CN118089770A

CN118089770A - Driving path planning method, device, equipment and storage medium

Info

Publication number: CN118089770A
Application number: CN202410292938.9A
Authority: CN
Inventors: 杨照坤; 孙立志; 苗连青; 张鑫磊
Original assignee: Shenzhen Technology University
Current assignee: Shenzhen Technology University
Priority date: 2024-03-14
Filing date: 2024-03-14
Publication date: 2024-05-28

Abstract

The invention discloses a driving path planning method, a driving path planning device, driving path planning equipment and a storage medium. Comprising the following steps: acquiring vehicle driving information and road network information of a target vehicle, wherein the vehicle driving information comprises a vehicle starting point position and a vehicle ending point position, and the road network information comprises road nodes and corresponding node attributes thereof; determining an optimal planning path according to the vehicle running information and the road network information; and controlling the target vehicle to run according to the optimal planning path. The vehicle command center is used for acquiring the vehicle running information and road network information of the target vehicle, and determining an optimal planning path for the target vehicle according to the vehicle starting point and the vehicle end point and combining the road network information, so that the target vehicle can be effectively helped to save the running distance, time and energy consumption. Different vehicles are scheduled, so that the vehicles can automatically drive based on the optimal planning path, traffic efficiency and safety are improved, scheduling cost is saved, and travel experience of a user is optimized.

Description

Driving path planning method, device, equipment and storage medium

Technical Field

The present invention relates to the field of automatic driving technologies, and in particular, to a driving path planning method, device, equipment, and storage medium.

Background

With the development of social economy and the acceleration of the urban process, urban traffic problems are increasingly prominent. In order to improve traffic efficiency, reduce congestion and optimize travel experience, a travel path planning technique has been developed.

In the prior art, drivers often use online maps to determine the path of travel. However, this method has many limitations such as untimely and inaccurate map updating, and driving safety of some fixed-road routers is not guaranteed in mining areas or remote mountainous areas, and there is a certain cost pressure.

Disclosure of Invention

The invention provides a driving path planning method, a driving path planning device, driving path planning equipment and a storage medium, so that driving path planning is carried out on complex road conditions, automatic driving of vehicles is controlled through an optimal planning path, and scheduling of different vehicles can be realized.

According to an aspect of the present invention, there is provided a driving path planning method, including:

acquiring vehicle driving information and road network information of a target vehicle, wherein the vehicle driving information comprises a vehicle starting point position and a vehicle ending point position, and the road network information comprises road nodes and corresponding node attributes thereof;

determining an optimal planning path according to the vehicle running information and the road network information;

and controlling the target vehicle to run according to the optimal planning path.

Optionally, acquiring the vehicle running information and the road network information of the target vehicle includes: determining each road node of a designated area, and acquiring node attributes corresponding to each road node, wherein the node attributes comprise node positions and connectivity; acquiring the current coordinates of a target vehicle, and taking the current coordinates as a vehicle starting point position; acquiring a target position input by a user based on each road node, and taking the target position as a vehicle terminal position; and generating road network information according to the corresponding relation between each road node and the node attribute.

Optionally, determining the optimal planned path according to the vehicle driving information and the road network information includes: determining the node distance between each road node and the starting point position of the vehicle in the road network information, and taking the road node with the nearest node distance as a target node; and determining an optimal planning path from the target node to the vehicle terminal position through the road network information.

Optionally, determining an optimal planned path from the target node to the vehicle end position through the road network information includes: determining each planning path from the target node to the vehicle terminal position through the road network information; determining the communication condition of each planning path according to connectivity, and taking the planning paths with the communication condition as the communication as candidate planning paths; calculating path weights corresponding to each candidate planning path according to a preset path planning algorithm; and taking the candidate planning path with the largest path weight as the optimal planning path.

Optionally, after taking the road node closest to the road node as the target node, the method further includes: and transmitting the node position coordinates of the target node to the target vehicle to guide the target vehicle to drive to the target node.

Optionally, after controlling the target vehicle to travel according to the optimal planned path, the method further includes: obtaining an obstacle detection situation based on the optimal planning path, wherein the obstacle detection situation comprises the existence of an obstacle and the absence of the obstacle; when the obstacle detection condition is that an obstacle exists, setting the communication condition of the optimal planning path as non-passing, and taking each planning path with the communication condition as a second candidate planning path; calculating second path weights corresponding to each second candidate planning path according to a preset path planning algorithm; taking the second candidate planning path with the largest second path weight as an updating planning path; and controlling the target vehicle to run according to the updated planning path.

