CN117058904A

CN117058904A - Vehicle control method, device, electronic equipment and storage medium

Info

Publication number: CN117058904A
Application number: CN202311110156.0A
Authority: CN
Inventors: 邱亚星; 刘子昊; 刘艳荣; 孙明芳; 白红霞
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: 2023-11-14

Abstract

The disclosure provides a vehicle control method, a vehicle control device, electronic equipment and a storage medium, relates to the technical field of computers, and particularly relates to the technical field of artificial intelligence such as intelligent transportation, cloud computing, big data, deep learning and the like. Comprising the following steps: under the condition that the road section to be driven by the vehicle meets the preset condition, current first driving data of the vehicle, first state data of a first signal lamp and first passing data of a first intersection where the first signal lamp is located are obtained, then a first driving strategy of the vehicle passing through the first intersection is determined according to the obtained data, then under the condition that the first driving strategy indicates that the vehicle can pass through the first intersection, second state data of a second signal lamp and second passing data of a second intersection where the second signal lamp is located are obtained, then a second driving strategy of the vehicle is determined according to a plurality of items in the obtained data, and then a target driving strategy of the vehicle passing through the first intersection and/or the second intersection is determined based on the first driving strategy and the second driving strategy.

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

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 meets a preset condition, current first driving data of the vehicle, first state data of a first signal lamp in the driving direction of the vehicle and first traffic data of a first intersection where the first signal lamp is located are obtained, wherein the first state data is determined based on historical state data of the first signal lamp, and the traffic data comprises historical traffic data and/or current traffic data;

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

acquiring second state data of a second signal lamp in the vehicle driving direction and second traffic data of a second intersection where the second signal lamp is located under the condition that the first driving strategy indicates that the vehicle can pass through the first intersection, wherein the second state data is determined based on historical state data of the second signal lamp;

determining a second driving strategy of the vehicle according to a plurality of items of the first driving data, the first state data, the first passing data, the second state data and the second passing data;

and determining a target driving strategy of the vehicle passing through the first intersection and/or the second intersection based on the first driving strategy and the second driving strategy.

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

the first acquisition module is used for acquiring current first driving data of the vehicle, first state data of a first signal lamp in the driving direction of the vehicle and first traffic data of a first intersection where the first signal lamp is located under the condition that a road section to be driven by the vehicle meets preset conditions, wherein the first state data is determined based on historical state data of the first signal lamp, and the traffic data comprises historical traffic data and/or current traffic data;

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

the second obtaining module is used for obtaining second state data of a second signal lamp in the driving direction of the vehicle and second traffic data of a second intersection where the second signal lamp is located under the condition that the first driving strategy indicates that the vehicle can pass through the first intersection, wherein the second state data is determined based on historical state data of the second signal lamp;

a second determining module, configured to determine a second driving policy of the vehicle according to a plurality of the first driving data, the first state data, the first traffic data, the second state data, and the second traffic data;

and the third determining module is used for determining a target driving strategy of the vehicle passing through the first intersection and/or the second intersection based on the first driving strategy and the second driving strategy.

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 to be driven by a vehicle meets a preset condition, current first driving data of the vehicle, first state data of a first signal lamp in a driving direction of the vehicle and first traffic data of a first intersection where the first signal lamp is located are obtained, then a first driving strategy of the vehicle passing through the first intersection is determined according to the first driving data, the first state data and the first traffic data, and under the condition that the first driving strategy indicates that the vehicle can pass through the first intersection, second state data of a second signal lamp in the driving direction of the vehicle and second traffic data of a second intersection where the second signal lamp is located are obtained, then a second driving strategy of the vehicle is determined according to a plurality of items of the first driving data, the first state data, the first traffic data, the second state data and the second traffic data, and then a target driving strategy of the vehicle passing through the first intersection and/or the second intersection is determined based on the first driving strategy and the second driving strategy. Therefore, through lower information interaction cost, estimation of the driving strategy of the vehicle passing through a single intersection and two continuous intersections can be realized, reliable basis is provided for improving the driving safety of the vehicle and reducing the 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 schematic structural view of a vehicle control apparatus according to an embodiment of the present disclosure;

fig. 5 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: and under the condition that the road section to be driven by the vehicle meets the preset condition, acquiring current first driving data of the vehicle, first state data of a first signal lamp in the driving direction of the vehicle and first traffic data of a first intersection where the first signal lamp is located.

Wherein the first status data is determined based on historical status data of the first signal lamp, and the traffic data comprises historical traffic data and/or current traffic data.

