Vehicle path planning method, electronic equipment and storage medium

Abstract

The invention provides a vehicle path planning method, electronic equipment and a storage medium. The method comprises the steps that vehicle path information and real-time road condition information are acquired, the vehicle path information is used for representing an initial navigation path planned by a target vehicle based on map data and destination information, and the real-time road condition information is used for representing driving state information of related vehicles in a current driving road; path planning is carried out based on the vehicle path information and the real-time road condition information, a target path planning result corresponding to the target vehicle is obtained, and the target path planning result is used for determining a target navigation path corresponding to the target vehicle; and issuing the target path planning result to the target vehicle, so that the target vehicle executes the target path planning result. According to the invention, a technical problem of low passing efficiency of a vehicle path planning method in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of automatic driving, in particular, to a vehicle path planning method, an electronic device and a storage medium. BACKGROUND

[0002] In the technical field of automatic driving, Vehicle-to-Infrastructure Communication (V2I) is widely regarded as an important means to improve road safety and traffic efficiency. Especially in the environment of urban multi-road intersections and high-density traffic flow, autonomous vehicles need a technology that can optimize the path and dynamically adjust the driving strategy from a global perspective to quickly respond to unexpected traffic changes such as road maintenance and construction, and ensure smooth operation of traffic flow and driving safety. However, in related technologies, the communication efficiency between vehicles and between vehicles and Road-Side Units (RSUs) is low, especially when unexpected traffic events such as road maintenance or construction occur, vehicles often fall into a cycle of repeatedly calculating similar paths due to the lack of real-time traffic information from a macroscopic perspective, and cannot adjust the driving plan in time, resulting in a decrease in overall traffic efficiency and poor user experience, thereby limiting the application efficiency and safety of autonomous vehicles in complex traffic environments.

[0003] At present, no effective solution has been proposed to solve the above problems. SUMMARY

[0004] Embodiments of the present disclosure provide a vehicle path planning method, an electronic device and a storage medium to at least solve the technical problem of low traffic efficiency in related vehicle path planning methods.

[0005] According to an aspect of an embodiment of the present disclosure, a vehicle path planning method is provided, comprising: obtaining vehicle path information and real-time traffic information, wherein the vehicle path information is used to represent an initial navigation path planned by a target vehicle based on map data and destination information, and the real-time traffic information is used to represent driving state information of related vehicles in a current driving road; performing path planning based on the vehicle path information and the real-time traffic information to obtain a target path planning result corresponding to the target vehicle, wherein the target path planning result is used to determine a target navigation path corresponding to the target vehicle; and issuing the target path planning result to the target vehicle to enable the target vehicle to execute the target path planning result.

[0006] Optionally, the path planning is performed based on the vehicle path information and the real-time traffic information, and the target path planning result corresponding to the target vehicle is obtained by: obtaining road network structure information and historical driving data associated with a target road section, wherein the historical driving data is used to represent vehicle driving data of the target road section in a historical time period, and the target road section is at least part of the initial navigation path; performing overall path planning based on the road network structure information, the historical driving data and the real-time traffic information to obtain an initial path planning result; and performing local path planning using the initial path planning result and the vehicle path information to obtain the target path planning result.

[0007] Optionally, the overall path planning is performed based on the road network structure information, the historical driving data and the real-time traffic information to obtain the initial path planning result by: performing comparison analysis based on the historical driving data and the real-time traffic information to obtain a traffic prediction result, wherein the traffic prediction result is used to represent traffic flow information and congestion trend information associated with the target road section in a future time period; and performing path calculation according to the traffic prediction result and the road network structure information to obtain the initial path planning result.

[0008] Optionally, the local path planning is performed using the initial path planning result and the vehicle path information to obtain the target path planning result by: performing matching processing using real-time position information of the target vehicle and the initial path planning result to obtain a position matching result, wherein the position matching result is used to determine a local path to be passed through by the target vehicle in the future time period; querying nearby traffic data corresponding to the real-time position information based on the position matching result, wherein the nearby traffic data includes at least one of the following: vehicle density information, average vehicle speed information and lane availability information of the local path to be passed through; and performing update processing on the local path to be passed through using a preset local path planning manner and the nearby traffic data to obtain the target path planning result.

[0009] Optionally, the update processing on the local path to be passed through using the preset local path planning manner and the nearby traffic data to obtain the target path planning result includes: obtaining state node data corresponding to the target vehicle, wherein the state node data is used to represent a passing state of the target vehicle in the local path to be passed through; determining a state transition cost of the target vehicle based on the nearby traffic data and the state node data, wherein the state transition cost includes a time cost and an energy consumption cost when the target vehicle transitions from a first state to a second state; and determining the target path planning result according to the state transition cost.

[0010] Optionally, the target path planning result is sent to the target vehicle by: obtaining a path planning priority of the target vehicle, wherein the path planning priority is used to determine a path planning order corresponding to the target vehicle; and sending the target path planning result to the target vehicle according to the path planning priority.

[0011] Optionally, the vehicle path planning method in the embodiments of the present disclosure further comprises: caching the vehicle path information and the real-time road condition information to a target storage area in real time; in response to a communication connection between the target vehicle being interrupted, performing path planning by using the vehicle path information and the real-time road condition information in the target storage area to obtain a target path planning result.

[0012] According to another aspect of the embodiments of the present disclosure, a vehicle path planning method is further provided, comprising: sending vehicle path information, wherein the vehicle path information is used to represent an initial navigation path planned by a target vehicle based on map data and destination information; receiving a target path planning result corresponding to the target vehicle, wherein the target path planning result is obtained by performing path planning based on the vehicle path information and real-time road condition information, the real-time road condition information is used to represent driving state information of a related vehicle in a current driving road, and the target path planning result is used to determine a target navigation path corresponding to the target vehicle; and controlling the target vehicle according to the target path planning result.

[0013] According to another aspect of the embodiments of the present disclosure, a vehicle path planning device is further provided, comprising: an acquisition module, configured to acquire vehicle path information and real-time road condition information, wherein the vehicle path information is used to represent an initial navigation path planned by a target vehicle based on map data and destination information, and the real-time road condition information is used to represent driving state information of a related vehicle in a current driving road; a first planning module, configured to perform path planning based on the vehicle path information and the real-time road condition information to obtain a target path planning result corresponding to the target vehicle, wherein the target path planning result is used to determine a target navigation path corresponding to the target vehicle; and an execution module, configured to issue the target path planning result to the target vehicle to enable the target vehicle to execute the target path planning result.

