Vehicle path planning method and system based on space-ground integrated network

Abstract

The invention discloses a vehicle path planning method and system based on a space-ground integrated network. The method comprises the following steps: acquiring a target point coordinate of a user navigation path; acquiring current position coordinates of a user vehicle; selecting path deviation, and obtaining a shortest path according to the path deviation; calculating the positioning precision and the path precision, and judging whether the positioning precision and the path precision reach the standard or not; acquiring the traffic condition of the shortest path at the current moment, and judging whether the shortest path scheme is reasonable or not; outputting the shortest path scheme as a path planning result; acquiring current position coordinates of a user vehicle and traffic conditions of a road where the user vehicle is located through a low-orbit satellite, and recalculating a shortest path scheme according to the position coordinates acquired by the low-orbit satellite and the traffic conditions of the road; and optimizing the navigation path by adopting a heuristic path optimization method and then outputting a result. According to the invention, global coverage and high-precision positioning, dynamic information fusion and real-time optimization, traffic flow optimization and reasonable resource allocation are realized, and the safety and emergency response capability are enhanced.

Description

Technical Field

[0001] The present invention 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 Art

[0002] With the rapid development of intelligent transportation systems (ITS) and vehicle-to-everything (V2X) technologies, vehicle path planning faces multiple challenges of dynamization, high precision, and security. Traditional path planning methods mainly rely on medium-earth orbit satellite navigation systems such as GPS and Beidou, combined with static electronic maps to achieve basic navigation functions. However, the existing technologies have significant defects in practical applications, seriously 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 altitude of medium-earth orbit satellites is about 20,000 kilometers. The signal transmission distance is long and vulnerable to interference such as atmospheric refraction and ionospheric delay. The positioning accuracy can usually only reach the meter level, making it difficult to meet the requirements of high-precision scenarios such as autonomous driving. In urban canyons with dense high-rise buildings, underground tunnels, or remote mountainous areas, signal shielding problems frequently occur, leading to vehicle positioning failures. In addition, existing navigation terminals only support one-way signal reception and cannot interact with satellites according to personalized requirements such as vehicle type and task priority, making it difficult to achieve dynamic path customization and optimization.

[0004] Secondly, the integration and utilization efficiency of dynamic traffic information is low. Although V2X technology enables vehicles to obtain multi-dimensional data such as traffic flow, accident warnings, and weather changes in real time, existing planning algorithms still mainly rely on static weight allocation and lack the ability to dynamically respond to emergencies. For example, traditional algorithms such as Dijkstra and A* can calculate the shortest path, but they cannot effectively integrate real-time congestion indices and meteorological disaster prediction data, resulting in a disconnect between the planning results and the actual situation. At the same time, the system lacks a closed-loop collaborative mechanism for traffic state prediction and path decision-making, which is prone to the Braess's paradox phenomenon of "local optimization exacerbating global congestion" 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 real-time requirements. Existing solutions overly rely on cloud-based centralized computing. In scenarios with weak network coverage or high latency (such as latency exceeding 500 ms), the lag in path updates can lead to a more than 37% increase in the error rate of detour decisions. The computing resources and energy consumption limitations of in-vehicle terminals force algorithms to adopt 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 security of path planning.

[0006] Finally, there are significant flaws in the system security redundancy design. The current navigation systems generally lack a multi-modal heterogeneous redundancy mechanism. When satellite signals are lost, they cannot seamlessly switch to backup modes such as inertial navigation, roadside unit-assisted positioning, or visual road sign matching. Experimental data shows that 90% of existing systems generate 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 technologies have four core flaws: insufficient reliability of satellite navigation, inefficient utilization of dynamic information, limited real-time computing, and lack of safety redundancy, which make it difficult for the path planning system to balance the requirements of high precision, strong adaptability, and low latency. With the exponential growth of urban traffic complexity, it is urgent to break through the traditional technology bottlenecks and achieve truly dynamic global optimal path planning through innovations in multi-source perception fusion, edge intelligent computing, and heterogeneous redundancy architectures. Summary of the Invention

[0008] Object of the Invention: Aiming at the deficiencies in the existing technologies, the present invention provides a vehicle path planning method and system based on a space-ground integrated network. Through the deep integration of the space-ground integrated network and vehicle networking technologies, the present invention realizes high-precision, strong-adaptability, and low-latency vehicle path planning, effectively solves the core flaws of the existing technologies, and provides 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 includes the following steps:

