CN110398960B

CN110398960B - Intelligent driving path planning method, device and equipment

Info

Publication number: CN110398960B
Application number: CN201910612580.2A
Authority: CN
Inventors: 邓堃; 张军; 卢红喜; 孙伟
Original assignee: Zhejiang Geely Holding Group Co Ltd; Zhejiang Geely Automobile Research Institute Co Ltd
Current assignee: Zhejiang Geely Holding Group Co Ltd; Zhejiang Geely Automobile Research Institute Co Ltd
Priority date: 2019-07-08
Filing date: 2019-07-08
Publication date: 2024-01-19
Anticipated expiration: 2039-07-08
Also published as: CN110398960A

Abstract

The application discloses a method, a device and equipment for planning an intelligent driving path, wherein the method comprises the steps of receiving intelligent driving planning information pushed by a vehicle-road cooperative base station; extracting initial path information for intelligent driving of the vehicle from the intelligent driving planning information; acquiring personalized demand data of a user for intelligent driving of the vehicle; and adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving. The accuracy of the intelligent driving planning information of the vehicle, which is determined by the vehicle-road cooperative base station, is higher, the vehicle-mounted calculation processing capacity and complexity requirements of the automatic driving vehicle can be reduced, more differentiated target path information can be provided for each user, and the actual application requirements are met.

Description

Intelligent driving path planning method, device and equipment

Technical Field

The present disclosure relates to the field of intelligent traffic technologies, and in particular, to a method, an apparatus, and a device for path planning in intelligent driving.

Background

Autopilot technology is a technical hotspot of the current automotive industry and is currently mainly divided into six autopilot classes, L0-L5, according to SAE, wherein L0 refers to vehicles without any autopilot function, L1-L2 autopilot is essentially still A Driving Assistance System (ADAS), L3 autopilot may be referred to as a quasi autopilot system, and L4-L5 autopilot may be considered as a truly meaningful autopilot system.

In a conventional autonomous vehicle, ambient information is detected by a sensor mounted on the vehicle, an autonomous path is calculated based on the detected ambient information, and autonomous is performed based on the autonomous path. However, whether the conventional L1-L2 class autonomous vehicle is equipped with sensors including, for example, forward-looking radar, forward-looking camera, ultrasonic radar, etc., or sensors including forward-looking lidar, multiple-angle radar and side radar, high-precision forward-looking camera, side-looking camera, rear-looking camera, etc., the sensing range and sensing capability of these sensors are limited (for example, a driving scene that cannot cover a larger range, a driving scene that cannot cover a more complex, etc.) included in the L3 and above autonomous vehicle. Because the current self-driving vehicle has limited self-driving environment sensing capability, the acquired surrounding environment information is limited, so that the accuracy of the calculated self-driving path is low, and intelligent driving experience is affected. In addition, the automatic driving path calculated by the existing automatic driving vehicle is single, and the vehicle-mounted calculation processing capacity and complexity of the automatic driving vehicle are high in requirements, and cannot meet the actual application requirements.

Disclosure of Invention

Based on this, the present application aims to provide a method, a device and equipment for planning a path of intelligent driving, so as to solve at least one technical problem. The technical scheme is as follows:

in one aspect, the present application provides a path planning method for intelligent driving, including:

receiving intelligent driving planning information pushed by a vehicle-road cooperative base station; the intelligent driving planning information is determined by the vehicle-road cooperative base station based on initial vehicle-road cooperative information of the vehicle;

extracting initial path information for intelligent driving of the vehicle from the intelligent driving planning information;

acquiring personalized demand data of a user for intelligent driving of the vehicle;

adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving;

the initial vehicle-road cooperative information comprises initial navigation information and current positioning information.

On the other hand, the application also provides a path planning method for intelligent driving, which comprises the following steps:

acquiring vehicle information sent by a vehicle; the vehicle information comprises initial vehicle-road cooperative information and personalized demand data for intelligent driving of the vehicle by a user;

Determining intelligent driving planning information of the vehicle based on the initial vehicle path cooperative information; the intelligent driving planning information comprises initial path information for intelligent driving of the vehicle;

pushing the target path information to the vehicle;

On the other hand, the application also provides an intelligent driving path planning device, which comprises:

the receiving module is used for receiving intelligent driving planning information pushed by the vehicle-road cooperative base station; the intelligent driving planning information is determined by the vehicle-road cooperative base station based on initial vehicle-road cooperative information of the vehicle;

the extraction module is used for extracting initial path information for intelligent driving of the vehicle in the intelligent driving planning information;

the acquisition module is used for acquiring personalized demand data for intelligent driving of the vehicle by a user;

the adjustment module is used for adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving;

the acquisition module is used for acquiring vehicle information sent by the vehicle; the vehicle information comprises initial vehicle-road cooperative information and personalized demand data for intelligent driving of the vehicle by a user;

the planning module is used for determining intelligent driving planning information of the vehicle based on the initial vehicle-road cooperative information; the intelligent driving planning information comprises initial path information for intelligent driving of the vehicle;

the pushing module is used for pushing the target path information to the vehicle;

In another aspect, the present application further provides an intelligent driving path planning apparatus, where the apparatus includes a vehicle, and a terminal of the vehicle includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the intelligent driving path planning method as described in any one of the above; and/or

The device comprises at least one vehicle-road cooperative base station, wherein a server in the vehicle-road cooperative base station comprises a processor and a memory, at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the intelligent driving path planning method.