[0014] Optionally, the first planning module is further configured to: acquire road network structure information and historical driving data associated with a target road section, wherein the historical driving data is used to represent vehicle driving data of the target road section in a historical time period, and the target road section is at least part of a road section in the initial navigation path; perform overall path planning based on the road network structure information, the historical driving data and the real-time road condition information to obtain an initial path planning result; and perform local path planning by using the initial path planning result and the vehicle path information to obtain the target path planning result.

[0015] Optionally, the first planning module is further configured to: perform comparison and analysis based on the historical driving data and the real-time road condition information to obtain a road condition prediction result, wherein the road condition prediction result is used to represent traffic flow information and congestion trend information associated with the target road section in a future time period; and perform path calculation according to the road condition prediction result and the road network structure information to obtain the initial path planning result.

[0016] Optionally, the first planning module is further configured to: perform matching processing on the real-time position information of the target vehicle and the initial path planning result, to obtain a position matching result, the position matching result being used to determine a to-be-passed local path of the target vehicle in a future time period; query nearby road condition data corresponding to the real-time position information based on the position matching result, wherein the nearby road condition data comprises at least one of the following: vehicle density information, average vehicle speed information, and lane availability information of the to-be-passed local path; and perform update processing on the to-be-passed local path based on a preset local path planning manner and the nearby road condition data, to obtain a target path planning result.

[0017] Optionally, the first planning module is further configured to: obtain state node data corresponding to the target vehicle, the state node data being used to indicate a passing state of the target vehicle in the to-be-passed local path; determine a state transition cost of the target vehicle based on the nearby road condition data and the state node data, wherein the state transition cost comprises a time cost and an energy consumption cost when the target vehicle transitions from a first state to a second state; and determine the target path planning result according to the state transition cost.

[0018] Optionally, the execution module is further configured to: obtain a path planning priority of the target vehicle, wherein the path planning priority is used to determine a path planning sequence corresponding to the target vehicle; and deliver the target path planning result to the target vehicle according to the path planning priority.

[0019] Optionally, the vehicle path planning apparatus in the embodiment of the present disclosure further comprises: a cache module, configured to cache vehicle path information and real-time road condition information to a target storage area in real time; and a second planning module, configured to perform path planning on the vehicle path information and the real-time road condition information in the target storage area, to obtain a target path planning result, in response to a communication connection between the target vehicle being interrupted.

[0020] Optionally, the vehicle path planning apparatus in the embodiment of the present disclosure further comprises: a sending module, configured to send vehicle path information, wherein the vehicle path information is used to indicate an initial navigation path planned by the target vehicle based on map data and destination information; a receiving module, configured to receive a target path planning result corresponding to the target vehicle, wherein the target path planning result is obtained based on path planning on the vehicle path information and real-time road condition information, the real-time road condition information being used to indicate driving state information of a related vehicle in a current driving road, and the target path planning result being used to determine a target navigation path corresponding to the target vehicle; and a control module, configured to control the target vehicle according to the target path planning result.

[0021] According to another aspect of the embodiment of the present disclosure, an electronic device is also provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the vehicle path planning method in the embodiment of the present disclosure.

[0022] According to another aspect of the embodiments of the present disclosure, a computer readable storage medium is also provided, which includes a stored executable program, wherein the executable program controls a device where the storage medium is located to perform the vehicle path planning method in the embodiments of the present disclosure when the executable program is running.

[0023] According to another aspect of the embodiments of the present disclosure, a computer program product is also provided, which includes computer instructions, and the computer instructions implement the vehicle path planning method in the embodiments of the present disclosure when executed by a processor.

[0024] In the embodiments of the present disclosure, by obtaining the vehicle path information and the real-time road condition information, and then performing path planning based on the vehicle path information and the real-time road condition information, the target path planning result corresponding to the target vehicle is obtained, and finally the target path planning result is issued to the target vehicle, so that the target vehicle executes the target path planning result, which achieves the purpose of optimizing the vehicle driving route, thereby realizing the technical effect of improving the road traffic efficiency, and further solving the technical problem of low traffic efficiency existing in the vehicle path planning method in the related art. BRIEF DESCRIPTION OF DRAWINGS

[0025] The accompanying drawings, which are included to provide a further understanding of the present disclosure and constitute a part of the present disclosure, illustrate the illustrative embodiments of the present disclosure and specific examples thereof and are used to explain the present disclosure, but should not be used to limit the present disclosure. In the drawings:

[0026] Figure 1 is a flowchart of a vehicle path planning method according to one of the embodiments of the present disclosure;

[0027] Figure 2 is a schematic diagram of another vehicle path planning method according to one of the embodiments of the present disclosure;

[0028] Figure 3 is a flowchart of another vehicle path planning method according to one of the embodiments of the present disclosure;

[0029] Figure 4 is a structural block diagram of a vehicle path planning device according to one of the embodiments of the present disclosure;

[0030] Figure 5 is a structural block diagram of another vehicle path planning device according to one of the embodiments of the present disclosure. DETAILED DESCRIPTION

[0031] In order to better understand the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, not all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present disclosure.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0033] In the related art, the communication efficiency between vehicles and between vehicles and RSUs is low. Especially in the presence of sudden traffic events such as road maintenance or construction, vehicles often fall into a cycle of repeatedly calculating similar paths due to the lack of real-time traffic information with a macroscopic perspective, and cannot adjust the driving plan in time, resulting in a decrease in overall traffic efficiency and poor user experience, thereby limiting the application efficiency and safety of autonomous vehicles in complex traffic environments.

[0034] Specifically, under the current autonomous driving technology system, vehicles mainly rely on sensors such as cameras, millimeter wave radars, and laser radars mounted on the vehicles for environment perception and path planning. However, the detection range of the above-mentioned vehicle-mounted sensors is relatively limited, generally covering a distance of tens to hundreds of meters. Therefore, in the face of sudden traffic events several kilometers away, such as congestion caused by traffic accidents, vehicles often still follow the original route until they approach the congestion point to take action. The limitations of sensors also easily cause ineffective lane changes, exacerbating traffic congestion and significantly reducing overall traffic efficiency. At the same time, in the existing autonomous driving system, the cooperative communication and collaboration mechanism between vehicles and between vehicles and traffic infrastructure is weak. Each autonomous vehicle independently plans and decides on a path, cannot effectively share traffic information, and lacks the ability to optimize the driving path from a global perspective.

