CN117116075A

CN117116075A - Vehicle control method, device, electronic equipment and storage medium

Info

Publication number: CN117116075A
Application number: CN202311110454.XA
Authority: CN
Inventors: 邱亚星; 刘子昊; 刘艳荣; 孙明芳; 张岩
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2023-08-30
Filing date: 2023-08-30
Publication date: 2023-11-24

Abstract

The disclosure provides a vehicle control method, a device, electronic equipment and a storage medium, relates to the technical field of computers, in particular to the technical field of artificial intelligence such as intelligent traffic, cloud computing, big data, deep learning and the like, and comprises the following steps: under the condition that a road section to be driven by the vehicle comprises a plurality of continuous straight-going signal lamps, current first driving data of the vehicle, n pieces of first state data corresponding to the continuous n signal lamps respectively and n pieces of first driving data of n intersections where the n signal lamps are respectively located are obtained, then a first driving strategy of the vehicle passing through the n intersections is determined according to the obtained data, and then the vehicle is controlled to continuously pass through the n intersections according to the first driving strategy under the condition that the first driving strategy indicates that the vehicle can continuously pass through the n intersections. Therefore, through lower information interaction cost, estimation of running strategies of vehicles passing through a plurality of intersections continuously is realized, and reliable basis is provided for improving running safety and reducing oil consumption.

Description

Vehicle control method, device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of computers, in particular to the technical field of artificial intelligence such as intelligent transportation, cloud computing, big data, deep learning and the like, and specifically relates to a vehicle control method, a device, electronic equipment and a storage medium.

Background art

With the continuous improvement of living standard, driving and traveling become more and more common ways in people's life. However, in the driving process of the vehicle, traffic accidents are likely to occur because the driver can not brake in time due to the misjudgment of the signal lamp at the intersection. It is therefore desirable to provide a vehicle control method for predicting whether a road is green or not to allow traffic to proceed, so as to improve driving safety.

Disclosure of Invention

The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.

To this end, the present disclosure proposes a vehicle control method, apparatus, electronic device, and storage medium.

According to a first aspect of the present disclosure, there is provided a vehicle control method including:

under the condition that a road section to be driven by a vehicle comprises a plurality of continuous straight traffic lights, current first driving data of the vehicle, n pieces of first state data corresponding to the continuous n pieces of traffic lights respectively and n pieces of first traffic data of n intersections where the n pieces of traffic lights are respectively located are obtained, wherein the state data are determined based on historical state data of the traffic lights, the traffic data comprise historical traffic data and/or current traffic data, and n is an integer larger than 1;

Determining a first driving strategy of the vehicle passing through the n intersections according to the first driving data, the n first state data and the n first traffic data;

and controlling the vehicle to continuously pass through the n intersections according to the first driving strategy under the condition that the first driving strategy indicates that the vehicle can continuously pass through the n intersections by green light.

According to a second aspect of the present disclosure, there is provided a vehicle control apparatus including:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring current first driving data of a vehicle, n pieces of first state data corresponding to n continuous signal lamps respectively and n pieces of first traffic data of n intersections where the n signal lamps are respectively positioned under the condition that a road section to be driven by the vehicle comprises a plurality of continuous straight signal lamps, wherein the state data is determined based on historical state data of the signal lamps, the traffic data comprises historical traffic data and/or current traffic data, and n is an integer larger than 1;

the determining module is used for determining a first driving strategy of the vehicle passing through the n intersections according to the first driving data, the n first state data and the n first traffic data;

And the control module is used for controlling the vehicle to continuously pass through the n intersections according to the first driving strategy under the condition that the first driving strategy indicates that the vehicle can continuously pass through the n intersections by green light.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle control method according to the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the vehicle control method according to the first aspect.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement the steps of the vehicle control method according to the first aspect.