Optionally, after controlling the target vehicle to travel according to the optimal planned path, the method further includes: acquiring running state information of a target vehicle; when the running state information meets the corresponding preset conditions, generating a vehicle control instruction according to the running state information, wherein the vehicle control instruction comprises starting, pausing or stopping; the target vehicle is controlled based on the vehicle control instruction.

According to another aspect of the present invention, there is provided a traffic path planning apparatus including:

The system comprises a vehicle running information and road network information acquisition module, a road network information acquisition module and a road network information acquisition module, wherein the vehicle running information acquisition module is used for acquiring vehicle running information and road network information of a target vehicle, the vehicle running information comprises a vehicle starting point position and a vehicle ending point position, and the road network information comprises road nodes and corresponding node attributes thereof;

The optimal planning path determining module is used for determining an optimal planning path according to the vehicle running information and the road network information;

and the target vehicle running control module is used for controlling the target vehicle to run according to the optimal planning path.

According to another aspect of the present invention, there is provided an electronic apparatus including:

At least one processor; and

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

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a driving path planning method according to any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement a driving path planning method according to any one of the embodiments of the present invention when executed.

According to the technical scheme, the vehicle command center is used for acquiring the vehicle running information and road network information of the target vehicle, and an optimal planning path is determined for the target vehicle according to the vehicle starting point and the vehicle end point and by combining the road network information, so that the target vehicle can be effectively helped to save the running distance, time and energy consumption. Different vehicles are scheduled, so that the vehicles can automatically drive based on the optimal planning path, traffic efficiency and safety are improved, scheduling cost is saved, and travel experience of a user is optimized.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, 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 driving path planning method according to a first embodiment of the present invention;

FIG. 2 is a flow chart of another method for planning a driving path according to a first embodiment of the present invention;

Fig. 3 is a flowchart of another driving path planning method according to the second embodiment of the present invention;

Fig. 4 is a schematic diagram of a path planning application process according to a second embodiment of the present invention;

Fig. 5 is a schematic structural diagram of a driving path planning device according to a third embodiment of the present invention;

Fig. 6 is a schematic structural diagram of an electronic device for implementing a driving path planning method according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures 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 the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a driving path planning method according to an embodiment of the present invention, where the method may be performed by a driving path planning device, and the driving path planning device may be implemented in hardware and/or software, and the driving path planning device may be configured in a command center computer controller. As shown in fig. 1, the method includes:

S110, acquiring vehicle driving information and road network information of a target vehicle, wherein the vehicle driving information comprises a vehicle starting point position and a vehicle ending point position, and the road network information comprises road nodes and corresponding node attributes thereof.

The target vehicle is a vehicle monitored and managed by the vehicle command center, and can be an unmanned vehicle, and the vehicle command center is used for planning and controlling the driving path of the target vehicle, so that the target vehicle can finally realize automatic driving based on the calculated optimal planning path. A vehicle command center refers to a mechanism or facility that commands and dispatches vehicles. The vehicle command center can monitor, schedule and command the target vehicle in real time according to the acquired vehicle running information and road network information so as to ensure safe and efficient operation of the target vehicle. The road network information is various data related to a road network on which the target vehicle travels, and includes each road node and its corresponding node attribute. Road network information may be obtained through various map data and traffic monitoring devices, such as electronic maps, satellite maps, traffic cameras, traffic flow monitoring devices, and the like.

Specifically, the vehicle command center may acquire vehicle travel information and road network information of the target vehicle. Wherein the vehicle travel information includes a vehicle start position and a vehicle end position, and the vehicle travel information may be used to determine a travel route and a travel distance of the target vehicle. The road network information can be used for evaluating feasibility and safety of different routes, and comprises each road node and corresponding node attributes thereof, wherein the node attributes comprise node positions and connectivity, the node positions refer to longitude and latitude coordinates of the road nodes, the connectivity refers to road attributes and is used for indicating whether the road node is connected with other nodes or not, for example, when an intersection is formed, a road is not behind a road node A, the connectivity of the road node A is not communicated, and the road node A is not connected with other roads.

It is known that the vehicle travel information includes a vehicle start position, i.e., the current coordinates of the target vehicle, and a vehicle end position, which can be acquired by various sensors and positioning devices on the vehicle, such as a Global Positioning System (GPS), an Inertial Navigation System (INS), a vehicle odometer, a vehicle accelerometer, and the like. The vehicle end position is a target road node selected by a user from road network information, and the vehicle command center takes the position of the target road node as a target position.