[0010] Step 1: The user vehicle accesses the vehicle network and the intelligent transportation system, obtains the target point coordinates of the user's navigation path, and sets it as the starting point;

[0011] Step 2: Obtain basic positioning through medium-orbit satellites to obtain the current position coordinates of the user vehicle, and set it as the ending point;

[0012] Step 3: The user selects the path bias to reach the path target point, and obtains the shortest path plan according to the path bias;

[0013] Step 4: Calculate the positioning accuracy of the actual position of the user vehicle at this time relative to the position shown on the navigation map and the path accuracy when the user vehicle travels along the shortest path plan in Step 3. The path accuracy refers to the average value of the positioning accuracies of all sampling points in a fixed-length route, and determine whether both the positioning accuracy and the path accuracy meet the standards. If they meet the standards, enter Step 5; if they do not meet the standards, enter Step 7;

[0014] Step 5: Obtain the traffic conditions of the shortest path at the current moment through vehicle network data, and determine whether the shortest path plan is reasonable. If it is reasonable, enter Step 6; if it is not reasonable, enter Step 8;

[0015] Step 6: Output the shortest path plan as the result of path planning;

[0016] Step 7: Obtain the current position coordinates of the user's vehicle and the traffic conditions of the road where it is located through low-orbit satellites, recalculate the shortest path plan based on the position coordinates and road traffic conditions obtained by the low-orbit satellites, and enter Step 5;

[0017] Step 8: After optimizing the navigation path using the heuristic path optimization method, enter Step 6.

[0018] Preferred option, the path deviation includes the fast arrival mode, and the fast arrival mode is judged based on the road travel time. Specifically:

[0019] Take the road travel time as the weight of road selection, and the calculation is as follows:

[0020] When taking the shortest time as the selection criterion for the optimal path, the selection of the optimal path is based on time as the search weight, and the delay time at intersections needs to be considered. Therefore, the travel time on each path is expressed as

[0021]

[0022] where t represents the total travel time of this section of the road, l represents the path length, v α represents the average speed of this section of the road, and t′ represents the downstream intersection delay time.

[0023] Preferred option, the intersection delay time includes the delay time at free-flow intersections and the delay time at signal-controlled intersections.

[0024] The delay time at free-flow intersections is calculated as follows through the P-K formula and Little's equation:

[0025]

[0026] where p represents the utilization rate, q represents the arrival rate, and Var(s) represents the service time variance; these data are obtained by querying the passing records of the nearest vehicles through the vehicle networking.

[0027] The delay time at signal-controlled intersections is calculated according to Webster's formula:

[0028]

[0029] where T represents the signal cycle length, λ represents the green ratio, Q represents the traffic flow of the approach lane, and X represents the saturation degree. These data can be obtained through the intelligent transportation system and vehicle networking.

[0030] Preferred option, the path deviation further includes an avenue mode, and the avenue mode is based on the road traffic capacity. Specifically:

[0031] The road traffic capacity refers to the ability of road facilities to direct traffic flow and is also a measure of the vehicle load on the road. If the road traffic capacity is poor, the probability of traffic jams and accidents on this road will increase, and the requirements for driving conditions will also increase. Therefore, roads with poor traffic capacity also need to consider the factors therein by adjusting the weight for route planning;

[0032] Assume that the vehicle is driving on the road at 90% of the speed limit in a uniform and orderly manner. The calculation formula for the road traffic capacity is as follows:

[0033] N 0 = 900v β / L

[0034] N = kN 0

[0035] where, v β is the road speed limit, N is the road traffic capacity index, N 0 is the ideal road traffic capacity, L represents the safe driving distance of the vehicle, and k is the correction coefficient;

[0036] The real-time traffic data is provided by the intelligent transportation system. The correction coefficient k is obtained according to the following method:

[0037] Influence of lane width: where w 0 is the lane width;

[0038] Influence of intersections: where, C 0 represents the effective passing time ratio of the intersection, and S 0 represents the intersection spacing;

[0039] Take the minimum value of the two influences as the final correction coefficient k.

