Path planning method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a path planning method and device, equipment and a storage medium. Comprising the following steps: acquiring a current driving state and a current planned path of a vehicle, and acquiring real-time traffic data corresponding to the current planned path according to the current planned path; determining a current congestion state and an estimated congestion state corresponding to the current planned path according to the current driving state and the real-time traffic data; and determining a target planned path of the vehicle according to the current congestion state and the estimated congestion state. According to the invention, comprehensive, rapid and predictive planning for the vehicle driving path is realized, and the change of the congestion state of the road section in the vehicle driving process is fully considered, so that the planned path can help the vehicle to better avoid the congestion road section, and the driving travel quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a path planning method, device, equipment and storage medium. Background Art

[0002] With the rapid development and popularization of autonomous driving technology, autonomous vehicles have gradually become an important part of the future intelligent transportation system. In the current practice of autonomous driving technology, vehicles mainly rely on built-in high-precision maps and navigation systems to plan driving paths, and use on-board sensors to perceive the surrounding environment in real time to make decisions and adjustments.

[0003] This method is often unable to cope with the complex and ever-changing urban traffic environment, especially the congestion during peak hours. Moreover, congestion has gradually become the norm in modern cities. Existing solutions for vehicle route planning can only consider the congestion of the road section at the time of vehicle route planning, and automatically or manually select a route plan with fewer congested sections or a route plan that can get rid of congested sections from multiple alternative route planning plans.

[0004] However, urban road congestion is constantly changing. People often choose a route and then drive directly along the route without paying attention to changes in road conditions. However, since the time required for vehicles to travel to different sections of the route planning scheme is different, the congestion of the section may change after a certain period of time. Limited by the coverage of sensors and data processing capabilities, it is difficult for vehicles to plan routes that are beyond their perception range in a comprehensive, fast, and flexible manner, resulting in the inability to avoid congested sections in a timely manner, affecting the quality of driving. Summary of the invention

[0005] The present invention provides a path planning method, device, equipment and storage medium. Based on the vehicle-road-cloud integration technology, the vehicle driving path is planned comprehensively, quickly and predictably according to the information available at the current moment of path planning, and the changes in the congestion status of the road section during the vehicle driving process are fully considered, so that the planned path can help the vehicle better avoid congested sections and improve the driving quality.

[0006] In a first aspect, an embodiment of the present invention provides a path planning method, including:

[0007] Obtain the current driving status and current planned path of the vehicle, and obtain the real-time traffic data corresponding to the current planned path according to the current planned path;

[0008] Determine the current congestion state and the estimated congestion state corresponding to the current planned route according to the current driving state and the real-time traffic data;

[0009] The target planning path of the vehicle is determined based on the current congestion status and the estimated congestion status.

[0010] In a second aspect, an embodiment of the present invention further provides a path planning device, including:

[0011] The data acquisition module is used to obtain the current driving status and the current planned path of the vehicle, and obtain the real-time traffic data corresponding to the current planned path according to the current planned path;

[0012] A congestion state determination module, used to determine the current congestion state and the estimated congestion state corresponding to the current planned path according to the current driving state and real-time traffic data;

[0013] The planned path determination module is used to determine the target planned path of the vehicle based on the current congestion status and the estimated congestion status.

[0014] In a third aspect, an embodiment of the present invention further provides a path planning device, including:

[0015] at least one processor; and a memory communicatively coupled to the at least one processor;

[0016] The memory stores a programmable program that can be executed by at least one processor, and the programmable program is executed by at least one processor so that the at least one processor can implement the path planning method of any embodiment of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising executable instructions, which, when executed by a programmable processor, are used to execute the path planning method of any embodiment of the present invention.

[0018] The embodiment of the present invention provides a path planning method, device, equipment and storage medium, which obtains the current driving state and the current planned path of the vehicle, and obtains the real-time traffic data corresponding to the current planned path according to the current planned path; determines the current congestion state and the estimated congestion state corresponding to the current planned path according to the current driving state and the real-time traffic data; and determines the target planned path of the vehicle according to the current congestion state and the estimated congestion state. By adopting the above technical solution, when it is necessary to adjust the path planning of the vehicle, the required path planning vehicle's own driving information and the traffic state of each road section in the area where the vehicle is located are obtained based on the vehicle-road-cloud integration technology, and then based on the acquired information, the current congestion state of the current planned path of the vehicle at the current moment and the estimated congestion state that may occur during the driving process of the vehicle are determined, so that when determining the target planned path finally adopted by the vehicle, the influence of the current congestion state and the estimated congestion state on the vehicle's driving path can be considered at the same time, and a comprehensive, fast and predictive planning for the vehicle's driving path is achieved, and the changes in the congestion state of the road section during the vehicle's driving process are fully considered, so that the planned path can help the vehicle better avoid congested sections and improve the quality of driving travel.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a path planning method provided in Embodiment 1 of the present invention;

[0022] Figure 2 A flow chart of a path planning method provided in Embodiment 2 of the present invention;

[0023] Figure 3 A schematic diagram of the structure of a path planning device provided in Embodiment 3 of the present invention;

[0024] Figure 4 A schematic diagram of the structure of a path planning device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 A flow chart of a path planning method provided in the first embodiment of the present invention. The embodiment of the present invention can be applied to verify the feasibility and optimize the planned path of the vehicle to obtain a planned path that can better avoid congested sections. The path planning method can be executed by a path planning device, which can be implemented by software and / or hardware, and the path planning device can be configured in a path planning device. Optionally, the path planning device can be an electronic device, which can be a notebook, a desktop computer, a smart tablet, etc. It can be understood that the path planning device should be a cloud device in the vehicle-road-cloud integration technology, and the embodiment of the present invention does not limit the specific type of the device.

