicle is used for indicating the identification of the unmanned vehicle capable of providing the embarkation service, and the passenger can search the corresponding target unmanned vehicle according to the device number of the unmanned vehicle. The verification code is used to verify a correspondence between a passenger who sends a boarding request and a target unmanned vehicle that can provide a boarding service.
Preferably, after the first control module 304 returns a message of success request to the terminal device of the passenger and controls the target unmanned vehicle to drive to the first geographic location where the passenger is located, the apparatus further comprises:
a first sending module 305, configured to send a preset message to other unmanned vehicles except the target unmanned vehicle to notify the other unmanned vehicles that the target unmanned vehicle already provides the passenger with the boarding service.
The preset message may be "this pickup request has been responded to".
A second sending module 306, configured to return a message of failed request to the passenger when the first determining module 303 determines that the target unmanned vehicle does not exist.
When it is determined that the target unmanned vehicle does not exist, it is considered that no target unmanned vehicle can provide the service corresponding to the boarding request, and a message of request failure is returned to the terminal device of the passenger.
Preferably, after determining that the target unmanned vehicle is not present, the apparatus further comprises:
a third obtaining module 307, configured to obtain a first unmanned vehicle closest to the first geographic location;
a fourth obtaining module 308, configured to obtain a fourth geographic location where a rechargeable electric pile or a second unmanned vehicle is located between the first geographic location and the destination;
a second determining module 309, configured to determine whether the remaining power of the first unmanned vehicle can travel to the fourth geographic location according to the pre-trained power distance model;
a second control module 310, configured to control the first unmanned vehicle to drive to the first geographic location where the passenger is located when it is determined that the remaining power of the first unmanned vehicle is capable of driving to the fourth geographic location.
In this embodiment, the server queries a traffic condition between a geographic position where the unmanned vehicle is located and a passenger destination through a high-definition three-dimensional map, acquires a first unmanned vehicle closest to a first geographic position where the passenger is located, acquires a fourth geographic position where a rechargeable electric pile or a second unmanned vehicle between the first geographic position where the passenger is located and the destination is located, and determines whether the remaining electric quantity of the first unmanned vehicle can be driven to the fourth geographic position where the rechargeable electric pile or the second unmanned vehicle is located.
Preferably, the determining, by the second determining module 309, whether the remaining power of the first unmanned vehicle can travel to the fourth geographic location according to the pre-trained power distance model includes:
inputting the remaining power of the first unmanned vehicle and the road condition information between the third geographic position and the fourth geographic position into the pre-trained power distance model to obtain a third distance that the remaining power of the first unmanned vehicle can travel;
calculating a third sub-distance between the third geographic position and the first geographic position and a fourth sub-distance between the first geographic position and the fourth geographic position, and summing the third sub-distance and the fourth sub-distance to obtain a fourth distance;
judging whether the third distance is greater than the fourth distance;
determining that the remaining capacity of the first unmanned vehicle can travel to the fourth geographic location of the rechargeable electric pile or the second unmanned vehicle when it is determined that the third distance is greater than or equal to the fourth distance;
determining that the remaining power of the first unmanned vehicle cannot travel to the fourth geographic location of the rechargeable electric pile or the second unmanned vehicle when it is determined that the third distance is less than the fourth distance.
In this embodiment, the remote server calculates a third distance that the remaining charge of the first unmanned vehicle can travel, and at the same time, calculates a fourth distance between the schematic geographic location of the first unmanned vehicle and a fourth geographic location of the rechargeable electric pile or the second unmanned vehicle.
When the third distance is greater than or equal to the fourth distance, it is indicated that the residual electric quantity of the first unmanned vehicle closest to the first geographic position of the passenger can travel to the rechargeable electric pile or the fourth geographic position of the second unmanned vehicle, the first unmanned vehicle is controlled to drive to the passenger, meanwhile, the second unmanned vehicle is controlled to lock the door, the boarding request of other passengers is not received, and the passenger is waited to board the first unmanned vehicle and automatically start after arriving. Or after the passenger boards the first unmanned vehicle, the first unmanned vehicle is controlled to run to the chargeable electric pile, and charging is suspended.
Preferably, after determining that the remaining amount of power of the first unmanned vehicle is capable of traveling to the fourth geographic location, before controlling the first unmanned vehicle to travel to the first geographic location where the passenger is located, the apparatus further comprises:
and sending an inquiry message of transfer required in the middle to the terminal equipment of the passenger so that the passenger can select whether to board the first unmanned vehicle.
The second control module 310 controls the first unmanned vehicle to travel to a first geographic location where the passenger is located, including:
controlling the first unmanned vehicle to drive toward the first geographic location where the passenger is located when a confirmation selection of the passenger is received.
The second control module 310 is further configured to control the second unmanned vehicle to lock the door when the confirmation selection of the passenger is received.