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 efficiency of determining the driving strategy is improved. Meanwhile, in general, 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 out the current first state data of the first signal lamp based on the historical state data of the first signal lamp, and the first state data is not required to be acquired by information interaction with multiple parties, thereby reducing the information communication cost for acquiring the state data of the signal lamp.

The average traffic speed and the average traffic volume may be an average value of the first intersection in all the historical periods, or may be a historical average traffic speed and a historical average traffic volume corresponding to the current 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 the first intersection can be used as the first traffic data, or the current traffic speed and the current traffic volume of the first 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.

Optionally, the vehicle control system may determine, after acquiring the current navigation route and the current position of the vehicle, a road section to be driven by the vehicle according to the current position and the navigation route of the vehicle, and then determine that the road section to be driven meets a preset condition when the road section to be driven is a straight road section and the road section to be driven includes a plurality of continuous signal lamps. Therefore, unnecessary resource waste is avoided by making preset conditions for triggering the driving strategy decision, and the effect and efficiency of the driving strategy decision are improved.

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 a road section to be driven by a vehicle meets a preset condition, the current driving speed v of the vehicle and the distance s from the first signal lamp are obtained, and first state data of the first signal lamp (for example, the current state of the signal lamp is a green lamp, the remaining time of the current state is 20s, the next state is a yellow lamp, the duration of the next state is 10s, etc.) and first traffic data of an intersection i where the first signal lamp is located are obtained (as shown in fig. 2), fig. 2 is a schematic diagram of the road section to be driven by the vehicle, wherein after the first intersection i is located on the road section to be driven, the road section to be driven may further include that the intersection where the second signal lamp is located is i+1, and the intersection where the third signal lamp is located is i+2.

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 is smaller than 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 accords with the preset condition, the vehicle control system may first start the driving strategy prediction function, monitor the distance change between the vehicle and the first signal lamp, 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 signal lamp is smaller than 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.

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

In the embodiment of the disclosure, the vehicle control system may input the first driving data, the first state data and the first traffic data into a pre-trained prediction model to obtain a first driving strategy of the vehicle passing through the first intersection, which is output by the prediction model.

The 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 by the present disclosure.

In the embodiment of the disclosure, before the prediction of the driving strategy is performed on line, the historical track data of the vehicle passing through each straight road section, the historical state data of each signal lamp corresponding to each track data and the historical traffic data of each intersection can be acquired, then the historical track data is labeled by using the posterior information of whether the vehicle passes through the intersection or not in each track data, a training data set is constructed, and then the model is trained according to the data set to obtain a prediction model.

S103: and under the condition that the first driving strategy indicates that the vehicle can pass through the first intersection, acquiring second state data of a second signal lamp in the driving direction of the vehicle and second traffic data of a second intersection where the second signal lamp is positioned.

Wherein the second status data is determined based on historical status data of the second signal lamp.

In the embodiment of the disclosure, when the first driving strategy predicts that the vehicle can pass through the first intersection by green light, the vehicle control system can continuously acquire the second state data of the second signal lamp and the second passing data of the second intersection where the second signal lamp is located on the navigation route, and then judges whether the vehicle can continuously pass through the first intersection and the second intersection.

S104: and determining a second driving strategy of the vehicle according to the first driving data, the first state data, the first traffic data, the second state data and the second traffic data.

In the embodiment of the disclosure, when the model is trained, not only can the model be trained by using the related data of a single intersection to generate a prediction model for whether the single intersection can pass through by a green light, but also the model can be jointly trained by using the data of two intersections to generate a model capable of predicting whether the two intersections can pass through a continuous green light, so that the vehicle control system can input multiple items of first driving data, first state data, first passing data, second state data and second passing data into the corresponding prediction model to obtain a second driving strategy of the vehicle.

Alternatively, the vehicle control system may determine a second driving strategy for the vehicle passing through the second intersection based on the first driving data, the second state data, and the second communication data. Alternatively, the second driving policy for the vehicle to continuously pass through the first intersection and the second intersection may be determined based on the first driving data, the first state data, the first traffic data, the second state data, and the second traffic data. Therefore, the second driving strategy can be obtained from multiple modes to judge whether the vehicle can continuously pass through the second intersection, the accuracy and the reliability of the determined driving strategy are further improved, and the possibility of strategy decision errors is reduced.

In the embodiment of the disclosure, the vehicle control system may input the first driving data, the second state data and the second traffic data to the first prediction model corresponding to the second intersection, so as to obtain a second driving policy of the vehicle passing through the second intersection, which is output by the first prediction model.