[0029] like Figure 1 As shown, a path planning method provided by an embodiment of the present invention specifically includes the following steps:

[0030] S101, obtaining the current driving state and the current planned path of the vehicle, and obtaining real-time traffic data corresponding to the current planned path according to the current planned path.

[0031] In this embodiment, the current driving state can be specifically understood as information related to the vehicle's driving itself during the vehicle's driving process at the current moment. For example, the current driving state may include the vehicle's position, speed, and lane at the current moment, etc., which is not limited in this embodiment of the present invention. The current planned path can be specifically understood as the path planning scheme used by the vehicle at the current moment, or it can be the driving route of the vehicle at the current moment in the area governed by the cloud device.

[0032] In this embodiment, real-time traffic data can be specifically understood as the road condition data of each road section corresponding to the current planned path at the current moment, which is obtained by the cloud device due to the roadside equipment set up for the road section corresponding to the current planned path. Exemplarily, in the area managed by the cloud device, edge devices can be set up around each road section, and information capture can be completed through sensors such as cameras and radars configured on the edge devices, and the edge devices perform preliminary processing on the captured information, and send the obtained perceived target information and perceived event information to the roadside equipment corresponding to the road section. At the same time, each network training vehicle that is connected to the roadside equipment in communication can also send its own vehicle information to the roadside equipment. After the roadside equipment integrates and simply calculates the various types of information it receives, it can obtain real-time traffic data of all road sections in the corresponding area that can be sent to the cloud device. Optionally, the above-mentioned real-time traffic data may include map information, traffic light information, congestion information, and networked vehicle information, etc., which is not limited by the embodiment of the present invention. It can be understood that the road section corresponding to the currently planned path may be located within the management range of different roadside equipment. The cloud device can obtain the real-time traffic data corresponding to the road section related to the currently planned path from the corresponding roadside equipment based on the area passed by the currently planned path, and integrate the real-time traffic data corresponding to the currently planned path.

[0033] Specifically, when a vehicle enters the cloud device management area, the cloud device can communicate with the vehicle and receive the current driving status and current planned path uploaded by the vehicle. The cloud device can clarify the entry point, exit point and path within its own management area based on the vehicle's current planned path. After clarifying the path, it can determine the multiple roadside devices on the path. The cloud device can obtain the real-time traffic data collected and counted by each roadside device, and integrate the real-time traffic data obtained from each roadside device to determine the real-time traffic data corresponding to the current planned path.

[0034] S102: Determine a current congestion state and an estimated congestion state corresponding to a current planned route according to a current driving state and real-time traffic data.

[0035] In this embodiment, the current congestion state can be specifically understood as a state indicating whether there is a congested section on the currently planned path at the current moment. The estimated congestion state can be specifically understood as a state indicating whether the vehicle will encounter a congested section during the driving process if it continues to travel along the currently planned path.

[0036] Specifically, after clarifying the real-time traffic data corresponding to the current planned path, it is possible to determine whether there is congestion in each roadside equipment management section on the current planned path based on the real-time traffic data, and to obtain the current congestion status corresponding to the current planned path by integrating the congestion conditions of all sections. At the same time, it is also possible to determine the time required for the vehicle to reach different locations on the current planned path based on the current driving status, and then predict whether there will be congestion at different locations on the current planned path when the vehicle arrives based on the time it takes for the vehicle to arrive at different sections and real-time traffic data. The estimated congestion status corresponding to the current planned path can be obtained by integrating the prediction results.

[0037] In an embodiment of the present invention, when judging congestion on the planned path currently located by the vehicle, the congestion situation of the planned path at the current moment and whether the vehicle is likely to encounter congestion while traveling along the planned path are determined at the same time, fully considering the characteristics of road congestion changing over time, so that the vehicle can not only avoid the currently congested path during driving, but also avoid the congested path that may be encountered in the future driving process, and also allow the vehicle to choose a path where congestion is eliminated when the vehicle travels to the corresponding position even if there is congestion at the current moment, thereby ensuring the feasibility of planning the path based on the current congestion status and the estimated congestion status.

[0038] S103: Determine a target planning path for the vehicle according to the current congestion status and the estimated congestion status.

[0039] In this embodiment, the target planned path can be specifically understood as a travel path plan with the least congestion that can be planned for the vehicle based on the vehicle's driving status and road conditions at the current moment.