[0040] Preferred option, the calculation method of the shortest path plan in step three adopts the Dijkstra method. Specifically:

[0041] Let the weighted directed graph G=(V, E), where V represents the node set with n nodes, E represents the arc set with m arcs, (u, v) is the arc from u to v in E, and W(u, v) is the non-negative weight value of the arc (u, v); divide all nodes into two groups. The first group is the set S of nodes whose shortest paths have been determined. At the initial state, there is only one starting point v in the set S 0 ; the second group is the set (V - S) of nodes whose shortest paths have not been determined. After each shortest path v is obtained0 , …, v k , v will be k added to the set S until all nodes are added to the set S, and then the solution process of this shortest path is completed; v j is the end point of each intermediate path after the whole path is divided into intermediate paths, and v k is the end point of the whole path;

[0042] Let d i (1 ≤ i ≤ n) be the length of the shortest path currently found from the starting point v 0 to other nodes v i ; The initial state of d i is: if there is an arc from v 0 to v i , then d i is the weight of the arc (v 0 , v i ), otherwise let d i be ∞; if the first shortest path is (v 0 , v j ), then j satisfies d j = min{d i | v i ∈ V};

[0043] Then the next shortest path with the end point v k is (v 0 , v k ) or (v 0 , v j , v k ); Generally, the set S is a set that stores the nodes with the determined shortest paths. Then the intermediate node of the next shortest path must be a node in the set S, and its length is d j = min{d i | v i ∈ V - S}. After each shortest path is obtained, its 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 ). By repeatedly executing the above calculation method, the shortest paths from the starting point v 0 to other nodes in the map can be generated.

[0044] Preferred option, the positioning accuracy calculation in step four is specifically:

[0045] The positioning accuracy is used to measure the deviation between the actual position of the user's vehicle and the position shown on the navigation map. Assuming that the actual position coordinates of the vehicle are (x, y), and the position coordinates shown on the navigation map are (x 1 , y 1 ), the Euclidean distance formula is used to calculate the distance error between the two, which is used as a measure of the positioning accuracy:

[0046]

[0047] The criterion for judging compliance is as follows: If the positioning accuracy of j consecutive points is greater than T, it is judged that there is a positioning error, that is, it does not meet the standard. Among them, 3 ≤ j ≤ 5, and T = 5 meters; when the positioning accuracy of j consecutive points does not meet the standard, the path accuracy corresponding to these sampling points also does not meet the standard.

[0048] Preferred option. The basis for judging whether the shortest path plan in step five is reasonable is whether there are difficult-to-pass situations in the roads planned by the shortest path plan, that is, there are traffic jams, traffic accidents, road repairs, natural disasters, and whether there are impassable situations.

[0049] Preferred option. Step seven is specifically as follows:

[0050] First, judge the subordination relationship with the surrounding roads according to the current position, update the vehicle position, and obtain a new position, that is, the nearest point on the subordinate road from the current position is the updated new position of the vehicle. Use the shortest path plan calculation method in step three according to the new position;

[0051] The method for updating the vehicle position is specifically as follows: Using the vehicle positioning method of map matching, on the premise of clarifying the existence of errors, according to the spatial and subordination relationship between the vehicle position and the road, and then select the nearest position on the corresponding road:

[0052] From the perspective of selecting the positioning error area, the probability statistical matching method represents the positioning error area in map matching in the form of a positioning error ellipse according to the probability statistical phase and offset theory; for map matching, the area inside the error ellipse is the candidate area; when searching for the best matching road section in the map database, that is, when searching for the road section that can best match the actual driving position of the vehicle considering the positioning accuracy, selecting the road section that intersects the current positioning error ellipse as the candidate road section can find the effective candidate road section faster, that is, the candidate road section that intersects the current positioning error ellipse, reducing the map matching calculation time; the calculation process of the positioning error ellipse is as follows:

[0053]

[0054] δ x and δ yare the standard deviations of the positioning errors in the due east and due north directions respectively, and δ xy is their covariance, 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 δ 0 is the posterior variance of the unit weight.

[0055] Preferred option, the heuristic path optimization method in step eight is specifically as follows:

[0056] Combined with Dijkstra's method, a heuristic shortest - path search method is used. It introduces heuristic information in Dijkstra's method to avoid unreasonable paths and improves the effectiveness of the optimal path search in the road network;

[0057] Dijkstra's method in step three solves the shortest path in the order of increasing length. Therefore, the search space of this Dijkstra's method can be represented as a circular area. Among them, the end point T refers to the edge of the circular area. Searching outward with the origin B as the center and the radius increasing continuously, the shortest path is determined after finding the end point T;

[0058] To provide a weight estimate of the distance between any target point and a node, an estimation function is introduced. Define the heuristic estimation function of node v as f′(v)=g(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 best path from the current node v to the target node;

[0059] Among all the intermediate nodes with the same distance from the starting point, the smaller the angle between the connection line of any intermediate node and the starting point and the connection line of the starting point and the end point, the smaller the straight - line distance from this intermediate node to the end point. Let the intermediate node be A, the distance between A and the starting point B be d, and the angle between the line segment BA and BT be α, then there is

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

[0061] Replace the node weight d(v) in Dijkstra's method with f′(v), and the shortest - path heuristic search can be realized. Among them, the formulation of the weight parameter is based on the needs of users and the vehicle - to - everything network.

