A vehicle path planning method and system based on a space-ground integrated network

Abstract

The application discloses a vehicle path planning method and system based on a space-ground integrated network, which comprises the following steps: obtaining target point coordinates of a user navigation path; obtaining current position coordinates of a user vehicle; selecting a path bias, and obtaining a shortest path according to the path bias; calculating positioning accuracy and path accuracy, and judging whether the positioning accuracy and the path accuracy both meet the standards; obtaining traffic conditions of the shortest path at the current time, and judging whether the shortest path scheme is reasonable; outputting the shortest path scheme as a path planning result; obtaining the current position coordinates of the user vehicle and traffic conditions of a road where the user vehicle is located through a low-orbit satellite, and recalculating the shortest path scheme according to the position coordinates and the road traffic conditions obtained through the low-orbit satellite; and outputting a result after optimizing the navigation path by using a heuristic path optimization method. The application realizes global coverage and high-precision positioning, dynamic information fusion and real-time optimization, traffic flow optimization and reasonable resource allocation, and further enhances safety and emergency response capability.

Description

TECHNICAL FIELD

[0001] The application relates to a vehicle path planning method and system based on a space-ground integrated network and belongs to the technical field of vehicle path planning. BACKGROUND

[0002] With the rapid development of intelligent transportation systems (ITS) and vehicle-to-everything (V2X) technologies, vehicle path planning faces multiple challenges of dynamicity, high precision, and safety. Traditional path planning methods mainly rely on GPS, Beidou, and other medium-orbit satellite navigation systems, combined with static electronic maps to achieve basic navigation functions. However, existing technologies have significant defects in practical applications, severely restricting the planning efficiency in complex traffic scenarios.

[0003] Firstly, the physical limitations of satellite navigation systems result in coverage blind spots and insufficient signal reliability. The orbital height of medium-orbit satellites is about 20,000 kilometers, the signal transmission distance is long, and it is easily disturbed by atmospheric refraction, ionospheric delay, etc. The positioning accuracy is usually only meter-level, which is difficult to meet the high-precision scene demand of automatic driving. In urban canyons with high-rise buildings, underground tunnels, or remote mountainous areas, signal shielding problems occur frequently, leading to vehicle positioning failure. In addition, existing navigation terminals only support one-way signal reception, and cannot interact with satellites according to individual needs such as vehicle type, task priority, etc., making it difficult to achieve dynamic path customization optimization.

[0004] Secondly, the fusion and utilization efficiency of dynamic traffic information is low. Although V2X technology enables vehicles to obtain real-time traffic flow, accident warning, weather changes, and other multi-dimensional data, existing planning algorithms still mainly rely on static weight distribution, lacking dynamic response capability to sudden events. For example, traditional Dijkstra, A*, and other algorithms can calculate the shortest path, but cannot effectively integrate real-time congestion index and weather disaster prediction data, leading to a disconnection between planning results and actual situations. At the same time, the system lacks a closed-loop coordination mechanism for traffic state prediction and path decision, which can easily cause the "local optimization exacerbating global congestion" Breach paradox phenomenon in large-scale road networks, making it difficult to improve the overall efficiency of group traffic flow.

[0005] Thirdly, the limitations of the computing architecture restrict the real-time requirements. Existing solutions rely too much on cloud centralized computing, and in weak network coverage or high delay scenarios (such as delay exceeding 500ms), path update lag can cause a 37% or more increase in detour decision-making error rate. The computing resources and energy consumption limitations of vehicle-mounted terminals force algorithms to use simplified heuristic rules, sacrificing the optimization potential of complex models such as deep reinforcement learning. Tests show that this compromise increases the probability of suboptimal solution selection in dynamic environments by 42.6%, significantly reducing the economy and safety of path planning.

[0006] Finally, the system security redundancy design has a major flaw. Current navigation systems generally lack multi-modal heterogeneous redundancy mechanisms. When satellite signals are lost, they cannot seamlessly switch to backup modes such as inertial navigation, roadside unit assisted positioning, or visual landmark matching. Experimental data shows that 90% of existing systems produce a path deviation error of more than 50 meters within 60 seconds of signal interruption, which poses a serious safety hazard to autonomous vehicles.

[0007] In summary, the existing technology has four major core defects: insufficient satellite navigation reliability, inefficient use of dynamic information, limited real-time computing, and lack of security redundancy, which makes it difficult for path planning systems to balance the demands of high precision, strong adaptability, and low latency. With the exponential growth of urban traffic complexity, it is urgent to break through the traditional technical bottlenecks through the innovation of multi-source perception fusion, edge intelligent computing, and heterogeneous redundancy architecture, to achieve truly dynamic global optimal path planning. SUMMARY

[0008] The purpose of the application is to address the deficiencies in the prior art by providing a vehicle path planning method and system based on a space-ground integrated network. The application achieves high-precision, strong adaptability, and low-latency vehicle path planning through the deep integration of space-ground integrated networks and Internet of Vehicles technology, effectively addressing the core deficiencies of existing technology and providing an innovative solution for the further development of intelligent transportation systems.