[0040] Specifically, after clarifying the current congestion status and the estimated congestion status, it can be known whether the vehicle will encounter a congested section if it continues to travel along the currently planned route. If there will be no congested section, it can be considered that there is no need to spend more computing resources to re-plan the path for the vehicle, and the current planned path can be directly used as the target planned path for the vehicle. If it is determined that a congested section will be encountered, the vehicle's driving path can be re-planned and selected, and the path with the shortest travel time can be determined as the vehicle's target planned path. It can be understood that the path obtained after re-planning and selecting the vehicle's driving path can still be the current planned path, and the current planned path will not be directly abandoned because the current planned path may be congested.

[0041] The technical solution of this embodiment obtains the current driving state and the current planned path of the vehicle, and obtains the real-time traffic data corresponding to the current planned path according to the current planned path; determines the current congestion state and the estimated congestion state corresponding to the current planned path according to the current driving state and the real-time traffic data; and determines the target planned path of the vehicle according to the current congestion state and the estimated congestion state. By adopting the above technical solution, when it is necessary to adjust the path planning of the vehicle, the required path planning vehicle's own driving information and the traffic state of each road section in the area where the vehicle is located are obtained based on the vehicle-road-cloud integration technology, and then based on the obtained information, the current congestion state of the current planned path of the vehicle at the current moment and the estimated congestion state that may occur during the driving process of the vehicle are determined, so that when determining the target planned path finally adopted by the vehicle, the influence of the current congestion state and the estimated congestion state on the vehicle's driving path can be considered at the same time, realizing a comprehensive, fast and predictive planning for the vehicle's driving path, fully considering the changes in the congestion state of the road section during the vehicle's driving process, so that the planned path can help the vehicle better avoid congested sections and improve the quality of driving travel.

[0042] Embodiment 2

[0043] Figure 2 A flow chart of a path planning method provided in Embodiment 2 of the present invention. The embodiment of the present invention is further optimized on the basis of the above-mentioned optional technical solutions, by dividing the current planned path into multiple current planned sections according to the roadside equipment that receives real-time traffic data, and determining the time required for the vehicle to travel to the corresponding current planned section in a normal state based on the average vehicle travel time of each current planned section and the current driving state of the vehicle, and then based on the time required for the vehicle to travel to the current planned section and the real-time traffic data corresponding to the current planned section, the congestion state that the current planned section may be in when the vehicle travels to the current planned section can be estimated to obtain the corresponding estimated section congestion state, and then the congestion states of each estimated section are integrated to obtain the estimated congestion state corresponding to the entire current planned path. After clarifying the current congestion status and estimated congestion status of the current planned path, the two can be combined to determine whether the vehicle can continue to travel along the current planned path. If it is determined that the vehicle can continue to travel along the current planned path, the most suitable lane for driving under the current planned path is selected for the vehicle, realizing comprehensive, fast and predictable planning at the lane level. Not only is the change of the congestion status of the road section during vehicle driving fully considered at the road section level, but also the impact of traffic flow conditions on different lanes in the same road section on vehicle driving. The congestion effect, so that the planned path can help vehicles better avoid congested sections, thereby improving the quality of driving trips.

[0044] like Figure 2As shown, a path planning method provided by an embodiment of the present invention specifically includes the following steps:

[0045] S201. Acquire the current driving state and the current planned path of the vehicle, and acquire real-time traffic data corresponding to the current planned path according to the current planned path.

[0046] S202: Determine a currently planned road segment set according to the currently planned path.

[0047] In this embodiment, the current planned road segment set can be specifically understood as a set of road segments obtained by splitting the current planned path according to different collectors of real-time traffic data.

[0048] Specifically, according to the management area of ​​the roadside equipment passed by the current planned path, or according to the different roads included in the current planned path, the current planned path is divided into multiple current planned sections, and each current planned section is integrated into a current planned section set according to the driving order from the starting point to the end point.

[0049] S203: Determine an estimated arrival time of the vehicle at each current planned road section in the current planned road section set according to the average vehicle travel time of each current planned road section and the current driving status.

[0050] In this embodiment, the average vehicle travel time can be specifically understood as the average time required for a vehicle to travel through the current planned road section at a speed limit without congestion. The estimated arrival time can be specifically understood as the time it takes for a vehicle to travel from the current time to the corresponding current planned road section.

[0051] Specifically, the vehicle's current driving status and driving conditions such as the vehicle's current location and speed can be determined based on the vehicle's current driving status. For each currently planned section in the currently planned section set, the multiple sections that the vehicle needs to pass through in the process of traveling from the current location to the currently planned section can be determined. The time it takes for the vehicle to travel through the current section at the current speed and the average vehicle travel time of the multiple sections that the vehicle needs to pass through are summed up as the time required for the vehicle to travel from the current location to the above-mentioned currently planned section. The sum of this time and the current time can be used as the estimated arrival time of the vehicle at the above-mentioned currently planned section. Based on the above method, the estimated arrival time of the vehicle when it travels to the starting position of each currently planned section in the currently planned section set can be determined.

