1. An automatic driving method, characterized by comprising the steps of:

acquiring current position information and destination information of a vehicle;

determining a plurality of preselected paths according to the current position information and the destination information;

acquiring traffic conditions of a plurality of preselected paths;

according to the traffic conditions of a plurality of preselected paths, evaluating the driving automation degrees of the preselected paths to obtain a path with the highest driving automation degree as an automatic driving path, wherein the driving automation degree is represented by the interference degree of a driver in the driving process;

and controlling the vehicle to drive according to the automatic driving path.

2. The method of claim 1, further comprising:

when a plurality of paths with the highest driving automation degree exist, the driving time of the plurality of paths with the highest driving automation degree is estimated;

and selecting the path with the shortest driving time from the plurality of paths with the highest driving automation degree as an automatic driving path.

3. The method of claim 1, wherein the evaluating the driving automation degree of the plurality of preselected paths according to the traffic condition of the plurality of preselected paths to obtain the path with the highest driving automation degree as the automatic driving path comprises:

acquiring multi-terminal cooperation information, wherein the multi-terminal cooperation information comprises at least one of vehicle-side information, road-side information and cloud-side information;

and constructing a digital twin model of the multiple preselected paths according to the multi-terminal cooperation information, and evaluating the driving automation degree of the multiple preselected paths according to a digital twin result to obtain a path with the highest driving automation degree as an automatic driving path.

4. The method of claim 2, wherein the predicting driving times for the plurality of paths with the highest degree of driving automation comprises:

constructing a digital twin model of the multiple paths with the highest automation degree according to multi-terminal cooperation information, wherein the multi-terminal cooperation information comprises at least one of cloud information, road-side information and vehicle-side information;

predicting the congestion conditions of the multiple paths according to the digital twin model;

and predicting the driving time of the multiple paths with the highest automation degree according to the congestion conditions of the multiple paths.

5. The method of claim 2, wherein the predicting driving times for the plurality of paths with the highest degree of driving automation comprises:

when the multiple paths with the highest driving automation degree comprise paths passing through traffic lights and vehicles are predicted to be in a no-passing state when reaching the traffic lights, lane changing of the vehicles is predicted, when the vehicles run on any one of lanes in a passing state under the jurisdiction of the traffic lights, the vehicles pass through the time of the road section under the jurisdiction of the traffic lights and the vehicles run on the original lane, and when the traffic lights are waited to be converted from the no-passing state to a running state, the vehicles pass through the time of the road section under the jurisdiction of the traffic lights;

and selecting the shortest time of the vehicle passing through the traffic light in the jurisdiction section as the driving time passing through the traffic light.

6. An autopilot device, comprising:

the information acquisition module is used for acquiring the current position information and the destination information of the vehicle;

the preselected path determining module is used for determining a plurality of preselected paths according to the current position information and the destination information;

the traffic condition determining module is used for acquiring traffic conditions of a plurality of preselected paths;

the automatic evaluation module is used for evaluating the driving automation degrees of a plurality of preselected paths according to the traffic conditions of the preselected paths to obtain the path with the highest driving automation degree as an automatic driving path, and the driving automation degree is represented by the interference degree of a driver in the driving process;

and the driving module is used for controlling the vehicle to drive according to the automatic driving path.

7. The apparatus of claim 6, further comprising:

the driving time evaluation module is used for predicting the driving time of a plurality of paths with the highest driving automation degree when the paths with the highest driving automation degree have a plurality of paths;

and the path selection module is used for selecting the path with the shortest driving time from the plurality of paths with the highest driving automation degree as the automatic driving path.

8. The apparatus of claim 6, wherein the automated evaluation module comprises:

the first cooperation information acquisition module is used for acquiring multi-terminal cooperation information, and the multi-terminal cooperation information comprises at least one of vehicle-side information, road-side information and cloud information;

and the automatic evaluation submodule is used for constructing a digital twinning model of the multiple preselected paths according to the multi-terminal cooperation information, evaluating the driving automation degree of the multiple preselected paths according to a digital twinning result, and obtaining a path with the highest driving automation degree as an automatic driving path.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the autopilot method of any of claims 1-5 are implemented when the program is executed by the processor.