[0009] Technical solution: A vehicle path planning method based on a space-ground integrated network, comprising the following steps:

[0010] Step one, the user's vehicle accesses the Internet of Vehicles and the intelligent transportation system, and obtains the target point coordinates of the user's navigation path, which is set as the starting point.

[0011] Step two, obtain the basic positioning through the medium orbit satellite to obtain the current position coordinates of the user's vehicle, which is set as the ending point.

[0012] Step three, the user selects the path bias to reach the path target point, and obtains the shortest path scheme according to the path bias.

[0013] Step four, calculate the positioning accuracy of the user's vehicle's actual location relative to the location shown on the navigation map and the path accuracy when the user's vehicle travels along the shortest path scheme in step three. Path accuracy refers to the average value of the positioning accuracy of all sampling points in a fixed length route. Determine whether the positioning accuracy and path accuracy meet the standards. If they meet the standards, go to step five. If they do not meet the standards, go to step seven.

[0014] Step five, obtain the traffic conditions of the shortest path at the current time through the Internet of Vehicles data, and determine whether the shortest path scheme is reasonable. If it is reasonable, go to step six. If it is not reasonable, go to step eight.

[0015] Step six, output the shortest path scheme as the path planning result;

[0016] Step seven, obtain the current position coordinates of the user's vehicle and the traffic situation of the road where the vehicle is located through the low-orbit satellite, and recalculate the shortest path scheme according to the position coordinates and the road traffic situation obtained by the low-orbit satellite, and enter step five;

[0017] Step eight, after the navigation path is optimized by the heuristic path optimization method, enter step six.

[0018] Preferably, the path bias includes a fast arrival mode, and the fast arrival mode takes road travel time as the judgment basis, specifically:

[0019] The road travel time is taken as the weight of road selection, and is calculated as follows:

[0020] When taking the shortest time as the selection standard of the optimal path, the selection of the optimal path takes time as the search weight, and needs to consider the time of intersection delay, so the driving time on each path is expressed as:

[0021]

[0022] Wherein, t represents the total driving time of the road, l represents the path length, v α represents the average speed of the road, and t' represents the downstream intersection delay time.

[0023] Preferably, the time of intersection delay includes the time of free-flow intersection delay and the time of signal-controlled intersection delay,

[0024] The time of free-flow intersection delay is calculated by the P-K formula and the Little equation as follows:

[0025]

[0026] Wherein, p represents the utilization rate, q represents the arrival rate, and Var(s) represents the service time variance; these data are obtained by searching the nearest vehicle passing record through the Internet of Vehicles;

[0027] The time of signal-controlled intersection delay is obtained according to the Webster formula:

[0028]

[0029] Wherein, T represents the signal cycle length, λ represents the green ratio, Q represents the traffic flow of the inlet, and X represents the saturation degree; these data can be obtained through the intelligent traffic system and the Internet of Vehicles.

[0030] Preferably, the path bias also includes a wide avenue mode, which is determined based on road capacity, specifically:

[0031] Road capacity refers to the ability of road facilities to manage traffic flow and is also a measure of the load on vehicles. If the road capacity is poor, the probability of traffic jams and accidents on that road will increase, and the requirements for driving conditions will also increase. Therefore, roads with poor road capacity also need to consider the factors in route planning by adjusting the weights.

[0032] Assuming vehicles travel on the road at 90% of the speed limit and in a uniform and orderly manner, the formula for calculating road capacity is as follows:

[0033] N0 = 900v β / L

[0034] N = kN0

[0035] Among them, v β N is the road speed limit, N is the road capacity index, N0 is the ideal road capacity, L represents the safe driving distance of vehicles, and k is the correction factor.

[0036] Real-time traffic data is provided by the intelligent transportation system, and the correction factor k is obtained as follows:

[0037] The impact of lane width: Where w0 is the lane width;

[0038] Impact of the intersection: Where C0 represents the effective passage time ratio of the intersection, and S0 represents the intersection spacing;

[0039] The minimum value between the two influences is taken as the final correction coefficient k.

[0040] Preferably, the method for calculating the shortest path in step three is Dijkstra's method, specifically:

[0041] Let G = (V, E) be a weighted directed graph, where V represents a set of nodes with n nodes, E represents a set of arcs with m arcs, (u, v) is an arc in E from u to v, and W(u, v) is the non-negative weight of arc (u, v). Divide all nodes into two groups: the first group is the set S of nodes whose shortest paths have been determined, initially containing only one starting point v0; the second group is the set (VS) of nodes whose shortest paths have not yet been determined. Subsequently, as each shortest path v0, ..., v... is found... k , then v k Add each node to set S until all nodes are added to set S. This completes the process of finding the shortest path. jis the end point of each intermediate path after the whole path is divided into intermediate paths, v k is the end point of the whole path;

[0042] Let d i (1≤i≤n) be the length of the shortest path from the start point v0 to other nodes v i found at present; the initial state of d i is that if there is an arc from v0 to v i , then d i is the weight of the arc (v0, v i ), otherwise let d i be ∞; if the first shortest path is (v0, v j ), then j satisfies d j = min{d i | v i ∈ V};

[0043] Then the next shortest path of v k is (v0, v k ) or (v0, v j , v k ); in general, the set S is the set of nodes storing the determined shortest path, then the intermediate node of the next shortest path must be the node in the set S, and the length is d j = min{d i | v i ∈ V-S}, after each shortest path is obtained, the end point v j will be added to the set S, and then d i is updated to d i = min{d i , d j + W(v j , v i )}, through multiple loop execution of the above calculation method, the shortest path from the start point v0 to each node in the map can be generated.