The vehicle control method, the device, the electronic equipment and the storage medium provided by the disclosure have the following beneficial effects:

In this embodiment, under the condition that it is determined that a road section on which a vehicle is to travel includes a plurality of continuous straight-going signal lamps, current first traveling data of the vehicle, n first state data corresponding to n continuous signal lamps respectively, and n first traveling data of n intersections where the n signal lamps are respectively located are obtained, then a first traveling strategy of the vehicle passing through the n intersections is determined according to the first traveling data, the n first state data and the n first traveling data, and then, under the condition that the first traveling strategy indicates that the vehicle can continuously pass through the n intersections by green light, the vehicle is controlled to continuously pass through the n intersections according to the first traveling strategy. Therefore, through lower information interaction cost, the estimation of the running strategy that the vehicle continuously passes through a plurality of intersections can be realized, reliable basis is provided for improving running safety and reducing oil consumption, and the driving experience of a user is optimized.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, which serve to better understand the present disclosure, and are not to be construed as limiting the present disclosure, wherein:

FIG. 1 is a flow chart of a method of controlling a vehicle according to an embodiment of the present disclosure;

FIG. 2 is a schematic illustration of a vehicle section to be traveled provided by the present disclosure;

FIG. 3 is a flow chart diagram of a vehicle control method according to another embodiment of the present disclosure;

FIG. 4 is a flow chart diagram of a vehicle control method according to another embodiment of the present disclosure;

FIG. 5 is a flow chart diagram of a vehicle control method according to another embodiment of the present disclosure;

fig. 6 is a schematic structural view of a vehicle control apparatus according to an embodiment of the present disclosure;

fig. 7 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The embodiment of the disclosure relates to the technical field of artificial intelligence such as intelligent traffic, cloud computing, big data, deep learning and the like.

Artificial intelligence (Artificial Intelligence), english is abbreviated AI. It is a new technical science for researching, developing theory, method, technology and application system for simulating, extending and expanding human intelligence.

Intelligent transportation refers to the utilization of advanced information technology and intelligent equipment to improve the efficiency and safety of transportation systems. The method realizes the collection, processing and sharing of real-time data by connecting traffic facilities, vehicles and users so as to optimize traffic flow, reduce congestion, improve traffic safety, save energy and reduce emission.

Cloud computing is an internet-based computing model that delivers computing services in an on-demand, flexible, and scalable manner by providing computing resources (e.g., servers, storage, databases, etc.) to users. The method transfers the computing tasks and data storage from the local device to the cloud server, so that a user can access and use the computing resources through the network anytime and anywhere.

Big data, or huge amount of data, refers to information that the amount of data involved is so large that it cannot be retrieved, managed, processed, and consolidated in a reasonable time through the mainstream software tools, thus helping the business decision to be more aggressive.

Deep learning is the inherent regularity and presentation hierarchy of learning sample data, and the information obtained during such learning is helpful in interpreting data such as text, images and sounds. The final goal of deep learning is to enable a machine to analyze learning capabilities like a person, and to recognize text, images, and sound data.

In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.

The following describes a vehicle control method, apparatus, electronic device, and storage medium of the embodiments of the present disclosure with reference to the accompanying drawings.

It should be noted that, the execution body of the vehicle control method in this embodiment is a vehicle control device, and the device may be implemented in a software and/or hardware manner, and the device may be configured in an electronic device, where the electronic device may include, but is not limited to, a terminal, a server, an in-vehicle device, and the like. In the embodiments of the present disclosure, a vehicle control system is used as an example to execute a vehicle control method.

Fig. 1 is a flowchart of a vehicle control method according to an embodiment of the present disclosure.

As shown in fig. 1, the vehicle control method includes:

s101: when it is determined that a road section on which a vehicle is to travel includes a plurality of continuous straight-going signal lamps, current first traveling data of the vehicle, n pieces of first state data corresponding to the continuous n signal lamps respectively, and n pieces of first traffic data of n intersections at which the n signal lamps respectively are located are acquired.

The state data is determined based on historical state data of the signal lamp, the traffic data comprises historical traffic data and/or current traffic data, and n is an integer greater than 1.

Wherein, the driving data can include at least one of the driving speed and the distance between the signal lamps; the status data may include at least one of a current light state of the signal light, a remaining time of the current light state, a next light state of the signal light, a duration of the next light state, and a period of the signal light; the traffic data may include at least one of an average traffic speed, an average traffic volume, which is not limited by the present disclosure.

In the method, the vehicle control system determines multidimensional data related to traffic of the signal lamp based on historical data and/or real-time driving data, so that reliability and accuracy of a generated vehicle driving strategy are guaranteed, and determination efficiency of the driving strategy is improved. Meanwhile, under general conditions, some state data of each signal lamp are unchanged, such as a signal lamp period and each lamp state duration, so that the present disclosure can mine current first state data of the corresponding signal lamp based on historical state data of each signal lamp, and information interaction with multiple parties is not needed to obtain the first state data, thereby reducing information communication cost for obtaining the state data of the signal lamp.

