Vehicle passing track management system based on Beidou satellite positioning

Abstract

The invention discloses a vehicle passing track management system based on Beidou satellite positioning, and relates to the technical field of vehicle management. The management system comprises a positioning module; the positioning module is based on satellite positioning and sends position data to the central processing module in real time. According to the management system, a changing score is designed for each road section through an integral algorithm, the vehicle can select the road section to move according to the maximum score value, meanwhile, the score of the road section is increased along with the increase of the interval time, and the score coefficient is changed due to the influence of the dynamic state, so that the vehicle cruise in the area without being damaged. According to the method, more areas can be covered, optimization of vehicle cruise enables the vehicles to be better distributed in the whole area, the situation that the vehicles in some areas are too concentrated or too sparse is avoided, and the situation that the customer waits for a long time due to the fact that the nearest vehicle is too far away from the order is reduced when the order is received subsequently.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and specifically to a vehicle passing trajectory management system based on Beidou satellite positioning. Background Art

[0002] The Beidou satellite positioning technology is widely used to obtain the real-time position of vehicles. The system receives the position data sent by Beidou satellites through the positioning module and transmits these data to the central processing module in real time, providing basic data support for subsequent path planning and safety management.

[0003] Patent Publication No. "CN107784824A", patent name "Driverless taxi dispatching system and dispatching method". The above dispatching system can select a taxi for a passenger according to the passenger's ride request information. Under the condition of meeting the passenger's ride request, it can reasonably dispatch driverless taxis from within the stations that meet the preset measurement conditions and, after selecting a taxi, notify the taxi and the passenger. When the taxi completes the passenger delivery task, the system can also reasonably arrange the stations where the taxi needs to stop according to the preset measurement conditions, improving the dispatching efficiency of the system. The dispatching system and method of the above invention can provide personalized services more accurately and pertinently.

[0004] During the operation of driverless taxis, they will adopt an operation mode similar to that of traditional cruising taxis. When driving on the road, they wait for customer orders at the same time. This method is used to reduce the customer waiting time. In the above dispatching method, it is proposed that when the taxi completes the passenger delivery task, the system can also reasonably arrange the stations where the taxi needs to stop according to the preset measurement conditions. The arranged stations are basically located at traffic key points. However, relative to the entire area, there will be some locations far from the traffic key points, which will cause users at this location to wait for a long time due to the long distance when sending orders, affecting the user experience. Therefore, a vehicle passing trajectory management system based on Beidou satellite positioning is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a vehicle passing trajectory management system based on Beidou satellite positioning to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A vehicle passing trajectory management system based on Beidou satellite positioning, the management system includes:

[0007] A positioning module; the positioning module is based on satellite positioning and sends position data to the central processing module in real time;

[0008] An instruction sending module: the instruction sending module sends instruction information to the central processing module;

[0009] Central processing module; The central processing module receives and analyzes instruction information, establishes a position data set based on the obtained position data, and establishes a road network map through the map information stored inside the central processing module. The road network map includes nodes and road segments, and divides the road network map into several regions;

[0010] The selection method selects at least one application position data from the position data set based on the instruction information, and obtains the historical selection of the selection method;

[0011] Generate a first path based on the instruction information, the road network map, and the application position data, and transmit the first path to the sub-processor module installed on the vehicle based on the historical selection;

[0012] The central processing module obtains real-time traffic conditions information, and the central processing module generates modification information for modifying the first path based on the traffic conditions information. The modification information is transmitted to the sub-processor module based on the historical selection;

[0013] Sub-processor module: The sub-processor module receives the first path and the modification information, and obtains the modified path in real time through the modification method;

[0014] The instruction information includes a cruising instruction information for controlling the moving trajectory of vehicles within the region. The selection method selects the application position data located in the target region from the position data set based on the cruising instruction information, and obtains the cruising historical selection;

[0015] The generation method of the cruising first path includes: establishing a cruising distance threshold between cruising vehicles within the same region, and generating the cruising first path through an integral algorithm. The integral algorithm includes: designing a changing score for each road segment based on the road network map through a fractional algorithm. The vehicle selects the road segment to move according to the maximum score, and obtains a moving path including at least two road segments based on a prediction method, and obtains the cruising first path.

[0016] Furthermore, establish a counting method for calculating the number of vehicles within the region. The counting method includes: establishing a real number X and an imaginary number Y. The number of vehicles is equal to the real number X plus the imaginary number Y. When it is detected that a vehicle is located within the target region, but the vehicle status is marked as unused, it is included in the real number X of the target region. When the vehicle is located within the target region and the vehicle status is marked as used, it is not included in the real number X of the target region. When the destination of the vehicle is within the target region and the vehicle status is marked as used, it is included in the imaginary number Y of the target region, and controls the switching of the vehicle status based on the attributes of the instruction information;

[0017] The instruction information includes scheduling instruction information for controlling vehicle data within a region, establishing a limit range for restricting the number of vehicles in different regions, and triggering the scheduling instruction information when it is detected that the number of vehicles within the region is not within the limit range of the corresponding region. The scheduling instruction information includes a first scheduling instruction for reducing the number of vehicles within the region and a second scheduling instruction for increasing the number of vehicles within the region;