The intelligent driving path planning method, device and equipment provided by the application have the following beneficial effects:

the intelligent driving planning information pushed by the vehicle-road cooperative base station is received; the intelligent driving planning information is determined by the vehicle-road cooperative base station based on initial vehicle-road cooperative information of the vehicle; extracting initial path information for intelligent driving of the vehicle from the intelligent driving planning information; acquiring personalized demand data of a user for intelligent driving of the vehicle; and adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving. The vehicle-road cooperative base station can obtain larger-range and more accurate environment perception information, so that the accuracy of the intelligent driving planning information of the vehicle, which is determined by the vehicle-road cooperative base station, is higher, and the vehicle-mounted calculation processing capacity and complexity requirements of the automatic driving vehicle can be reduced. In addition, the personalized demand data of the users are also considered when the target path information is determined, so that more differentiated target path information is provided for each user, and the actual application demands are met.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

In order to more clearly illustrate the technical solutions and advantages of embodiments of the present application or of the prior art, the following description will briefly introduce the drawings that are required to be used in the embodiments or the prior art descriptions, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a path planning method for intelligent driving according to an embodiment of the present application.

Fig. 2 is a flowchart of a path planning method for intelligent driving according to another embodiment of the present application.

Fig. 3 is a block diagram of an intelligent driving path planning apparatus according to an embodiment of the present application.

Fig. 4 is a flowchart of a path planning method for intelligent driving according to another embodiment of the present application.

Fig. 5 is a block diagram of a path planning apparatus for intelligent driving according to another embodiment of the present application.

Fig. 6 is a schematic hardware structure of an apparatus for implementing the method provided in the embodiment of the present application.

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings. It will be apparent that the described embodiments are merely one embodiment of the present application and not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

Reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic may be included in at least one implementation of the present application. In the description of the present application, it should be understood that the terms "upper," "lower," "left," "right," "top," "bottom," and the like indicate an orientation or a positional relationship based on that shown in the drawings, and are merely for convenience of description and simplification of the description, and do not indicate or imply that the apparatus or element in question must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application. 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 include one or more of the feature, either explicitly or implicitly. Moreover, the terms "first," "second," and the like, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein.

The methods and apparatus related to embodiments of the present application are described in detail below with reference to the accompanying drawings.

In the related art, a conventional autonomous vehicle detects surrounding environment information from sensors mounted on the vehicle, calculates an autonomous driving route based on the detected surrounding environment information, and performs autonomous driving based on the autonomous driving route. However, whether the conventional L1-L2 class autonomous vehicles or the L3 and above autonomous vehicles include various sensors, the sensing range and sensing capabilities of these sensors are limited (e.g., not capable of covering a larger range of driving scenarios, not capable of covering more complex driving scenarios, etc.). Because the current self-driving vehicle has limited self-driving environment sensing capability, the acquired surrounding environment information is limited, and the accuracy of the calculated self-driving path is low, so that intelligent driving experience is influenced. In addition, the automatic driving path calculated by the existing automatic driving vehicle is single, and the vehicle-mounted calculation processing capacity and complexity of the automatic driving vehicle are high in requirements, and cannot meet the actual application requirements. In order to solve the technical problem, the application provides a method, a device and equipment for planning an intelligent driving path.

Fig. 1 is a flowchart of a path planning method for intelligent driving provided in an embodiment of the present application. The method is applied to a terminal, and can be executed by a path planning device, the device can be realized by a software and/or hardware mode, and the device can be integrated in the terminal. Referring to fig. 1, the method may include:

s102, intelligent driving planning information pushed by a vehicle-road cooperative base station is received; the intelligent driving planning information is determined by the vehicle path cooperative base station based on the initial vehicle path cooperative information of the vehicle.

In the embodiment of the application, the vehicle terminal can establish communication connection with the vehicle-road cooperative base station. After the communication connection is established, the vehicle-road cooperative base station can determine intelligent driving planning information of the vehicle according to the initial vehicle-road cooperative information of the vehicle, and send the intelligent driving planning information to the corresponding vehicle. Of course, the vehicle may send a planning information acquisition request to the road cooperative base station in advance, and then the road cooperative base station determines intelligent driving planning information of the vehicle based on the information acquisition request.

The initial vehicle-road cooperative information comprises initial navigation information and current positioning information. The initial navigation information can include destination location information and an initial navigation line; the current positioning information may include a specific location where the vehicle is located, for example, a lane line location where the vehicle is located, a longitude and latitude where the vehicle is located, an altitude, and the like.

In an alternative embodiment, the initial vehicle-road coordination information may further include first environment awareness information. The first environmental perception information is surrounding environment information of the environment where the vehicle is located, and can be detected by a sensor of the own vehicle, and can also be detected by a sensor of other surrounding vehicles to detect the current area so as to obtain and share the information. In another alternative embodiment, the initial vehicle path coordination information may also include, but is not limited to, in-vehicle bus data information.

It should be noted that, each item of information in the initial vehicle-road cooperative information may be transmitted to the vehicle-road cooperative base station after being encapsulated by data. For example, the vehicle terminal may establish wireless communication, authentication, handshaking (determining communication protocol, determining transmission frequency, and other needs to be agreed upon), and establish a transmission channel with the vehicle-road cooperative base station; and then, packaging various information in the initial vehicle-road cooperative information of the vehicle by wireless communication and transmitting the packaged information to the vehicle-road cooperative base station.

In the embodiment of the application, the vehicle-road cooperative base station at least has the edge computing capability and the wireless communication capability. In one embodiment, these capabilities of the vehicle cooperative base station may be implemented by a server or cluster of servers, which may include, for example, wireless communication servers and edge computing servers, but typically do not have chassis drive-by-wire, in-vehicle bus devices.