[0044] Optionally, the positioning accuracy in step four is calculated as follows:

[0045] The positioning accuracy is used to measure the deviation degree of the actual position of the user's vehicle and the displayed position on the navigation map. Assuming that the actual position coordinates of the vehicle are (x, y), and the displayed position coordinates on the navigation map are (x1, y1), the distance error between the two is calculated using the Euclidean distance formula, which is used as a measurement method of the positioning accuracy:

[0046]

[0047] The criterion for judging whether the standard is met is that the positioning accuracy of the continuous j points is greater than T, and then it is judged that the positioning error occurs, that is, it does not meet the standard, wherein 3≤j≤5, T=5 meters; when the positioning accuracy of the continuous j points does not meet the standard, the path accuracy corresponding to these sampling points also does not meet the standard.

[0048] Preferably, the basis for judging whether the shortest path scheme is reasonable in step five is whether there is a difficult-to-pass situation in the road planned by the shortest path scheme, that is, there is a traffic jam, traffic accident, road repair, natural disaster situation, and whether there is an impassable situation.

[0049] Preferably, the step seven is specifically:

[0050] First, the membership relationship with the surrounding road is judged according to the current position, the vehicle position is updated, and the new position is obtained, that is, the nearest point on the membership road to the current position is the new position updated by the vehicle, and the shortest path scheme calculation method in step three is used according to the new position;

[0051] The method for updating the vehicle position is specifically: using the vehicle positioning method of map matching, under the premise of clear error, according to the vehicle position and the space and membership relationship of the road, the corresponding nearest position of the road is selected:

[0052] From the perspective of selecting the positioning error area, the probability statistical matching method represents the positioning error area in the map matching in the form of a positioning error ellipse according to the probability statistical phase and the offset theory; for map matching, the error ellipse is the candidate area; when finding the best matching road segment in the map database, that is, finding the road segment that can best match the actual driving position of the vehicle from the map database under the consideration of positioning accuracy, selecting the road segment intersecting with the current positioning error ellipse as the candidate road segment can quickly find the effective candidate road segment, that is, the candidate road segment intersecting with the current positioning error ellipse, which reduces the map matching calculation time; the positioning error ellipse calculation process is as follows:

[0053]

[0054] δ x and δ y are the standard deviations of the positioning error in the due east and due north directions respectively, δ xy is the covariance of the two, a is the major axis of the error ellipse, b is the minor axis of the error ellipse, θ is the angle between the major axis of the ellipse and the due north direction, and δ0 is the posterior variance of the unit weight.

[0055] Preferably, the heuristic path optimization method in step eight is specifically:

[0056] The shortest path search method combined with Dijkstra method uses heuristic information to avoid unreasonable paths in Dijkstra method, and improves the effectiveness of the best path search of road network.

[0057] The Dijkstra method in step three is to solve the shortest path in the order of length increment, so the search space of the Dijkstra method can be represented as a circular region, wherein the terminal point T refers to the edge of the circular region, and the original point B is searched outward as the center, the radius is constantly increasing, and the shortest path is determined after the terminal point T is found.

[0058] In order to provide the weight estimation of the distance between any target point and node, an estimation function is introduced, and the heuristic estimation function of node v is defined as f'(v)=g(v)+h'(v), g(v) is the actual cost from the starting point s to the current node v, and h'(v) is the estimated cost of the best path from the current node v to the target node.

[0059] In all intermediate nodes with equal distance from the starting point, the smaller the angle between the line connecting any intermediate node and the starting point and the line connecting the starting point and the terminal point, the smaller the straight line distance from the intermediate node to the terminal point, assuming that the intermediate node is A, the distance between A and the starting point B is d, and the angle between the line segment BA and BT is α, then

[0060] When searching for the shortest path, the node weight d(v) is used as the actual cost from the starting point to the current node, and the straight line distance d'(v) from the current node to the target node is used as the estimated cost, so the heuristic estimation function is f'(v)=d(v)+d'(v).

[0061] Using f'(v) instead of the node weight d(v) in the Dijkstra method can realize the heuristic search of the shortest path, wherein the weight parameter is determined according to the needs of users and vehicle networking.

[0062] The system for realizing the vehicle path planning method based on the space-ground integrated network comprises a driving intention compiling module, a medium-orbit satellite signal receiving module, a low-orbit satellite signal receiving module, a path planning module, and a vehicle navigation module.

[0063] The driving intention compiling module is used to obtain the navigation target and the selected path preference of a user, and send them to the path planning module.

[0064] The medium-orbit satellite signal receiving module is used to obtain the basic positioning to obtain the current position coordinates of the vehicle of the user, and send them to the path planning module.

[0065] The low-orbit satellite signal receiving module is configured to acquire positioning signals with higher precision from low-orbit satellites than from medium-orbit satellites through ground receiving base stations when the positioning precision is not up to standard, and send the signals to the path planning module.