[0018] When the real number X is greater than the maximum value in the limit range, it is considered that the number of vehicles within the target region is greater than the limit range. When X + Y is less than the minimum value in the limit range, it is considered that the number of vehicles within the target region is less than the limit range;

[0019] The selection method includes a first selection method applied to the first scheduling instruction and a second selection method applied to the second scheduling instruction. The first selection method includes: based on the number of vehicles within the target region being less than the limit range, obtaining the minimum difference quantity, selecting vehicles from the surrounding regions by the first selection method in combination with the minimum difference quantity, sending the first scheduling instruction to the selected vehicles, and selecting the corresponding application location data from the location data set;

[0020] The second selection method includes: based on the number of vehicles within the target region being greater than the limit range, obtaining the quantity to be reduced regarding vehicles in the target region, selecting vehicles from the target region by the second selection method in combination with the quantity to be reduced, sending the second scheduling instruction to the selected vehicles, and selecting the corresponding application location data from the location data set;

[0021] The scheduling first path for controlling the number of vehicles within the region is calculated by combining the path algorithm of the scheduling instruction information. The calculation method of the scheduling first path includes: based on the corresponding location data of the selected vehicles and the first scheduling instruction, generating a scheduling first path for guiding the selected vehicles to move to the target region.

[0022] Furthermore, the first selection method includes: combining the minimum difference quantity, obtaining the number of scheduled vehicles in the adjacent region. The number of scheduled vehicles refers to the difference between the number of vehicles within the region and the minimum value in the limit range. When the number of scheduled vehicles in the adjacent region is less than the minimum difference quantity, gradually expand the adjacent region to obtain the number of scheduled vehicles in the expanded adjacent region until the number of scheduled vehicles is greater than or equal to the minimum difference quantity. According to the current range of the adjacent region, select the same number of vehicles as the minimum difference quantity from the current range of the adjacent region and control the vehicles to move to the target region;

[0023] The second selection method includes: combining the quantity to be subtracted, selecting a number of vehicles equal to the quantity to be subtracted from the target area, obtaining the number of blank vehicles in the adjacent area, where the number of blank vehicles refers to the difference between the number of vehicles inside the area and the maximum value in the restricted range. When the number of blank vehicles in the adjacent area is less than the quantity to be subtracted, gradually expand the adjacent area to obtain the number of blank vehicles in the expanded adjacent area until the number of blank vehicles is greater than or equal to the quantity to be subtracted. Based on the current range of the adjacent area, select a number of vehicles equal to the quantity to be subtracted from the target area and control the vehicles to move to the adjacent area.

[0024] Furthermore, the score algorithm includes: setting a score coefficient K, the road segment score = score coefficient K × interval time. When a vehicle is on a road segment, the interval time is adjusted to zero. When there are no vehicles on the road segment, the road segment score increases with the increase of the interval time. When the vehicle leaves the road segment, the interval time starts to increase, and a coefficient method is designed to control the specific value of the score coefficient K;

[0025] The coefficient method includes: the score coefficient K is adjusted based on static road attributes and dynamic states. The static road attributes include length and traffic capacity, and the dynamic state includes the real-time congestion degree of the road segment. Numerical representations are made for the static road attributes and dynamic states.

[0026] Furthermore, the instruction information includes order instruction information;

[0027] The selection method includes a fourth selection method applied to order instruction information. The fourth selection method includes: obtaining the starting point position in the order instruction information, and selecting the application position data closest to the starting point position from the position data set according to the nearest principle;

[0028] Combined with the path algorithm of the order instruction information, calculate the order first path for controlling the movement trajectory of vehicles within the area. The calculation method of the order first path includes: obtaining the starting point position and the ending point position of the order instruction information, and establishing the order first path based on the current application position data of the vehicle, the starting point position and the ending point position through the fastest principle;

[0029] The fastest principle includes using a prediction algorithm to simulate the time required for the vehicle to pass through the application position data, the starting point position and the ending point position in turn according to the road network map, and selecting the order first path according to the shortest time.

[0030] Furthermore, the instruction information includes designated instruction information, and the designated instruction information includes return instruction information, repair instruction information and charging instruction information;

[0031] The selection method includes the fifth selection method applied to the specified instruction information, and the fifth selection method includes: obtaining the target position and the specified vehicle in the specified instruction information, and obtaining the application position data corresponding to the specified vehicle from the position dataset according to the specified vehicle;

[0032] Combined with the path algorithm of the specified instruction information, calculate the specified first path for controlling the specified vehicle. The calculation method of the specified first path includes: establishing the specified first path based on the application position data corresponding to the specified vehicle and the target position through the fastest principle.

[0033] Furthermore, the modification method includes: based on the modification information, recalculate the path, adjust the path nodes and adjust the driving parameters, and input the recalculated path and driving parameters into the first path to obtain the modified path.