Alternatively, the wireless communication server may use cellular network communication, or may use V2X communication on a line without a cellular network; the transmission data is exchanged by wireless communication with each road participant. The edge computing server can process wireless communication data by utilizing big data, a private cloud server and the like; fusing the positioning perception information of the road participants in the communication range of the base station, and calculating to generate a fused positioning perception model; the planned unmanned path and chassis control amount for all vehicles with drive-by-wire devices are calculated. Optionally, the edge computing server may be used for data processing, automatic driving, decision planning, real-time control computing and storage, has the capability of big data, machine learning, real-time control, may process environment-aware big data, extract valuable information by machine learning, and perform computation of relevant signals such as unmanned path planning, hierarchical decision, real-time control, etc.

Optionally, the vehicle-road cooperative base station also has positioning sensing capability. Correspondingly, the vehicle-road cooperative base station can also comprise a positioning sensing server, and the positioning sensing server is preferably provided with a high-precision and large-range positioning sensing function. For example, the location aware server may provide a high accuracy location of the base station using a high accuracy positioning device; and detecting information such as dynamic and static targets, traffic signs, road routes and the like in the field of view by using equipment such as the installed high-performance radar, cameras, laser radar sensors and the like.

Of course, in other embodiments, these capabilities of the vehicle cooperative base station are not limited to being implemented by the servers described above, but may also be implemented by embedded systems, modules, or other computer devices.

It should be noted that, the number of the vehicle-road cooperative base stations is not limited to one, and in practical application, the vehicle-road cooperative base stations may be a plurality of vehicle-road cooperative base stations arranged in a distributed manner, and communication coverage areas among the plurality of vehicle-road cooperative base stations may be partially overlapped.

In this embodiment of the present application, the terminal of the vehicle may be a vehicle-mounted terminal, which may be implemented by an entity device such as a vehicle recorder, a T-box, or may be implemented by software and/or hardware. As a variant, the vehicle-mounted terminal can be replaced by a mobile terminal which is connected to the vehicle signal, so that an automatic driving control of the vehicle can be achieved by the mobile terminal. The vehicle may include: common manually driven vehicles, such as L0-L5 class autopilot vehicles, motor/tricycles or other motor vehicles, etc.; which typically include location-aware devices, on-board drive-by-wire devices, wireless communication devices, some vehicles may include edge computing devices. According to different types of motor vehicles, the vehicle-mounted drive-by-wire equipment has positioning sensing equipment, edge computing equipment and vehicle-mounted drive-by-wire equipment with different performances. Optionally, the location-aware device may include a location device and an environment-aware device. The wireless communication device may include, but is not limited to, a communication device (e.g., a 5G communication device) with high throughput, high bandwidth, high reliability, low latency, etc., and may also communicate and exchange data with other devices (e.g., 3G/4G/6G/LTE, etc.) with similar capabilities. The vehicle-mounted drive-by-wire equipment can comprise equipment connected to a vehicle-mounted bus or a gateway, such as a vehicle-mounted sensor, man-machine interaction, a power assembly, a chassis, a vehicle network and the like; by controlling the work of the vehicle-mounted drive-by-wire equipment, acceleration and deceleration, steering, gear shifting, parking and the like of the vehicle are controlled through electronic signals, so that intelligent driving with higher automatic driving level is realized.

In this embodiment of the present application, the intelligent driving planning information may be calculated by the vehicle-road coordination base station based on the environment awareness information fused in the target area and the initial vehicle-road coordination information of the vehicle. The intelligent driving planning information may include path planning information. The path planning information is used for describing a planned path when the vehicle performs intelligent driving.

Alternatively, the path planning information may be a series of waypoint information in a geodetic coordinate system. Each of the waypoint information may include position information, chassis control amount information such as speed information, heading angle information, curvature information, and the like. The chassis control information includes a chassis control amount required for the in-vehicle drive-by-wire apparatus when describing a planned path determined when the vehicle performs intelligent driving. The chassis control amount may include at least one of acceleration and deceleration, steering wheel torque, accelerator opening, brake pedal displacement, gear information, and steering wheel angle.

S104, extracting initial path information for intelligent driving of the vehicle from the intelligent driving planning information.

In the embodiment of the application, as various information can be included in the intelligent driving planning information, the initial path information for intelligent driving of the vehicle can be acquired through extraction.

Alternatively, the initial path information may include a global planned path matching the start-destination location and a local planned path matching the actual driving environment information; road segment planning information including a plurality of road segments may also be included.

S106, personalized demand data of intelligent driving of the vehicle by the user is obtained.

In the embodiment of the application, the personalized demand data is determined based on the customized demand of the vehicle, the information security demand, the driving comfort demand, the privacy demand of the driver or the passenger, the emergency or burst scene demand, and the like. For example, the personalized demand data may include at least one of a demand road type, demand path location data, and demand dock location data.

The personalized demand data may be determined directly by the user through information input by voice, manual, etc., or indirectly by analyzing the historical driving data of the vehicle.

In addition, under the condition that the personalized demand data comprises a plurality of demand data, the personalized demand data can be processed according to the priority level for each demand data by combining different actual scenes, and can be weighted for each demand data, and each personalized demand is fused to obtain weighted personalized demand data.

It should be noted that the personalized demand data is not fixed, and can be adaptively adjusted according to actual driving conditions.

S108, adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving.