The average traffic speed and the average traffic volume may be an average value of all the historical time periods at each intersection, or may be a historical average traffic speed and a historical average traffic volume corresponding to the current time period. For example, when the current time for the vehicle to pass through the intersection is 8-9 a.m., the acquired traffic data may be historical data corresponding to 8-9 a.m.

In the embodiment of the disclosure, the historical average traffic speed and the average traffic volume of each intersection can be used as the first traffic data, or the current traffic speed and the current traffic volume of each intersection can be obtained as the first traffic data, or the historical traffic data and the current traffic data can be averaged, and the like, which is not limited in the disclosure.

In the embodiment of the disclosure, the road section to be driven by the vehicle can be determined according to the historical driving track of the vehicle. For example, the historical driving track of the vehicle from 8 points to 8 points 20 in the morning is that the vehicle enters the X section of the straight road section from the intersection A until the intersection A+N exits, and after the vehicle enters the X section from the intersection A in 8 points 5 minutes in the morning, the vehicle can be determined to continuously pass through N signal lamps in the straight road section.

In the embodiment of the disclosure, under the condition that it is determined that a road section to be driven by a vehicle meets a condition that the road section includes a plurality of continuous straight-going signal lamps, the current driving speed v of the vehicle and the distance s from the first signal lamp are obtained, and first state data (for example, the current light state of the signal lamp is a green light, the remaining time of the current light state is 20s, the next light state is a yellow light, the duration of the next light state is 10s, etc.) corresponding to n signal lamps i, i+1, … …, i+n-1 respectively are obtained, and first traffic data of n intersections i, i+1, … …, i+n-1 where the vehicle is located are respectively shown in fig. 2, and fig. 2 is a schematic diagram of the road section to be driven by the vehicle.

Alternatively, the vehicle control system may acquire the current first driving data of the vehicle when it is determined that the distance between the vehicle and the first signal lamp of the n signal lamps is less than or equal to the distance threshold.

The distance threshold may be a fixed value, or may be a value determined according to practical situations, for example, when there are more vehicles passing through the road, the distance threshold may be less, etc., which is not limited in the present disclosure.

In the embodiment of the disclosure, when the road section to be driven by the user vehicle includes a plurality of continuous straight traffic lights, the vehicle control system may first start the driving strategy prediction function, monitor the distance change between the vehicle and the first traffic light, and then start to acquire the first driving data such as the current speed of the vehicle when the distance between the vehicle and the first traffic light is less than or equal to the distance threshold value, so that unnecessary data collection and resource waste can be avoided, and conditions are provided for improving the generating efficiency of the driving strategy.

Alternatively, the vehicle control system may determine the type of the road section to be driven before acquiring the first driving data, the first state data, and the first traffic data, and then determine the value of n according to the type of the road section to be driven.

In the embodiment of the present disclosure, the type of the road section to be driven may be an urban arterial road, a general road, or the like. Because the corresponding signal lamp density, the vehicle flow and the road section passing smoothness are different according to different road section types, the vehicle control system can set different maximum values for starting to predict the number of continuous passing intersections for different road sections, the road section is wider, the corresponding maximum value n can be larger, for example, the value of n can be 4 for an urban arterial road; for a normal road, n may have a value of 3. By determining different starting predicted values n for different types of road sections, the situation that the decision time is too long due to the fact that a large number of predicted intersections are selected for the crowded road sections is avoided, decision errors are reduced, and the efficiency of traffic prediction is further improved.

S102: and determining a first driving strategy of the vehicle passing through n intersections according to the first driving data, the n first state data and the n first traffic data.

The driving strategy may include at least one of indication information of whether the green light can pass through the intersection, driving speed range of the green light passing through the intersection, accelerator pedal opening, brake speed, brake pedal opening, brake distance and the like, which is not limited in the present disclosure. The driving advice which is more reasonable and safe can be provided for the user through various driving strategy information, the probability of accidents caused by improper driving behaviors is effectively reduced, the increase of oil consumption caused by unnecessary acceleration driving in front of the lamp is avoided, and the air pollution is reduced.

Optionally, the vehicle control system may input the first driving data, the n first state data and the n first traffic data into a first prediction model corresponding to the preset n intersections, so as to obtain a first driving policy of the vehicle passing through the n intersections output by the first prediction model, where the first prediction model is a model generated by training based on historical traffic data of the n intersections, historical state data of the n signal lamps and track data of the vehicle passing through the n intersections.