In this embodiment, the passenger is allowed to autonomously select whether he or she wishes to suspend charging or transfer by sending a query message to the passenger. When the passenger wants to suspend charging or change the passenger, the first unmanned vehicle is controlled to drive to the passenger, and meanwhile the second unmanned vehicle is controlled to lock the door to wait for the first unmanned vehicle, so that the passenger is given multiple choices, and the passenger riding experience is improved.
In summary, the control device for the unmanned vehicle according to the embodiment of the present invention obtains the geographic location and the destination of the passenger, and the remaining electric quantity and the geographic location of the plurality of unmanned vehicles by initially receiving the boarding request; and judging that a target unmanned vehicle exists according to a pre-trained electric quantity distance model, wherein the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination, returning a message of successful request to the terminal equipment of the passenger, and controlling the target unmanned vehicle to drive to the geographic position of the passenger. According to the pre-trained electric quantity distance model, the distance which can be traveled by the residual electric quantity is calculated under the condition that the road quality and the traffic congestion degree are fully considered, the practical situation is met, the problem that whether the unmanned vehicle can deliver the passenger to the destination or not can not be accurately estimated is solved, and the passenger experience of taking the unmanned vehicle is improved.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a dual-screen device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.
EXAMPLE III
Fig. 4 is a schematic diagram of a server according to a third embodiment of the present invention.
The remote server 3 includes: a memory 41, at least one processor 42, a computer program 43 stored in said memory 41 and executable on said at least one processor 42, and at least one communication bus 44.
The steps in the above-described method embodiments are implemented when the computer program 43 is executed by the at least one processor 42.
Illustratively, the computer program 43 may be divided into one or more modules/units, which are stored in the memory 41 and executed by the at least one processor 42 to perform the steps in the above-described method embodiments of the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 43 in the remote server 3.
The remote server 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. It will be appreciated by those skilled in the art that the schematic diagram 4 is merely an example of the remote server 3 and does not constitute a limitation of the remote server 3, and may include more or less components than those shown, or some components in combination, or different components, for example, the remote server 3 may also include input and output devices, network access devices, buses, etc.
The at least one Processor 42 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. The processor 42 may be a microprocessor or the processor 42 may be any conventional processor or the like, the processor 42 being the control center of the remote server 3 and connecting the various parts of the entire remote server 3 using various interfaces and lines.
The memory 41 may be used to store the computer program 43 and/or the module/unit, and the processor 42 may implement various functions of the remote server 3 by running or executing the computer program and/or the module/unit stored in the memory 41 and calling data stored in the memory 41. The memory 41 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the remote server 3, and the like. In addition, the memory 41 may include a high speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The modules/units integrated by the remote server 3 may be stored in a computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
In the several embodiments provided in the present invention, it should be understood that the disclosed unmanned vehicle and method may be implemented in other ways. For example, the above described unmanned vehicle embodiment is merely illustrative, and for example, the division of the units is only one logical functional division, and there may be other divisions when actually implemented.
In addition, functional units in the embodiments of the present invention may be integrated into the same processing unit, or each unit may exist alone physically, or two or more units are integrated into the same unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit scope of the technical solutions of the present invention.
Claims (9)
1. A method for controlling an unmanned vehicle, applied to a server, the method comprising:
when a taking request is received, acquiring a first geographic position and a destination where a passenger is located;
judging whether a target unmanned vehicle exists or not according to a pre-trained electric quantity distance model, wherein the remaining electric quantity of the target unmanned vehicle can carry the passenger to the destination;
when the target unmanned vehicle is determined to exist, returning a message of successful request to the terminal device of the passenger and controlling the target unmanned vehicle to drive to the first geographic position where the passenger is located, wherein the message of successful request comprises: providing a device number of a target unmanned vehicle carrying service and a verification code for correspondingly starting the target unmanned vehicle;
obtaining a first unmanned vehicle closest to the first geographic location after determining that the target unmanned vehicle is not present; acquiring a fourth geographical position where a chargeable electric pile between the first geographical position and the destination is located; judging whether the residual electric quantity of the first unmanned vehicle can drive to the fourth geographic position or not according to the pre-trained electric quantity distance model; when it is determined that the remaining capacity of the first unmanned vehicle can travel to the fourth geographic location, controlling the first unmanned vehicle to travel to the first geographic location where the passenger is located, and controlling the first unmanned vehicle to travel to the chargeable electric pile after the passenger boards the first unmanned vehicle.