[0062] A system for implementing a vehicle path planning method based on a space - ground integrated network, including a driving intention compilation 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 compilation module is used to obtain the user's navigation target and the selected path bias, and send them to the path planning module;

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

[0065] The low-earth orbit satellite signal receiving module is used to obtain a higher-precision positioning signal from low-earth orbit satellites than medium-earth orbit satellites through a ground receiving base station when the positioning accuracy does not meet the standard, and send it to the path planning module;

[0066] The path planning module is used to obtain the shortest path that meets the path bias and has qualified positioning accuracy and path accuracy according to the signals sent by the driving intention compilation module, the medium-earth orbit satellite signal receiving module, and the low-earth orbit satellite signal receiving module, and send it to the vehicle navigation module;

[0067] The vehicle navigation module is used to display the shortest path plan route to the user, and in this embodiment, it can also display maps, navigation instructions, and traffic information.

[0068] Beneficial effects: The patent solution realizes efficient and safe vehicle path planning globally by integrating the space-ground integrated network and vehicle networking technology, making full use of the dynamic information advantages of low-earth orbit satellites, GPS / BDS, and V2X, and has the following remarkable beneficial effects:

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

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

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

[0072] This solution 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 hours, the system can predict congested areas in advance and optimize the path to avoid traffic bottlenecks; after an accident occurs, it can quickly provide a safe alternative route, significantly shortening the driving time and improving travel efficiency.

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

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

[0075] 4. Enhance safety and emergency response capabilities

[0076] Through the heterogeneous redundancy design of multi-source data, this solution automatically switches to the backup positioning mode when satellite signals are lost, ensuring the continuity of route planning. At the same time, based on real-time traffic data, the system can early warn of potential dangers and optimize routes, reducing the accident risk by more than 30%, significantly enhancing road safety and emergency response capabilities.

[0077] In summary, through the deep integration of the space-ground integrated network and vehicle networking technologies, the present invention realizes high-precision, strong adaptability, and low-latency vehicle route planning, effectively solving the core defects of the existing technologies, and providing an innovative solution for the further development of intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0079] Figure 1 is the flowchart of the method of the present invention;

[0080] Figure 2 is the schematic diagram of the positioning error ellipse of the present invention;

[0081] Figure 3 is the path optimization search space diagram of this aspect;

[0082] Figure 4 is the schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0083] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0084] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention.

[0085] In the present invention, unless otherwise clearly specified and defined, the first feature being "on" or "under" the second feature may include the first and second features being in direct contact, or may also include the first and second features not being in direct contact but being in contact through additional features therebetween. Moreover, the first feature being "above", "over" and "on top of" the second feature includes the first feature being directly above and obliquely above the second feature, or merely indicating that the first feature has a higher horizontal height than the second feature. The first feature being "under", "beneath" and "underneath" the second feature includes the first feature being directly below and obliquely below the second feature, or merely indicating that the first feature has a lower horizontal height than the second feature.

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

[0087] Step 1: The user vehicle accesses the vehicle networking and intelligent transportation system, obtains the target point coordinates of the user's navigation path, and sets them as the starting point;

[0088] Step 2: Obtain basic positioning through medium-orbit satellites to obtain the current position coordinates of the user vehicle, and set them as the ending point;

[0089] Step 3: The user selects the path bias for reaching the path target point, and obtains the shortest path plan according to the path bias;

[0090] The path bias includes a fast arrival mode, and the fast arrival mode is judged based on the road travel time. Specifically:

[0091] Take the road travel time as the weight for road selection, and calculate as follows:

[0092] When taking the shortest time as the selection criterion for the optimal path, the selection of the optimal path is based on time as the search weight, and the delay time at intersections needs to be considered. Therefore, the travel time on each path is expressed as

[0093]

[0094] where t represents the total travel time of this section of the road, l represents the path length, v αIt represents the average speed of this section of the road, and \(t'\) represents the downstream intersection delay time.