The first prediction model may be constructed based on a deep neural network model (Deep Neural Networks, DNN), or may be constructed based on other classification models, which is not limited in this disclosure.

In the embodiment of the disclosure, before the prediction of the driving strategy is performed on line, the historical traffic data of n intersections, the historical state data of n signal lamps and the historical track data of vehicles passing through the n intersections can be obtained first, then the historical track data are labeled by using the posterior information of whether the vehicles pass through the intersections or not in each track data, a training data set is constructed, then the first prediction model corresponding to the n intersections is obtained according to the training of the data set, so that the efficiency and accuracy of the driving strategy continuously passing through the multiple intersections can be improved on line, and the driving experience of a user is optimized.

S103: in the case where the first travel strategy indicates that the vehicle can pass through n intersections in succession by a green light, the vehicle is controlled to pass through n intersections in succession in accordance with the first travel strategy.

In the embodiment of the disclosure, the first driving strategy may include information of a speed range, an accelerator opening, and the like, and the vehicle control system may directly control the vehicle to drive based on the first driving strategy, or may broadcast the first driving strategy to a driver, so that the driver controls the vehicle.

In this embodiment, when it is determined that a road section on which a vehicle is to travel includes a plurality of continuous straight-going signal lamps, the vehicle control system acquires current first traveling data of the vehicle, n first state data corresponding to n signal lamps respectively, and n first traffic data of n intersections where the n signal lamps respectively are located, then determines a first traveling policy of the vehicle passing through the n intersections according to the first traveling data, the n first state data, and the n first traffic data, and then, when the first traveling policy indicates that the vehicle can continuously pass through the n intersections by green lights, controls the vehicle to continuously pass through the n intersections according to the first traveling policy. Therefore, through lower information interaction cost, the estimation of the running strategy that the vehicle continuously passes through a plurality of intersections can be realized, reliable basis is provided for improving running safety and reducing oil consumption, and the driving experience of a user is optimized.

Fig. 3 is a flowchart illustrating a vehicle control method according to another embodiment of the present disclosure.

As shown in fig. 3, the vehicle control method includes:

s301: when it is determined that a road section on which a vehicle is to travel includes a plurality of continuous straight-going signal lamps, current first traveling data of the vehicle, n pieces of first state data corresponding to the continuous n signal lamps respectively, and n pieces of first traffic data of n intersections at which the n signal lamps respectively are located are acquired.

S302: and determining a first driving strategy of the vehicle passing through n intersections according to the first driving data, the n first state data and the n first traffic data.

The descriptions of S301 to S302 may be specifically referred to the above embodiments, and are not repeated herein.

S303: under the condition that the first driving strategy indicates that the vehicle cannot pass through n intersections by continuous green light, n-1 state data corresponding to n-1 signal lamps in the driving direction of the vehicle, n-1 passing data of n-1 intersections where the n-1 signal lamps are respectively located, and first driving data are input into a preset second prediction model corresponding to the n-1 intersections, so that a second driving strategy of the vehicle passing through the n-1 intersections, which is output by the second prediction model, is obtained.

The second prediction model is a model which is generated based on the historical traffic data of n-1 intersections, the historical state data of n-1 signal lamps and the track data of vehicles passing through n-1 intersections in a historical mode.

In the embodiment of the disclosure, when the vehicle control system determines that the vehicle cannot continuously pass through n intersections by green light, the number of intersections predicted simultaneously can be reduced, and n-1 state data corresponding to the first n-1 signal lamps on the road section to be driven, n-1 traffic data of the n-1 intersections where the first n-1 signal lamps are located, and the first driving data are input into the second prediction model to obtain a second driving strategy which is output by the second prediction model and passes through the n-1 intersections.

S304: and under the condition that the second driving strategy indicates that the vehicle cannot continuously pass through n-1 intersections, the operation of acquiring the driving strategy is carried out based on n-2 state data and n-2 traffic data in a returning mode until the driving strategy that the vehicle passes through the first intersection is determined.

In the embodiment of the disclosure, when the driving strategies of the intersections indicate that the continuous passing of the intersections is impossible, the vehicle control system can reduce the number of the intersections predicted at the same time, delete the state data of the signal lamps with the farthest distance on the navigation route and the traffic data of the corresponding intersections, input the state data into the prediction models corresponding to the corresponding number of the intersections again to obtain the driving strategies, and repeatedly execute the operation until the driving strategies indicating that the continuous green light can pass are obtained, wherein the driving strategies at the moment at least enable the green light of the vehicle to pass through the first intersection.