2. The method of claim 1, wherein said determining whether a target unmanned vehicle exists based on a pre-trained charge distance model, a remaining charge of the target unmanned vehicle being capable of carrying the passenger to the destination comprises:
inputting the remaining power of the unmanned vehicle and road condition information between the second geographic position where the unmanned vehicle is located and the first geographic position where the passenger is located into the pre-trained power distance model to obtain a first distance where the remaining power of the unmanned vehicle can travel;
calculating a first sub-distance between the second geographic position and the first geographic position and a second sub-distance between the first geographic position and the destination, and summing the first sub-distance and the second sub-distance to obtain a second distance;
when the first distance is judged to be greater than or equal to the second distance, determining the unmanned vehicle located at the second geographic position as the target unmanned vehicle;
and when the first distance is judged to be smaller than the second distance, determining that the target unmanned vehicle does not exist.
3. The method of claim 1, wherein the training process of the electrical quantity distance model comprises:
collecting different residual electric quantity and road condition information of the unmanned vehicle and driving distances corresponding to the road condition information and the residual electric quantity as sample data;
randomly dividing the sample data into a training set with a first preset proportion and a verification set with a second preset proportion, training a neural network by using the training set to obtain an electric quantity distance model, and verifying the accuracy of the electric quantity distance model obtained by training by using the verification set;
if the accuracy is greater than or equal to a preset accuracy threshold, ending the training;
if the accuracy is smaller than the preset accuracy threshold, increasing the number of samples in the training set and retraining the electric quantity distance model until the accuracy is larger than or equal to the preset accuracy threshold.
4. The method of claim 1, wherein after said returning a message to the passenger's terminal device requesting success and controlling the target unmanned vehicle to travel to the first geographic location where the passenger is located, the method further comprises:
sending a preset message to other unmanned vehicles except the target unmanned vehicle to inform the other unmanned vehicles that the target unmanned vehicle provides the passenger with boarding service.
5. The method of claim 1, wherein said determining whether the remaining power of the first unmanned vehicle can travel to the fourth geographic location based on the pre-trained power distance model comprises:
inputting the remaining power of the first unmanned vehicle and road condition information between the third geographic position and the fourth geographic position of the first unmanned vehicle into the pre-trained power distance model to obtain a third distance which can be traveled by the remaining power of the first unmanned vehicle;
calculating a third sub-distance between the third geographic position and the first geographic position and a fourth sub-distance between the first geographic position and the fourth geographic position, and summing the third sub-distance and the fourth sub-distance to obtain a fourth distance;
determining that the remaining capacity of the first unmanned vehicle can travel to the fourth geographic location when it is determined that the third distance is greater than or equal to the fourth distance;
and when the third distance is judged to be smaller than the fourth distance, determining that the remaining capacity of the first unmanned vehicle cannot travel to the fourth geographic position.
6. The method of claim 5, wherein after determining that the remaining charge of the first unmanned vehicle is capable of traveling to the fourth geographic location, prior to controlling the first unmanned vehicle to travel to the first geographic location where the passenger is located, the method further comprises:
sending an inquiry message of transfer required in the middle to a terminal device of the passenger so that the passenger can select whether to board the first unmanned vehicle;
said controlling said first unmanned vehicle to travel to said first geographic location where said passenger is located, comprising: controlling the first unmanned vehicle to drive toward the first geographic location where the passenger is located when a confirmation selection of the passenger is received.
7. An unmanned vehicle control apparatus, operating in a server, the apparatus comprising:
the obtaining module is used for obtaining a first geographic position and a destination of a passenger when the taking request is received;
the judging module is used for judging whether a target unmanned vehicle exists or not according to a pre-trained electric quantity distance model, and the remaining electric quantity of the target unmanned vehicle can load the passenger to the destination;
a control module, configured to, when the determining module determines that the target unmanned vehicle exists, return a message that a request is successful to a terminal device of the passenger and control the target unmanned vehicle to move to the first geographic location where the passenger is located, where the message that the request is successful includes: providing a device number of a target unmanned vehicle carrying service and a verification code for correspondingly starting the target unmanned vehicle;
a third obtaining module, configured to obtain a first unmanned vehicle closest to the first geographic location after determining that the target unmanned vehicle does not exist;
the fourth acquisition module is used for acquiring a fourth geographical position where the chargeable electric pile between the first geographical position and the destination is located;
the second judgment module is used for judging whether the residual electric quantity of the first unmanned vehicle can drive to the fourth geographic position or not according to the pre-trained electric quantity distance model;
the second control module is used for controlling the first unmanned vehicle to drive to the first geographic position where the passenger is located when the fact that the residual electric quantity of the first unmanned vehicle can drive to the fourth geographic position is determined, and controlling the first unmanned vehicle to drive to the chargeable electric pile after the passenger boards the first unmanned vehicle.
8. A server, characterized in that the server comprises a processor and a memory, the processor being configured to implement the unmanned vehicle control method according to any of claims 1-6 when executing the computer program stored in the memory.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the unmanned vehicle control method according to any one of claims 1 to 6.
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| CN108932867A (en) * | 2018-09-12 | 2018-12-04 | 奇瑞汽车股份有限公司 | It takes the system of returning the car and takes returning method |
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