[0034] Furthermore, the management system includes:

[0035] Sensor module: The sensor module installed on the vehicle monitors the environmental information outside the vehicle in real time;

[0036] Driving and trajectory coordination module: Combine the environmental information, the modified path and the position data, and calculate the navigation data for guiding the vehicle to move through the coordination algorithm;

[0037] The coordination algorithm includes a parking algorithm and a driving-out algorithm, and obtains the parking data and fine-tuning data for guiding the vehicle to move through the parking algorithm and the fine-tuning algorithm;

[0038] The parking algorithm includes: obtaining the parking area, planning the parking space in combination with the environmental information in the parking area, establishing the trajectory for controlling the vehicle to move to the parking space based on the real-time environmental information, and obtaining the parking data;

[0039] The fine-tuning algorithm includes: adjusting the position and speed of the vehicle on the road section according to the modified path and in real-time combination with the environmental information around the vehicle, and obtaining the fine-tuning data based on the position adjustment and speed adjustment.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] Vehicle passing trajectory management system based on Beidou satellite positioning. The management system designs a changing score for each road section through an integral algorithm. Vehicles can choose the road section to move according to the maximum score. At the same time, the road section score increases with the increase of the interval time, and the score coefficient changes under the influence of the dynamic state, enabling the vehicle to cover more areas when cruising within the area. The optimization of vehicle cruising enables the vehicle to be better distributed throughout the area, avoiding the situation where vehicles are too concentrated or too sparse in some areas, so as to reduce the situation where the distance between the nearest vehicle and the order is too far and the customer waiting time is too long when receiving orders subsequently.

[0042] At the same time, the management system combines the integral algorithm with the road network map and modifies the No. 1 cruising path through a modification method. The No. 1 cruising path is obtained based on a prediction method to predict the subsequent road sections. When other cruising vehicles move to the predicted subsequent road sections, the score of that road section is cleared, so as to adjust the prediction method and thus adjust the No. 1 cruising path in real time, thereby reducing the situation of over-cruising the same road section.

[0043] The management system establishes a counting method and a limit range for calculating the number of vehicles in the area. When the number of vehicles in the area exceeds or is lower than the limit range, cross-regional scheduling is automatically triggered to reduce the number of vehicles in the target area or increase the number of vehicles in the target area, reduce vehicle idleness or congestion, and improve the overall operation efficiency. The system can automatically trigger scheduling instructions to ensure the reasonable distribution of the number of vehicles in each area. Brief Description of the Drawings

[0044] Figure 1 It is a schematic diagram of the management system of the present invention;

[0045] Figure 2 It is a schematic diagram of the cruising instruction of the present invention;

[0046] Figure 3 It is a schematic diagram of the scheduling instruction of the present invention;

[0047] Figure 4 It is a classification diagram of the instructions of the present invention. Detailed Embodiment

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Such as Figure 1 - Figure 4As shown in the figure, the present invention provides a technical solution: a vehicle passing trajectory management system based on Beidou satellite positioning. The management system includes:

[0050] A positioning module; the positioning module is based on satellite positioning and sends position data to the central processing module in real time;

[0051] An instruction sending module: the instruction sending module sends instruction information to the central processing module;

[0052] A central processing module; the central processing module receives and analyzes the instruction information, establishes a position data set based on the obtained position data, establishes a road network map through the map information stored inside the central processing module. The road network map includes nodes and road segments, and divides the road network map into several regions;

[0053] The selection method selects at least one application position data from the position data set based on the instruction information, and obtains the historical selection of the selection method;

[0054] Generate a first path based on the instruction information, the road network map and the application position data. The first path is transmitted to the sub-processor module installed on the vehicle based on the historical selection;

[0055] The central processing module obtains the road condition information in real time. The central processing module generates modification information for modifying the first path based on the road condition information. The modification information is transmitted to the sub-processor module based on the historical selection;

[0056] A sub-processor module: the sub-processor module receives the first path and the modification information, and obtains the modified path in real time through the modification method;

[0057] The instruction information includes cruise instruction information for controlling the movement trajectory of vehicles within the area. The selection method selects the application position data located in the target area from the position data set based on the cruise instruction information, and obtains the cruise historical selection;

[0058] The generation method of the cruise first path includes: establishing a cruise distance threshold between cruise vehicles within the same area, generating the cruise first path through an integral algorithm. The integral algorithm includes: designing a changing score for each road segment through a fractional algorithm and based on the road network map. The vehicle selects the road segment to move according to the maximum score, and obtains a movement path including at least two road segments based on a prediction method, and obtains the cruise first path.

[0059] Establish a counting method for calculating the number of vehicles in a region. The counting method includes: establishing a real number X and an imaginary number Y, where the number of vehicles is equal to the sum of the real number X and the imaginary number Y. When a vehicle is detected within the target region but its status is marked as unused, it is included in the real number X of the target region. When a vehicle is within the target region and its status is marked as used, it is not included in the real number X of the target region. When the destination of a vehicle is within the target region and its status is marked as used, it is included in the imaginary number Y of the target region. Control the switching of the vehicle status based on the attributes of the instruction information;

[0060] The instruction information includes scheduling instruction information for controlling vehicle data within a region. Establish a limit range for restricting the number of vehicles in different regions. When the number of vehicles detected within a region is not within the limit range of the corresponding region, trigger the scheduling instruction information. The scheduling instruction information includes a first scheduling instruction for reducing the number of vehicles within a region and a second scheduling instruction for increasing the number of vehicles within a region;

[0061] When the real number X is greater than the maximum value within the limit range, it is considered that the number of vehicles within the target region is greater than the limit range. When X + Y is less than the minimum value within the limit range, it is considered that the number of vehicles within the target region is less than the limit range;