10. A storage medium having computer instructions stored thereon, wherein the instructions, when executed by a processor, implement the steps of the autopilot method of any of claims 1-5.

Background

The automatic driving technology mainly adopts single-vehicle intelligence, one vehicle is used as a terminal, and driving behaviors are judged according to surrounding conditions. With the continuous iterative promotion of automobile electronization and hard software technology, the automatic driving technology is also greatly improved. However, the current automatic driving technology does not consider the traffic situation, and often drives according to a certain specific route, but the specific route may have accidents such as traffic accidents and temporary traffic control, and the related technology generally requires the driver to take over manually, resulting in low intelligent degree of automatic driving.

Disclosure of Invention

In view of this, embodiments of the present invention provide an automatic driving method, an automatic driving device, and an electronic device, so as to overcome the defect of low intelligent degree of automatic driving in the prior art.

According to a first aspect, an embodiment of the present invention provides an automatic driving method, including the steps of: acquiring current position information and destination information of a vehicle; determining a plurality of preselected paths according to the current position information and the destination information; acquiring traffic conditions of a plurality of preselected paths; according to the traffic conditions of a plurality of preselected paths, evaluating the driving automation degrees of the preselected paths to obtain a path with the highest driving automation degree as an automatic driving path, wherein the driving automation degree is represented by the interference degree of a driver in the driving process; and controlling the vehicle to drive according to the automatic driving path.

Optionally, the automatic driving method further comprises: when a plurality of paths with the highest driving automation degree exist, the driving time of the plurality of paths with the highest driving automation degree is estimated; and selecting the path with the shortest driving time from the plurality of paths with the highest driving automation degree as an automatic driving path.

Optionally, the evaluating the driving automation degrees of the multiple preselected paths according to the traffic conditions of the multiple preselected paths to obtain a path with the highest driving automation degree as an automatic driving path includes: acquiring multi-terminal cooperation information, wherein the multi-terminal cooperation information comprises at least one of vehicle-side information, road-side information and cloud-side information; and constructing a digital twin model of the multiple preselected paths according to the multi-terminal cooperation information, and evaluating the driving automation degree of the multiple preselected paths according to a digital twin result to obtain a path with the highest driving automation degree as an automatic driving path.

Optionally, the predicting the driving time of the plurality of paths with the highest driving automation degree includes: constructing a digital twin model of the multiple paths with the highest automation degree according to multi-terminal cooperation information, wherein the multi-terminal cooperation information comprises at least one of cloud information, road-side information and vehicle-side information; predicting the congestion conditions of the multiple paths according to the digital twin model; and predicting the driving time of the multiple paths with the highest automation degree according to the congestion conditions of the multiple paths.

Optionally, the predicting the driving time of the plurality of paths with the highest driving automation degree includes: when the multiple paths with the highest driving automation degree comprise paths passing through traffic lights and vehicles are predicted to be in a no-passing state when reaching the traffic lights, lane changing of the vehicles is predicted, when the vehicles run on any one of lanes in a passing state under the jurisdiction of the traffic lights, the vehicles pass through the time of the road section under the jurisdiction of the traffic lights and the vehicles run on the original lane, and when the traffic lights are waited to be converted from the no-passing state to a running state, the vehicles pass through the time of the road section under the jurisdiction of the traffic lights; and selecting the shortest time of the vehicle passing through the traffic light in the jurisdiction section as the driving time passing through the traffic light.

According to a second aspect, an embodiment of the present invention provides an automatic driving apparatus, including: the information acquisition module is used for acquiring the current position information and the destination information of the vehicle; the preselected path determining module is used for determining a plurality of preselected paths according to the current position information and the destination information; the traffic condition determining module is used for acquiring traffic conditions of a plurality of preselected paths; the automatic evaluation module is used for evaluating the driving automation degrees of a plurality of preselected paths according to the traffic conditions of the preselected paths to obtain the path with the highest driving automation degree as an automatic driving path, and the driving automation degree is represented by the interference degree of a driver in the driving process; and the driving module is used for controlling the vehicle to drive according to the automatic driving path.