[0052] For example, assuming that the current planned section set includes [section 1, section 2, ..., section n], a total of n currently planned sections, at the current moment, it can be determined that the vehicle is traveling in section 2 according to the current driving state. At this time, the estimated arrival time for section 5 is calculated as an example. The time required for the vehicle to travel the remaining distance in section 2 at the speed corresponding to the current driving state can be determined as T1, and the average vehicle travel time corresponding to sections 3 and 4 can be determined as T2 and T3 respectively. The current moment is determined as T0, and the estimated arrival time when the vehicle travels to the starting position of section 5 can be expressed as T=T0+T1+T2+T3, that is, the time for the vehicle to travel through the remaining position of section 2, and the sum of the average vehicle travel time of the two sections that have not been entered subsequently. By analogy, it can be clarified that the estimated arrival time when the vehicle travels to the starting position of each current planned section in the current planned section set based on the current driving state.

[0053] S204. For each currently planned road section in the currently planned road section set, determine the current road section congestion status of the currently planned road section according to the real-time traffic data of the road section corresponding to the currently planned road section, and determine the estimated road section congestion status of the currently planned road section according to the real-time traffic data and estimated arrival time of the road section corresponding to the currently planned road section.

[0054] In this embodiment, the real-time traffic data of the road section can be specifically understood as the real-time traffic data corresponding to the current planned road section, which can indicate the traffic conditions of the current planned road section at the current moment. The congestion status of the current road section can be specifically understood as the congestion status corresponding to the current planned road section at the current moment. The estimated congestion status of the road section can be specifically understood as the status information obtained by estimating the congestion status that the current planned road section may be in when the vehicle travels to the current planned road section.

[0055] Specifically, in order to clarify the current congestion state and the estimated congestion state on the entire currently planned path, it is necessary to first clarify the congestion state of each currently planned section set at the current time and under the estimated conditions. At this time, the congestion state at the current time and the time of vehicle arrival can be determined for each currently planned section in the currently planned section set. Since each currently planned section corresponds to a roadside device for collecting real-time traffic conditions on it, the real-time traffic condition data collected by the roadside device for the current planned section can be used as the real-time traffic data of the road section of the current planned road, so that the cloud device can determine the congestion of the current planned section at the current time based on the acquired real-time traffic data of the road section, and obtain the corresponding current section congestion state. At the same time, based on the estimated arrival time for the current planned section determined above, and the real-time traffic data of the road section corresponding to the current planned section, the scene when the vehicle travels to the current planned section can be estimated and simulated to obtain the corresponding estimated section congestion state.

[0056] It can be understood that the information related to the congestion status can be directly included in the real-time traffic data of the road section, that is, the cloud device can directly read the current congestion status of the current planned road section at the current moment from the real-time traffic data of the road section, or the cloud device can complete the calculation of the congestion status of the current road section according to the specific information contained in the real-time traffic data of the road section. The essential difference is only in the calculation subject of the current congestion status of the road section, which can be a roadside device that collects real-time traffic data of the road section, or a cloud device. The embodiment of the present invention does not limit this.

[0057] Optionally, the current road congestion state of the currently planned road section is determined according to the real-time traffic data of the road section corresponding to the currently planned road section, which can be specifically achieved in the following ways:

[0058] 1) Determine the current congestion coefficient of the currently planned road section based on the real-time traffic data of the road section corresponding to the currently planned road section.

[0059] 2) If the current congestion coefficient is greater than or equal to the preset congestion coefficient threshold, the congestion state of the current road section is determined to be congested; otherwise, the congestion state of the current road section is determined to be unblocked.

[0060] In this embodiment, the congestion coefficient can be specifically understood as a parameter used to evaluate the smoothness of traffic in a vehicle driving section. For example, the congestion coefficient can be calculated based on the real-time average headway and the free flow average headway value of the section to be calculated. The calculation formula is as follows:

[0061]

[0062] Among them, TPI is the congestion coefficient, N is the total number of vehicles observed on the road section to be calculated, and h i is the observed headway between the i-th vehicle and the preceding vehicle. Generally, a headway of not less than 9 seconds can be used as a threshold for distinguishing free-flow traffic states, and can also be empirically adjusted according to the actual road conditions, which is not limited in the embodiments of the present invention.

[0063] In this embodiment, the preset congestion coefficient threshold value can be specifically understood as a numerical threshold value that is preset according to actual conditions and is used to determine whether congestion exists on a road section.

[0064] Specifically, according to the actual content contained in the real-time traffic data of the road section corresponding to the current planned road section, the current congestion coefficient of the current planned road section is directly obtained therefrom, or based on the vehicle driving status contained in the real-time traffic data of the road section, the vehicle driving status related coefficient is substituted into the above-mentioned congestion coefficient determination formula to obtain the current congestion coefficient of the current planned road section. The current congestion coefficient is compared with the preset congestion coefficient threshold. If the current congestion coefficient is greater than or equal to the preset congestion coefficient threshold, it can be considered that the current planned road section is in a state of vehicle congestion at the current moment, and the congestion state of the current road section is determined as congested; if the current congestion coefficient is less than the preset congestion coefficient threshold, it can be considered that the current planned road section can be driven without congestion at the current moment, and the congestion state of the current road section is determined as unblocked.

[0065] Optionally, the target planning path of the vehicle is determined according to the current congestion state and the estimated congestion state, which can be specifically achieved in the following ways:

[0066] 1) Determine the current traffic volume of the currently planned section based on the real-time traffic data of the section corresponding to the currently planned section.