The road nodes refer to points with specific functions and actions in the road network information, and the road nodes can be intersections, turning points, starting points, end points, intersections, toll stations, parking lots and the like of roads. The types of road nodes may be divided according to different classification criteria, such as: intersection point: points in the road network where two or more roads meet, such as intersections, T-junctions, roundabout, and the like. Turning point: the points in the road network where the road turns, such as curves and corners, etc. Start and end point: the start point and the end point of the road in the road network, such as the entrance and the exit of the expressway, the start point and the end point of the urban road, etc. Crossing: points in the road network where roads are connected to other roads or buildings, such as intersections, school gates, bus stops, and the like. Toll station: the toll collection points in the road network, such as toll stations of expressways, parking lots of urban roads, and the like.

Specifically, map data or other related information may be used to determine each road node of the specified area, and obtain a node attribute corresponding to each road node. The node attributes include node position and connectivity, wherein the node position can be represented by longitude and latitude, and the connectivity can be represented by an adjacency table or adjacency matrix in graph theory. The current coordinates of the target vehicle may then be obtained using a positioning technique and taken as the vehicle origin location. The user may determine the target position based on the address or latitude and longitude of the road network information input destination, and use it as the vehicle end position. Finally, the controller can generate road network information according to the corresponding relation between each road node and the node attribute. Road network information may be represented by a directed graph in graph theory, where nodes represent road nodes, edges represent road links, and weights of the edges represent the length of the road or other relevant information.

And S120, determining an optimal planning path according to the vehicle driving information and the road network information.

The optimal planned path is a path with the largest weight value determined through calculation between a given vehicle starting position and a vehicle end position. The calculation of the optimal planned path can help the target vehicle to select the optimal route, avoid congestion, reduce fuel consumption, and the like. It should be noted that the calculation result of the optimal planned path may be affected by various factors, such as actual traffic conditions, road condition changes, and weather changes. Therefore, the calculation of the optimal planned path needs to be continuously updated and optimized to adapt to the actual situation change.

Specifically, after this information is obtained, a path planning algorithm may be used to determine the optimal planned path. The path planning algorithm can calculate the optimal paths of different types such as the shortest path, the fastest path or the lowest cost path according to the vehicle running information and the road network information. The target vehicle is then controlled to travel in accordance with the optimal planned path by sending the optimal planned path to a control system of the target vehicle.

Fig. 2 is a flowchart of a driving path planning method according to an embodiment of the present invention, and step S120 mainly includes steps S121 to S122 as follows:

S121, determining node distances between each road node and the starting point position of the vehicle in the road network information, and taking the road node with the nearest node distance as a target node.

Specifically, the vehicle command center determines all road nodes included in the road network information, calculates a node distance between each node and the starting point position of the vehicle, and the node distance can be obtained through map data or other data sources. The closest road node may then be taken as the target node. This ensures that the selected target node is the closest node to the vehicle start position, thereby reducing the travel distance and time.

In a specific embodiment, if the node distances are equal, the traffic condition of the road node can be considered, and the road node with better traffic condition is selected as the target node, so as to reduce the running time and the congestion. The geographical environment around the node may also be considered. And selecting a node with relatively flat terrain and relatively good road conditions as a target node to improve the driving safety and comfort. In addition, the controller can also randomly select one node as a target node in a random manner.

Specifically, the position coordinates of the target node may be transmitted to the target vehicle by using a wireless communication technology, and converted into a coordinate format that can be understood by the vehicle. Other information such as travel routes, road conditions, traffic conditions, etc. may also be provided in order to better guide the driving of the target vehicle to the target node. Such information may help the target vehicle to better plan the route of travel, avoid congestion and other dangerous situations, and ultimately drive to the target node.

S122, determining an optimal planning path from the target node to the vehicle terminal position through the road network information.

Specifically, after the target node is determined, the road network information may be further used to determine an optimal planned path from the target node to the vehicle end position. The optimal planned path may be implemented by a preset path planning algorithm, for example, dijkstra's algorithm or a-x algorithm. The determined optimal planning path can help the target vehicle to effectively reduce the driving distance and time and improve the transportation efficiency and safety.