[0035] According to an embodiment of the present disclosure, a method embodiment of a vehicle path planning method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0036] The method embodiment can be executed in an electronic device or similar computing device comprising a memory and a processor. Taking a computer terminal as an example, the computer terminal can include one or more processors (the processor can include but is not limited to a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a micro controller unit (MCU), a field programmable gate array (FPGA), a neural-network processor unit (NPU), a tensor processing unit (TPU), an artificial intelligence (AI) type processor, etc.) and a memory for storing data. Optionally, the above computer terminal can also include a transmission device for communication function, an input and output device, and a display device. Those skilled in the art can understand that the above structural description is only illustrative, and does not limit the structure of the above computer terminal. For example, the computer terminal can include more or less components than the above structural description, or have a different configuration from the above structural description.

[0037] The memory can be used to store computer programs, for example, software programs of application software and modules, such as the computer program corresponding to the vehicle path planning method in the embodiments of the present disclosure. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, implements the vehicle path planning method described above. The memory can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory can further include a memory remotely arranged with respect to the processor, which can be connected to the mobile terminal through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0038] The transmission device is configured to receive or transmit data via a network. The network can include, for example, a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device includes a network interface controller (NIC) that is configured to connect to other network devices via a base station to communicate with the Internet. In one example, the transmission device can be a radio frequency (RF) module that is configured to communicate with the Internet via a wireless connection.

[0039] The display device can be, for example, a touch screen liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display can enable a user to interact with a user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI) that a user can interact with via finger contacts and / or gestures on the touch-sensitive surface. The user interactions with the GUI can optionally include one or more of the following: creating a webpage, drawing, text editing, composing an electronic mail message, playing a game, viewing a video, viewing a digital photo, viewing a digital video, viewing a digital music, and / or browsing the Internet.

[0040] Figure 1 is a flowchart of a vehicle path planning method according to an embodiment of the present disclosure, as shown in Figure 1 The method comprises the following steps:

[0041] In step S11, vehicle path information and real-time road condition information are obtained, wherein the vehicle path information is used to represent an initial navigation path planned by a target vehicle based on map data and destination information, and the real-time road condition information is used to represent driving state information of a related vehicle in a current driving road;

[0042] In step S12, path planning is performed based on the vehicle path information and the real-time road condition information, and a target path planning result corresponding to the target vehicle is obtained, wherein the target path planning result is used to determine a target navigation path corresponding to the target vehicle;

[0043] In step S13, the target path planning result is issued to the target vehicle, so that the target vehicle executes the target path planning result.

[0044] The vehicle path information is a driving instruction and route planning calculated by a vehicle navigation system intelligent algorithm based on high-definition map data and destination information set by the driver, which is used to indicate the path trajectory of the vehicle from the current position to the final destination. Specifically, the vehicle path information includes but is not limited to the driving direction of the target vehicle, the turning point, the expected road network node, and the estimated driving time based on the current traffic condition.

[0045] The real-time traffic information is dynamic state data of the current road network collected and processed by the RSU in real time through integrated sensor networks such as cameras, geomagnetic sensors, microwave radars, and V2X communication with surrounding vehicles. Specifically, the real-time traffic information includes but is not limited to road traffic flow, vehicle distribution, vehicle position information, driving speed, driving direction, signal light state, road construction information, traffic accident location and influence range, and other multi-dimensional information in the current driving road.

[0046] Illustratively, after a self-driving vehicle equipped with a 5G-V2X communication module starts, based on its built-in high-definition digital map data and the destination information set by the driver, an initial navigation path is planned, and the initial navigation path is transmitted to the road side unit (RSU) within its surrounding coverage. At the same time, the RSU obtains and processes the dynamic driving state information of the relevant vehicles in the current driving road, such as position, speed, driving direction, and real-time traffic data such as signal light state, road construction details, and accident location, through integrated multi-modal sensor networks and V2X communication technology. Further, based on the received vehicle path information and real-time collected traffic information, the RSU comprehensively calculates and plans a target navigation path from the current position to the destination for the target vehicle based on traffic flow prediction, road traffic capacity, and congestion trend, and sends the planned target navigation path to the target vehicle through V2X communication technology, so that the target vehicle drives according to the target navigation path.

[0047] Illustratively, the vehicle-mounted communication module of the target vehicle can use the Advanced Encryption Standard (AES) to encrypt the initial navigation path, and then send it to the RSU around the target vehicle, to ensure the security and timeliness of data transmission.

[0048] Based on the steps S11 to S14, by obtaining the vehicle path information and the real-time traffic information, and then performing path planning based on the vehicle path information and the real-time traffic information, the target path planning result corresponding to the target vehicle is obtained, and finally the target path planning result is issued to the target vehicle to make the target vehicle execute the target path planning result, so as to achieve the purpose of optimizing the vehicle driving route, thereby realizing the technical effect of improving the road traffic efficiency, and further solving the technical problem of low traffic efficiency existing in the vehicle path planning method in the related art.

[0049] The vehicle path planning method in the embodiments of the present disclosure will be further introduced below.

[0050] Optionally, in step S12, the path planning based on the vehicle path information and the real-time traffic information to obtain the target path planning result corresponding to the target vehicle comprises:

[0051] In step S121, the road network structure information associated with the target road section and the historical driving data are obtained, wherein the historical driving data are used to represent the vehicle driving data of the target road section in a historical time period, and the target road section is at least part of the initial navigation path;

[0052] In step S122, the overall path planning is performed based on the road network structure information, the historical driving data and the real-time traffic information to obtain the initial path planning result;

[0053] In step S123, the local path planning is performed by using the initial path planning result and the vehicle path information to obtain the target path planning result.

[0054] The road network structure information is the network topology structure of the target road section, including but not limited to the connection mode of the target road section, the intersection layout, the number of lanes, the road type (such as trunk road, secondary trunk road), the direction restriction of the road, the road geometric shape parameters, and the road attributes such as the road surface condition and the speed limit information.