In this embodiment, when the first driving policy indicates that the vehicle cannot pass through n intersections continuously, the vehicle control system inputs n-1 status data corresponding to n-1 signal lamps in the driving direction of the vehicle, n-1 traffic data of n-1 intersections where the n-1 signal lamps are located respectively, and the first driving data, to a second prediction model corresponding to the preset n-1 intersections, so as to obtain a second driving policy of the vehicle passing through the n-1 intersections, which is output by the second prediction model, and then returns to execute an operation of obtaining the driving policy based on the n-2 status data and the n-2 traffic data until it is determined that the vehicle passes through the driving policy of the first intersection, when the second driving policy indicates that the vehicle cannot pass through the n-1 intersections continuously. Therefore, the number of the intersections predicted simultaneously can be reduced sequentially, the running strategy that the vehicle can continuously pass through the most intersections is obtained, the generation efficiency of the running strategy is further improved, the optimal running mode for continuously passing through a plurality of intersections is provided for the user, and the driving experience of the user is improved.

Fig. 4 is a flowchart illustrating a vehicle control method according to another embodiment of the present disclosure.

As shown in fig. 4, the vehicle control method includes:

S401: when it is determined that a road section on which a vehicle is to travel includes a plurality of continuous straight-going signal lamps, current first traveling data of the vehicle, n pieces of first state data corresponding to the continuous n signal lamps respectively, and n pieces of first traffic data of n intersections at which the n signal lamps respectively are located are acquired.

S402: and determining a first driving strategy of the vehicle passing through n intersections according to the first driving data, the n first state data and the n first traffic data.

The descriptions of S401 to S402 may be specifically referred to the above embodiments, and are not repeated herein.

S403: in the case where the first driving strategy indicates that the vehicle can pass through n intersections in succession, the number m of the continuous straight-going signal lamps included in the adjacent road sections after the n intersections is determined according to the navigation route of the vehicle.

The adjacent road segments refer to road segments including a plurality of continuous straight-going signal lamps after n intersections in a navigation route of the vehicle, may be road segments continuous with the nth intersection, and may be continuous straight-going road segments separated from the nth intersection by one or more turn signal lamps, which is not limited in the present disclosure.

In the embodiment of the disclosure, after determining that the vehicle can pass through n intersections with continuous green lights, the vehicle control system can continuously predict continuous straight road sections after the n intersections in the navigation route.

S404: and under the condition that m is larger than 1 and smaller than n, m state data corresponding to m signal lamps respectively are obtained, m traffic data of m intersections where the m signal lamps are respectively positioned, and second driving data before the vehicle is driven into the adjacent road section.

The second driving data may be information such as a current driving speed of the vehicle, where the vehicle control system determines that a distance between the vehicle and a first signal lamp of the m signal lamps is less than or equal to a distance threshold.

Optionally, when m is greater than n, the vehicle control system may acquire n second state data corresponding to the first n signal lamps in the m signal lamps respectively, and n second traffic data of n intersections where the first n signal lamps are located respectively, and then input the second driving data, the n second state data, and the n second traffic data into a preset first prediction model corresponding to the n intersections, so as to acquire a fourth driving policy of the vehicle passing through the first n intersections, where the fourth driving policy is output by the first prediction model.

In the embodiment of the disclosure, when the number m of the continuous straight-going signal lamps in the adjacent road sections is greater than n, the vehicle control system may not be capable of predicting the continuous traffic situation of m intersections, so that the first n intersections in the m intersections can be predicted first to obtain a fourth driving strategy that the vehicle passes through the first n intersections, then the number of intersections which can be passed through by green lamps is judged step by step according to the indication of the fourth driving strategy, and then the remaining intersections are predicted again to generate the driving strategy. Therefore, the decision efficiency of the driving strategy can be improved, and the influence of more decision data on the accuracy of the prediction result is avoided.

S405: and inputting the second driving data, the m state data and the m traffic data into a third prediction model corresponding to the preset m intersections to obtain a third driving strategy of the vehicle passing through the m intersections, wherein the third driving strategy is output by the third prediction model.