The first prediction model is a prediction model for predicting a second intersection driving strategy, which is generated through training according to signal lamp historical state data, historical traffic data and vehicle historical track data of the second intersection.

Or, the vehicle control system may also input the first driving data, the first state data, the first traffic data, the second state data and the second traffic data into the second prediction model to obtain a second driving strategy that the vehicle output by the second prediction model continuously passes through the first intersection and the second intersection, where the second prediction model is a model generated based on data joint training associated with the first intersection and the second intersection respectively. Therefore, the efficiency of determining the driving strategy can be improved through the corresponding prediction model, and the driving experience is improved.

S105: and determining a target driving strategy of the vehicle passing through the first intersection and/or the second intersection based on the first driving strategy and the second driving strategy.

In the embodiment of the disclosure, the vehicle control system may combine the generated first driving strategy and the second driving strategy to obtain the target driving strategy to determine whether the vehicle can pass through the first intersection only by green light or whether the vehicle can pass through the first intersection and the second intersection by continuous green light.

Alternatively, the vehicle control system may determine the target travel strategy for the vehicle to pass through the first intersection based on the first travel strategy if the second travel strategy indicates that the vehicle cannot pass through the second intersection with the first travel strategy indicating that the vehicle can pass through the first intersection with the green light.

Alternatively, when the second driving strategy indicates that the vehicle cannot continuously pass through the first intersection and the second intersection by the green light, and the first driving strategy indicates that the vehicle can pass through the first intersection by the green light, the target driving strategy of the vehicle passing through the first intersection may be determined based on the first driving strategy.

Or, if the second driving strategy indicates that the vehicle can continuously pass through the first intersection and the second intersection, determining the target driving strategy that the vehicle continuously passes through the first intersection and the second intersection based on the second driving strategy.

In the embodiment of the disclosure, the vehicle control system can determine different target driving strategies according to different conditions of the determined first driving strategy and the determined second driving strategy, so that the comprehensiveness and accuracy of the driving strategies of two continuous intersections are improved, the optimal driving strategy is obtained, and the driving experience of a user is further improved.

In the embodiment of the disclosure, after determining the target driving strategy, the vehicle control system may broadcast the target driving strategy to the user, and the user controls the vehicle based on the target driving strategy. Alternatively, if in an autonomous driving scenario, the vehicle control system may control the vehicle directly based on the target driving strategy.

In this embodiment, when it is determined that a road section to be driven by the vehicle meets a preset condition, the vehicle control system acquires current first driving data of the vehicle, first state data of a first signal lamp in a driving direction of the vehicle, and first traffic data of a first intersection where the first signal lamp is located, then determines a first driving strategy of the vehicle passing through the first intersection according to the first driving data, the first state data, and the first traffic data, and when the first driving strategy indicates that the vehicle can pass through the first intersection, acquires second state data of a second signal lamp in the driving direction of the vehicle, and second traffic data of a second intersection where the second signal lamp is located, then determines a second driving strategy of the vehicle according to a plurality of items of the first driving data, the first state data, the first traffic data, the second state data, and the second traffic data, and then determines a target driving strategy of the vehicle passing through the first intersection and/or the second intersection based on the first driving strategy and the second driving strategy. Therefore, through lower information interaction cost, estimation of the driving strategy of the vehicle passing through a single intersection and two continuous intersections can be realized, reliable basis is provided for improving the driving safety of the vehicle and reducing the 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: and under the condition that the road section to be driven by the vehicle meets the preset condition, acquiring current first driving data of the vehicle, first state data of a first signal lamp in the driving direction of the vehicle and first traffic data of a first intersection where the first signal lamp is located.

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

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

S303: and under the condition that the first driving strategy indicates that the vehicle cannot pass through the first intersection, acquiring second driving data of the vehicle, wherein the second driving data is the driving data when the distance between the vehicle and the second signal lamp is smaller than a distance threshold value.

In this embodiment of the present disclosure, when the vehicle control system determines that the vehicle cannot pass through the first intersection by using the green light, the distance between the vehicle and the second signal lamp may be monitored after the vehicle passes through the first intersection, and then when the distance is smaller than the distance threshold, information such as the running speed of the vehicle at this time may be obtained as the second running data.

S304: and acquiring second state data of a second signal lamp in the driving direction of the vehicle and second traffic data of a second intersection where the second signal lamp is located, wherein the second state data is determined based on historical state data of the second signal lamp.

The description of S304 may be specifically referred to the above embodiments, and will not be repeated here.