[0062] The selection method includes a first selection method applied to the first scheduling instruction and a second selection method applied to the second scheduling instruction. The first selection method includes: based on the number of vehicles within the target region being less than the limit range, obtain the minimum difference quantity, select vehicles from the surrounding regions through the first selection method in combination with the minimum difference quantity, send the first scheduling instruction to the selected vehicles, and select the corresponding application location data from the location data set;

[0063] The second selection method includes: based on the number of vehicles within the target region being greater than the limit range, obtain the quantity to be reduced regarding vehicles in the target region, select vehicles from the target region through the second selection method in combination with the quantity to be reduced, send the second scheduling instruction to the selected vehicles, and select the corresponding application location data from the location data set;

[0064] Calculate the first scheduling path for controlling the number of vehicles in a region by combining the path algorithm of the scheduling instruction information. The calculation method of the first scheduling path includes: based on the location data corresponding to the selected vehicles and the first scheduling instruction, generate a first scheduling path for guiding the selected vehicles to move to the target region.

[0065] The first selection method includes: combining the minimum difference quantity to obtain the number of dispatching vehicles in adjacent areas. The number of dispatching vehicles refers to the difference between the minimum value of the number of vehicles within the area and the restricted range. When the number of dispatching vehicles in the adjacent area is less than the minimum difference quantity, gradually expand the adjacent area to obtain the number of dispatching vehicles in the expanded adjacent area until the number of dispatching vehicles is greater than or equal to the minimum difference quantity. Based on the current range of the adjacent area, select the same number of vehicles as the minimum difference quantity from the current range of the adjacent area and control the vehicles to move to the target area;

[0066] The second selection method includes: combining the quantity to be subtracted, select the same number of vehicles as the quantity to be subtracted from the target area, and obtain the number of blank vehicles in the adjacent area. The number of blank vehicles refers to the difference between the maximum value of the number of vehicles within the area and the restricted range. When the number of blank vehicles in the adjacent area is less than the quantity to be subtracted, gradually expand the adjacent area to obtain the number of blank vehicles in the expanded adjacent area until the number of blank vehicles is greater than or equal to the quantity to be subtracted. Based on the current range of the adjacent area, select the same number of vehicles as the quantity to be subtracted from the target area and control the vehicles to move to the adjacent area.

[0067] The score algorithm includes: setting a score coefficient K, the road segment score = score coefficient K × interval time. When the vehicle is on the road segment, the interval time is adjusted to zero. When there is no vehicle on the road segment, the road segment score increases with the increase of the interval time. When the vehicle leaves the road segment, the interval time starts to increase, and the specific value of the score coefficient K is controlled by the design coefficient method;

[0068] The coefficient method includes: the score coefficient K is adjusted based on static road attributes and dynamic states. The static road attributes include length and traffic capacity, and the dynamic states include the real-time congestion degree of the road segment. Numerical representations are made for the static road attributes and dynamic states.

[0069] The instruction information includes order instruction information;

[0070] The selection method includes the fourth selection method applied to the order instruction information. The fourth selection method includes: obtaining the starting point position in the order instruction information, and selecting the application position data closest to the starting point position from the position data set according to the nearest principle;

[0071] Combined with the path algorithm of the order instruction information, calculate the order first path for controlling the vehicle movement trajectory within the area. The calculation method of the order first path includes: obtaining the starting point position and the ending point position of the order instruction information, and establishing the order first path based on the current application position data of the vehicle, the starting point position and the ending point position through the fastest principle;

[0072] The fastest principle includes using a prediction algorithm based on a road network map to simulate the time required for a vehicle to pass through the application location data, starting point location, and ending point location in sequence, and selecting the first-order path of the order according to the shortest time.

[0073] The instruction information includes designated instruction information, and the designated instruction information includes return instruction information, maintenance instruction information, and charging instruction information;

[0074] The selection method includes the fifth selection method applied to the designated instruction information. The fifth selection method includes: obtaining the target location and the designated vehicle in the designated instruction information, and obtaining the application location data corresponding to the designated vehicle from the location data set according to the designated vehicle;

[0075] Combined with the path algorithm of the designated instruction information, calculate the first-order designated path for controlling the designated vehicle. The calculation method of the first-order designated path includes: establishing the first-order designated path based on the application location data corresponding to the designated vehicle and the target location and through the fastest principle.

[0076] The modification method includes: based on the modification information, recalculate the path, adjust the path nodes and driving parameters, and input the recalculated path and driving parameters into the first-order path to obtain the modified path.

[0077] The management system includes:

[0078] The sensor module: The sensor module installed on the vehicle monitors the environmental information outside the vehicle in real time;

[0079] The driving and trajectory coordination module: combines the environmental information, modified path, and location data, and calculates the navigation data for guiding the vehicle to move through the coordination algorithm;

[0080] The coordination algorithm includes a parking algorithm and a driving-out algorithm, and obtains the parking data and fine-tuning data for guiding the vehicle to move through the parking algorithm and the fine-tuning algorithm;

[0081] The parking algorithm includes: obtaining the parking area, planning the parking space in combination with the environmental information in the parking area, establishing the trajectory for controlling the vehicle to move to the parking space based on the real-time environmental information, and obtaining the parking data;

[0082] The fine-tuning algorithm includes: adjusting the position and speed of the vehicle on the road section according to the modified path and in real-time combination with the environmental information around the vehicle, and obtaining the fine-tuning data based on the position adjustment and speed adjustment.