[0066] The path planning module is configured to acquire a shortest path that meets path deviation and positioning precision and path precision standards according to signals sent by the driving intention compiling module, the medium-orbit satellite signal receiving module, and the low-orbit satellite signal receiving module, and send the shortest path to the vehicle navigation module.

[0067] The vehicle navigation module is configured to display the shortest path scheme route to a user, and in this embodiment, can also display a map, navigation instructions, and traffic information.

[0068] Beneficial effects: The patent scheme fully utilizes the dynamic information advantages of low-orbit satellites, GPS / BDS, and V2X by fusing space-ground integrated networks and vehicle networking technology, and achieves efficient and safe vehicle path planning on a global scale, and has the following significant beneficial effects:

[0069] 1. Global coverage and high-precision positioning

[0070] By integrating the high-intensity signals of low-orbit satellites and the wide-area coverage capability of GPS / BDS, a space-ground integrated network is constructed, solving the signal loss problem of traditional navigation systems in shielding environments. The high-resolution remote sensing data and atmospheric monitoring information of low-orbit satellites, combined with real-time traffic data of ground V2X, significantly improve the positioning precision and the accuracy of path planning, ensuring that vehicles can obtain reliable navigation services in any region and environment around the world.

[0071] 2. Dynamic information fusion and real-time optimization

[0072] This scheme uses the space-ground integrated network to collect multi-dimensional data such as traffic conditions, road conditions, weather changes, and emergencies in real time, and dynamically adjusts the path planning through intelligent algorithms. For example, during peak traffic periods, the system can predict congestion areas in advance and optimize the path to avoid traffic bottlenecks; after an accident occurs, a safe alternative route is quickly provided, significantly reducing travel time and improving travel efficiency.

[0073] 3. Traffic flow optimization and resource rational allocation

[0074] By monitoring traffic conditions and dynamically adjusting paths in real time, this scheme achieves global optimization of traffic flow and rational allocation of road resources. Experiments show that the system can improve vehicle traffic efficiency during peak hours by more than 20%, while reducing fuel consumption and carbon emissions, contributing to green transportation development.

[0075] 4. Enhanced safety and emergency response capability

[0076] The scheme automatically switches to a backup positioning mode when satellite signals are lost, ensuring the continuity of path planning through the heterogeneous redundancy design of multi-source data. At the same time, based on real-time traffic data, the system can early warning potential dangers and optimize the path, reducing the accident risk by more than 30%, significantly improving road safety and emergency response capability.

[0077] In summary, the present application realizes high-precision, strong adaptability and low-latency vehicle path planning through the deep integration of space-ground network and Internet of Vehicles technology, effectively solves the core defects of the prior art, and provides an innovative solution for the further development of intelligent transportation systems. BRIEF DESCRIPTION OF DRAWINGS

[0078] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only a part of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on the provided drawings.

[0079] Figure 1 The method flowchart of the present application;

[0080] Figure 2 The positioning error ellipse diagram of the present application;

[0081] Figure 3 The path optimization search space diagram of the present application;

[0082] Figure 4 The system structure diagram of the present application. DETAILED DESCRIPTION

[0083] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0084] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore it cannot be understood as a limitation of the present application.

[0085] In the present application, unless otherwise explicitly specified and limited, "on" or "under" of a first feature to a second feature can include that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, "on", "above" and "over" of a first feature to a second feature includes that the first feature is directly above and obliquely above the second feature, or only indicates that the first feature is higher in horizontal height than the second feature. "Under", "below" and "underneath" of a first feature to a second feature includes that the first feature is directly below and obliquely below the second feature, or only indicates that the first feature is lower in horizontal height than the second feature.

[0086] As shown in Figure 1 A vehicle path planning method based on a space-earth integrated network, comprising the following steps:

[0087] Step one, the user's vehicle accesses the Internet of vehicles and intelligent transportation system, obtains the target point coordinates of the user's navigation path, and sets the starting point;

[0088] Step two, obtain the current position coordinates of the user's vehicle by the medium orbit satellite to obtain the current position coordinates of the user's vehicle, and set the end point;

[0089] Step three, the user selects the path bias of the path target point, and obtains the shortest path scheme according to the path bias;

[0090] The path bias includes a fast arrival mode, and the fast arrival mode takes road passing time as the judgment basis, specifically:

[0091] The road passing time is taken as the weight of road selection, and is calculated as follows:

[0092] When the time is the shortest as the selection standard of the optimal path, the selection of the optimal path is taken as the search weight, and the time of intersection delay needs to be considered, so the driving time on each path is expressed as,

[0093]

[0094] Wherein, t represents the total driving time of the road section, l represents the path length, v α represents the average speed of the road section, and t' represents the downstream intersection delay time.

[0095] The intersection delay time includes the free traffic intersection delay time and the signal control intersection delay time,

[0096] The free traffic intersection delay time is calculated by the P-K formula and the Little equation, and the delay is as follows:

[0097]

[0098] Where p represents utilization rate, q represents arrival rate, and Var(s) represents service time variance; these data are obtained by searching for the nearest vehicle through the vehicle network and recording data.