In this embodiment, when the first driving policy indicates that the vehicle can continuously pass through n intersections, the vehicle control system determines, according to the navigation route of the vehicle, the number m of continuous straight-going signal lamps included in adjacent road segments after the n intersections, and then obtains m state data corresponding to the m signal lamps respectively when m is greater than 1 and less than n, m traffic data of the m intersections where the m signal lamps are respectively located, and second driving data before the vehicle enters the adjacent road segments, and then inputs the second driving data, the m state data and the m traffic data into a third prediction model corresponding to the preset m intersections, so as to obtain a third driving policy of the vehicle passing through the m intersections, which is output by the third prediction model. Therefore, the estimation of the driving strategy can be respectively carried out on all straight road sections in the vehicle navigation route, the accuracy and the reliability of the driving strategy are further improved, the whole-process driving decision of the navigation route is realized, and the driving experience of a user is further optimized.

Fig. 5 is a flowchart of a vehicle control method according to another embodiment of the present disclosure.

As shown in fig. 5, the vehicle control method includes:

s501: under the condition that a road section on which the vehicle is to run comprises a continuous straight traffic light, under the condition that the distance between the vehicle and one signal light is smaller than or equal to a distance threshold value, third running data of the vehicle, third state data of one signal and third traffic data of an intersection where the signal light is located are obtained.

S502: and inputting the third driving data, the third state data and the third data into a fourth prediction model corresponding to the preset single road junction so as to obtain a fifth driving strategy of the vehicle passing through the road junction, which is output by the fourth prediction model.

S503: the vehicle is controlled to continuously pass through an intersection according to a fifth driving strategy.

The descriptions of S501 to S503 may be specifically referred to the above embodiments, and are not repeated herein.

In this embodiment, the vehicle control method may also be used for predicting green light traffic of a single straight signal lamp on a road section to be driven, so as to enhance applicability of a generated driving strategy and further improve efficiency of green light traffic prediction at an intersection.

Fig. 6 is a schematic structural diagram of a vehicle control apparatus according to an embodiment of the present disclosure.

As shown in fig. 6, the vehicle control apparatus 600 includes:

the obtaining module 601 is configured to obtain, when it is determined that a road section on which the vehicle is to travel includes a plurality of continuous straight traffic lights, current first traveling data of the vehicle, n first state data corresponding to n continuous traffic lights, respectively, and n first traffic data of n intersections where the n traffic lights are located, respectively, where the state data is determined based on historical state data of the traffic lights, the traffic data includes historical traffic data and/or current traffic data, and n is an integer greater than 1;

the determining module 602 is configured to determine a first driving policy of the vehicle passing through n intersections according to the first driving data, the n first state data, and the n first traffic data;

the control module 603 is configured to control the vehicle to continuously pass through n intersections according to the first driving policy, where the first driving policy indicates that the vehicle can continuously pass through the n intersections by green light.

In some embodiments, the determining module 602 may be further configured to:

and inputting the first driving data, the n first state data and the n first traffic data into a first prediction model corresponding to the preset n intersections to obtain a first driving strategy of the vehicle passing through the n intersections, wherein the first prediction model is a model generated by training on the basis of the historical traffic data of the n intersections, the historical state data of the n signal lamps and the track data of the vehicle passing through the n intersections.

In some embodiments, the determining module 602 may be further configured to:

under the condition that the first driving strategy indicates that a vehicle cannot pass through n intersections by continuous green lights, n-1 state data corresponding to continuous n-1 signal lamps in the driving direction of the vehicle, n-1 passing data of n-1 intersections where the n-1 signal lamps are respectively located, and first driving data are input into a second prediction model corresponding to the preset n-1 intersections, so that a second driving strategy of the vehicle passing through the n-1 intersections, which is output by the second prediction model, is obtained;

and under the condition that the second driving strategy indicates that the vehicle cannot continuously pass through n-1 intersections, the operation of acquiring the driving strategy is carried out based on n-2 state data and n-2 traffic data in a returning mode until the driving strategy that the vehicle passes through the first intersection is determined.

In some embodiments, the determining module 602 may be further configured to:

under the condition that the first driving strategy indicates that the vehicle can continuously pass through n intersections, determining the number m of continuous straight-going signal lamps contained in adjacent road sections after the n intersections according to the navigation route of the vehicle;

under the condition that m is larger than 1 and smaller than n, m state data corresponding to m signal lamps respectively are obtained, m traffic data of m intersections where the m signal lamps are respectively located, and second driving data before the vehicle is driven into an adjacent road section are obtained;

And inputting the second driving data, the m state data and the m traffic data into a third prediction model corresponding to the preset m intersections to obtain a third driving strategy of the vehicle passing through the m intersections, wherein the third driving strategy is output by the third prediction model.