[0083] The vehicle in this application specifically refers to a driverless taxi. In the dynamic cruising mode of driverless taxis, some driverless taxis adopt an operation method similar to that of traditional cruising taxis. When driving on the road, they wait for customer orders at the same time. When the central processing module receives an order, it will send an instruction to the driverless taxi closest to the customer, enabling the driverless taxi to quickly adjust its driving route and head to the passenger pick-up location. Dynamic cruising enables driverless taxis to respond to passengers' travel needs in real time, reducing passengers' waiting time. Moreover, dynamic cruising enables driverless taxis to cover a wider area. The cruising distance threshold is a key parameter in the vehicle passing trajectory management system based on Beidou satellite positioning, which is used to control the dynamic cruising of driverless taxis within a specific area. The cruising distance threshold defines the minimum distance between different driverless taxis in the same area, ensuring that vehicles maintain a certain spacing during cruising and avoiding being too concentrated or dispersed. Therefore, when planning the No. 1 cruising path under the cruising instruction, it is necessary to consider the limitation of the cruising distance threshold at the same time. Note that the cruising distance threshold only applies to vehicles under the cruising instruction, and there is no cruising distance threshold between vehicles under the cruising instruction and vehicles under the order instruction.

[0084] The road network map includes nodes and road segments. Among them, the road segments are roads, and the nodes are the intersection positions of roads. The road segments only refer to roads. The No. 1 cruising path is generated through an integral algorithm. A changing score is designed for each road segment through a scoring algorithm. When moving to the node position, the road segment is selected according to the maximum score. Since a cruising instruction is required to enable the vehicle to cruise within the target area, in order to ensure the operation of the driverless taxi, a prediction algorithm is designed. Based on the current road segment and direction of the cruising driverless taxi, the road segment that will be selected at the next node is predicted, and based on the road segment that will be selected, the road segment that will be retracted and selected at the second node position is predicted. Repeating the above operations can achieve the path prediction of the cruising driverless taxi. However, there may be other similar vehicles that are already on the predicted road segment in advance, resulting in the road segment score being cleared to zero. Therefore, the prediction method obtains a moving path that includes at least two road segments. At the same time, the upper limit can be increased according to the actual situation, reducing the load on the central processing module when the predicted road segment score is cleared to zero and re-prediction is required. When the predicted road segment has been cleared to zero, the No. 1 cruising path can be modified through a modification method at this time. The management system designs a changing score for each road segment through an integral algorithm. The vehicle can select the road segment to move according to the maximum score, thus effectively avoiding congested road segments and improving the driving efficiency. At the same time, the road segment score increases with the increase of the interval time, and the score coefficient changes under the influence of the dynamic state, reducing the entry into congested road segments, enabling the vehicle to cover more areas when cruising within the area, so as to reduce the situation that the distance between the nearest vehicle and the order is too far and the customer waiting time is too long when receiving an order subsequently.

[0085] The integral algorithm is only applicable to driverless taxis under the cruising instruction. Even if driverless taxis pass through multiple sections under other instructions, the interval time of the sections will not be reset. In the same area, for driverless taxis waiting at fixed stations, the sections where they stop will continuously reset the interval time of those sections. When a driverless taxi in use moves to a section, the score of that section will not be reset.

[0086] Section score = score coefficient K × interval time, where score coefficient K = static road attribute × dynamic state. The static road attribute and dynamic state are numerically represented so that the specific value of score coefficient K can be obtained through this numerical representation. Since the section is two-way, the score coefficient K varies based on the different directions of the section, resulting in different specific values of the score coefficient K. The score coefficient K is affected by the static road attribute and dynamic state. Once the impact of the static road attribute on the score coefficient K is determined, it generally will not change. When the section is too congested, the score coefficient K can be negative, causing the section score to be negative, thus avoiding the possibility of vehicles moving from this congested section. At the same time, because the section is two-way, there may be a situation where one direction is congested while the other direction is unobstructed during two-way traffic. Therefore, for the same section but different directions, the congestion levels are calculated separately.

[0087] Meanwhile, the real-time congestion level is numerically represented, and a numerical set of congestion levels is established. For example, the numerical set of congestion levels is (-1, 0.5, 0.8, 1). When it is too congested, the congestion level is adjusted to -1. -1 represents extremely congested, which affects traffic flow. 0.5 represents slightly congested, indicating that although there is certain traffic pressure on this section, the overall traffic flow still maintains a certain degree of fluidity. 1 represents unobstructed, indicating that the traffic condition of this section is good and vehicles can drive smoothly. There is no value of zero in the numerical set of congestion levels because a value of zero would cause the section score to be zero. When it is too congested, the section score becomes negative. Section score = score coefficient K × interval time. The interval time increases with time, and the score coefficient mainly changes with the dynamic state. The value obtained for the static road attribute remains basically unchanged. Among static road attributes, the specific values of some main traffic arteries are relatively larger than those of diversion roads. Therefore, for main traffic arteries, the cruising times will be more, while the cruising times for diversion roads will be less. This design can take into account the actual traffic situation, enabling the cruising path to correspond based on the actual traffic situation. The management system combines the integral algorithm with the road network map and modifies the No. 1 cruising path through a modification method. The No. 1 cruising path is obtained based on a prediction method to predict subsequent sections. When other cruising vehicles move to the predicted subsequent sections, the score of that section is reset, thus adjusting the prediction method in real time and adjusting the No. 1 cruising path in real time to reduce the situation of over-cruising the same section.