Specifically, the vehicle command center determines all possible paths from the target node to the vehicle terminal position as each planned path by using road network information according to the current position of the vehicle and the target node. For each planned path obtained, it is also necessary to check their connectivity. Connectivity refers to whether there are broken road segments or non-passable nodes in the path. If there is a non-connected portion in the path, it needs to be excluded from the candidate planned path. The vehicle command center takes the communicated planned path as a candidate planned path for further processing. The candidate planned paths are the basis for subsequent path weight calculation and optimal path selection.

Further, to evaluate the merits of each candidate planned path, a preset path planning algorithm may be used to calculate their corresponding path weights. The path weight may be calculated based on a variety of factors, such as path length, road class, traffic flow, and travel time. Depending on the specific requirements and application scenario, a suitable path planning algorithm and weight calculation method may be selected. Finally, by comparing the path weights of the candidate planning paths, the candidate planning path with the largest path weight can be selected as the optimal planning path. The optimal planning path is the optimal running path from the target node to the vehicle end position.

It should be noted that the path weight is used to measure the quality of different paths. The path weight may be calculated based on a variety of factors, including path length, road conditions, traffic conditions, travel time, energy consumption, and the like.

Among them, a shorter path is generally considered as a better choice for the path length, because it can reduce travel time and energy consumption. However, the path length is not the only consideration, and other factors may also have an effect on the merits of the path. Road conditions mean that different roads may have different gradients, curvatures, road conditions, etc., which may affect the speed of travel and energy consumption of the vehicle. Therefore, in calculating the path weight, the factors of the road condition may be taken into consideration to select a path more suitable for the vehicle to travel. In addition, energy consumption is also a factor that may affect path weights. For an electric vehicle or a hybrid vehicle, selecting a path with lower energy consumption may extend the range. Therefore, factors of energy consumption may be considered in calculating the path weights.

And S130, controlling the target vehicle to run according to the optimal planning path.

In summary, the optimal planned path from the target node to the vehicle end position may be determined using the road network information and the preset path planning algorithm. And controlling the target vehicle based on the optimal planning path, thereby being beneficial to improving the running efficiency of the vehicle, reducing the running time and the running cost and improving the traveling experience of the user.

Specifically, during the running process of the vehicle, the vehicle command center can monitor the state of the vehicle in real time, can send instructions such as start, pause, stop and the like to the vehicle, and can control the vehicle to advance or stop. Specifically, the vehicle command center can realize accurate control of the target vehicle by acquiring the running state information of the target vehicle.

The operating state information may include, among other things, the speed, position, direction, battery level, and engine state of the vehicle. By acquiring such information in real time, the behavior of the vehicle can be better understood. When the running state information satisfies the corresponding preset conditions, a vehicle control instruction may be generated according to the information. These preset conditions may be set according to a specific application scenario, for example, when the vehicle speed exceeds a set threshold, a deceleration instruction is generated; when the vehicle battery level is below a certain level, a charge instruction or the like is generated. The vehicle control instructions may include start, pause, or terminate operations. The start command may cause the vehicle to begin running, the pause command may cause the vehicle to temporarily stop running, and the stop command may cause the vehicle to stop running and shut down the engine. These instructions may be executed by a control system of the vehicle to achieve precise control of the vehicle. According to the technical scheme, remote control and management of the target vehicle can be achieved, and the running efficiency and safety of the vehicle are improved. Meanwhile, the running state of the vehicle can be monitored in real time, so that potential problems can be found and solved in time, and the normal running of the vehicle can be ensured.

Example two

Fig. 3 is a flowchart of a driving path planning method according to a second embodiment of the present invention, where a process of generating an updated planned path is added on the basis of the first embodiment. As shown in fig. 3, the method includes:

s210, obtaining an obstacle detection situation based on the optimal planning path, wherein the obstacle detection situation comprises the existence of an obstacle and the absence of the obstacle.

In particular, in path planning algorithms, handling obstacles and accidents is very important. The obstacle may be a large stone or a very large pit or the like present in the path. In order to ensure the running safety and efficiency of the target vehicle, the vehicle command center may acquire the obstacle detection situation of the target vehicle based on the optimal planned path. Obstacle detection conditions can be divided into two categories: there are obstacles and no obstacles. If an obstacle on the optimal planned path is unavoidable, the path planning algorithm needs to calculate an updated planned path to bring the target vehicle to the target destination.

And S220, when the obstacle detection condition is that an obstacle exists, setting the communication condition of the optimal planning path as non-passing, and taking each planning path with the communication condition as a second candidate planning path.