[0095] The time of the intersection delay includes the time of the free-flow intersection delay and the time of the signal-controlled intersection delay.

[0096] The time of the free-flow intersection delay is calculated by the following delay obtained from the P-K formula and the Little's equation:

[0097]

[0098] Among them, \(p\) represents the utilization rate, \(q\) represents the arrival rate, and \(Var(s)\) represents the service time variance; these data are obtained by querying the passing records of the nearest vehicles through the vehicle networking.

[0099] The time of the signal-controlled intersection delay is obtained according to the Webster formula:

[0100]

[0101] Among them, \(T\) represents the signal cycle length, \(\lambda\) represents the green ratio, \(Q\) represents the traffic flow of the approach lane, and \(X\) represents the saturation degree. These data can be obtained through the intelligent transportation system and the vehicle networking.

[0102] The path deviation also includes the broad avenue mode, and the broad avenue mode is judged based on the road capacity. Specifically:

[0103] The road capacity refers to the ability of the road facilities to dredge the traffic flow, and it is also a measure of the vehicle load on the road. If the road capacity is poor, the probability of traffic jams and accidents on this road will increase, and the requirements for driving conditions will also increase. Therefore, for roads with poor road capacity, it is also necessary to consider the factors among them through adjusting the weights for path planning.

[0104] Assume that the vehicle travels on the road at 90% of the speed limit in a uniform and orderly manner. Then the calculation formula for the road capacity is as follows:

[0105] N 0 =900v β / L

[0106] N = kN 0

[0107] Among them, \(v\) β is the road speed limit, \(N\) is the road capacity index, \(N\) 0 is the ideal road capacity, \(L\) represents the safe driving distance of the vehicle, and \(k\) is the correction coefficient.

[0108] The real-time traffic data is provided by the intelligent transportation system, and the correction coefficient \(k\) is obtained according to the following method:

[0109] Influence of lane width: where w 0 is the lane width;

[0110] Influence of intersection: where C 0 represents the effective passing time ratio of the intersection, and S 0 represents the intersection spacing;

[0111] Take the minimum value of the two influences as the final correction coefficient k.

[0112] The calculation method of the shortest path scheme in the third step adopts the Dijkstra method, specifically:

[0113] Let the weighted directed graph G=(V, E), where V represents the set of n nodes, E represents the set of m arcs, (u, v) is the arc from u to v in E, and W(u, v) is the non-negative weight value of the arc (u, v); divide all nodes into two groups. The first group is the set S of nodes for which the shortest path has been determined. Initially, there is only one starting point v 0 in the set S; the second group is the set (V - S) of nodes for which the shortest path has not been determined. After each shortest path v 0 ,…,v k is found, v x is added to the set S until all nodes are added to the set S, and then the solution process of this shortest path is completed; v j is the end point of each intermediate path after dividing the whole path into intermediate paths, and v k is the end point of the whole path;

[0114] Let d i (1 ≤ i ≤ n) be the shortest path length from the starting point v 0 to other nodes v i ; the initial state of d i is: if there is an arc from v 0 to v i , then d i is the weight value of the arc (v 0 ,v i ), otherwise let d i be ∞; if the first shortest path is (v 0 ,v j ), then j satisfies d j = min{d i ∣v i ∈ V};

[0115] Then the next shortest path with the end point v k is (v 0, v k ) or (v 0 , v j , v k ); Generally, the set S stores the nodes with the determined shortest paths. Then, the intermediate node of the next shortest path must be a node in the set S, and its length is d j = min{d i | v i ∈ V - S}, After obtaining a shortest path each time, its 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 )}, By repeatedly executing the above calculation method, the shortest paths from the starting point v 0 to 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 position shown on the navigation map and the path accuracy when the user's vehicle travels along the shortest path plan in Step Three. The path accuracy refers to the average value of the positioning accuracies of all sampling points in a fixed-length route. Determine whether both the positioning accuracy and the path accuracy meet the standards. If they meet the standards, proceed to Step Five; if they do not meet the standards, proceed to Step Seven;

[0117] The specific calculation of the positioning accuracy in Step Four is as follows:

[0118] The positioning accuracy is used to measure the deviation degree between the actual position of the user's vehicle and the position shown on the navigation map. Assume that the actual position coordinates of the vehicle are (x, y), and the position coordinates shown on the navigation map are (x 1 , y 1 ). Use the Euclidean distance formula to calculate the distance error between the two, and use this as a measure of the positioning accuracy:

[0119]

[0120] The standard for judging compliance is: If the positioning accuracies of consecutive j points are all greater than T, it is judged that a positioning error has occurred, that is, it does not meet the standard, where 3 ≤ j ≤ 5 and T = 5 meters; When the positioning accuracies of consecutive j points do not meet the standards, the path accuracies corresponding to these sampling points also do not meet the standards.