[0088] The cruising instruction enables the dynamic cruising of driverless taxis within the area. The driverless taxis can switch between the dynamic cruising mode and the fixed-station waiting mode. When there are different commercial centers and transportation hubs in the area, some of the driverless taxis in the area will switch to the fixed-station waiting mode at the locations of commercial centers and transportation hubs. The central processing module receives the position data and establishes a position data set, so as to be able to understand the positions of driverless taxis in real time, which is convenient for subsequent applications.

[0089] When the user sends an instruction through the mobile phone as the instruction sending module, the instruction sent is non-designated scheduling information, that is, it does not instruct which vehicle to execute the instruction. It only needs to select a vehicle according to the nearest principle around. However, when the user finds that there are items left in the driverless taxi after taking it, a designated scheduling can be issued to instruct the previous driverless taxi to move to the target area. When the battery of the driverless taxi is insufficient, at this time, the instruction sending module in the driverless taxi sends a designated scheduling instruction, that is, to instruct the vehicle to go to the corresponding charging area or battery replacement area. Similarly, the maintenance instruction information is also designated instruction information, and the rest of the instruction information is non-designated instruction information.

[0090] At the same time, the central processing module internally stores an instruction sending module. Through the real-time position data, the number of vehicles within the area can be obtained. When it is detected that the internal vehicle data exceeds the limit range of the corresponding area, the instruction sending module stored inside the central processing module sends a message to the central processing module.

[0091] Divide the map data into several regions, and control the number of driverless taxis within a single region. When a driverless taxi leaves its original region due to an order or other instructions and moves a user to a new region, resulting in a decrease or increase in the number of driverless taxis in that region, monitor whether the number of driverless taxis in that region is within the corresponding limit range. When receiving an order instruction message, a driverless taxi changes from an unused state to a used state. Based on the destination in the order instruction message, this driverless taxi will be counted in the vehicle number of the target region. The number of such vehicles is regarded as the additional number. The vehicle number of the target region is X + Y, where the real number X is the number of vehicles actually in the target region and in the unused state, and the imaginary number Y is the number of vehicles not in the target region but with the destination being the target region and will complete the switch from the used state to the unused state in the target region. When the destination of the vehicle is other regions and it is in the used state, it will directly act on X, reducing the specific value of X by one. When the vehicle arrives at the destination, completes the order, and changes from the used state to the unused state, then the value of Y is reduced by one, and the specific value of X is increased by one. Compare the vehicle number X + Y with the limit range. When the real number X is greater than the maximum value in the limit range, it is regarded that the vehicle number inside the target region is greater than the limit range. When X + Y is less than the minimum value in the limit range, it is regarded that the vehicle number inside the target region is less than the limit range. The minimum difference number is equal to the real number X.

[0092] When there is a shortage of vehicles in the target region, give priority to dispatching vehicles from the regions adjacent to the target region. However, when there are not enough vehicles in the adjacent regions, it will be gradually expanded, that is, obtain dispatchable vehicles from the regions in contact with the adjacent regions, excluding dispatching vehicles from the target region, and repeat the expansion until enough vehicles can be obtained. At the same time, when dispatching, the vehicles in the adjacent regions also need to meet the corresponding limit range. The management system establishes a counting method and a limit range for calculating the number of vehicles in the region. When the number of regional vehicles exceeds or is lower than the limit range, it automatically triggers cross-regional dispatching to reduce or increase the number of vehicles in the target region, reduce vehicle idle or congestion, and improve the overall operation efficiency. The system can automatically trigger dispatch instructions to ensure a reasonable distribution of the number of vehicles in each region.

[0093] After a driverless taxi arrives in a new region, without instructions, it will not return to its original region. The driverless taxi will cruise in the new region or wait at a fixed station, or when the number of driverless taxis in this new region exceeds the corresponding limit range, then reduce the number of driverless taxis in the new region according to the second selection method and the second dispatch instruction.

[0094] When the driverless taxi is under the influence of the order instruction information and needs to drive out of the original area, the number of driverless taxis in the original area will decrease by one. The driverless taxi passing through this area will not be regarded as affecting the number of driverless taxis in this area. Only when there is destination information in the instruction information of the driverless taxi and when the driverless taxi moves to the target area, the number of driverless taxis in the target area will increase by one. When the number of driverless taxis in the area exceeds the limit, the driverless taxis in this area will move to the adjacent area. When reducing the number of driverless taxis in the target area, the driverless taxi will not move across regions.

[0095] Since there are different commercial centers and transportation hubs in each area, the specific number of driverless taxis in each area may vary, so the limit range of each area will also be inconsistent. At the same time, when controlling the vehicles in the area to cruise, the driverless taxis waiting at fixed stations will not be cruised.