[0055] The historical driving data are the vehicle driving records in the target road section in a certain time period, including the position, speed, acceleration, driving direction, lane changing frequency, number of stops and stop duration, signal light response mode, and time distribution characteristics of traffic flow of the vehicle at a specific time point.

[0056] Exemplarily, the RSU in the coverage range around the target vehicle can download and store the road network topology structure of the target road section and its surrounding area from the high-precision map service in the cloud, and can download the historical driving data related to the target road section in the historical similar time period from the cloud traffic big data platform. Subsequently, the RSU fuses the obtained road network structure information, historical driving data and current real-time traffic information, performs overall path planning, comprehensively evaluates the traffic capacity of the road network, predicts possible traffic congestion points, and calculates the initial path planning result from the current position of the target vehicle to the destination, so that the target vehicle avoids the road section with high historical congestion rate and selects the path combination with better traffic efficiency. Then, the RSU performs local path planning based on the specific driving route of the target vehicle and the dynamic distribution of vehicles in the current road section. For possible traffic bottlenecks or temporary obstacles, such as lane changing, detouring and the like, the driving trajectory of the target vehicle is optimized, and finally the target path planning result is generated, so as to ensure that the vehicle can smoothly and efficiently pass through under the guidance of the globally optimal path and the flexible adjustment of the local path.

[0057] Based on the above steps S121 to S123, through the close cooperation of the RSU and the cloud high-precision map and historical traffic database, combined with the dynamic analysis and feedback of real-time traffic information, the adaptability of the vehicle to the complex traffic environment is improved, so that the vehicle can make the best path decision based on historical and real-time data, thereby improving the road traffic efficiency.

[0058] Optionally, in step S122, the overall path planning based on the road network structure information, the historical driving data and the real-time traffic information obtains the initial path planning result, which includes:

[0059] Step S1221, based on the historical driving data and the real-time traffic information, comparison and analysis are performed to obtain a road condition prediction result, wherein the road condition prediction result is used to represent the traffic flow information and congestion trend information associated with the target road section in the future time period;

[0060] Step S1222, according to the road condition prediction result and the road network structure information, path calculation is performed to obtain the initial path planning result.

[0061] The above traffic flow information is the expected number of vehicles, distribution density, average driving speed and time and space distribution characteristics of traffic flow in the target road section and its surrounding area in the predicted time period. Specifically, the above traffic flow information includes the vehicle inflow and outflow in different time periods (such as morning peak, evening peak, weekend, holiday, etc.), and the increase and decrease trend of traffic flow in a specific area or a specific direction.

[0062] The congestion trend information is information about the location, degree and duration of possible congestion on the target road segment in the prediction period, which is analyzed based on historical driving data and real-time traffic information. Specifically, the congestion trend information includes, but is not limited to, predictive congestion caused by regular traffic peak hours, and unplanned congestion caused by unexpected events such as traffic accidents, road construction, changes in weather conditions, etc.

[0063] By way of example, the RSUs within the coverage range around the target vehicle can use a Long Short-Term Memory (LSTM) model in a deep learning network to compare and analyze the historical driving data and real-time traffic information associated with the target path, predict the traffic flow information and congestion trend information of the target road segment in the next three hours, and thus form a traffic prediction result. Further, the traffic capacity of the main road and the secondary road in the city is taken into account, and the target vehicle is guided to avoid the main road with long-term congestion and to select a route composed of secondary roads with higher traffic efficiency.

[0064] By way of example, the traffic prediction result and the road network structure information can be input into a Dijkstra improved algorithm based on traffic flow prediction to calculate an initial overall optimal path from the current vehicle position to the destination, and thus obtain an initial path planning result.

[0065] Based on the steps S1221 to S1222, through the intelligent cooperation of the RSU and the autonomous vehicle, an optimal driving path that adapts to future traffic conditions can be dynamically planned, and the vehicle traffic efficiency and traffic safety can be effectively improved, so that not only the spatiotemporal distribution of traffic flow is predicted, but also sudden congestion can be flexibly responded to, the vehicle can be guided to reasonably change lanes, the best secondary road combination can be selected, and intelligent traffic management and optimization can be achieved.

[0066] Optionally, in step S123, the initial path planning result and the vehicle path information are used for local path planning to obtain a target path planning result, which includes:

[0067] In step S1231, the real-time position information of the target vehicle and the initial path planning result are matched to obtain a position matching result, which is used to determine the local path to be passed through by the target vehicle in the future period;

[0068] In step S1232, the nearby traffic data corresponding to the real-time position information is queried based on the position matching result, wherein the nearby traffic data includes at least one of the following: vehicle density information, average vehicle speed information, lane availability information of the local path to be passed through;

[0069] In step S1233, the local path to be passed through is updated using a preset local path planning method and the nearby traffic data to obtain the target path planning result.

[0070] The above-mentioned to-be-traveled local path is a specific road segment between the current location of the target vehicle and the destination, which generally covers the specific road segment that the vehicle is expected to travel in the next few minutes to half an hour, including but not limited to the currently traveled road, the upcoming intersection, the target lane to be changed, and the possible special road segment (such as the construction zone, the school zone, etc.).

[0071] The above-mentioned vehicle density information is the real-time number and distribution of vehicles on the to-be-traveled local path, which specifically reflects the traffic congestion degree of the current road segment, including but not limited to the number of vehicles per unit length or area, and the distribution density of vehicles on the lane, for evaluating the congestion of the road.

[0072] The above-mentioned lane availability information is the state information of whether the lane of the to-be-traveled local path can be normally traveled, including but not limited to whether the lane is closed, whether the lane change is allowed, and the length of the vehicle queue on the lane, etc.

[0073] The above-mentioned preset local path planning mode includes but is not limited to the dynamic programming algorithm, the A* search algorithm, the ant colony algorithm, the model predictive control algorithm, and the reinforcement learning algorithm.