In general, a vehicle-road coordination base station performs path planning by considering indexes such as multi-vehicle coordination, vehicle-road coordination, traffic jam, path following, driving comfort, fuel economy and the like, and does not consider the actual personalized requirements of each vehicle user (driver or passenger), so that the planned driving path is relatively single. The starting point for these demands may be from the experience of driving, the properties of the vehicle, the quality of service, the privacy of the data, etc. Thus, during actual intelligent driving, there may be at least two situations: in the first case, the vehicle user has higher requirements on driving smoothness, hopes to be smoother and more natural in the running process of the vehicle, and does not need to be accelerated and decelerated too rapidly; in the second case, the vehicle user suddenly wants to go to a new location (e.g., restaurant or shopping mall) temporarily or to stop at a location temporarily or want to travel along a small road during the travel of the vehicle along the initial path.

For the first case, the personalized demand data may include driving smoothness demand data. At this time, the adjusting the initial path information based on the personalized demand data may obtain target path information of the vehicle for intelligent driving, that is, the step S108 may include:

determining a chassis control quantity threshold of the vehicle according to the driving smoothness requirement data, wherein the chassis control quantity threshold comprises the maximum amplitude of acceleration, deceleration and steering of the vehicle and the respective maximum change rate of the maximum amplitude;

judging whether each running data in the current chassis control quantity of the vehicle exceeds the corresponding running data in the chassis control quantity threshold value or not;

and if the judgment result is yes, adjusting the initial path information according to the chassis control quantity threshold value to obtain target path information of the vehicle for intelligent driving.

The target path information of the vehicle may be a series of road point information in a geodetic coordinate system. Each road point information includes position information, chassis control amount information such as speed information, heading angle information, curvature information, and the like. The chassis control amount information in the target path information satisfies the chassis control amount threshold.

For the second case, the personalized demand data may include at least one of a demand road type, demand route location data, and demand dock location data. At this time, the step of adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving, that is, the step S108 may include:

s1082, obtaining high-precision map information pushed by the vehicle-road cooperative base station.

In an embodiment, the high-precision map information may be a high-precision three-dimensional map or a high-precision two-dimensional map reflecting an area where the vehicle is located. The high-precision map information can be determined by a high-precision map server in the vehicle-road cooperative base station, or can be determined by the vehicle-road cooperative base station according to the acquired environment information perceived by the road participants and the map information of the area. The vehicle may send a high-precision map information acquisition request or a path adjustment request to the road cooperative base station in advance, and then the road cooperative base station determines high-precision map information of the vehicle based on the request and sends the high-precision map information to the vehicle.

S1084, adjusting the initial path information based on the personalized demand data and the high-precision map information to obtain target path information of the vehicle for intelligent driving.

In one embodiment, the personalized demand data may include a demand road type. The demand road type may be a road type that the user wishes to drive, such as a major road, a minor road, a lake-following road, a road requiring a bridge crossing, and the like. At this time, the S1084 may specifically include:

based on the road type and the high-precision map information, identifying a road section to be adjusted and other road sections in the initial path information;

personalized adjustment is carried out on the road section to be adjusted to obtain adjustment path information;

acquiring partial initial path information corresponding to other road sections based on the initial path information;

and obtaining target path information of the vehicle for intelligent driving based on the partial initial path information and the adjustment path information.

In another embodiment, the personalized demand data may include demand pathway location data or demand dock location data. The demand route location data may be, for example, a route marker name, a route road longitude and latitude coordinate, and the like. The dock location data may include dock location geographic location, dock longitude and latitude coordinates, dock marker names, and the like. At this time, the S1084 may specifically include:

Determining whether the demand path location data exists in the initial path information;

if not, determining a line intersection point of a path where the path-required position data is located and initial path information based on the high-precision map information;

replacing the lines between the line intersections in the initial path information with corresponding partial lines in the path;

and based on the part of lines and other adjustment lines in the initial path information, splicing to obtain target path information of the vehicle for intelligent driving.

In addition, if the requirements of the vehicle user on the driving smoothness are high, the vehicle is expected to run smoothly and naturally in the running process, and acceleration and deceleration are not too rapid. For this case, the personalized demand data may include driving smoothness demand data. At this time, the adjusting the initial path information based on the personalized demand data may obtain target path information of the vehicle for intelligent driving, that is, the step S108 may include:

According to the method and the device, the vehicle-road cooperative base station can obtain larger-range and more accurate environment perception information, so that the accuracy of intelligent driving planning information of the vehicle, which is determined by the vehicle-road cooperative base station, is higher, and the vehicle-mounted computing processing capacity and complexity requirements of an automatic driving vehicle can be reduced. In addition, the personalized demand data of the users are also considered when the target path information is determined, so that more differentiated target path information is provided for each user, and the actual application demands are met.

In another embodiment, during the actual driving process, some emergency situations may occur, for example, emergency traffic situations such as a child suddenly running into the driving path of the vehicle. At this time, the method may further include:

s110, acquiring the environment sensing information of the surrounding environment of the vehicle in real time.

And S112, judging whether the vehicle can generate emergency traffic conditions or not based on the environment perception information.

And S114, if the emergency traffic situation is judged to occur, determining temporary path information of the intelligent driving of the vehicle for collision avoidance according to the environment sensing information.

S116, replacing the target path information with the temporary path information until the emergency traffic situation is eliminated.

And if the emergency traffic situation is judged not to be cleared, replacing the target path information with the temporary path information so that the vehicle can intelligently drive according to the temporary path information. And if the emergency traffic situation is judged not to be cleared, restoring to the target path information, so that the vehicle can intelligently drive according to the target path information.

In an emergency traffic situation, the temporary path information of the intelligent driving of the vehicle for collision avoidance can be determined, so that the vehicle is controlled to respond quickly, traffic accidents are reduced, and the safety of the intelligent driving is improved.