[0067] 2) Substitute the current traffic flow and the estimated arrival time corresponding to the current planned section into the pre-built section congestion prediction model corresponding to the current planned section to determine the estimated section congestion status of the current planned section.

[0068] In this embodiment, the current traffic volume can be specifically understood as the number of vehicles passing through the currently planned road section at the current moment. The road section congestion prediction model can be specifically understood as a regression model constructed based on the historical traffic volume of the road section over a period of time and the historical time corresponding to different historical traffic volumes, which can be used to predict the road section congestion according to the traffic volume and the time. Exemplarily, the road section congestion prediction model can be an autoregressive moving average model (ARMA), or other models that can realize the prediction of road section congestion, which is not limited by the embodiment of the present invention. It can be understood that each currently planned road section in the currently planned road section set can construct its own corresponding road section congestion prediction model.

[0069] Specifically, the current traffic flow of the current planned section at the current moment can be determined based on the real-time traffic data of the section corresponding to the current planned section, and the current traffic flow and the estimated arrival time corresponding to the current planned section are used as the input of the pre-built section congestion prediction model corresponding to the current planned section, and the output of the section congestion prediction model is determined as the estimated section congestion status of the current planned section.

[0070] Optionally, when there is no pre-built road congestion prediction model for the current planned road section, the current congestion coefficient of the current planned road section at the current moment can be determined based on the real-time traffic data of the road section and the above-mentioned congestion coefficient calculation formula. The historical congestion coefficient of the current planned road section at the preset arrival time point within the historical time period is obtained from the storage of the cloud device or the storage of the roadside device corresponding to the current planned road section, and the estimated congestion coefficient of the current planned road section is determined according to the following formula, and the estimated congestion coefficient is determined in the same way as the above-mentioned current congestion state through the preset congestion coefficient threshold, so as to determine the estimated road section congestion state. Among them, the method of determining the estimated congestion coefficient is as follows:

[0071] Estimated congestion coefficient = current congestion coefficient + (current congestion coefficient - historical congestion coefficient)

[0072] S205: Integrate the congestion status of each current road section and the estimated congestion status of each road section respectively to determine the current congestion status and the estimated congestion status corresponding to the current planned path.

[0073] Specifically, the congestion status of each current road section is integrated. If any of the current road sections is congested, the current congestion status corresponding to the current planned path can be determined as congested; the congestion status of each estimated road section is integrated. If any of the estimated road sections is congested, the estimated congestion status corresponding to the current planned path can be determined as congested; otherwise, the current congestion status and the estimated congestion status corresponding to the current planned path can be determined as unblocked.

[0074] S206: If the current congestion state and the estimated congestion state are both unblocked, the current planned path is determined as the target planned path of the vehicle.

[0075] Specifically, when the current congestion status and the estimated congestion status are both unblocked, it can be considered that the vehicle will not encounter congestion if it continues to travel along the current planned path. At this time, it can be considered that there is no need to spend more computational costs to adjust the vehicle's driving path, and the current planned path can be determined as the vehicle's target planned path.

[0076] S207. If the current congestion status and / or the estimated congestion status is congestion, the real-time traffic data of all road sections in the domain are obtained, and the travel cost corresponding to each road section is determined based on the real-time traffic data of each road section, and the road section combination with the lowest sum of travel costs is determined as the target planning path of the vehicle.

[0077] In this embodiment, the travel cost can be specifically understood as the time required for a vehicle to pass through a corresponding road section.

[0078] Specifically, when any or all of the current congestion status and the estimated congestion status are congested, it can be considered that the vehicle is likely to encounter congestion if it continues to travel along the currently planned route. At this time, it is necessary to re-plan and select the required driving route of the vehicle within the area that can be managed by the cloud device to minimize the possibility of the vehicle encountering congestion during driving. At this time, the real-time traffic data of all road sections in the area that can be managed by the cloud device can be obtained, and the possible travel time of the vehicle when passing through each road section can be determined based on the real-time traffic data of each road section, and it is used as the travel cost of passing through each road section. Then, when the vehicle's entry and exit points in the area that can be managed by the cloud device are clearly defined, the sections that can connect the entry and exit points are arranged and combined, and the sum of the travel costs of each combination is calculated, and the section combination with the lowest travel cost is determined as the target planning path for the vehicle.

[0079] For example, the A* algorithm can be used to calculate the travel cost of each road section. The specific implementation method is as follows:

[0080] The travel cost of the nth road segment in the cloud device manageable area is expressed as f(n), the actual cost of the road segment, that is, the estimated travel time of the vehicle passing through the road segment, is expressed as g(n), and the estimated cost of the road segment, that is, the basic travel time of the road segment, is expressed as h(n). The travel cost can be expressed by the following formula:

[0081] f(n)=g(n)+h(n)

[0082] Assume that the length of the road section is d, and the speed limit or the average speed of vehicles passing through the road section when traffic is smooth is v base , the current real-time average speed of the road section is v actual , the congestion impact coefficient is f(TPI), and the average waiting time of the traffic light on this road section is t wait , then the various parts of the above-mentioned toll costs can be expressed by the following formulas:

[0083]

[0084]

[0085]

[0086] In summary, the travel cost f(n) of each road section in the area that the cloud device can manage can be determined. After the travel cost of each road section is determined, the sum of the communication costs of different road section combinations can be obtained, and then the road section combination with the lowest sum of travel costs can be determined as the target planning path of the vehicle. It can be understood that the target planning path finally determined can be consistent with the current planning path.