[0074] For example, it is assumed that the target vehicle starts at 17:45 on the urban trunk road, and the destination is the conference center located on the other side of the city. The initial path planning result is to pass through the traffic hub in the city center. The RSU obtains the position matching result by the position matching algorithm, that the target vehicle will travel the to-be-traveled local path including the road segment from the trunk road to the traffic hub, involving three main intersections and two areas allowing lane change, within 17:45 to 18:15. Based on the above-mentioned position matching result, the RSU queries and integrates the nearby road condition data related to the local path. The data shows that on the trunk road in front of the first intersection that the target vehicle is expected to pass, the vehicle density is 150 vehicles per kilometer, and the average speed is 30 kilometers per hour; while the inner lane near the second intersection is closed due to construction, causing the vehicle density to rise to 200 vehicles per kilometer, and the average speed to drop to 22 kilometers per hour; in addition, on a parallel secondary trunk road, the vehicle density is 100 vehicles per kilometer, and the average speed reaches 45 kilometers per hour, all lanes are open and there is no congestion. Further, the RSU uses the preset dynamic programming algorithm to update and optimize the to-be-traveled local path of the target vehicle, combined with the vehicle density, average speed, and lane availability information. The algorithm calculates that the target vehicle changes to the left lane to the outer lane of the parallel secondary trunk road at 17:55, which can avoid the congestion caused by the construction, while maintaining a high average speed, and is expected to pass the intersection on the secondary trunk road at 17:58 with a green light, without stopping and waiting, and continue to travel along the updated path until approaching the conference center.

[0075] Based on the steps S1231-S1233, the real-time position information of the target vehicle and the initial path planning result are matched to obtain a position matching result. Then, the nearby road condition data corresponding to the real-time position information is determined based on the position matching result. Finally, the preset local path planning method and the nearby road condition data are used to update the to-be-passed local path to obtain the target path planning result. The driving route of the target vehicle can be adjusted and optimized in real time to ensure that the target vehicle can avoid high-density traffic and congested road sections and select a path with higher traffic efficiency and safety in a complex and changeable urban traffic environment, thereby improving the road traffic efficiency and vehicle driving safety.

[0076] Optionally, in step S1233, the target path planning result is obtained by updating the to-be-passed local path using the preset local path planning method and the nearby road condition data, which includes:

[0077] In step S211, state node data corresponding to the target vehicle is obtained, and the state node data is used to represent the passing state of the target vehicle in the to-be-passed local path.

[0078] In step S212, the state transition cost of the target vehicle is determined based on the nearby road condition data and the state node data, wherein the state transition cost includes the time cost and the energy consumption cost when the target vehicle is transferred from a first state to a second state.

[0079] In step S213, the target path planning result is determined according to the state transition cost.

[0080] The above-mentioned state node data is the passing state information of the target vehicle at different positions and time points in the to-be-passed local path, including but not limited to the specific lane where the vehicle is located, the vehicle speed, the distance from the front vehicle, the acceleration of the vehicle, and the energy consumption state of the vehicle.

[0081] Exemplarily, in a certain section, the RSU monitors in real time that the target vehicle is in a long queue in the current lane and drives slowly, while there are fewer vehicles in the adjacent lane and the speed is faster. The RSU divides the path from the current position of the target vehicle to the ideal lane-changing position within a certain distance into multiple state nodes. According to the vehicle density, speed and other information of each state node, the cost of transferring from the current state to the next state is calculated, such as time cost, energy consumption cost, etc. By comparing the cumulative cost of different paths, the path with the minimum cost is selected as the locally optimal path to guide the target vehicle to enter the adjacent lane with faster speed by lane changing. When encountering a situation of temporary reduction of lanes due to road construction, the RSU can dynamically adjust the local path planning based on the dynamic programming algorithm according to the location of the construction area and the length of the vehicle queue, redefine the state nodes and state transition cost, and consider the cost of different behaviors such as vehicle detouring and waiting, so as to guide the vehicle to change lanes in advance or choose an alternative route to cope with sudden traffic changes.

[0082] Exemplarily, from the current position A to the ideal lane-changing position B on the to-be-traveled local path, the RSU defines state nodes 1 to 10. State node 1 represents the current position of the target vehicle, the speed is 30 km / h, the distance from the front vehicle is 10 m, and the energy consumption state is medium; state nodes 2 and 3 represent two states of accelerating to 35 km / h and decelerating to 25 km / h in the current lane, respectively; state nodes 4 to 7 are states in which the target vehicle dynamically adjusts the speed according to the distance from the front vehicle while keeping the lane unchanged; state nodes 8 and 9 represent two states of changing lanes to the adjacent lane with a speed of 30 km / h, including successful and failed lane changing; and state node 10 is the ideal lane-changing position B, where the vehicle has completed lane changing and the speed has increased to 40 km / h with good energy consumption state. Under the above multiple state node data, the target vehicle accelerates from state node 1 to state node 2 with a smaller time cost but an increased energy consumption cost; decelerates from state node 3 through the congestion area to state node 4 with a significantly increased time cost but a relatively small energy consumption cost; and successfully changes lanes from state node 8 to state node 9 with moderate time and energy consumption costs, but needs to be evaluated in real time according to lane availability and lane changing safety. Further, the RSU can analyze all state transition costs by the dynamic programming algorithm to find the path with the minimum cumulative cost, i.e., the target path planning result, to guide the target vehicle to efficiently travel on the to-be-traveled local path, including when to accelerate, decelerate, when to change lanes, and how to adjust the speed and travel strategy according to the real-time road conditions to minimize time loss and energy consumption, and achieve the best travel efficiency.

[0083] Based on the above steps S21 to S23, by acquiring the plurality of state node data of the target vehicle on the local path to be passed, combining the real-time nearby road condition information to determine the cost of state transition, and finally calculating the lowest cost path through the dynamic programming algorithm, the real-time optimization of the driving strategy of the target vehicle can be realized, the passing efficiency and safety of the autonomous vehicle are improved, and the congested and high-energy consumption driving options are effectively avoided.

[0084] Optionally, in step S13, the path planning result is sent to the target vehicle, including:

[0085] In step S131, the path planning priority of the target vehicle is acquired, wherein the path planning priority is used to determine the path planning sequence corresponding to the target vehicle.

[0086] In step S132, the target path planning result is sent to the target vehicle according to the path planning priority.