[0096] When the driverless taxi receives the order instruction information and the specified instruction information, the vehicle state of the driverless taxi will be converted into the used state. When the driverless taxi receives the cruise instruction information or the instruction information to wait at a fixed station, the driverless taxi will not be converted into the used state and will still remain in the unused state. When the driverless taxi moves based on the cruise instruction information, although the driverless taxi is in cruise, the vehicle state is still in the unused state.

[0097] The central processing module obtains the road condition information in real time. The central processing module can jointly understand the road condition in real time by cooperating with systems such as navigation software. After receiving the order information, it selects the nearest cruising driverless taxi or the driverless taxi waiting at the fixed station according to the nearest principle. The driverless taxi obtains the starting point location and the ending point location, and based on the current application location data of the vehicle, the starting point location and the ending point location, it establishes the order No. 1 path according to the fastest principle. Both the nearest principle and the fastest principle require the shortest time-consuming. At the same time, when picking up and dropping off customers, it actually obtains the road condition information, generates the modification information for modifying the No. 1 path according to the road condition information, and thus modifies the order No. 1 path in real time.

[0098] The sensor module is installed on the vehicle and is used to monitor the environmental information outside the vehicle in real time. This information may include road conditions, the positions and speeds of surrounding vehicles, pedestrian dynamics, traffic signal status, etc. These real-time data provide key inputs for the subsequent driving and trajectory coordination module. The parking area can be calculated by passing through the pick-up point location and combining with the environmental data, and a parking space can be planned in the parking area, so as to control the vehicle to park in the parking space for the user to get on and off. At the same time, the path is fine-tuned by combining the environmental information around the vehicle, which includes adjusting the position and speed of the vehicle on the road section to adapt to the changes in road conditions, such as avoiding pedestrians and bypassing obstacles.

[0099] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The vehicle traffic trajectory management system based on Beidou satellite positioning is characterized by: The management system comprises: Positioning module: The positioning module is based on satellite positioning and sends position data to the central processing module in real time; Instruction sending module: The instruction sending module sends instruction information to the central processing module; Central processing module: The central processing module receives and analyzes the instruction information, establishes a location data set based on the acquired location data, and establishes a road network map through the map information stored in the central processing module, wherein the road network map includes nodes and road sections, and the road network map is divided into a plurality of areas; The selection method selects at least one application location data from the location data set based on the instruction information, and obtains a historical selection of the selection method; Generate a No. 1 route according to the command information, the road network map and the application location data, and transmit the No. 1 route to a sub-processor module installed on the vehicle based on historical selection; The central processing module obtains the road condition information in real time, and the central processing module generates modification information for modifying the No. 1 path based on the road condition information, and the modification information is transmitted to the sub-processor module according to the historical selection; Subprocessor module: The subprocessor module receives the first path and the modification information, and obtains the modification path in real time through the modification method; The instruction information includes cruise instruction information for controlling the movement trajectory of vehicles within the area, and the selection method selects application location data located in the target area from the location data set based on the cruise instruction information to obtain the cruise history selection; The method for generating a cruising path No. 1 includes: establishing a cruising distance threshold between cruising vehicles in the same area, generating a cruising path No. 1 through an integration algorithm, the integration algorithm including: designing a variable score for each road section based on a road network diagram through a score algorithm, the vehicle selects a road section to move according to the maximum score, and obtains a moving path including at least two road sections based on a prediction method to obtain the cruising path No.

1.

2. The vehicle traffic trajectory management system based on Beidou satellite positioning according to claim 1 is characterized in that: Establishing a counting method for calculating the number of vehicles in an area, the counting method comprising: establishing a real number X and an imaginary number Y, the number of vehicles being equal to the real number X plus the imaginary number Y, when it is detected that a vehicle is located in a target area but the vehicle state is marked as unused, then the number is counted in the real number X of the target area, when the vehicle is located in the target area and the vehicle state is marked as used, then the number is not counted in the real number X of the target area, when the destination of the vehicle is in the target area and the vehicle state is marked as used, then the number is counted in the imaginary number Y of the target area, and the switching of the vehicle state is controlled based on the attribute of the instruction information; The instruction information includes dispatch instruction information for controlling vehicle data within the area, establishing a restriction range for limiting the number of vehicles in different areas, and triggering the dispatch instruction information when it is detected that the number of vehicles within the area is not within the restriction range of the corresponding area, and the dispatch instruction information includes a first dispatch instruction for reducing the number of vehicles within the area and a second dispatch instruction for increasing the number of vehicles within the area; When the real number X is greater than the maximum value in the restricted range, it is considered that the number of vehicles in the target area is greater than the restricted range; when X+Y is less than the minimum value in the restricted range, it is considered that the number of vehicles in the target area is less than the restricted range; The selection method includes a first selection method applied to a first dispatch instruction and a second selection method applied to a second dispatch instruction, wherein the first selection method includes: based on the number of vehicles in the target area being less than a restricted range, obtaining a minimum difference number, selecting a vehicle from a surrounding area by combining the first selection method with the minimum difference number, sending a first dispatch instruction to the selected vehicle, and selecting application location data corresponding to the selected vehicle from the location data set; The second selection method includes: based on the number of vehicles in the target area being greater than the restricted range, obtaining the number of vehicles to be reduced in the target area, selecting a vehicle from the target area by combining the number of vehicles to be reduced with the second selection method, sending a second dispatch instruction to the selected vehicle, and selecting application location data corresponding to the selected vehicle from the location data set; The path algorithm combined with the dispatch instruction information calculates the dispatch path No. 1 for controlling the number of vehicles in the area. The calculation method of the dispatch path No. 1 includes: based on the corresponding position data of the selected vehicle and the No. 1 dispatch instruction, generating a dispatch path No. 1 for guiding the selected vehicle to move to the target area.