[0099] The delay time at traffic light-controlled intersections can be calculated using Webster's formula:

[0100]

[0101] Where T represents the signal cycle length, λ represents the green light ratio, Q represents the traffic flow at the approach lane, and X represents the saturation level, these data can be obtained through intelligent traffic systems and vehicle-to-everything (V2X) networks.

[0102] The path bias also includes a wide avenue mode, which is determined based on road capacity, specifically:

[0103] Road capacity refers to the ability of road facilities to manage traffic flow and is also a measure of the load on vehicles. If the road capacity is poor, the probability of traffic jams and accidents on that road will increase, and the requirements for driving conditions will also increase. Therefore, roads with poor road capacity also need to consider the factors in route planning by adjusting the weights.

[0104] Assuming vehicles travel on the road at 90% of the speed limit and in a uniform and orderly manner, the formula for calculating road capacity is as follows:

[0105] N0 = 900v β / L

[0106] N = kN0

[0107] Among them, v β N is the road speed limit, N is the road capacity index, N0 is the ideal road capacity, L represents the safe driving distance of vehicles, and k is the correction factor.

[0108] Real-time traffic data is provided by the intelligent transportation system, and the correction factor k is obtained as follows:

[0109] The impact of lane width: Where w0 is the lane width;

[0110] Impact of the intersection: Where C0 represents the effective passage time ratio of the intersection, and S0 represents the intersection spacing;

[0111] The minimum value between the two influences is taken as the final correction coefficient k.

[0112] The method for calculating the shortest path in step three is Dijkstra's method, specifically as follows:

[0113] Let G = (V, E) be a weighted directed graph, where V represents a set of nodes with n nodes, E represents a set of arcs with m arcs, (u, v) is an arc in E from u to v, and W(u, v) is the non-negative weight of arc (u, v). Divide all nodes into two groups: the first group is the set S of nodes whose shortest paths have been determined, initially containing only one starting point v0; the second group is the set (VS) of nodes whose shortest paths have not yet been determined. Subsequently, as each shortest path v0, ..., v... is found... k , then v x Add each node to set S until all nodes are added to set S. This completes the process of finding the shortest path. j It is the endpoint of each intermediate path after dividing the entire path into intermediate paths, v k It is the end point of the entire path;

[0114] Let d i (1≤i≤n) represents the current number of nodes found from the starting point v0 to other nodes v. i Shortest path length; d i The initial state is: if from v0 to v i If there is an arc, then d i For arc (v0, v) i The weight of ) is determined by , otherwise let d i The value is ∞; if the first shortest path is (v0, v j If j satisfies d, then j satisfies d. j =min{d i |v i ∈V};

[0115] Then the next endpoint is v. k The shortest path is (v0, v k ) or (v0, v j ,v k Generally, if set S is the set of nodes for which the shortest path has been determined, then the intermediate node of the next shortest path must be a node in set S, and its length is d. j =min{d i |v i ∈VS}, after finding a shortest path each time, its destination v j It will be added to set S, then d i Updated to d i =min{d i ,d j +W(v j ,v i By repeatedly executing the above calculation method, the shortest path from the starting point v0 to all other nodes in the map can be generated.

[0116] Step four, calculate the positioning accuracy of the actual position of the user's vehicle relative to the displayed position on the navigation map at this time, and the path accuracy of the user's vehicle when driving along the shortest path scheme in step three, the path accuracy refers to the average of the positioning accuracy of all sampling points in a fixed length route, and whether the positioning accuracy and the path accuracy meet the standard is judged, if they meet the standard, step five is entered, if they do not meet the standard, step seven is entered;

[0117] The positioning accuracy in step four is specifically:

[0118] The positioning accuracy is used to measure the deviation degree of the actual position of the user's vehicle and the displayed position on the navigation map, assuming that the actual position coordinates of the vehicle are (x, y) and the displayed position coordinates on the navigation map are (x1, y1), the distance error between the two is calculated using the Euclidean distance formula, which is used as a measure of positioning accuracy:

[0119]

[0120] The standard for judging whether it meets the standard is that the positioning accuracy of the continuous j points is greater than T, then it is judged that there is a positioning error, that is, it does not meet the standard, wherein 3≤j≤5, T=5 meters; when the positioning accuracy of the continuous j points does not meet the standard, the path accuracy corresponding to these sampling points also does not meet the standard.

[0121] Step five, obtain the traffic situation of the shortest path at the current time through the Internet of Vehicles data, judge whether the shortest path scheme is reasonable, if it is reasonable, step six is entered, if it is not reasonable, step eight is entered; the basis for judging whether the shortest path scheme is reasonable in step five is whether there is a difficult-to-travel situation in the road planned by the shortest path scheme, that is, there is a traffic jam, traffic accident, road repair, natural disaster situation, and whether there is a situation that cannot be traveled.