[0087] For example, when the autonomous vehicle is in a driving state, the RSU monitors that a road section in front is congested due to a sudden traffic accident through the integrated sensor assembly and real-time data analysis system. In this case, the RSU quickly starts the path reconstruction program, recalculates the optimal overall path of the affected vehicle and the best driving scheme of the local section according to the current traffic situation, including the specific location of the accident, the congestion range and degree, and the real-time passing capacity of the surrounding road network. In order to ensure the timeliness and urgency of path update, the RSU will set a priority queue, list the vehicles close to the accident site or with special urgent tasks as the highest priority, and prioritize the optimization of their paths. The updated path information is sent to the vehicle communication module of the target vehicle through V2X communication technology. After receiving the path update instruction from the RSU, the target vehicle adjusts the driving strategy immediately to ensure that it continues to move along the optimized path, thereby avoiding the congested section.

[0088] Based on the above steps S131 to S132, by acquiring the path planning priority of the target vehicle and sending the path planning result to the target vehicle according to the path planning priority, the quick passing of the emergency vehicle can be ensured, the driving path of the ordinary vehicle is optimized, the traffic congestion is reduced, the driving time is shortened, and the overall flow and safety of the road network are enhanced.

[0089] Optionally, the vehicle path planning method in the embodiments of the present disclosure further includes:

[0090] In step S221, the vehicle path information and the real-time road condition information are cached to the target storage area in real time.

[0091] Step S222, in response to the communication connection between the target vehicle being interrupted, path planning is performed using the vehicle path information in the target storage area and real-time traffic information, to obtain a target path planning result.

[0092] Exemplarily, the RSU continuously receives the vehicle path information uploaded by the target vehicle, while collecting and analyzing real-time traffic information through its environmental perception system, and caches the above information to the internal target storage area of the RSU, ensuring that even in the case of communication interruption with the target vehicle, path re-planning can be performed using the cached data. Once communication interruption is detected, the RSU immediately activates the autonomous planning mode, uses the vehicle path information in the storage area and real-time traffic information to quickly calculate a path planning result that adapts to the current traffic conditions through an optimization algorithm, and stores it as a target path planning result to be sent. When the communication connection is restored, the RSU transmits the updated path planning result to the target vehicle in a timely manner to guide the vehicle to adjust the driving route according to the latest traffic conditions.

[0093] Exemplarily, after the target vehicle receives the optimal path calculated by the RSU, it fuses this path information into its own trajectory planning algorithm. At critical moments such as approaching an intersection or needing to change lanes, the target vehicle adjusts the driving strategy in advance according to the real-time dynamic information provided by the RSU, such as signal light status, surrounding vehicle speed and direction, etc., including but not limited to speed control, acceleration / deceleration timing selection, and steering timing optimization, to ensure that the vehicle can pass smoothly and efficiently when traffic signals change or traffic density changes. Specifically, the target vehicle can use a model predictive control algorithm to dynamically plan a driving trajectory that meets safety standards and maximizes traffic efficiency, such as accelerating moderately to reduce waiting time at an intersection when predicting that the signal ahead will turn green, or smoothly changing lanes to take advantage of a more open lane when detecting a decrease in vehicles in the adjacent lane.

[0094] Based on the above steps S221 to S222, by caching the vehicle path information and real-time traffic information to the target storage area in real time, and then using the vehicle path information in the target storage area and real-time traffic information to perform path planning when the communication connection between the RSU and the target vehicle is interrupted, a target path planning result is obtained, which can ensure that even in the case of unstable communication, the RSU can provide continuous path guidance for the vehicle based on the latest traffic data, maintain the smooth operation of the autonomous vehicle, and avoid driving uncertainty caused by communication interruption, thereby improving the safety of vehicle driving.

[0095] Figure 2 is a schematic diagram of another vehicle path planning method according to an embodiment of the present disclosure, as Figure 2As shown, the roadside unit can perform path planning based on the vehicle path information and real-time traffic information to obtain a target path planning result corresponding to the target vehicle.

[0096] Exemplarily, the RSU can be connected through V2X communication to obtain, in real time, an initial navigation path planned by the target vehicle based on map data and destination information, i.e., the vehicle path information, and driving state information of relevant vehicles in the current driving road. On this basis, the RSU plans a generally optimal route from the current position to the destination for the target vehicle from a global perspective according to the urban traffic network structure, historical traffic big data and real-time traffic, and uses a Dijkstra improved algorithm based on traffic flow prediction and optimization. For example, the traffic capacity and congestion trend of the urban trunk road and secondary road are comprehensively considered to guide the vehicle to avoid the trunk road with long-time congestion and select a route composed of secondary roads with higher traffic efficiency. In addition, based on the vehicle distribution in the current road section, the RSU can use a local path planning algorithm (such as dynamic programming algorithm, A* algorithm, ant colony algorithm, etc.) to quickly analyze the vehicle dynamics of the current lane and adjacent lanes. For example, when it is detected that the vehicles in the current lane are queued for a long time and travel slowly, while the vehicles in the adjacent lane are few and travel fast, a local optimal path is planned to enter the adjacent lane by changing lanes. When the road construction leads to temporary reduction of lanes, the RSU will dynamically adjust the local path planning according to the position of the construction area and the length of the vehicle queue to guide the vehicle to change lanes in advance or select an alternative route to cope with the sudden change of traffic conditions.

[0097] Figure 3 is a flowchart of another vehicle path planning method according to an embodiment of the present disclosure, as shown in Figure 3 The method comprises the following steps:

[0098] Step S31, sending vehicle path information, wherein the vehicle path information is used to represent an initial navigation path planned by the target vehicle based on map data and destination information;

[0099] Step S32, receiving a target path planning result corresponding to the target vehicle, wherein the target path planning is obtained by performing path planning based on the vehicle path information and real-time traffic information, the real-time traffic information is used to represent driving state information of relevant vehicles in the current driving road, and the target path planning result is used to determine a target navigation path corresponding to the target vehicle;

[0100] Step S33, controlling the target vehicle according to the target path planning result.

[0101] Exemplarily, after the vehicle is started and the destination is set, an initial navigation path is planned based on the map data and the destination information, and the initial navigation path is sent to the RSU through the vehicle-mounted communication system. After receiving the initial navigation path, the RSU collects real-time road condition information by using various sensors equipped thereon, and plans a target path planning result from the current position to the destination based on the real-time road condition information and the initial navigation path. Further, the RSU sends the planned target navigation path to the target vehicle through the V2X communication technology. After receiving the target path planning result, the target vehicle travels according to the target path planning result.