[0087] Optionally, since the driving condition of a vehicle is not only affected by the congestion condition of the road section where the vehicle is located, but also by the traffic volume on lanes in different road sections, after determining the target planning path of the vehicle according to the current congestion condition and the estimated congestion condition, the following is further included:

[0088] When the target planned path is the current planned path, the current driving section and the current driving lane of the vehicle are determined according to the current driving state, and the current optimal lane set is determined according to the current driving section and real-time traffic data;

[0089] If the current driving lane does not belong to the current best lane set, the vehicle lane change prompt information is determined according to the difference in traffic volume between the current driving lane and the current best lane set.

[0090] In this embodiment, the current optimal lane set can be specifically understood as the lane traffic conditions of several intersections where the vehicle is about to pass determined from real-time traffic data based on the vehicle's scheduled driving status, and the lane combination with the smallest sum of traffic flow determined based on the traffic conditions of each lane.

[0091] Specifically, when the target planning path is the current planning path, since the cloud device has completed the acquisition of the real-time traffic data corresponding to the current driving path, the real-time traffic data can be directly applied. At this time, the current driving section of the vehicle and the current driving lane of the vehicle on the current driving section can be determined according to the current driving state, and the lane traffic conditions of several sections or intersections after the current driving section can be obtained from the real-time traffic data according to the current driving section of the vehicle. A lane is selected from each section for combination, and the combination with the smallest sum of lane traffic volume in each combination is determined as the current best lane set. Furthermore, when it is determined that the current driving lane of the vehicle is not in the current best lane set, the vehicle will not be directly switched to the lane corresponding to the current best lane set, but the traffic volume difference between the current driving lane and the current best lane set will be calculated first, and based on the difference, it is determined whether it is necessary to generate vehicle lane change prompt information to prompt the vehicle to change lanes.

[0092] Optionally, the vehicle lane change prompt information is determined according to the difference in traffic volume between the current driving lane and the current optimal lane set, which can be specifically implemented in the following manner:

[0093] 1) If the traffic volume difference between the current driving lane and the current optimal lane set is less than a preset traffic volume threshold, no vehicle lane change prompt information is generated, and when the vehicle drives to the next driving section of the current driving section, the process returns to the step of determining the current driving section and the current driving lane of the vehicle according to the current driving state, and determining the current optimal lane set according to the current driving section and real-time traffic data;

[0094] 2) If the traffic flow difference between the current driving lane and the current optimal lane set is greater than or equal to a preset traffic flow threshold, a vehicle lane change prompt message is generated to prompt the vehicle to switch to the current optimal lane set.

[0095] Specifically, when the traffic volume difference between the current driving lane and the current best lane set is less than the preset traffic volume threshold, it can be considered that the driving state difference between the current driving lane and the current best lane set is not large, and there is no need to change lanes to the current best lane set to make lane changes frequent. At this time, the vehicle lane change prompt information may not be generated, and the vehicle is allowed to remain in the current driving lane and drive out of the current driving section. When the vehicle drives to the next driving section of the current driving section, the step of determining the current best lane set is repeated to determine whether it is necessary to perform lane change prompt processing on the vehicle. When the traffic volume difference between the current driving lane and the current best lane set is greater than or equal to the preset traffic volume threshold, it can be considered that the driving state difference between the current driving lane and the current best lane set is large. If continuing to drive along the current driving lane will affect the forward speed of the vehicle, the vehicle lane change prompt information can be generated at this time to prompt the vehicle to switch to the current best lane set for driving.

[0096] It is understandable that the preset traffic flow threshold value can be preset according to actual traffic conditions, and the embodiment of the present invention does not limit this.

[0097] The technical solution of this embodiment is to divide the current planned path into multiple current planned sections according to the roadside equipment that receives real-time traffic data, and determine the time required for the vehicle to travel to the corresponding current planned section in a normal state based on the average vehicle travel time of each current planned section and the current driving status of the vehicle. Then, based on the time required for the vehicle to travel to the current planned section and the real-time traffic data corresponding to the current planned section, the congestion state that the current planned section may be in when the vehicle travels to the current planned section can be estimated to obtain the corresponding estimated section congestion state, and then the congestion states of each estimated section are integrated to obtain the estimated congestion state corresponding to the entire current planned path. After clarifying the current congestion status and estimated congestion status of the current planned path, the two can be combined to determine whether the vehicle can continue to travel along the current planned path. If it is determined that the vehicle can continue to travel along the current planned path, the most suitable lane for driving under the current planned path is selected for the vehicle, realizing comprehensive, fast and predictable planning at the lane level. Not only is the change of the congestion status of the road section during vehicle driving fully considered at the road section level, but also the impact of traffic flow conditions on different lanes in the same road section on vehicle driving. The congestion effect, so that the planned path can help vehicles better avoid congested sections, thereby improving the quality of driving trips.