Optionally, the automatic driving device further comprises: the driving time evaluation module is used for predicting the driving time of a plurality of paths with the highest driving automation degree when the paths with the highest driving automation degree have a plurality of paths; and the path selection module is used for selecting the path with the shortest driving time from the plurality of paths with the highest driving automation degree as the automatic driving path.

Optionally, an automated evaluation module comprising: the first cooperation information acquisition module is used for acquiring multi-terminal cooperation information, and the multi-terminal cooperation information comprises at least one of vehicle-side information, road-side information and cloud information; and the automatic evaluation submodule is used for constructing a digital twinning model of the multiple preselected paths according to the multi-terminal cooperation information, evaluating the driving automation degree of the multiple preselected paths according to a digital twinning result, and obtaining a path with the highest driving automation degree as an automatic driving path.

According to a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the automatic driving method according to the first aspect or any one of the embodiments of the first aspect when executing the program.

According to a fourth aspect, an embodiment of the present invention provides a storage medium having stored thereon computer instructions that, when executed by a processor, implement the steps of the automatic driving method according to the first aspect or any of the embodiments of the first aspect.

The technical scheme of the invention has the following advantages:

according to the automatic driving method provided by the embodiment, the traffic conditions of all the preselected paths are obtained in advance, and the automatic driving degree of the vehicle running on the paths is determined according to the traffic conditions, so that the paths with the highest automatic driving degree are selected for driving.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a specific example of an automatic driving method in the embodiment of the invention;

FIG. 2 is a diagram of an exemplary embodiment of an automatic driving method according to the present invention;

FIG. 3 is a diagram illustrating an exemplary embodiment of an automatic driving method according to the present invention;

FIG. 4 is a functional block diagram of a particular example of an autopilot device in an embodiment of the present invention;

fig. 5 is a schematic block diagram of a specific example of an electronic device in the embodiment of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

The embodiment provides an automatic driving method, which may be applied to a vehicle terminal, a road side, or a cloud side, and is not limited in the embodiment, as shown in fig. 1, including the following steps:

s101, acquiring current position information and destination information of a vehicle;

for example, the current position information of the vehicle may be acquired by acquiring positioning information loaded on the vehicle, such as GPS navigation information. The destination information may be set by the user in advance, and the manner of acquiring the vehicle destination information may be to acquire the setting information of the user or the destination information set in the reservation information. The present embodiment does not limit the manner of obtaining the current position information and the destination information of the vehicle, and those skilled in the art can determine the information as needed.

S102, determining a plurality of preselected paths according to the current position information and the destination information;

for example, according to the current position information and the destination information, the manner of determining the plurality of preselected paths may be to combine a plurality of sets of feasible routes in an exhaustive manner by taking the current position as a starting point and the destination position as an end point in a map; the method may further include selecting a plurality of groups of routes ranked in front as preselected routes according to a recommendation degree given by a third-party map application, where the current position is used as a starting point and the destination position is used as an end point in the map, and the method does not limit a manner of determining a plurality of preselected routes according to the current position information and the destination information, and those skilled in the art can determine the preselected routes as needed.

S103, acquiring traffic conditions of a plurality of pre-selected paths; the manner in which the traffic conditions for the plurality of preselected routes are obtained may be from urban traffic monitoring data.