[0102] Based on the steps S31 to S33, by sending the vehicle path information, receiving the target path planning result corresponding to the target vehicle, and then controlling the target vehicle according to the target path planning result, the purpose of optimizing the vehicle travel route is achieved, thereby achieving the technical effect of improving the road traffic efficiency, and further solving the technical problem of low traffic efficiency existing in the vehicle path planning method in the related art.

[0103] Through the description of the above implementation, those skilled in the art can clearly understand that the method according to the above embodiment can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases the former is a better implementation. Based on such understanding, the technical solutions of the disclosure can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a plurality of instructions for making a terminal device (which can be a mobile phone, computer, server, or network device, etc.) execute the method described in various embodiments of the disclosure.

[0104] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the disclosure are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0105] In the embodiments of the disclosure, a vehicle path planning device is also provided, which is used to implement the above embodiments and preferred embodiments, and has been described and will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware, or a combination of software and hardware implementations are also possible and contemplated.

[0106] Figure 4 is a structural block diagram of a vehicle path planning device according to an embodiment of the present disclosure, as shown in the figure, the device comprises: Figure 4

[0107] The acquisition module 401 is configured to acquire vehicle path information and real-time road condition information, wherein the vehicle path information is used to represent an initial navigation path of a target vehicle based on map data and destination information, and the real-time road condition information is used to represent driving state information of a related vehicle in a current driving road;

[0108] The first planning module 402 is configured to perform path planning based on the vehicle path information and the real-time road condition information, to obtain a target path planning result corresponding to the target vehicle, wherein the target path planning result is used to determine a target navigation path corresponding to the target vehicle.

[0109] The execution module 403 is configured to issue the target path planning result to the target vehicle, so that the target vehicle executes the target path planning result.

[0110] Optionally, the first planning module 402 is further configured to acquire road network structure information and historical driving data associated with a target section, wherein the historical driving data is used to represent vehicle driving data of the target section in a historical time period, and the target section is at least part of the initial navigation path; perform overall path planning based on the road network structure information, the historical driving data and the real-time road condition information, to obtain an initial path planning result; and perform local path planning by using the initial path planning result and the vehicle path information, to obtain the target path planning result.

[0111] Optionally, the first planning module 402 is further configured to perform comparison analysis based on the historical driving data and the real-time road condition information, to obtain a road condition prediction result, wherein the road condition prediction result is used to represent traffic flow information and congestion trend information associated with the target section in a future time period; and perform path calculation according to the road condition prediction result and the road network structure information, to obtain the initial path planning result.

[0112] Optionally, the first planning module 402 is further configured to perform matching processing by using real-time position information of the target vehicle and the initial path planning result, to obtain a position matching result, wherein the position matching result is used to determine a local path to be passed through by the target vehicle in the future time period; query and determine nearby road condition data corresponding to the real-time position information based on the position matching result, wherein the nearby road condition data includes at least one of the following: vehicle density information, average vehicle speed information, and lane availability information of the local path to be passed through; and perform update processing on the local path to be passed through by using a preset local path planning mode and the nearby road condition data, to obtain the target path planning result.

[0113] ​Optionally, the first planning module 402 is further configured to: acquire state node data corresponding to the target vehicle, the state node data being used to indicate a passing state of the target vehicle in the local path to be passed; determine a state transition cost of the target vehicle based on the nearby road condition data and the state node data, wherein the state transition cost comprises a time cost and an energy consumption cost when the target vehicle is transferred from a first state to a second state; and determine the target path planning result according to the state transition cost.

[0114] Optionally, the execution module 403 is further configured to: acquire a path planning priority of the target vehicle, wherein the path planning priority is used to determine a path planning sequence corresponding to the target vehicle; and distribute the target path planning result to the target vehicle according to the path planning priority.

[0115] Optionally, the vehicle path planning device in the embodiment of the present disclosure further comprises:

[0116] The cache module 404 is configured to cache the vehicle path information and the real-time road condition information to a target storage area in real time.

[0117] The second planning module 405 is configured to, in response to a communication connection between the target vehicle being interrupted, perform path planning by using the vehicle path information and the real-time road condition information in the target storage area, to obtain a target path planning result.

[0118] Figure 5 is a structural block diagram of another vehicle path planning device according to an embodiment of the present disclosure, as shown in Figure 5 The device comprises:

[0119] The sending module 501 is configured to send vehicle path information, wherein the vehicle path information is used to indicate an initial navigation path planned by the target vehicle based on map data and destination information.

[0120] The receiving module 502 is configured to receive a target path planning result corresponding to the target vehicle, wherein the target path planning result is obtained by performing path planning based on the vehicle path information and real-time road condition information, the real-time road condition information is used to indicate driving state information of a related vehicle in a current driving road, and the target path planning result is used to determine a target navigation path corresponding to the target vehicle.

[0121] The control module 503 is configured to control the target vehicle according to the target path planning result.

[0122] It should be noted that the above-mentioned modules can be realized by software or hardware, and for the latter, the following implementation manners can be used, but are not limited thereto: all the above-mentioned modules are located in the same processor; or the above-mentioned modules are located in different processors in any combination.

[0123] According to another aspect of the embodiments of the present disclosure, an electronic device is also provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the vehicle path planning method in the embodiments of the present disclosure.

[0124] Optionally, in the present embodiment, the processor can be configured to execute the following steps by a computer program:

[0125] S1, obtaining vehicle path information and real-time traffic information, wherein the vehicle path information is used to represent an initial navigation path planned by a target vehicle based on map data and destination information, and the real-time traffic information is used to represent driving state information of a related vehicle in a current driving road;

[0126] S2, performing path planning based on the vehicle path information and the real-time traffic information to obtain a target path planning result corresponding to the target vehicle, wherein the target path planning result is used to determine a target navigation path corresponding to the target vehicle;

[0127] S3, issuing the target path planning result to the target vehicle to enable the target vehicle to execute the target path planning result.

[0128] According to another aspect of the embodiments of the present disclosure, a computer-readable storage medium is also provided, comprising a stored executable program, wherein the executable program controls a device where the storage medium is located to execute the vehicle path planning method in the embodiments of the present disclosure when the executable program is running.