Fig. 2 is a flowchart of a path planning method for intelligent driving according to another embodiment of the present application. The interactive execution main body of the method is a vehicle terminal and a vehicle-road cooperative base station. Referring to fig. 2, the method may include:

s201, a vehicle terminal acquires triggering operation of a user on intelligent driving;

s202, the vehicle terminal sends a driving planning information acquisition request to a vehicle-road cooperative base station based on the triggering operation; the driving planning information acquisition request comprises initial vehicle path cooperative information of the vehicle;

s203, the vehicle-road cooperative base station determines intelligent driving planning information of the vehicle based on the driving planning information acquisition request;

s204, pushing the intelligent driving planning information to the vehicle terminal by the vehicle-road cooperative base station;

s205, the vehicle terminal extracts initial path information for intelligent driving of the vehicle from the intelligent driving planning information;

s206, the vehicle terminal acquires personalized demand data for intelligent driving of the vehicle by a user;

s207, the vehicle terminal adjusts the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving.

In an embodiment, the method may further comprise:

s208, the vehicle terminal acquires environment perception information of the surrounding environment of the vehicle in real time;

s209, the vehicle terminal judges whether an emergency traffic situation occurs to the vehicle based on the environment sensing information;

s210, if the vehicle terminal judges that emergency traffic conditions occur, determining temporary path information of the vehicle for intelligent driving of collision avoidance according to the environment sensing information;

s211, the vehicle terminal replaces the target path information with the temporary path information until the emergency traffic situation is eliminated.

It should be noted that details and advantages not disclosed in the embodiments of the present application are referred to the above embodiments, and are not described herein.

The following are device embodiments of the present application, which may be used to perform the method embodiments described above. For details and advantages not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method of the present application.

Referring to fig. 3, a block diagram of an intelligent driving path planning apparatus according to an embodiment of the present application is shown. The intelligent driving path planning device has the function of realizing the terminal side in the method example, and the function can be realized by hardware or can be realized by executing corresponding software by hardware. The intelligent driving path planning apparatus 30 may include:

The receiving module 31 is configured to receive intelligent driving planning information pushed by the vehicle-road cooperative base station; the intelligent driving planning information is determined by the vehicle-road cooperative base station based on initial vehicle-road cooperative information of the vehicle;

the extracting module 32 is configured to extract initial path information for intelligent driving of the vehicle in the intelligent driving planning information;

an obtaining module 33, configured to obtain personalized demand data for intelligent driving of the vehicle by a user;

the adjustment module 34 is configured to adjust the initial path information based on the personalized demand data, so as to obtain target path information of the vehicle for intelligent driving;

Optionally, the personalized demand data includes at least one of a demand road type, demand path location data, and demand dock location data. Accordingly, the adjustment module 34 may further include:

an obtaining unit 341, configured to obtain high-precision map information pushed by a vehicle-road cooperative base station;

and an adjustment unit 342, configured to adjust the initial path information based on the personalized demand data and the high-precision map information, so as to obtain target path information of the vehicle for intelligent driving.

Optionally, the apparatus 30 may further include:

the real-time perception information acquisition module is used for acquiring environment perception information of the surrounding environment of the vehicle in real time;

the judging module is used for judging whether the vehicle can generate emergency traffic conditions or not based on the environment perception information;

the temporary path determining module is used for determining temporary path information of the vehicle for intelligent driving of collision avoidance according to the environment sensing information if the emergency traffic situation is judged to occur;

and the path replacement module is used for replacing the target path information with the temporary path information until the emergency traffic situation is eliminated.

Fig. 4 is a flowchart of a path planning method for intelligent driving according to still another embodiment of the present application. The method is applied to the vehicle-road cooperative base station, and can be concretely executed by a path planning device, the device can be realized by software and/or hardware, and the device can be integrated in the vehicle-road cooperative base station. Referring to fig. 4, the method may include:

s402, acquiring vehicle information sent by a vehicle; the vehicle information comprises initial vehicle-road cooperative information and personalized demand data for intelligent driving of the vehicle by a user.

In the embodiment of the application, the vehicle-road cooperative base station can establish communication connection with the vehicle terminal. After the communication connection is established, the vehicle-road cooperative base station can acquire vehicle information uploaded by the vehicle, then determine target path information for intelligent driving based on the vehicle information, and send the target path information to the corresponding vehicle so as to enable the vehicle to perform intelligent driving.

In the embodiment of the application, the vehicle information may include initial vehicle-road cooperative information and personalized demand data for intelligent driving of the vehicle by a user.

Optionally, the initial vehicle-road cooperative information includes initial navigation information and current positioning information. The initial navigation information can include destination location information and an initial navigation line; the current positioning information may include a specific location where the vehicle is located, for example, a lane line location where the vehicle is located, a longitude and latitude where the vehicle is located, an altitude, and the like.

Optionally, the personalized demand data is determined based on a customized demand of the vehicle, an information security demand, a driving comfort demand, a privacy demand of a driver or a passenger, an emergency or sudden scene demand, etc. For example, the personalized demand data may include at least one of a demand road type, demand path location data, and demand dock location data.

It should be noted that, each item of information in the above vehicle information may be transmitted to the vehicle-road cooperative base station after being encapsulated by data. For example, the vehicle terminal may establish wireless communication, authentication, handshaking (determining communication protocol, determining transmission frequency, and other needs to be agreed upon), and establish a transmission channel with the vehicle-road cooperative base station; and then, packaging various information in the vehicle information of the vehicle by wireless communication and transmitting the packaged information to the vehicle-road cooperative base station.

S404, determining intelligent driving planning information of the vehicle based on the initial vehicle path cooperative information; the intelligent driving planning information includes initial path information for intelligent driving of the vehicle.