S104, evaluating the driving automation degrees of the multiple preselected paths according to the traffic conditions of the multiple preselected paths to obtain a path with the highest driving automation degree as an automatic driving path, wherein the driving automation degree is represented by the intervention degree of a driver in the driving process;

for example, the degree of automation of the driving may depend on the probability or number of times that a human take over/intervention is required while in the course of the automated driving, a lower probability or a lower number of times of human take over/intervention indicating a higher degree of automation, otherwise a lower degree of automation. The manner of evaluating the driving automation degrees of the multiple preselected paths may be to count factors affecting vehicle driving on each preselected path, such as traffic jam conditions, congestion duration, and emergency events (traffic accidents, temporary traffic control, etc.), assign a value to each factor affecting vehicle driving, set a corresponding weight for each factor, perform weighting processing on all factors on the preselected paths to obtain a calculated value, and may reflect the automated driving degree through the calculated value, where a higher calculated value may indicate a lower automated driving degree. The present embodiment does not limit the manner of evaluating the driving automation degree of a plurality of preselected routes according to the traffic conditions of the plurality of preselected routes, and those skilled in the art can determine the driving automation degree as needed. And selecting the path corresponding to the minimum value of the calculation result in the plurality of pre-selected paths as the automatic driving path.

And S105, controlling the vehicle to drive according to the automatic driving route. The manner of controlling the vehicle to drive according to the automatic driving route may be that a control instruction is issued to an ECU unit of the vehicle, and the control instruction is executed by the ECU unit.

According to the automatic driving method provided by the embodiment, the traffic conditions of all the preselected paths are obtained in advance, and the automatic driving degree of the vehicle running on the paths is determined according to the traffic conditions, so that the paths with the highest automatic driving degree are selected for driving.

Since there may be a plurality of routes with the highest degree of driving automation when evaluating the degree of driving automation, in order to further shorten the route driving time, as an optional implementation manner of this embodiment, the method further includes:

when a plurality of paths with the highest driving automation degree exist, the driving time of the plurality of paths with the highest driving automation degree is estimated; and selecting the path with the shortest driving time from the plurality of paths with the highest driving automation degree as an automatic driving path.

For example, the manner of estimating the driving time of the plurality of paths having the highest degree of driving automation may be to acquire the average driving time of the plurality of paths having the highest degree of driving automation in the current time period in the history data as the estimated time. The route with the shortest driving time is selected from the multiple routes with the highest driving automation degree as the automatic driving route, so that the driving time length can be shortened.

As an optional implementation manner of this embodiment, the evaluating the driving automation degrees of the multiple preselected routes according to the traffic conditions of the multiple preselected routes to obtain a route with the highest driving automation degree as an automatic driving route includes:

firstly, acquiring multi-terminal cooperation information, wherein the multi-terminal cooperation information comprises at least one of vehicle-terminal information, road-terminal information and cloud-terminal information;

illustratively, the vehicle-side information, the road-side information and the cloud-side information may be respectively from a vehicle-side self-sensing device, a road-side computing platform and a cloud-side computing platform. The vehicle-end information comprises environmental information in the driving process acquired by vehicle-mounted sensors such as a camera, a millimeter wave radar, an ultrasonic radar and a laser radar; the road end information comprises information collected by a plurality of vehicle end computing platforms and environment information collected by data collection equipment installed on the road side, wherein the information is received from the road end computing platforms; the cloud information comprises macro information formed by environment information uploaded by a plurality of road-side computing platforms and received from the cloud computing platforms.

Secondly, according to the multi-terminal cooperation information, a digital twinning model of the multiple pre-selected paths is constructed, and according to a digital twinning result, the driving automation degree of the multiple pre-selected paths is evaluated to obtain a path with the highest driving automation degree as an automatic driving path.

Illustratively, the digital twin model of the plurality of preselected paths may be constructed based on the multi-terminal cooperation information by classifying the multi-terminal cooperation information into vehicle geometric data, vehicle operation data, environment data, vehicle performance data, etc., and constructing the digital model based on different kinds of information, for example, the information classification may be divided into vehicle geometric data, vehicle operation data, environment data, vehicle performance data, etc., a vehicle geometric digital twin module is formed based on the vehicle geometric data, an operation state digital twin module is constructed based on the vehicle operation data, a map digital twin module is constructed based on the environment data, and a vehicle motion control digital twin module is constructed based on the vehicle performance data. Namely, the digital twin model comprises a vehicle geometric digital twin module, an operating state digital twin module, a map digital twin module and an automatic vehicle motion control digital twin module, wherein the vehicle geometric digital twin module is used for performing digital twin on a plurality of vehicle attribute information and the like in the environment information; the running state digital twinning module is used for carrying out digital twinning on a plurality of pieces of vehicle motion information, vehicle faults and vehicle flow in the environmental information; the map digital twinning module is used for carrying out digital twinning on road image information, obstacle information, traffic environment information and the like in the environment information; the automatic vehicle motion control digital twinning module is used for carrying out digital twinning on vehicle control according to vehicle dynamics.