Specifically, when the obstacle detection condition is that an obstacle exists, the communication condition of the optimal planned path needs to be set to be non-passing. This means that the target vehicle cannot travel along the optimally planned path because of obstacles present on the path. The second candidate planned path is an alternative in case an optimal planned path is not feasible. In order to find a viable alternative path, each planned path for which the connectivity is a connectivity may be taken as a second candidate planned path.

S230, calculating second path weights corresponding to the second candidate planning paths according to a preset path planning algorithm.

S240, taking the second candidate planning path with the largest second path weight as an updating planning path.

S250, controlling the target vehicle to run according to the updated planning path.

Specifically, a second path weight corresponding to each second candidate planning path is calculated according to a preset path planning algorithm, and the second candidate planning path with the largest second path weight is used as an updated planning path. The target vehicle will travel according to the updated planned path to avoid the obstacle and reach the destination. Through the technical scheme of the embodiment, the obstacle detection condition can be obtained in real time, and the planning path is dynamically adjusted according to the condition, so that the running safety and the running efficiency of the target vehicle are ensured.

In addition, if other paths need to be driven in the driving process of the target vehicle, the vehicle command center can stop the current path planning first and then execute a new path planning flow again. For example, when the vehicle is traveling, some emergency situations may be encountered, and other paths may be required to travel. For example, the target vehicle is running along an optimal planned path planned by the navigation system, but suddenly encounters a traffic accident or road construction, so that the road ahead cannot pass. In this case, the vehicle needs to travel other paths to avoid getting trapped in place. At this time, the controller can stop the current path planning and re-execute the new path planning flow, so that the target vehicle can adjust the driving path in time when encountering an emergency, and the target vehicle is prevented from being trapped in place.

The specific application scene is as follows: fig. 4 is a schematic diagram of a path planning application process provided by a second embodiment of the present invention, in fig. 4, a vehicle command center loads path network information, matches an optimal path on the path network based on the sent start point and end point information, and generates a path with highest weight by taking a matching principle as a weight; then, the command center matches the current position of the vehicle with the nearest point on the road network, and guides the vehicle to the road network (the nearest point to the starting position of the vehicle); and then the command center issues a path, and the vehicle runs according to the preset path. If the vehicle detects that the path needs to be re-planned, the vehicle command center can recalculate the weight, generate a new path and send the new path to the vehicle, and the vehicle runs according to the new path.

Example III

Fig. 5 is a schematic structural diagram of a driving path planning device according to a third embodiment of the present invention. As shown in fig. 5, the apparatus includes: a vehicle driving information and road network information obtaining module 310, configured to obtain vehicle driving information and road network information of a target vehicle, where the vehicle driving information includes a vehicle starting position and a vehicle ending position, and the road network information includes each road node and a node attribute corresponding to each road node;

an optimal planned path determining module 320, configured to determine an optimal planned path according to the vehicle driving information and the road network information;

The target vehicle driving control module 330 is configured to control the target vehicle driving according to the optimal planned path.

Optionally, the vehicle driving information and road network information obtaining module 310 is specifically configured to: determining each road node of a designated area, and acquiring node attributes corresponding to each road node, wherein the node attributes comprise node positions and connectivity; acquiring the current coordinates of a target vehicle, and taking the current coordinates as a vehicle starting point position; acquiring a target position input by a user based on each road node, and taking the target position as a vehicle terminal position; and generating road network information according to the corresponding relation between each road node and the node attribute.

Optionally, the optimal planned path determining module 320 specifically includes: a target node determining unit configured to: determining the node distance between each road node and the starting point position of the vehicle in the road network information, and taking the road node with the nearest node distance as a target node; an optimal planned path determination unit configured to: and determining an optimal planning path from the target node to the vehicle terminal position through the road network information.

Optionally, the optimal planned path determining unit is specifically configured to: determining each planning path from the target node to the vehicle terminal position through the road network information; determining the communication condition of each planning path according to connectivity, and taking the planning paths with the communication condition as the communication as candidate planning paths; calculating path weights corresponding to each candidate planning path according to a preset path planning algorithm; and taking the candidate planning path with the largest path weight as the optimal planning path.

Optionally, the apparatus further comprises: and the target node sending module is used for sending the node position coordinates of the target node to the target vehicle after taking the road node closest to the target node as the target node so as to guide the target vehicle to drive to the target node.