In some embodiments, the determining module 602 may be further configured to:

under the condition that m is larger than n, n pieces of second state data corresponding to the first n signal lamps in the m signal lamps and n pieces of second traffic data of n intersections where the first n signal lamps are located are obtained;

and inputting the second driving data, the n second state data and the n second traffic data into a first prediction model corresponding to the preset n intersections to obtain a fourth driving strategy of the vehicle passing through the first n intersections, wherein the fourth driving strategy is output by the first prediction model.

In some embodiments, the above-mentioned acquisition module 601 may also be used to:

and under the condition that the distance between the vehicle and the first signal lamp in the n signal lamps is smaller than or equal to a distance threshold value, acquiring current first driving data of the vehicle.

determining the type of a road section to be driven;

and determining the value of n according to the type of the road section to be driven.

In some embodiments, the vehicle control apparatus 600 may be further configured to:

under the condition that a road section to be driven by the vehicle comprises a continuous straight-going signal lamp, under the condition that the distance between the vehicle and the signal lamp is smaller than or equal to a distance threshold value, third driving data of the vehicle, third state data of a signal and third traffic data of an intersection where the signal lamp is positioned are obtained;

inputting the third driving data, the third state data and the third driving data into a fourth prediction model corresponding to a preset single road port to obtain a fifth driving strategy of the vehicle passing through one road port, wherein the fifth driving strategy is output by the fourth prediction model;

the vehicle is controlled to continuously pass through an intersection according to a fifth driving strategy.

In some embodiments, the vehicle control apparatus 600 described above, wherein,

the travel data includes at least one of: the distance between the running speed and the signal lamp;

the status data includes at least one of: the current state of the signal lamp, the remaining time of the current state, the next state of the signal lamp, the duration of the next state and the period of the signal lamp;

the traffic data includes at least one of: average traffic speed, average traffic volume.

In some embodiments, the vehicle control device 600 described above, wherein the driving strategy includes at least one of: the method comprises the steps of indicating information of whether a green light can pass through an intersection, a driving speed range of the green light passing through the intersection, an accelerator pedal opening, a braking speed, a brake pedal opening and a braking distance.

It should be noted that the foregoing explanation of the vehicle control method is also applicable to the vehicle control device of the present embodiment, and is not repeated here.

In this embodiment, when it is determined that a road section on which a vehicle is to travel includes a plurality of continuous straight-going signal lamps, the vehicle control system acquires current first traveling data of the vehicle, n first state data corresponding to n signal lamps respectively, and n first traffic data of n intersections where the n signal lamps respectively are located, then determines a first traveling policy of the vehicle passing through the n intersections according to the first traveling data, the n first state data, and the n first traffic data, and then, when the first traveling policy indicates that the vehicle can continuously pass through the n intersections by green lights, controls the vehicle to continuously pass through the n intersections according to the first traveling policy. Therefore, through lower information interaction cost, the estimation of the running strategy that the vehicle continuously passes through a plurality of intersections can be realized, reliable basis is provided for improving running safety and reducing oil consumption, and the driving experience of a user is optimized. .

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 7 illustrates a schematic block diagram of an example electronic device 700 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in device 700 are connected to I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as method XX. For example, in some embodiments, the vehicle control method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the vehicle control method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the vehicle control method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise. In the description of the present disclosure, the words "if" and "if" are used to be interpreted as "at … …" or "at … …" or "in response to a determination" or "in the … … case".

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A vehicle control method comprising:

2. The method of claim 1, wherein the determining a first travel strategy for the vehicle to pass through the n intersections based on the first travel data, the n first status data, and the n first traffic data comprises:

And inputting the first driving data, the n first state data and the n first traffic data into a first prediction model corresponding to preset n intersections to obtain a first driving strategy of the vehicle passing through the n intersections, wherein the first prediction model is a model generated by training on the basis of historical traffic data of the n intersections, historical state data of the n signal lamps and track data of the vehicle passing through the n intersections.