In this embodiment of the present application, the intelligent driving planning information may be calculated by the vehicle-road coordination base station based on the environment awareness information fused in the target area and the initial vehicle-road coordination information of the vehicle. The intelligent driving planning information may include path planning information describing a planned path of the vehicle when intelligent driving is performed. The vehicle-road cooperation base station can plan the path by taking indexes such as multi-vehicle cooperation, vehicle-road cooperation, traffic jam, path following, travelling comfort, fuel economy and the like into consideration.

Alternatively, the initial path information of the vehicle may be a series of waypoint information in the geodetic coordinate system. Each of the waypoint information may include position information, chassis control amount information such as speed information, heading angle information, curvature information, and the like. The chassis control information includes a chassis control amount required for the in-vehicle drive-by-wire apparatus when describing a planned path determined when the vehicle performs intelligent driving. The chassis control amount may include at least one of acceleration and deceleration, steering wheel torque, accelerator opening, brake pedal displacement, gear information, steering wheel rotation angle, turn lights, hazard lights.

Optionally, the initial vehicle-road coordination information may further include first environment awareness information. The first environmental perception information is surrounding environment information of the environment where the vehicle is located, and can be detected by a sensor of the own vehicle, and can also be detected by a sensor of other surrounding vehicles to detect the current area so as to obtain and share the information. In another alternative embodiment, the initial vehicle path coordination information may also include, but is not limited to, in-vehicle bus data information.

It should be noted that, the vehicle-road cooperative base station implementing the present invention has at least an edge computing capability and a wireless communication capability. In one embodiment, these capabilities of the vehicle cooperative base station may be implemented by a server or cluster of servers, which may include, for example, wireless communication servers and edge computing servers, but typically do not have chassis drive-by-wire, in-vehicle bus devices.

Alternatively, the wireless communication server may use cellular network communication, or may use V2X communication on a line without a cellular network; the transmission data is exchanged by wireless communication with each road participant. The edge computing server can process wireless communication data by utilizing big data, a private cloud server and the like; fusing the positioning perception information of the road participants in the communication range of the base station, and calculating to generate a fused positioning perception model; a planned unmanned path is calculated for all vehicles having a drive-by-wire device.

Optionally, the edge computing server may be used for data processing, automatic driving, decision planning, real-time control computing and storage, has the capability of big data, machine learning, real-time control, may process environment-aware big data, extract valuable information by machine learning, and perform computation of relevant signals such as unmanned path planning, hierarchical decision, real-time control, etc.

In an embodiment, the vehicle-road cooperative base station may further have a positioning sensing capability. Correspondingly, the vehicle-road cooperative base station can comprise a positioning sensing server, and the positioning sensing server is preferably provided with a high-precision and wide-range positioning sensing function. For example, the location aware server may provide a high accuracy location of the base station using a high accuracy positioning device; and detecting information such as dynamic and static targets, traffic signs, road routes and the like in the field of view by using equipment such as the installed high-performance radar, cameras, laser radar sensors and the like.

In a specific embodiment, the determining the intelligent driving planning information of the vehicle based on the initial vehicle path cooperative information, that is, the step S404 may include:

s4041, positioning the absolute position of the vehicle-road cooperative base station by using a positioning sensing server of the vehicle-road cooperative base station, and then detecting information such as a dynamic and static target, a road route, a traffic sign and the like in a target range to establish an environment sensing model with the base station position as an origin.

S4042, each road participant connected to the vehicle-road cooperative base station receives the encapsulated communication data (encapsulated information) transmitted.

Wherein the road participant may include at least one of other motor vehicles, non-motor vehicles, road infrastructure, other vehicle-road cooperative base stations, and obstacles.

S4043, according to the obtained encapsulation information of each road participant, fusing each item of information (such as navigation information, positioning information, perception information, vehicle-mounted bus information and the like) in the encapsulation information into the environment perception model of the vehicle-road cooperative base station by calculation, and obtaining the fused environment perception model.

S4044, calculating the fused positioning perception information of all road participants according to the fused environment perception model;

S4045, calculating intelligent driving planning information of a plurality of vehicles with the vehicle-mounted drive-by-wire chassis according to the fused positioning sensing information and navigation path information provided by each road participant.

The intelligent driving planning information comprises initial path information for intelligent driving of the vehicle. The initial path information can be calculated by the vehicle-road cooperative base station according to high-precision map information of the cloud and dynamic and static target information around the vehicle. In an embodiment, the step S4045 may specifically be:

first, a piece of vehicle navigation path information (for example, longitude and latitude coordinate points in a series of global positioning systems) connecting the start point and the end point is calculated from the high-precision map information and the start point and the end point (destination) of the vehicle, and the vehicle navigation path information can guide the vehicle to travel from the start point to the end point along traffic sign marks such as lane lines on the structured road.

Then, based on the vehicle navigation path information and the fused positioning sensing information (i.e., the surrounding dynamic and static target information), initial path information (e.g., a series of road point information in the geodetic coordinate system) of the vehicle can be calculated. Each of the waypoint information may include position information, chassis control amount information such as speed information, heading angle information, curvature information, and the like. In general, the initial path information is consistent with the navigation path when there are no other traffic participants in the direction of travel of the vehicle; however, in practice, other traffic participants such as other vehicles, pedestrians, obstacles and the like will exist on the navigation path, so that it is necessary to further plan an initial path message for smooth and efficient traffic according to the fused positioning sensing information.

S406, adjusting initial path information in the intelligent driving planning information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving.