Optionally, the apparatus further comprises: the updating planning path determining module is used for acquiring an obstacle detection condition based on the optimal planning path after the target vehicle is controlled to run according to the optimal planning path, wherein the obstacle detection condition comprises the existence of an obstacle and the absence of the obstacle; when the obstacle detection condition is that an obstacle exists, setting the communication condition of the optimal planning path as non-passing, and taking each planning path with the communication condition as a second candidate planning path; calculating second path weights corresponding to each second candidate planning path according to a preset path planning algorithm; taking the second candidate planning path with the largest second path weight as an updating planning path; and controlling the target vehicle to run according to the updated planning path.

Optionally, the apparatus further comprises: the vehicle running state detection module is used for acquiring running state information of the target vehicle after controlling the target vehicle to run according to the optimal planning path; when the running state information meets the corresponding preset conditions, generating a vehicle control instruction according to the running state information, wherein the vehicle control instruction comprises starting, pausing or stopping; the target vehicle is controlled based on the vehicle control instruction.

The driving path planning device provided by the embodiment of the invention can execute the driving path planning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 6 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a driving path planning method. Namely: acquiring vehicle driving information and road network information of a target vehicle, wherein the vehicle driving information comprises a vehicle starting point position and a vehicle ending point position, and the road network information comprises road nodes and corresponding node attributes thereof; determining an optimal planning path according to the vehicle running information and the road network information; and controlling the target vehicle to run according to the optimal planning path.

In some embodiments, a driving path planning method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of a driving path planning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a driving path planning method in any other suitable way (e.g. by means of firmware).

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

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

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

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

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

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

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

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

Claims

1. The driving path planning method is characterized by comprising the following steps of:

determining an optimal planning path according to the vehicle driving information and the road network information;

2. The method according to claim 1, wherein the acquiring vehicle travel information and road network information of the target vehicle includes:

Determining each road node of a designated area, and acquiring node attributes corresponding to each road node, wherein the node attributes comprise node positions and connectivity;

acquiring current coordinates of a target vehicle, and taking the current coordinates as a starting point position of the vehicle;

Acquiring a target position input by a user based on each road node, and taking the target position as the vehicle end position;

and generating the road network information according to the corresponding relation between each road node and the node attribute.

3. The method of claim 2, wherein said determining an optimal planned path from said vehicle travel information and said road network information comprises:

Determining the node distance between each road node in the road network information and the starting point position of the vehicle, and taking the road node with the nearest node distance as a target node;

And determining an optimal planning path from the target node to the vehicle terminal position through the road network information.

4. A method according to claim 3, wherein said determining an optimal planned path from the target node to the vehicle end position via the road network information comprises:

determining each planning path from the target node to the vehicle terminal position through the road network information;

determining the communication condition of each planning path according to the connectivity, and taking the planning paths with the communication condition as the communication as candidate planning paths;

Calculating path weights corresponding to the candidate planning paths according to a preset path planning algorithm;

and taking the candidate planning path with the largest path weight as the optimal planning path.

5. A method according to claim 3, wherein after said setting the closest road node as the target node, the method further comprises:

And sending the node position coordinates of the target node to a target vehicle so as to guide the target vehicle to drive to the target node.

6. The method according to claim 4, characterized in that after the controlling the target vehicle to travel according to the optimal planned path, the method further comprises:

Acquiring an obstacle detection situation based on the optimal planned path, wherein the obstacle detection situation comprises the existence of an obstacle and the absence of the obstacle;

when the obstacle detection condition is that an obstacle exists, setting the communication condition of the optimal planning path as non-passing, and taking each planning path with the communication condition as communication as a second candidate planning path;

Calculating a second path weight corresponding to each second candidate planning path according to a preset path planning algorithm;

Taking the second candidate planning path with the largest second path weight as an updating planning path;

and controlling the target vehicle to run according to the updated planning path.

7. The method according to claim 1, characterized in that after the control of the target vehicle travel according to the optimal planned path, the method further comprises:

Acquiring running state information of a target vehicle;

When the running state information meets the corresponding preset conditions, generating a vehicle control instruction according to the running state information, wherein the vehicle control instruction comprises starting, pausing or stopping;

and controlling a target vehicle based on the vehicle control instruction.

8. A traffic path planning apparatus, comprising:

9. An electronic device, the electronic device comprising:

At least one processor; and

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

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A computer storage medium storing computer instructions for causing a processor to perform the method of any one of claims 1-7 when executed.