[0121] Step 5: Obtain the traffic conditions of the shortest path at the current moment through vehicle networking data, and determine whether the shortest path plan is reasonable. If it is reasonable, proceed to Step 6; if it is not reasonable, proceed to Step 8. The basis for determining whether the shortest path plan in Step 5 is reasonable is whether there are difficult-to-pass situations in the roads planned by the shortest path plan, that is, there are traffic jams, traffic accidents, road repairs, natural disasters, and whether there are impassable situations.

[0122] Step 6: Output the shortest path plan as the path planning result.

[0123] Step 7: Obtain the current position coordinates of the user's vehicle and the traffic conditions of the road where it is located through a low-earth orbit satellite, recalculate the shortest path plan based on the position coordinates and road traffic conditions obtained by the low-earth orbit satellite, and enter Step 5.

[0124] The specific content of Step 7 is as follows:

[0125] First, judge the subordination relationship between the current position and the surrounding roads, update the vehicle position to obtain a new position, that is, the nearest point on the subordinated road from the current position is the new position updated by the vehicle, and use the shortest path plan calculation method in Step 3 according to the new position.

[0126] The specific method for updating the vehicle position is as follows: Using the vehicle positioning method of map matching, on the premise of clarifying the existence of errors, according to the spatial and subordination relationship between the vehicle position and the road, and then select the nearest position on the corresponding road:

[0127] From the perspective of selecting the positioning error area, the probability statistical matching method represents the positioning error area in map matching in the form of a positioning error ellipse according to the probability statistical phase and offset theory; for map matching, the area inside the error ellipse is the candidate area; when looking for the best matching road section in the map database, that is, looking for the road section that can best fit the actual driving position of the vehicle from the map database considering the positioning accuracy, selecting the road section that intersects with the current positioning error ellipse as the candidate road section can find the effective candidate road section faster, that is, the candidate road section that intersects with the current positioning error ellipse, reducing the map matching calculation time; as Figure 2 shown, the calculation process of the positioning error ellipse is as follows:

[0128]

[0129] δ x and δ y are the standard deviations of the positioning errors in the due east and due north directions respectively, δ xy is the covariance between 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 δ 0 is the posterior variance of the unit weight.Figure 2 Where P is the vehicle position.

[0130] Step 8: After optimizing the navigation path using the heuristic path optimization method, proceed to Step 6:

[0131] The specific heuristic path optimization method in Step 8 is as follows:

[0132] Combined with Dijkstra's method, a heuristic shortest path search method is used, which introduces heuristic information in Dijkstra's method to avoid unreasonable paths and improves the effectiveness of the best path search in the road network;

[0133] As Figure 3 shown, Dijkstra's method in Step 3 solves the shortest path in the order of increasing length. Therefore, the search space of this Dijkstra's method can be represented as a circular area, where the end point T refers to the edge of the circular area. Searching outward from the origin B with the radius continuously increasing, the shortest path is determined after finding the end point T;

[0134] To provide a weight estimate of the distance between any target point and a node, an estimation function is introduced. Define the heuristic estimation function of node v as f′(v) = g(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 best path from the current node v to the target node;

[0135] Among all the intermediate nodes at the same 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 end point, the smaller the straight-line distance from this intermediate node to the end point. Let the intermediate node be A, the distance between A and the starting point B be d, and the angle between the line segment BA and BT be α, then there is

[0136] When searching for the shortest path, use the node weight d(v) 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 as the estimated cost. Then the heuristic estimation function is f′(v) = d(v) + d′(v);

[0137] Replace the node weight d(v) in Dijkstra's method with f′(v) to achieve the heuristic search for the shortest path, where the determination of the weight parameter is based on the needs of users and the vehicle networking.

[0138] As Figure 4 shown, the system for implementing the vehicle path planning method based on the space-ground integrated network includes a driving intention 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.