According to the constructed digital twin model, data deduction can be carried out, so that traffic condition prediction in a future period is realized, when the driving automation degrees of a plurality of preselected paths are evaluated, the evaluation basis comes from the prediction of the future traffic condition instead of depending on the current traffic condition, the accuracy of the driving automation degree evaluation is greatly improved, the possibility that an automatic driving vehicle needs manual taking over is further avoided, and the method is more intelligent.

As an optional implementation manner of this embodiment, the predicting the driving time of the multiple paths with the highest driving automation degree includes:

constructing a digital twin model of a plurality of paths with the highest automation degree according to multi-terminal cooperation information, wherein the multi-terminal cooperation information comprises at least one of cloud information, road end information and vehicle end information; for details, refer to the corresponding parts of the above embodiments, and are not described herein again.

Predicting the congestion conditions of a plurality of paths according to the digital twin model; illustratively, the digital twin model can perform data deduction according to the current traffic condition, so as to realize the prediction of the traffic condition in the future for a period of time, and further obtain the congestion condition of a plurality of paths.

And predicting the driving time of the multiple paths with the highest automation degree according to the congestion conditions of the multiple paths.

For example, the manner of predicting the driving time according to the congestion conditions of the multiple paths may be to perform data deduction according to a digital twin model, predict the congestion which the vehicle may face, the time for ending the congestion and the driving speed of the vehicle in a future time period, and then obtain the driving time of the non-congestion road section according to the speed of the vehicle and the relationship between the routes. And obtaining the driving time of the plurality of paths according to the driving time of the non-congestion road section and the congestion time of the congestion road end.

According to the automatic driving method provided by the embodiment, the congestion condition in the future time period of the multiple paths of the digital twin model is estimated, so that the driving time is determined according to the congestion condition, compared with the method of directly determining the driving time according to historical data, the driving time is more consistent with the actual condition, the determined driving time is more accurate, and further, the automatic driving time can be effectively saved.

As an optional implementation manner of this embodiment, the predicting the driving time of the multiple paths with the highest driving automation degree includes:

when the multiple paths with the highest driving automation degree comprise paths passing through traffic lights and vehicles are predicted to be in a no-passing state when reaching the traffic lights, lane changing of the vehicles is predicted, when the vehicles run on any one of lanes in a passing state under the jurisdiction of the traffic lights, the vehicles pass through the time of the road section under the jurisdiction of the traffic lights and the vehicles run on the original lane, and when the traffic lights are waited to be converted from the no-passing state to a running state, the vehicles pass through the time of the road section under the jurisdiction of the traffic lights;

and selecting the shortest time of the vehicle passing through the traffic light in the jurisdiction section as the driving time passing through the traffic light.

Illustratively, fig. 2 is a cross road with traffic lights for traffic control, when the vehicle is going straight: judging whether the right-turn lane is in a passing prohibition state, if so, acquiring road information of a first lane from a map, determining a turning-around position in the lane, determining the traffic condition from the turning-around position to a crossroad to obtain the driving time of the vehicle when the vehicle drives on a second lane when the vehicle drives on the second lane according to a digital twin model, and if the total driving time of the second lane is shorter than the total driving time of the first lane, changing the lane to plan the driving path of the vehicle according to the second lane.