For the first case, the personalized demand data may include driving smoothness demand data. At this time, the adjusting the initial path information based on the personalized demand data may obtain target path information of the vehicle for intelligent driving, that is, the S406 may include:

For the second case, the personalized demand data may include at least one of a demand road type, demand route location data, and demand dock location data. At this time, the adjusting the initial path information based on the personalized demand data may obtain target path information of the vehicle for intelligent driving, that is, the S406 may include:

and S4062, acquiring high-precision map information.

In an embodiment, the high-precision map information may be a high-precision three-dimensional map or a high-precision two-dimensional map reflecting an area where the vehicle is located. The high-precision map information can be a high-precision map stored in the cloud, can be determined by a high-precision map server in the vehicle-road cooperative base station, or can be determined by the vehicle-road cooperative base station according to the acquired environment information perceived by the road participants and the map information of the area.

And S4064, adjusting the initial path information based on the personalized demand data and the high-precision map information to obtain target path information of the vehicle for intelligent driving.

In one embodiment, if the personalized demand data includes a demand road type. The demand road type may be a road type that the user wishes to drive, such as a major road, a minor road, a lake-following road, a road requiring a bridge crossing, and the like. At this time, the S4064 may specifically include:

In another embodiment, if the personalized demand data includes demand route location data or demand dock location data. The demand route location data may be, for example, a route marker name, a route road longitude and latitude coordinate, and the like. The dock location data may include dock location geographic location, dock longitude and latitude coordinates, dock marker names, and the like. At this time, the S4064 may specifically include:

And S408, pushing the target path information to the vehicle.

In the embodiment of the application, the target path information can be sent to the vehicle through the wireless communication server in the vehicle-road cooperative base station.

In an alternative embodiment, the vehicle information and the target path information are blockchain encrypted. This is because different vehicles or owners have different needs for privacy, some owners are willing to disclose private information (e.g., personal identification information, personal driving habit data, path information traveled by an individual, etc.) of individuals, vehicles, driving, etc., and some owners are relatively sensitive to disclosure of these personal privacy information, and are reluctant to disclose these private information to a vehicle-road coordination base station. Therefore, intelligent driving planning information, state information, positioning information and the like of the vehicle, which are sent by the vehicle-road cooperative base station, cannot be directly exchanged and stored, or private information of the vehicle is revealed. Therefore, through a blockchain encryption mode, the anonymization processing and the advanced encryption processing are carried out on the related vehicle information and the target path information which are related to the vehicle owner or the driver and need to be transmitted and communicated, so that the disclosure of the privacy information is reduced, and meanwhile, the information interaction is ensured.

and S410, acquiring the environment sensing information of the surrounding environment of the vehicle in real time.

And S412, judging whether the vehicle can generate emergency traffic conditions or not based on the environment perception information.

And S414, if the emergency traffic situation is judged to occur, determining temporary path information of the intelligent driving of the vehicle for collision avoidance according to the environment sensing information.

S416, replacing the target path information with the temporary path information until the emergency traffic situation is eliminated.

Referring to fig. 5, a block diagram of a path planning apparatus for intelligent driving according to another embodiment of the present application is shown. The intelligent driving path planning device has the function of realizing the vehicle-road cooperative base station side in the method example, and the function can be realized by hardware or by executing corresponding software by the hardware. The intelligent driving path planning apparatus 50 may include:

An acquisition module 51, configured to acquire vehicle information sent by a vehicle; the vehicle information comprises initial vehicle-road cooperative information and personalized demand data for intelligent driving of the vehicle by a user;

a planning module 52 for determining intelligent driving planning information for the vehicle based on the initial roadway synergy information; the intelligent driving planning information comprises initial path information for intelligent driving of the vehicle;

the adjustment module 53 is configured to adjust the initial path information based on the personalized demand data, so as to obtain target path information of the vehicle for intelligent driving;

a pushing module 54, configured to push the target path information to the vehicle;

Optionally, the vehicle information and the target path information are blockchain-encrypted. The apparatus 50 may further include an encryption unit for encrypting the target path information and/or the uploaded vehicle information.

Optionally, the personalized demand data includes at least one of a demand road type, demand path location data, and demand dock location data. Accordingly, the adjusting module 53 may include:

An acquisition unit configured to acquire high-precision map information;

and the adjusting unit is used for adjusting the initial path information based on the personalized demand data and the high-precision map information to obtain target path information of the vehicle for intelligent driving.

Optionally, the apparatus 50 may further include:

the real-time perception acquisition module is used for acquiring environment perception information of the surrounding environment of the vehicle in real time;

and the replacing module is used for replacing the target path information pushed to the vehicle with the temporary path information until the emergency traffic situation is eliminated.

The embodiment of the application also provides an intelligent driving path planning device. The device comprises a vehicle, wherein the terminal of the vehicle comprises a processor and a memory, at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the intelligent driving path planning method executed by the vehicle terminal; and/or

The device comprises at least one vehicle-road cooperative base station, wherein a server in the vehicle-road cooperative base station comprises a processor and a memory, at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the intelligent driving path planning method executed by the vehicle-road cooperative base station.

Further, fig. 6 shows a schematic hardware structure of any device for implementing the method provided by the embodiments of the present application, where the device may be a computer terminal, a mobile terminal or other devices, and the device may also participate in forming or including an apparatus provided by the embodiments of the present application. As shown in fig. 6, the computer terminal 10 may include one or more processors 102 (shown as 102a, 102b, … …,102 n) 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA or the like processing device), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 6 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 6, or have a different configuration than shown in FIG. 6.

It should be noted that the one or more processors 102 and/or other data processing circuits described above may be referred to generally herein as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).

The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the methods described in the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, that is, implement a neural network processing method as described above. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 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 transmission means 106 is arranged to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).