3. The vehicle traffic trajectory management system based on Beidou satellite positioning according to claim 2 is characterized in that: The number one selection method includes: combining the minimum difference number, obtaining the number of dispatched vehicles in the adjacent area, the number of dispatched vehicles refers to the difference between the number of vehicles in the area and the minimum value in the restricted range, when the number of dispatched vehicles in the adjacent area is less than the minimum difference number, gradually expanding the adjacent area to obtain the number of dispatched vehicles in the expanded adjacent area, until the number of dispatched vehicles is greater than or equal to the minimum difference number, according to the current range of the adjacent area, selecting vehicles equal to the minimum difference number from the current range of the adjacent area, and controlling the vehicles to move to the target area; The second selection method includes: combining the number to be reduced, selecting a number of vehicles equal to the number to be reduced from the target area, obtaining the number of blank vehicles in the adjacent area, the number of blank vehicles refers to the difference between the number of vehicles inside the area and the maximum value in the restricted range, when the number of blank vehicles in the adjacent area is less than the number to be reduced, gradually expanding the adjacent area to obtain the number of blank vehicles in the expanded adjacent area, until the number of blank vehicles is greater than or equal to the number to be reduced, based on the current range of the adjacent area, selecting a number of vehicles equal to the number to be reduced from the target area, and controlling the vehicles to move to the adjacent area.

4. The vehicle traffic trajectory management system based on Beidou satellite positioning according to claim 1 is characterized in that: The score algorithm includes: setting a score coefficient K, the road section score = score coefficient K × interval time, when the vehicle is on the road section, the interval time is adjusted to zero, there is no vehicle on the road section, the road section score increases with the increase of the interval time, when the vehicle leaves the road section, the interval time starts to increase, and the design coefficient method controls the specific value of the score coefficient K; The coefficient method includes: the fractional coefficient K is adjusted based on static road attributes and dynamic status, the static road attributes include length and traffic capacity, the dynamic status includes the real-time congestion level of the road section, and the static road attributes and dynamic status are numerically represented.

5. The vehicle traffic trajectory management system based on Beidou satellite positioning according to claim 1 is characterized in that: The instruction information includes order instruction information; The selection method includes a fourth selection method applied to order instruction information, and the fourth selection method includes: obtaining the starting point position in the order instruction information, and selecting the application position data closest to the starting point position from the position data set according to the nearest principle; The path algorithm of the order instruction information is combined to calculate the order No. 1 path for controlling the movement trajectory of the vehicle in the area. The calculation method of the order No. 1 path includes: obtaining the starting point position and the end point position of the order instruction information, and establishing the order No. 1 path based on the current application position data of the vehicle, the starting point position and the end point position and through the fastest principle; The fastest principle includes using a prediction algorithm to simulate the time required for a vehicle to pass through the application location data, the starting point location and the end point location in sequence based on a road network diagram, and selecting order No. 1 path according to the shortest time.

6. The vehicle traffic trajectory management system based on Beidou satellite positioning according to claim 1 is characterized in that: The instruction information includes specific instruction information, and the specific instruction information includes return instruction information, maintenance instruction information and charging instruction information; The selection method includes a fifth selection method applied to the designated instruction information, the fifth selection method including: obtaining a target location and a designated vehicle in the designated instruction information, and obtaining application location data corresponding to the designated vehicle from a location data set according to the designated vehicle; The designated path No. 1 for controlling the designated vehicle is calculated by a path algorithm combined with the designated instruction information. The calculation method of the designated path No. 1 includes: establishing the designated path No. 1 based on the application position data and target position corresponding to the designated vehicle and through the fastest principle.

7. The vehicle traffic trajectory management system based on Beidou satellite positioning according to claim 1 is characterized in that: The modification method includes: based on the modification information, recalculating the path, adjusting the path nodes and adjusting the driving parameters, and inputting the recalculated path and driving parameters into the first path to obtain the modified path.

8. The vehicle traffic trajectory management system based on Beidou satellite positioning according to claim 1 is characterized in that: The management system comprises: Sensor module: The sensor module installed on the vehicle monitors the environmental information outside the vehicle in real time; Driving and trajectory coordination module: combines environmental information, modified paths and location data, and obtains navigation data to guide vehicle movement through collaborative algorithm calculation; The collaborative algorithm includes a parking algorithm and an exit algorithm, and the parking data and the fine-tuning data for guiding the movement of the vehicle are obtained through the parking algorithm and the fine-tuning algorithm; The parking algorithm includes: acquiring a parking area, planning a parking space in combination with environmental information in the parking area, establishing a trajectory for controlling the vehicle to move to the parking space based on real-time environmental information, and acquiring parking data; The fine-tuning algorithm includes: adjusting the position and speed of the vehicle on the road section according to the modified path and in real time in combination with the environmental information around the vehicle, and obtaining fine-tuning data based on the position adjustment and speed adjustment.

Images