3. The method of claim 1, wherein after the determining the first travel strategy for the vehicle to pass through the n intersections, further comprising:

when the first driving strategy indicates that the vehicle cannot pass through the n intersections by continuous green lights, inputting n-1 state data corresponding to n-1 signal lamps in the driving direction of the vehicle, n-1 traffic data of n-1 intersections where the n-1 signal lamps are located respectively, and the first driving data into a second prediction model corresponding to the preset n-1 intersections, so as to obtain a second driving strategy of the vehicle passing through the n-1 intersections, which is output by the second prediction model;

And under the condition that the second driving strategy indicates that the vehicle cannot continuously pass through the n-1 intersections, returning to execute the operation of acquiring the driving strategy based on the n-2 state data and the n-2 traffic data until determining the driving strategy of the vehicle passing through the first intersection.

4. The method of claim 1, wherein after the determining the first travel strategy for the vehicle to pass through the n intersections, further comprising:

determining the number m of continuous straight-going signal lamps contained in adjacent road sections after the n intersections according to the navigation route of the vehicle under the condition that the first driving strategy indicates that the vehicle can continuously pass through the n intersections by green lights;

under the condition that m is larger than 1 and smaller than n, m state data corresponding to the m signal lamps respectively are obtained, m traffic data of m intersections where the m signal lamps are respectively located, and second driving data before the vehicle is driven into the adjacent road section;

and inputting the second driving data, the m state data and the m traffic data into a third prediction model corresponding to the preset m intersections, so as to obtain a third driving strategy of the vehicle passing through the m intersections, wherein the third driving strategy is output by the third prediction model.

5. The method of claim 4, wherein after said determining the number m of consecutive straight-going signal lights contained in the adjacent road segments after said n intersections, further comprising:

when m is larger than n, n pieces of second state data corresponding to the first n signal lamps in the m signal lamps respectively and n pieces of second traffic data of n intersections where the first n signal lamps are located respectively are obtained;

and inputting the second driving data, the n second state data and the n second traffic data into a first prediction model corresponding to preset n intersections to obtain a fourth driving strategy of the vehicle passing through the first n intersections, wherein the fourth driving strategy is output by the first prediction model.

6. The method of claim 1, wherein the obtaining current first travel data of the vehicle comprises:

7. The method of claim 1, wherein prior to the acquiring the current first driving data of the vehicle and the n state data corresponding to the n signal lamps, further comprising:

Determining the type of the road section to be driven;

and determining the value of the n according to the type of the road section to be driven.

8. The method of any of claims 1-7, further comprising:

under the condition that a road section to be driven by the vehicle comprises one continuous straight running signal lamp, under the condition that the distance between the vehicle and the signal lamp is smaller than or equal to a distance threshold value, acquiring third driving data of the vehicle, third state data of the signal and third traffic data of an intersection where the signal lamp is positioned;

inputting the third driving data, the third state data and the third traffic data into a fourth prediction model corresponding to a preset single road junction to obtain a fifth driving strategy of the vehicle passing through the one road junction, wherein the fifth driving strategy is output by the fourth prediction model;

and controlling the vehicles to continuously pass through the intersection according to the fifth driving strategy.

9. The method of any one of claim 1 to 7, wherein,

10. The method of claim 9, wherein the driving strategy comprises at least one of: the method comprises the steps of indicating information of whether a green light can pass through an intersection, a driving speed range of the green light passing through the intersection, an accelerator pedal opening, a braking speed, a brake pedal opening and a braking distance.

11. A vehicle control apparatus comprising:

12. The apparatus of claim 11, wherein the means for determining is further for:

13. The apparatus of claim 11, wherein the means for determining is further for:

14. The apparatus of claim 11, wherein the means for determining is further for:

15. The apparatus of claim 14, wherein the means for determining is further for:

16. The apparatus of claim 11, wherein the acquisition module is further configured to:

17. The apparatus of claim 11, wherein the acquisition module is further configured to:

determining the type of the road section to be driven;

18. The apparatus of any of claims 11-17, further comprising:

19. The apparatus of any one of claim 11 to 17, wherein,

20. The apparatus of claim 19, wherein the travel strategy comprises at least one of: the method comprises the steps of indicating information of whether a green light can pass through an intersection, a driving speed range of the green light passing through the intersection, an accelerator pedal opening, a braking speed, a brake pedal opening and a braking distance.

21. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle control method of any one of claims 1-10.

22. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are for causing the computer to perform the vehicle control method of any one of claims 1-10.

23. A computer program product comprising a computer program which, when executed by a processor, implements the steps of the vehicle control method according to any one of claims 1-10.