[0139] The driving intention compilation module is used to obtain the user's navigation target and the selected path bias, and send them to the path planning module;

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

[0141] The low Earth orbit satellite signal receiving module is used to obtain a positioning signal with higher accuracy from low Earth orbit satellites than medium Earth orbit satellites through a ground receiving base station when the positioning accuracy does not meet the standard, and send it to the path planning module;

[0142] The path planning module is used to obtain the shortest path that meets the path bias and has qualified positioning accuracy and path accuracy according to the signals sent by the driving intention compilation module, the medium Earth orbit satellite signal receiving module, and the low Earth orbit satellite signal receiving module, and send it to the vehicle navigation module;

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

[0144] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0145] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle path planning method based on a ground-ground integrated network, characterized in that: The following steps are involved: Step 1: The user's vehicle is connected to the Internet of Vehicles and the intelligent transportation system, and the coordinates of the target point of the user's navigation path are obtained and set as the starting point; Step 2: Obtain basic positioning through the medium-orbit satellite to obtain the current position coordinates of the user's vehicle and set it as the end point; Step 3: The user selects a path direction to reach the path target point, and obtains the shortest path solution based on the path direction; Step 4: Calculate the positioning accuracy of the actual position of the user's vehicle relative to the position displayed on the navigation map and the path accuracy of the user's vehicle when traveling along the shortest path solution in step 3. The path accuracy refers to the average positioning accuracy of all sampling points in a fixed-length route. Determine whether both the positioning accuracy and the path accuracy meet the standards. If they meet the standards, proceed to step 5. If not, proceed to step 7. Step 5: Obtain the traffic conditions of the shortest path at the current moment through the Internet of Vehicles data, and determine whether the shortest path solution is reasonable. If it is reasonable, proceed to step 6; if it is not reasonable, proceed to step 8; Step 6: Output the shortest path solution as the path planning result; Step 7: Obtain the current position coordinates of the user's vehicle and the traffic conditions of the road through the low-orbit satellite, recalculate the shortest path plan based on the position coordinates and road traffic conditions obtained by the low-orbit satellite, and proceed to step 5; Step 8: After optimizing the navigation path using the heuristic path optimization method, proceed to step 6.

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

3. The vehicle path planning method based on the ground-ground integrated network according to claim 2 is characterized in that: The delay time of the intersection includes the delay time of the free-travel intersection and the delay time of the signal-controlled intersection. The delay time of the free-travel intersection is calculated by the PK formula and the Little equation as follows: Among them, 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 recent vehicle passing records through the Internet of Vehicles; The delay time of the signal-controlled intersection is calculated according to the Webster formula: Among them, T represents the signal cycle length, λ represents the green-to-signal ratio, Q represents the traffic flow of the entrance road, and X represents the saturation. These data can be obtained through the traffic intelligence system and the Internet of Vehicles.

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

5. The vehicle path planning method based on the ground-ground integrated network according to claim 1 is characterized in that: The calculation method of the shortest path solution in step 3 adopts Dijkstra method, which is specifically: Suppose there is a weighted directed graph G = (V, E), where V represents a node set with n nodes, E represents an arc set with m arcs, (u, v) is the arc from u to v in E, and W(u, v) is the non-negative weight of the arc (u, v); divide all nodes into two groups, the first group is the set S of nodes whose shortest paths have been determined. In the initial state, there is only one starting point v0 in the set S; the second group is the set (VS) of nodes whose shortest paths have not yet been determined. After each shortest path v0,…,v is obtained, k , then v k Add to the set S until all nodes are added to the set S, then the shortest path solution process is completed; v j is the end point of each intermediate path after the entire path is divided into intermediate paths, v k It is the end point of the entire path; Assume d i (1≤i≤n) is the number of nodes from the starting point v0 to other nodes v currently found i The shortest path length; d i The initial state is: if from v0 to v i There is an arc, then d i For the arc (v0,v i ), otherwise let d i is ∞; if the first shortest path is (v0,v j ), 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 ); In general, the set S is a set of nodes that stores the determined shortest paths, so the intermediate node of the next shortest path must be a node in the set S, and its length is d j =min{d i ∣v i ∈VS}, after each shortest path is found, its end point v j will be added to the set S, then d i Updated to d i =min{d i ,d j +W(v j ,v i )}, by executing the above calculation method multiple times in a loop, the shortest path from the starting point v0 to other nodes in the map can be generated.