[0098] Embodiment 3

[0099] Figure 3 A schematic diagram of the structure of a path planning device provided in Embodiment 3 of the present invention is shown in FIG. Figure 3 As shown, the path planning device includes a data acquisition module 31 , a congestion state determination module 32 and a planned path determination module 33 .

[0100] Among them, the data acquisition module 31 is used to obtain the current driving state and the current planned path of the vehicle, and obtain the real-time traffic data corresponding to the current planned path according to the current planned path; the congestion state determination module 32 is used to determine the current congestion state and the estimated congestion state corresponding to the current planned path according to the current driving state and the real-time traffic data; the planned path determination module 33 is used to determine the target planned path of the vehicle according to the current congestion state and the estimated congestion state.

[0101] The technical solution of the embodiment of the present invention, when it is necessary to adjust the path planning of the vehicle, completes the required path planning based on the vehicle-road-cloud integration technology to obtain the vehicle's own driving information and the traffic status of each road section in the area where the vehicle is located, and then completes the current congestion status of the vehicle's currently planned path at the current moment based on the acquired information, as well as the estimated congestion status that may occur during the vehicle's driving process. When determining the target planned path that the vehicle ultimately adopts, the impact of the current congestion status and the estimated congestion status on the vehicle's driving path can be considered at the same time, thereby achieving comprehensive, rapid and predictive planning for the vehicle's driving path, fully considering the changes in the congestion status of the road section during the vehicle's driving process, so that the planned path can help the vehicle better avoid congested sections, thereby improving the quality of driving travel.

[0102] Optionally, the congestion state determination module 32 includes:

[0103] The road segment set determination unit is used to determine the current planned road segment set according to the current planned path.

[0104] The arrival time determination unit is used to determine the estimated arrival time of the vehicle at each current planned road section in the current planned road section set according to the average vehicle travel time of each current planned road section and the current driving status.

[0105] The road section status determination unit is used to determine the current road section congestion status of each current planned road section in the current planned road section set according to the real-time traffic data of the road section corresponding to the current planned road section, and to determine the estimated road section congestion status of the current planned road section according to the real-time traffic data and estimated arrival time corresponding to the current planned road section.

[0106] The congestion state determination unit is used to integrate the congestion state of each current road section and the congestion state of each estimated road section respectively, and determine the current congestion state and the estimated congestion state corresponding to the current planned path.

[0107] Optionally, the road section state determination unit is specifically used to:

[0108] The congestion status of each current road section and the estimated congestion status of each road section are respectively integrated to determine the current congestion status and the estimated congestion status corresponding to the current planned path;

[0109] If the current congestion coefficient is greater than or equal to the preset congestion coefficient threshold, the current road section congestion state is determined as congested; otherwise, the current road section congestion state is determined as unblocked;

[0110] Determine the current traffic volume of the currently planned road section based on the real-time traffic data of the road section corresponding to the currently planned road section;

[0111] The current traffic flow and the estimated arrival time corresponding to the current planned road section are substituted into the pre-built road section congestion prediction model corresponding to the current planned road section to determine the estimated road section congestion status of the current planned road section.

[0112] Optionally, the planning path determination module 33 includes:

[0113] The first path determination unit is used to determine the current planned path as the target planned path of the vehicle if the current congestion state and the estimated congestion state are both unblocked.

[0114] The second path determination unit is used to obtain the real-time traffic data of all road sections in the domain if the current congestion state and / or the estimated congestion state is congestion, and determine the travel cost corresponding to each road section according to the real-time traffic data of each road section, and determine the road section combination with the lowest sum of travel costs as the target planning path of the vehicle.

[0115] Optionally, the path planning device further includes: a lane change prompting module, which is used to:

[0116] After determining the target planned path of the vehicle according to the current congestion status and the estimated congestion status, when the target planned path is the current planned path, the current driving section and the current driving lane of the vehicle are determined according to the current driving status, and the current best lane set is determined according to the current driving section and real-time traffic data; if the current driving lane does not belong to the current best lane set, the vehicle lane change prompt information is determined according to the difference in traffic volume between the current driving lane and the current best lane set.

[0117] Optionally, according to the traffic volume difference between the current driving lane and the current optimal lane set, the vehicle lane change prompt information is determined, including:

[0118] If the traffic flow difference between the current driving lane and the current best lane set is less than the preset traffic flow threshold, no vehicle lane change prompt information is generated, and when the vehicle travels to the next driving section of the current driving section, the process returns to the steps of determining the current driving section and the current driving lane of the vehicle according to the current driving state, and determining the current best lane set according to the current driving section and real-time traffic data; if the traffic flow difference between the current driving lane and the current best lane set is greater than or equal to the preset traffic flow threshold, a vehicle lane change prompt information is generated to prompt the vehicle to switch to the current best lane set.

[0119] The path planning device provided in the embodiment of the present invention can execute the oblique photography data loading method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0120] Embodiment 4

[0121] Figure 4 A schematic diagram of the structure of a path planning device provided for Embodiment 4 of the present invention. The path planning device 40 may be intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The path planning device 40 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0122] like Figure 4 As shown, the path planning device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the path planning device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0123] Multiple components in the path planning device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the path planning device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0124] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a path planning method.