S305: and determining a third driving strategy for the vehicle to pass through the second intersection based on the second driving data, the second state data and the second communication data.

It should be noted that, when it is determined according to the third driving policy that the vehicle can pass through the second intersection by green light, the vehicle control system may further obtain related data of the third signal light and the third intersection, predict whether the vehicle can pass through the third intersection by green light, or predict whether the vehicle can pass through the second intersection and the third intersection by continuous green light at the same time; otherwise, when the third driving strategy indicates that the vehicle cannot pass through the second intersection, returning to execute S303, obtaining driving data when the distance between the vehicle and the third signal lamp is smaller than the distance threshold value, and determining the driving strategy. That is, in the present disclosure, the vehicle control system may predict two successive straight crossings at a time until it is completed to predict whether all traffic lights on the straight road section to be driven can pass by green light.

In this embodiment, the vehicle control system obtains second driving data of the vehicle when the first driving policy indicates that the vehicle cannot pass through the first intersection, then obtains second state data of a second signal lamp in the driving direction of the vehicle and second traffic data of a second intersection where the second signal lamp is located, and then determines a third driving policy that the vehicle passes through the second intersection based on the second driving data, the second state data and the second traffic data. Therefore, after the intersection can not be passed by the green light, the running strategy can be determined based on new vehicle running data, the accuracy of the generated running strategy is further improved, and the intermittent prediction of the traffic conditions of a plurality of intersections can be realized.

Fig. 4 is a schematic structural diagram of a vehicle control device according to an embodiment of the present disclosure.

As shown in fig. 4, the vehicle control apparatus 400 includes:

the first obtaining module 401 is configured to obtain, when it is determined that a road section on which the vehicle is to be driven meets a preset condition, current first driving data of the vehicle, first state data of a first signal lamp in a driving direction of the vehicle, and first traffic data of a first intersection at which the first signal lamp is located, where the first state data is determined based on historical state data of the first signal lamp, and the traffic data includes historical traffic data and/or current traffic data;

A first determining module 402, configured to determine a first driving strategy of the vehicle passing through the first intersection according to the first driving data, the first state data, and the first traffic data;

a second obtaining module 403, configured to obtain, when the first driving policy indicates that the vehicle can pass through the first intersection, second state data of a second signal lamp in a driving direction of the vehicle and second traffic data of a second intersection where the second signal lamp is located, where the second state data is determined based on historical state data of the second signal lamp;

a second determining module 404, configured to determine a second driving policy of the vehicle according to the first driving data, the first state data, the first traffic data, the second state data, and a plurality of items of the second traffic data;

the third determining module 405 is configured to determine a target driving policy of the vehicle passing through the first intersection and/or the second intersection based on the first driving policy and the second driving policy.

In some embodiments, the first obtaining module 401 may be further configured to:

acquiring a current navigation route of a vehicle and a current position of the vehicle;

determining a road section to be driven by the vehicle according to the current position and the navigation route of the vehicle;

And under the condition that the road section to be driven is a straight road section and the road section to be driven comprises a plurality of signal lamps, determining that the road section to be driven meets the preset condition.

In some embodiments, the second determining module 404 may be further configured to:

determining a second driving strategy of the vehicle passing through a second intersection according to the first driving data, the second state data and the second communication data; or,

and determining a second driving strategy for the vehicle to continuously pass through the first intersection and the second intersection according to the first driving data, the first state data, the first passing data, the second state data and the second passing data.

inputting the first driving data, the second state data and the second traffic data into a first prediction model corresponding to the second intersection to obtain a second driving strategy of the vehicle passing through the second intersection, wherein the second driving strategy is output by the first prediction model; or,

and inputting the first driving data, the first state data, the first passing data, the second state data and the second passing data into a second prediction model to obtain a second driving strategy that the vehicle output by the second prediction model continuously passes through the first intersection and the second intersection, wherein the second prediction model is a model generated by combined training with data respectively associated with the first intersection and the second intersection.

In some embodiments, the vehicle control apparatus 400 described above, wherein the driving 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.

and under the condition that the distance between the vehicle and the first signal lamp is smaller than the distance threshold value, acquiring current first driving data of the vehicle.

In some embodiments, the first determining module 402 may be further configured to:

acquiring second driving data of the vehicle under the condition that the first driving strategy indicates that the vehicle cannot pass through the first intersection, wherein the second driving data is the driving data when the distance between the vehicle and the second signal lamp is smaller than a distance threshold value;

and determining a third driving strategy for the vehicle to pass through the second intersection based on the second driving data, the second state data and the second communication data.