[0122] Step six, output the shortest path scheme as the path planning result;

[0123] Step seven, obtain the current position coordinates of the user's vehicle and the traffic situation of the road through the low-orbit satellite, and recalculate the shortest path scheme according to the position coordinates and the road traffic situation obtained by the low-orbit satellite, and enter step five;

[0124] The step seven is specifically:

[0125] First, the membership relationship with the surrounding roads is judged according to the current position, the vehicle position is updated, and the new position is obtained, that is, the nearest point on the membership road to the current position is the new position of the vehicle, and the shortest path scheme calculation method in step three is used according to the new position;

[0126] The method for updating the vehicle position is specifically: using a vehicle positioning method of map matching, under the premise of explicitly existing errors, according to the spatial relationship and the membership of the vehicle position and the road, and then selecting the position closest to the corresponding road:

[0127] From the perspective of selecting the positioning error area, the probability statistical matching method represents the positioning error area in the map matching in the form of an error ellipse according to the probability statistical phase and offset theory; for the map matching, the error ellipse is the candidate area; when searching for the best matching road segment in the map database, that is, searching for the road segment that can best match the actual driving position of the vehicle from the map database under the consideration of the positioning accuracy, selecting the road segment intersecting with the current positioning error ellipse as the candidate road segment can find the effective candidate road segment faster, that is, the candidate road segment intersecting with the current positioning error ellipse, and reduce the map matching calculation time; as shown in Figure 2 The positioning error ellipse calculation process is as follows:

[0128]

[0129] δ x and δ y are the standard deviations of the positioning errors in the positive east and positive north directions respectively, δ xy is the covariance of the two, a is the long semi-axis of the error ellipse, b is the short semi-axis of the error ellipse, θ is the angle between the long semi-axis of the ellipse and the positive north direction, and δ0 is the posterior variance of the unit weight. Figure 2 P in the formula is the vehicle position.

[0130] Step eight, after optimizing the navigation path by using the heuristic path optimization method, step six is entered:

[0131] The heuristic path optimization method in step eight is specifically:

[0132] The heuristic shortest path search method is combined with the Dijkstra method, which introduces heuristic information in the Dijkstra method to avoid unreasonable paths and improve the effectiveness of the best path search of the road network;

[0133] As shown in Figure 3 The Dijkstra method in step three is to solve the shortest path in the order of increasing length, so the search space of the Dijkstra method can be represented as a circular region, wherein the terminal point T refers to the edge of the circular region, and the original point B is taken as the center to search outward, the radius is constantly increasing, and the shortest path is determined after the terminal point T is found;

[0134] In order to provide the weight estimation of the distance between any target point and node, an estimation function is introduced, and the heuristic estimation function of node v is defined as f'(v) = g(v) + h'(v), g(v) is the actual cost from the starting point s to the current node v, and h'(v) is the estimated cost of the optimal path from the current node v to the target node;

[0135] Among all the intermediate nodes with equal distance from the starting point, the smaller the angle between the line connecting any intermediate node and the starting point and the line connecting the starting point and the terminal point, the smaller the straight-line distance of the intermediate node to the terminal point, assuming that the intermediate node is A, the distance between A and the starting point B is d, and the angle between the line segments BA and BT is α, then

[0136] When searching for the shortest path, the node weight d(v) is taken as the actual cost from the starting point to the current node, and the straight-line distance d'(v) from the current node to the target node is taken as the estimated cost, so the heuristic estimation function is f'(v) = d(v) + d'(v);

[0137] f'(v) is used instead of the node weight d(v) in the Dijkstra method, that is, the shortest path heuristic search is realized, and the weight parameter is determined according to the needs of users and the Internet of Vehicles.

[0138] As shown in Figure 4 The system for realizing the vehicle path planning method based on the space-ground integrated network comprises a driving intention compiling module, a medium-orbit satellite signal receiving module, a low-orbit satellite signal receiving module, a path planning module, and a vehicle navigation module.

[0139] The driving intention compiling module is used to acquire the navigation target and the selected path bias of a user, and send them to the path planning module.

[0140] The medium-orbit satellite signal receiving module is used to acquire basic positioning to acquire the current position coordinates of the vehicle of the user, and send them to the path planning module; in this embodiment, the medium-orbit satellite signal receiving module is specifically a GPS / BDS signal receiving module.

[0141] The low-orbit satellite signal receiving module is used to acquire positioning signals with higher accuracy from low-orbit satellites than from medium-orbit satellites through ground receiving base stations when the positioning accuracy is not up to standard, and send them to the path planning module.

[0142] The path planning module is used to acquire the shortest path that meets the path bias and has up-to-standard positioning accuracy and path accuracy according to the signals sent by the driving intention compiling module, the medium-orbit satellite signal receiving module, and the low-orbit satellite signal receiving module, and send them to the vehicle navigation module.

[0143] The vehicle navigation module is used to display the shortest path scheme route to the user.

[0144] The various embodiments described in this specification are presented by way of example, and each embodiment is presented with the understanding that it will not limit the scope of the disclosure. Each embodiment is provided to highlight a certain aspect of the disclosure, and the scope of the disclosure is not limited to that embodiment. The same or similar elements are denoted by the same or similar reference numbers throughout the drawings and the specification.