[0129] Optionally, in the present embodiment, the storage medium can be configured to store a computer program for executing the following steps:

[0130] S1, obtaining vehicle path information and real-time traffic information, wherein the vehicle path information is used to represent an initial navigation path planned by a target vehicle based on map data and destination information, and the real-time traffic information is used to represent driving state information of a related vehicle in a current driving road;

[0131] S2, performing path planning based on the vehicle path information and the real-time traffic information to obtain a target path planning result corresponding to the target vehicle, wherein the target path planning result is used to determine a target navigation path corresponding to the target vehicle;

[0132] S3, issuing the target path planning result to the target vehicle to enable the target vehicle to execute the target path planning result.

[0133] Optionally, in the embodiment, the storage medium can include, but is not limited to, a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various storage media that can store computer programs.

[0134] According to another aspect of the embodiments of the present disclosure, a computer program product is also provided, which includes computer instructions for implementing the vehicle path planning method in the embodiments of the present disclosure when executed by a processor.

[0135] Optionally, in the embodiment, the computer program product can be set as a computer program for performing the following steps:

[0136] S1, obtaining vehicle path information and real-time road condition information, wherein the vehicle path information is used to represent an initial navigation path planned by a target vehicle based on map data and destination information, and the real-time road condition information is used to represent driving state information of a related vehicle in a current driving road;

[0137] S2, performing path planning based on the vehicle path information and the real-time road condition information to obtain a target path planning result corresponding to the target vehicle, wherein the target path planning result is used to determine a target navigation path corresponding to the target vehicle;

[0138] S3, issuing the target path planning result to the target vehicle, so that the target vehicle executes the target path planning result.

[0139] The serial numbers of the embodiments of the present disclosure are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0140] In the above embodiments of the present disclosure, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0141] In the several embodiments provided by the present disclosure, it should be understood that the disclosed technology can be implemented in other ways. Of course, the embodiment described above is only illustrative, and for example, the division of units can be a logical function division, and actual implementation can be in another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0142] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0143] In addition, each functional unit in various embodiments of the present disclosure can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0144] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical scheme of the present disclosure essentially or the part that contributes to the prior art, or the whole or part of the technical scheme can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present disclosure. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various program code storage media.

[0145] The above is only the preferred embodiment of the present disclosure, and it should be pointed out that for those skilled in the art, without departing from the principles of the present disclosure, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present disclosure.

Claims

1. A vehicle route planning method, characterized in that, include: The vehicle path information and real-time traffic information are obtained, wherein the vehicle path information is used to represent the initial navigation path planned by the target vehicle based on map data and destination information, and the real-time traffic information is used to represent the driving status information of relevant vehicles on the current driving road. Based on the vehicle path information and the real-time traffic information, path planning is performed to obtain the target path planning result corresponding to the target vehicle, wherein the target path planning result is used to determine the target navigation path corresponding to the target vehicle; The target route planning result is sent to the target vehicle so that the target vehicle executes the target route planning result.

2. The vehicle routing method according to claim 1, characterized in that, Based on the vehicle route information and the real-time traffic information, route planning is performed to obtain the target route planning result corresponding to the target vehicle, including: Obtain road network structure information and historical driving data associated with the target road segment, wherein the historical driving data is used to represent vehicle driving data of the target road segment within a historical time period, and the target road segment is at least a portion of the road segment in the initial navigation path; Based on the road network structure information, the historical driving data and the real-time traffic information, an overall route planning is performed to obtain the initial route planning result; The initial path planning result and the vehicle path information are used to perform local path planning to obtain the target path planning result.

3. The vehicle routing method according to claim 2, characterized in that, Based on the road network structure information, the historical driving data, and the real-time traffic information, overall route planning is performed to obtain the initial route planning result, which includes: Based on the comparison and analysis of the historical driving data and the real-time traffic information, a traffic prediction result is obtained, wherein the traffic prediction result is used to represent the traffic flow information and congestion trend information associated with the target road segment in the future time period; Based on the traffic condition prediction results and the road network structure information, path calculation is performed to obtain the initial path planning results.

4. The vehicle routing method according to claim 2, characterized in that, Using the initial path planning result and the vehicle path information, local path planning is performed to obtain the target path planning result, which includes: The real-time location information of the target vehicle and the initial path planning result are matched to obtain a location matching result, which is used to determine the local path to be traveled by the target vehicle in the future time period. Based on the location matching result, query and determine the nearby traffic data corresponding to the real-time location information, wherein the nearby traffic data includes at least one of the following: vehicle density information, average vehicle speed information, and lane availability information of the local path to be traversed; The local path to be traveled is updated using a preset local path planning method and the nearby road condition data to obtain the target path planning result.

5. The vehicle routing method according to claim 4, characterized in that, The local path to be traversed is updated using a preset local path planning method and the nearby traffic data to obtain the target path planning result, including: Obtain the status node data corresponding to the target vehicle, the status node data being used to represent the passage status of the target vehicle in the local path to be traversed; The state transition cost of the target vehicle is determined based on the nearby road condition data and the state node data, wherein the state transition cost includes the time cost and energy cost of the target vehicle when it transitions from the first state to the second state; The target path planning result is determined based on the state transition cost.

6. The vehicle routing method according to claim 1, characterized in that, Sending the target path planning result to the target vehicle includes: Obtain the path planning priority of the target vehicle, wherein the path planning priority is used to determine the path planning order corresponding to the target vehicle; The target route planning results are sent to the target vehicle according to the route planning priority.

7. The vehicle routing method according to claim 1, characterized in that, The method further includes: The vehicle route information and the real-time traffic information are cached in real time to the target storage area; In response to the interruption of the communication connection with the target vehicle, the system performs path planning using the vehicle path information and the real-time traffic information in the target storage area to obtain the target path planning result.

8. A vehicle route planning method, characterized in that, include: Send vehicle path information, wherein the vehicle path information is used to represent the initial navigation path planned by the target vehicle based on map data and destination information; Receive the target path planning result corresponding to the target vehicle, wherein the target path planning result is obtained by path planning based on the vehicle path information and real-time traffic information, the real-time traffic information is used to represent the driving status information of relevant vehicles on the current driving road, and the target path planning result is used to determine the target navigation path corresponding to the target vehicle; The target vehicle is controlled based on the target path planning results.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the vehicle routing method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the vehicle routing method according to any one of claims 1 to 8.

11. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed by a processor, implement the vehicle routing method as described in any one of claims 1 to 8.

Images