Fig. 3 is a cross road with traffic lights for traffic control, when the vehicle is to turn left:

judging whether the right-turn lane is in a passing prohibition state, if so, acquiring road information of a first lane from a map, determining a turning-around position in the lane, determining the traffic condition from the turning-around position to the other side of the intersection to obtain the driving time of the vehicle when the vehicle drives on a second lane when the vehicle drives on the second lane according to a digital twin model, and if the total driving time of the second lane is shorter than the total driving time of the first lane, changing the lane to plan the driving path of the vehicle according to the second lane.

An embodiment of the present invention provides an automatic driving apparatus, as shown in fig. 4, including:

an information obtaining module 201, configured to obtain current position information and destination information of a vehicle; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

A preselected path determining module 202, configured to determine a plurality of preselected paths according to the current location information and the destination information; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

A traffic condition determining module 203, configured to obtain traffic conditions of a plurality of preselected routes; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

The automatic evaluation module 204 is configured to evaluate driving automation degrees of a plurality of preselected paths according to traffic conditions of the preselected paths to obtain a path with a highest driving automation degree as an automatic driving path, where the driving automation degree is represented by an intervention degree of a driver in a driving process; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

And the driving module 205 is configured to control the vehicle to drive according to the automatic driving route. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

As an optional implementation manner of this embodiment, the automatic driving device further includes:

the driving time evaluation module is used for predicting the driving time of a plurality of paths with the highest driving automation degree when the paths with the highest driving automation degree have a plurality of paths; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

And the path selection module is used for selecting the path with the shortest driving time from the plurality of paths with the highest driving automation degree as the automatic driving path. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

As an optional implementation manner of this embodiment, the automated evaluation module includes:

the first cooperation information acquisition module is used for acquiring multi-terminal cooperation information, and the multi-terminal cooperation information comprises at least one of vehicle-side information, road-side information and cloud information; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

And the automatic evaluation submodule is used for constructing a digital twinning model of the multiple preselected paths according to the multi-terminal cooperation information, evaluating the driving automation degree of the multiple preselected paths according to a digital twinning result, and obtaining a path with the highest driving automation degree as an automatic driving path. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

As an optional implementation manner of this embodiment, the driving time evaluation module includes:

the digital twin construction module is used for constructing a digital twin model of the paths with the highest automation degree according to multi-end cooperative information, and the multi-end cooperative information comprises at least one of cloud information, road end information and vehicle end information; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

The congestion prediction module is used for predicting the congestion conditions of the multiple paths according to the digital twin model; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

And the time pre-estimating module is used for pre-estimating the driving time of the multiple paths with the highest automation degree according to the congestion conditions of the multiple paths. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

As an optional implementation manner of this embodiment, the driving time evaluation module includes:

the traffic light section time estimation module is used for predicting that the vehicle changes lanes when the multiple paths with the highest driving automation degree comprise paths passing the traffic lights and the vehicles are in a no-passing state when reaching the traffic lights, so that the time of the vehicles passing the traffic light section and the time of the vehicles running in the original lane are determined when the vehicles run in any one of the passing-through state lanes governed by the traffic lights, and the time of the vehicles passing the traffic light section is determined when the traffic lights are converted from the no-passing state to the running-through state; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

And the red road lamp section driving time determining module is used for selecting the shortest time of the vehicle passing through the traffic light in the jurisdiction section as the driving time passing through the traffic light. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.

The embodiment of the present application also provides an electronic device, as shown in fig. 5, a processor 310 and a memory 320, where the processor 310 and the memory 320 may be connected by a bus or other means.

Processor 310 may be a Central Processing Unit (CPU). The Processor 310 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or any combination thereof.

The memory 320, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the automatic driving method in the embodiments of the present invention. The processor executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions, and modules stored in the memory.

The memory 320 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 320 may optionally include memory located remotely from the processor, which may be connected to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory 320 and, when executed by the processor 310, perform an autopilot method as in the embodiment shown in FIG. 1.

The details of the electronic device may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.

The present embodiment also provides a computer storage medium having stored thereon computer-executable instructions for performing any of the methods described above in method embodiment 1. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.