[0145] The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle routing method based on a space-ground integrated network, characterized in that: Includes the following steps: Step 1: The user's vehicle connects to the Internet of Vehicles and the intelligent transportation system to obtain the coordinates of the target point of the user's navigation route and set it as the starting point; Step 2: Obtain basic positioning via medium-orbit satellites to get the current location coordinates of the user's vehicle and set them as the destination; Step 3: The user selects the path bias to reach the target point, and the shortest path solution is derived based on the path bias. Step 4: Calculate the positioning accuracy of the user vehicle's actual location relative to the location displayed on the navigation map, and the path accuracy of the user vehicle when traveling along the shortest path scheme in Step 3. Path accuracy refers to the average positioning accuracy of all sampling points in a fixed-length route. Determine whether both positioning accuracy and path accuracy meet the standards. If they do, proceed to Step 5; otherwise, proceed to Step 7. Step 5: Obtain the traffic situation of the shortest path at the current moment through vehicle network data, and determine whether the shortest path solution is reasonable. If it is reasonable, proceed to step 6; otherwise, proceed to step 8. Step 6: Output the shortest path solution as the path planning result; Step 7: Obtain the current location coordinates of the user's vehicle and the traffic conditions of the road using low-orbit satellites. Recalculate the shortest path scheme based on the location coordinates and road traffic conditions obtained from the low-orbit satellites, and proceed to Step 5. Step 8: After optimizing the navigation path using a heuristic path optimization method, proceed to Step 6.

2. The vehicle routing method based on a space-ground integrated network according to claim 1, characterized in that: The path bias includes a fast arrival mode, which is determined based on road travel time, specifically: The road travel time is used as the weight for road selection, calculated as follows: When the shortest travel time is used as the criterion for selecting the optimal path, the selection of the optimal path uses time as the search weight, and the time of delay at intersections needs to be considered. Therefore, the travel time on each path is expressed as follows: Where t represents the total travel time for this road segment, l represents the path length, and v α t represents the average speed of this road segment, and t′ represents the delay time at the downstream intersection.

3. The vehicle routing method based on a space-ground integrated network according to claim 2, characterized in that: The intersection delay time includes the delay time at free-traffic intersections and the delay time at traffic light-controlled intersections. The delay time at the free-traffic intersection, calculated using the PK formula and Little's equation, is as follows: Where p represents utilization rate, q represents arrival rate, and Var(s) represents service time variance; these data are obtained by searching for the nearest vehicle through the vehicle network and recording data. The delay time at traffic light-controlled intersections can be calculated using Webster's formula: Where T represents the signal cycle length, λ represents the green light ratio, Q represents the traffic flow at the approach lane, and X represents the saturation level, these data can be obtained through intelligent traffic systems and vehicle-to-everything (V2X) networks.

4. The vehicle routing method based on a space-ground integrated network according to claim 1, characterized in that: The path bias also includes a wide avenue mode, which is determined based on road capacity, specifically: Road capacity refers to the ability of road facilities to manage traffic flow and is also a measure of the load on vehicles. If the road capacity is poor, the probability of traffic jams and accidents on that road will increase, and the requirements for driving conditions will also increase. Therefore, roads with poor road capacity also need to consider the factors in route planning by adjusting the weights. Assuming vehicles travel on the road at 90% of the speed limit and in a uniform and orderly manner, the formula for calculating road capacity is as follows: N0=900v β / L N = kN0 Among them, v β N is the road speed limit, N is the road capacity index, N0 is the ideal road capacity, L represents the safe driving distance of vehicles, and k is the correction factor. Real-time traffic data is provided by the intelligent transportation system, and the correction factor k is obtained as follows: The impact of lane width: Where w0 is the lane width; Impact of the intersection: Where C0 represents the effective passage time ratio of the intersection, and S0 represents the intersection spacing; The minimum value between the two influences is taken as the final correction coefficient k.

5. The vehicle routing method based on a space-ground integrated network according to claim 1, characterized in that: The method for calculating the shortest path in step three is Dijkstra's method, specifically as follows: Let G = (V, E) be a weighted directed graph, where V represents a set of nodes with n nodes, E represents a set of arcs with m arcs, (u, v) is an arc in E from u to v, and W(u, v) is the non-negative weight of arc (u, v). Divide all nodes into two groups: the first group is the set S of nodes whose shortest paths have been determined, initially containing only one starting point v0; the second group is the set (VS) of nodes whose shortest paths have not yet been determined. Subsequently, as each shortest path v0, ..., v... is found... k , then v k Add each node to set S until all nodes are added to set S. This completes the process of finding the shortest path. j It is the endpoint of each intermediate path after dividing the entire path into intermediate paths, v k It is the end point of the entire path; Let d i (1≤i≤n) represents the current number of nodes found from the starting point v0 to other nodes v. i Shortest path length; d i The initial state is: if from v0 to v i If there is an arc, then d i For arc (v0, v) i The weight of ) is determined by , otherwise let d i The value is ∞; if the first shortest path is (v0, v j If j satisfies d, then j satisfies d. j =min{d i |v i ∈V}; Then the next endpoint is v. k The shortest path is (v0, v k ) or (v0, v j ,v k Generally, if set S is the set of nodes for which the shortest path has been determined, then the intermediate node of the next shortest path must be a node in set S, and its length is d. j =min{d i |v i ∈VS}, after finding a shortest path each time, its destination v j It will be added to set S, then d i Updated to d i =min{d i ,d j +W(v j ,v i By repeatedly executing the above calculation method, the shortest path from the starting point v0 to all other nodes in the map can be generated.