In some embodiments, the third determining module 405 may be further configured to:

Determining a target driving strategy of the vehicle passing through the first intersection based on the first driving strategy under the condition that the second driving strategy indicates that the vehicle cannot pass through the second intersection by green light and the first driving strategy indicates that the vehicle can pass through the first intersection by green light; or,

determining a target driving strategy of the vehicle passing through the first intersection based on the first driving strategy under the condition that the second driving strategy indicates that the vehicle cannot continuously pass through the first intersection and the second intersection by green light and the first driving strategy indicates that the vehicle can pass through the first intersection by green light; or,

and determining a target driving strategy for the vehicle to continuously pass through the first intersection and the second intersection based on the second driving strategy under the condition that the second driving strategy indicates that the vehicle can continuously pass through the first intersection and the second intersection by green light.

In some embodiments, the vehicle control apparatus 400 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.

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. 5 illustrates a schematic block diagram of an example electronic device 500 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. 5, the apparatus 500 includes a computing unit 501 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 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 501 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 storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the vehicle control method described above may be performed. Alternatively, in other embodiments, the computing unit 501 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 that the road segment on which the vehicle is to travel meets the preset condition comprises:

acquiring a current navigation route of the vehicle and a current position of the vehicle;

determining a road section to be driven by the vehicle according to the current position of the vehicle and the navigation route;

3. The method of claim 1, wherein the determining a second driving strategy for the vehicle based on a plurality of the first driving data, the first status data, the first traffic data, the second status data, and the second traffic data comprises:

determining a second driving strategy of the vehicle passing through the second intersection according to the first driving data, the second state data and the second traffic data; or,

and determining a second driving strategy for the vehicle to continuously pass through the first intersection and the second intersection according to the first driving data, the first state data, the first traffic data, the second state data and the second traffic data.

4. The method of claim 3, wherein the determining a second travel strategy for the vehicle based on a plurality of the first travel data, the first status data, the first traffic data, the second status data, and the second traffic data comprises:

and inputting the first driving data, the first state data, the first traffic data, the second state data and the second traffic data into a second prediction model to obtain a second driving strategy that the vehicle output by the second prediction model continuously passes through the first intersection and the second intersection, wherein the second prediction model is a model generated by combined training based on the data respectively associated with the first intersection and the second intersection.

5. The method of claim 1, wherein,

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

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

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

7. The method of claim 6, wherein after the determining the first travel strategy for the vehicle to pass through the first intersection, further comprising:

acquiring second driving data of the vehicle under the condition that the first driving strategy indicates that the vehicle cannot pass through the first intersection by green light, wherein the second driving data is driving data when the distance between the vehicle and a second signal lamp is smaller than the distance threshold;

and determining a third driving strategy of the vehicle passing through the second intersection based on the second driving data, the second state data and the second traffic data.

8. The method of any of claims 1-7, wherein the determining a target travel strategy for the vehicle to pass through the first intersection and/or the second intersection based on the first travel strategy and the second travel strategy comprises:

determining a target driving strategy for the vehicle to pass through the first intersection based on the first driving strategy when the second driving strategy indicates that the vehicle cannot pass through the second intersection by a green light and the first driving strategy indicates that the vehicle can pass through the first intersection by a green light; or,

determining a target driving strategy of the vehicle passing through the first intersection based on the first driving strategy when the second driving strategy indicates that the vehicle cannot continuously pass through the first intersection and the second intersection by a green light and the first driving strategy indicates that the vehicle can pass through the first intersection by a green light; or,

and determining a target driving strategy for the vehicle to continuously pass through the first intersection and the second intersection based on the second driving strategy under the condition that the second driving strategy indicates that the vehicle can continuously pass through the first intersection and the second intersection by using green light.

9. The method of claim 8, 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.

10. A vehicle control apparatus comprising:

11. The apparatus of claim 10, wherein the first acquisition module is further configured to:

12. The apparatus of claim 10, wherein the second determination module is further configured to:

13. The apparatus of claim 12, wherein the second determination module is further configured to:

14. The apparatus of claim 10, wherein,

15. The apparatus of claim 10, wherein the first acquisition module is further configured to:

16. The apparatus of claim 15, wherein the first determination module is further configured to:

17. The apparatus of any of claims 10-16, wherein the third determining module is further configured to:

18. The apparatus of claim 17, 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.

19. 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 1-9.

20. 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 1-9.

21. 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 1-9.