6. The vehicle path planning method based on the ground-ground integrated network according to claim 1 is characterized by: The positioning accuracy calculation in step 4 is specifically as follows: Positioning accuracy is used to measure the degree of deviation between the actual position of the user's vehicle and the position displayed on the navigation map. Assuming that the actual position coordinates of the vehicle are (x, y) and the position coordinates displayed on the navigation map are (x1, y1), the distance error between the two is calculated using the Euclidean distance formula as a measure of positioning accuracy: The standard for judging whether the standard is met is: if the positioning accuracy of j consecutive points is greater than T, it is judged that a positioning error has occurred, that is, it does not meet the standard, where 3≤j≤5, T=5 meters; when the positioning accuracy of j consecutive points does not meet the standard, the path accuracy corresponding to these sampling points also does not meet the standard.

7. The vehicle path planning method based on the ground-ground integrated network according to claim 1 is characterized in that: The basis for judging whether the shortest path plan is reasonable in step 5 is whether there are any difficult-to-pass situations in the roads planned by the shortest path plan, that is, there are traffic jams, traffic accidents, road maintenance, natural disasters, and whether there are any impassable situations.

8. The vehicle path planning method based on the ground-ground integrated network according to claim 1 is characterized by: The step seven is specifically as follows: First, the affiliation relationship with the surrounding roads is determined based on the current position, and the vehicle position is updated to obtain a new position, that is, the point closest to the current position on the affiliated road is the new position updated by the vehicle, and the shortest path solution calculation method in step 3 is used according to the new position; The method of updating the vehicle position is as follows: using the vehicle positioning method of map matching, under the premise of clarifying the existence of errors, according to the spatial and affiliation relationship between the vehicle position and the road, the nearest position of the corresponding road is selected: From the perspective of selecting the positioning error area, the probability statistical matching method uses the probability statistical phase and offset theory to represent the positioning error area in map matching in the form of a positioning error ellipse; for map matching, the area within the error ellipse is the candidate area; When searching for the best matching road section in the map database, that is, when searching for the road section that best matches the actual driving position of the vehicle in the map database while considering the positioning accuracy, selecting the road section that intersects with the current positioning error ellipse as the candidate road section can more quickly find the effective candidate road section, that is, the candidate road section that intersects with the current positioning error ellipse, and reduce the map matching calculation time; the positioning error ellipse calculation process is as follows: δ x and δ y are the standard deviations of positioning errors in the due east and due north directions, δ xy is the covariance of the two, a is the major semiaxis of the error ellipse, b is the minor semiaxis of the error ellipse, θ is the angle between the major semiaxis of the ellipse and the north direction, and δ0 is the posterior variance of the unit weight.

9. The vehicle path planning method based on the space-ground integrated network according to claim 5 is characterized in that: The heuristic path optimization method in step eight is specifically as follows: Combined with Dijkstra method, a heuristic shortest path search method is used. Heuristic information is introduced into Dijkstra method to avoid unreasonable paths, thus improving the effectiveness of searching for the best path in the road network. The Dijkstra method in step 3 solves the shortest path in order of increasing length, so the search space of the Dijkstra method can be represented as a circular area, where the end point T refers to the edge of the circular area. The search starts from the origin B and goes outward with the radius increasing. After the end point T is found, the shortest path is determined. In order to provide a weighted estimate of the distance between any target point and the node, an estimation function is introduced, and the heuristic estimation function of 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 best path from the current node v to the target node; Among all the intermediate nodes with the same 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 end point, the smaller the straight-line distance from the intermediate node to the end 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 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. The heuristic estimation function is f′(v)=d(v)+d′(v); By replacing the node weight d(v) in the Dijkstra method with f′(v), the shortest path heuristic search can be implemented, where the weight parameters are formulated according to the needs of users and the Internet of Vehicles.

10. A system for implementing the vehicle path planning method based on the space-ground integrated network according to any one of claims 1 to 9, characterized in that: It includes driving intention compilation module, medium-orbit satellite signal receiving module, low-orbit satellite signal receiving module, path planning module, and vehicle navigation module. The driving intention compilation module is used to obtain the user's navigation target and the selected path deviation, 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 position coordinates of the user vehicle and send them to the path planning module; The low-orbit satellite signal receiving module is used to obtain a positioning signal with higher accuracy from a low-orbit satellite than a medium-orbit satellite through a ground receiving base station when the positioning accuracy does not meet the standard, and send it to the path planning module; The path planning module is used to obtain the shortest path that meets the path deviation and has the required positioning accuracy and path accuracy according to 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 send it to the vehicle navigation module; The vehicle navigation module is used to display the shortest path solution route to the user.

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