6. The vehicle routing method based on a space-ground integrated network according to claim 1, characterized in that: The positioning accuracy calculation in step four is specifically as follows: Positioning accuracy measures the deviation between the user's vehicle's actual location and the location displayed on the navigation map. Assuming the vehicle's actual location coordinates are (x, y) and the navigation map's displayed location coordinates are (x1, y1), the distance error between the two is calculated using the Euclidean distance formula, and this error is used as a measure of positioning accuracy. The criteria for determining compliance are as follows: if the positioning accuracy of j consecutive points is greater than T, then a positioning error has occurred, i.e., the standard is not met, where 3≤j≤5 and T=5 meters; if the positioning accuracy of j consecutive points is not met, then the path accuracy corresponding to these sampling points is also not met.

7. The vehicle routing method based on a space-ground integrated network according to claim 1, characterized in that: The basis for judging whether the shortest path scheme is reasonable in step five is whether there are any difficult-to-pass situations in the road planned by the shortest path scheme, that is, whether there are traffic jams, traffic accidents, road repairs, natural disasters, or whether there are any impassable situations.

8. The vehicle routing method based on a space-ground integrated network according to claim 1, characterized in that: Step seven specifically involves: First, determine the relationship between the current location and the surrounding roads, update the vehicle's position, and obtain the new position. That is, the point closest to the current location on the affiliated road is the new position of the vehicle. Then, use the shortest path calculation method in step three based on the new position. The specific method for updating vehicle location is as follows: using map matching for vehicle positioning, and assuming that errors exist, selecting the nearest location on the corresponding road based on the spatial and hierarchical relationship between the vehicle's location and the road. From the perspective of selecting the positioning error region, the probabilistic statistical matching method, based on the probabilistic statistical phase and offset theory, represents the positioning error region in map matching in the form of a positioning error ellipse; for map matching, the area inside the error ellipse is the candidate region. When searching for the best matching road segment in the map database—that is, finding the road segment that best matches the vehicle's actual driving position while considering positioning accuracy—selecting the road segment intersecting the current positioning error ellipse as a candidate road segment can find effective candidate road segments more quickly, reducing map matching calculation time. The calculation process for the positioning error ellipse is as follows: δ x and δ y These are the standard deviations of the positioning error in the due east and due north directions, δ xy δ0 is the covariance of the two, a is the major semi-axis of the error ellipse, b is the minor semi-axis of the error ellipse, θ is the angle between the orientation of the major semi-axis of the ellipse and the due north direction, and δ0 is the posterior variance of the unit weight.

9. The vehicle routing method based on a space-ground integrated network according to claim 5, characterized in that: The heuristic path optimization method in step eight is specifically as follows: By combining Dijkstra's method with a heuristic shortest path search method, which introduces heuristic information into Dijkstra's method to avoid unreasonable paths, the effectiveness of finding the best path in the road network is improved. In step three, Dijkstra's method solves the shortest path by increasing the length. Therefore, the search space of Dijkstra's method can be represented as a circular region, where the endpoint T refers to the edge of the circular region. The search is carried outward from the origin B as the center, and the radius increases continuously. After the endpoint T is found, the shortest path is determined. To provide a weighted estimate of the distance between any target point and a node, an estimation function is introduced. The heuristic estimation function for node v is defined as f′(v)=f(v)+h′(v), where g(v) is the actual cost from the starting point s to the current node v, and h′(v) is the estimated cost of the optimal path from the current node v to the target node. Among all intermediate nodes equidistant from the starting point, the smaller the angle between the line connecting any intermediate node and the starting point and the line connecting the starting point and the ending point, the smaller the straight-line distance from that intermediate node to the ending point. Let the intermediate node be A, the distance between A and the starting point B be d, and the angle between line segments BA and BT be α, then we have... When searching for the shortest path, the node weight d(v) is used as the actual cost from the starting point to the current node, and the straight-line distance d′(v) from the current node to the target node is used as the estimated cost. Then the heuristic estimation function is f′(v)=d(v)+d′(v). By replacing the node weight d(v) in Dijkstra's method with f′(v), the shortest path heuristic search can be realized, where the weight parameter is determined according to the needs of users and the Internet of Vehicles.

10. A system for implementing the vehicle routing method based on a space-ground integrated network as described in any one of claims 1-9, characterized in that, This includes a driving intent compilation module, a medium-Earth orbit satellite signal receiving module, a low-Earth orbit satellite signal receiving module, a path planning module, and a vehicle navigation module. The driving intent compilation module is used to obtain the user's navigation target and selected path bias, and send them to the path planning module; The medium-orbit satellite signal receiving module is used to obtain basic positioning to obtain the current location coordinates of the user vehicle and send them to the path planning module. The low-orbit satellite signal receiving module is used to obtain positioning signals from low-orbit satellites with higher accuracy than medium-orbit satellites through ground receiving base stations when the positioning accuracy is not up to standard, and send them to the path planning module. The path planning module is used to obtain the shortest path that meets the path deviation and the positioning accuracy and path accuracy standards based on the signals sent by the driving intention compilation module, the medium-orbit satellite signal receiving module, and the low-orbit satellite signal receiving module, and then send it to the vehicle navigation module. The vehicle navigation module is used to display the shortest route to the user.

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