It should be noted that: the foregoing sequence of the embodiments of the present application is only for describing, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device and server embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and references to the parts of the description of the method embodiments are only required.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

While the foregoing is directed to the preferred embodiments of the present application, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the present application, such changes and modifications are also intended to be within the scope of the present application.

Claims

1. The intelligent driving path planning method is characterized by being applied to a vehicle terminal and comprising the following steps of:

Receiving intelligent driving planning information pushed by a vehicle-road cooperative base station; the intelligent driving planning information is determined by the vehicle-road cooperative base station based on the fused environment perception information in the target area and the initial vehicle-road cooperative information of the vehicle; the intelligent driving planning information comprises initial path information for intelligent driving of the vehicle;

acquiring personalized demand data of a user for intelligent driving of the vehicle; the personalized demand data is obtained by processing the initial demand data input by various users according to the demand priority sequence matched with the current driving scene;

the initial vehicle-road cooperative information comprises initial navigation information and current positioning information;

when the personalized demand data includes driving smoothness demand data, the adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving includes:

Determining a chassis control amount threshold of the vehicle according to the driving smoothness requirement data; the chassis control amount threshold includes a maximum magnitude of acceleration, deceleration and steering of the vehicle and a respective maximum rate of change thereof;

and under the condition that the current chassis control quantity of the vehicle exceeds the chassis control quantity threshold value, adjusting the initial path information according to the chassis control quantity threshold value to obtain target path information of the vehicle for intelligent driving.

2. The method according to claim 1, wherein, in the case where the personalized demand data includes at least one of a demand road type, demand route location data, and demand stop location data, the adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving includes:

obtaining high-precision map information pushed by a vehicle-road cooperative base station;

and adjusting the initial path information based on the personalized demand data and the high-precision map information to obtain target path information of the vehicle for intelligent driving.

3. The method according to claim 1 or 2, characterized in that the method further comprises:

Acquiring environmental perception information of the surrounding environment of the vehicle in real time;

judging whether the vehicle can generate emergency traffic conditions or not based on the environment perception information;

if the emergency traffic situation is judged to occur, determining temporary path information of the vehicle for intelligent driving of collision avoidance according to the environment sensing information;

and replacing the target path information with the temporary path information until the emergency traffic situation is eliminated.

4. The intelligent driving path planning method is characterized by being applied to a vehicle-road cooperative base station and comprising the following steps:

acquiring vehicle information sent by a vehicle; the vehicle information comprises initial vehicle-road cooperative information and personalized demand data for intelligent driving of the vehicle by a user; the personalized demand data is obtained by processing the initial demand data input by various users according to the demand priority sequence matched with the current driving scene;

determining intelligent driving planning information of the vehicle based on the integrated environment perception information and the initial vehicle path cooperative information in the target area; the intelligent driving planning information comprises initial path information for intelligent driving of the vehicle;

pushing the target path information to the vehicle;

5. The method of claim 4, wherein the vehicle information and the target path information are blockchain encrypted.

6. The method of claim 4, wherein, in the case where the personalized demand data includes at least one of a demand road type, demand route location data, and demand stop location data, the adjusting the initial path information based on the personalized demand data to obtain target path information of the vehicle for intelligent driving includes:

acquiring high-precision map information;

7. The method according to any one of claims 4-6, further comprising:

and replacing the target path information pushed to the vehicle with the temporary path information until the emergency traffic situation is eliminated.

8. An intelligent driving path planning device, which is applied to a vehicle terminal, comprising:

the receiving module is used for receiving intelligent driving planning information pushed by the vehicle-road cooperative base station; the intelligent driving planning information is determined by the vehicle-road cooperative base station based on the fused environment perception information in the target area and the initial vehicle-road cooperative information of the vehicle; the intelligent driving planning information comprises initial path information for intelligent driving of the vehicle;

the acquisition module is used for acquiring personalized demand data for intelligent driving of the vehicle by a user; the personalized demand data is obtained by processing the initial demand data input by various users according to the demand priority sequence matched with the current driving scene;

In the case that the personalized demand data includes driving smoothness demand data, the adjustment module is specifically configured to: determining a chassis control amount threshold of the vehicle according to the driving smoothness requirement data; the chassis control amount threshold includes a maximum magnitude of acceleration, deceleration and steering of the vehicle and a respective maximum rate of change thereof; and under the condition that the current chassis control quantity of the vehicle exceeds the chassis control quantity threshold value, adjusting the initial path information according to the chassis control quantity threshold value to obtain target path information of the vehicle for intelligent driving.

9. An intelligent driving path planning device, which is characterized in that the intelligent driving path planning device is applied to a vehicle-road cooperative base station and comprises:

the acquisition module is used for acquiring vehicle information sent by the vehicle; the vehicle information comprises initial vehicle-road cooperative information and personalized demand data for intelligent driving of the vehicle by a user; the personalized demand data is obtained by processing the initial demand data input by various users according to the demand priority sequence matched with the current driving scene;

the planning module is used for determining intelligent driving planning information of the vehicle based on the integrated environment perception information and the initial vehicle path cooperative information in the target area; the intelligent driving planning information comprises initial path information for intelligent driving of the vehicle;

10. A intelligently driven path planning device, characterized in that the device comprises a vehicle, the terminal of which comprises a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the intelligently driven path planning method according to any one of claims 1 to 3; and/or

The device comprises at least one vehicle co-base station, wherein a server in the vehicle co-base station comprises a processor and a memory, at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to realize the intelligent driving path planning method according to any one of claims 4 to 7.

11. A computer readable storage medium comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the intelligent driving path planning method of any one of claims 1-3; and/or a route planning method of intelligent driving as claimed in any one of claims 4 to 7.