[0125] In some embodiments, the path planning method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the path planning device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the power distribution scheme determination method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the path planning method in any other appropriate manner (e.g., by means of firmware).

[0126] Optionally, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the path planning method provided by any embodiment of the present invention.

[0127] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs for implementing the 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0129] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein may be implemented on a power distribution scheme determining 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 trackball) through which a user can provide input to the power distribution scheme determining device. Other types of devices may also be used to provide interaction with a user; for example, the 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, voice input, or tactile input).

[0131] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0132] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0133] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0134] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A path planning method, characterized in that: include: Acquire the current driving state and the current planned path of the vehicle, and acquire the real-time traffic data corresponding to the current planned path according to the current planned path; Determine a current congestion state and an estimated congestion state corresponding to the current planned path according to the current driving state and the real-time traffic data; A target planned path for the vehicle is determined according to the current congestion state and the estimated congestion state.

2. The path planning method according to claim 1, characterized in that: The determining, according to the current driving state and the real-time traffic data, the current congestion state and the estimated congestion state corresponding to the current planned path comprises: Determine a currently planned road segment set according to the currently planned path; Determine the estimated arrival time of the vehicle at each of the currently planned road sections in the currently planned road section set according to the average vehicle travel time of each of the currently planned road sections and the current driving state; For each currently planned road section in the currently planned road section set, determine the current road section congestion state of the currently planned road section according to the road section real-time traffic data corresponding to the currently planned road section, and determine the estimated road section congestion state of the currently planned road section according to the road section real-time traffic data and estimated arrival time corresponding to the currently planned road section; The current congestion status of each road section and the estimated congestion status of each road section are respectively integrated to determine the current congestion status and the estimated congestion status corresponding to the current planned path.

3. The path planning method according to claim 2, characterized in that: The determining, according to the real-time traffic data of the road section corresponding to the currently planned road section, the current road section congestion state of the currently planned road section comprises: Determining a current congestion coefficient of the currently planned road section according to the real-time traffic data of the road section corresponding to the currently planned road section; If the current congestion coefficient is greater than or equal to a preset congestion coefficient threshold, the congestion state of the current road section is determined to be congested; otherwise, the congestion state of the current road section is determined to be unblocked.

4. The path planning method according to claim 2, characterized in that: The step of determining the estimated road congestion state of the currently planned road section according to the road section real-time traffic data and the estimated arrival time corresponding to the currently planned road section includes: Determine the current traffic volume of the currently planned road section according to the real-time traffic data of the road section corresponding to the currently planned road section; The current traffic flow and the estimated arrival time corresponding to the currently planned road section are substituted into a pre-built road section congestion prediction model corresponding to the currently planned road section to determine the estimated road section congestion status of the currently planned road section.

5. The path planning method according to claim 1, characterized in that: The determining the target planning path of the vehicle according to the current congestion state and the estimated congestion state includes: If the current congestion state and the estimated congestion state are both unblocked, determining the current planned path as the target planned path of the vehicle; If the current congestion status and / or the estimated congestion status is congestion, the real-time traffic data of all road sections in the domain are obtained, and the travel cost corresponding to each road section is determined based on the real-time traffic data of each road section, and the road section combination with the lowest sum of travel costs is determined as the target planning path of the vehicle.

6. The path planning method according to claim 1, characterized in that: After determining the target planning path of the vehicle according to the current congestion state and the estimated congestion state, the method further includes: When the target planned path is the current planned path, determining the current driving section and the current driving lane of the vehicle according to the current driving state, and determining the current optimal lane set according to the current driving section and the real-time traffic data; If the current driving lane does not belong to the current optimal lane set, vehicle lane change prompt information is determined according to a vehicle flow difference between the current driving lane and the current optimal lane set.

7. The path planning method according to claim 6, characterized in that: The determining of the vehicle lane change prompt information according to the vehicle flow difference between the current driving lane and the current optimal lane set includes: If the traffic flow difference between the current driving lane and the current optimal lane set is less than a preset traffic flow threshold, no vehicle lane change prompt information is generated, and when the vehicle drives to the next driving section of the current driving section, the process returns to the step of determining the current driving section and the current driving lane of the vehicle according to the current driving state, and determining the current optimal lane set according to the current driving section and the real-time traffic data; If the traffic flow difference between the current driving lane and the current optimal lane set is greater than or equal to a preset traffic flow threshold, a vehicle lane change prompt message is generated to prompt the vehicle to switch to the current optimal lane set for driving.

8. A path planning device, characterized in that: include: A data acquisition module, used to acquire the current driving state and the current planned path of the vehicle, and acquire the real-time traffic data corresponding to the current planned path according to the current planned path; A congestion state determination module, used to determine a current congestion state and an estimated congestion state corresponding to the current planned path according to the current driving state and the real-time traffic data; A planned path determination module is used to determine a target planned path for the vehicle according to the current congestion state and the estimated congestion state.

9. A path planning device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the path planning method according to any one of claims 1 to 7.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to perform the path planning method according to any one of claims 1 to 7 when executed by a computer processor.

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