Three-dimensional priority passing method, system and device based on edge calculation and V2X cooperation

Abstract

The invention provides a three-dimensional priority passing method, system and device based on edge calculation and V2X cooperation, and belongs to the technical field of traffic control. The method comprises the following steps: after an edge computing device receives an on-road driving signal from an emergency vehicle, determining corresponding DSRC data and C-V2X long-distance path planning data; based on the DSRC data and the C-V2X long-distance path planning data, real-time position information of the emergency vehicle is determined, whether the emergency vehicle enters an interchange driving area or not is judged according to the real-time position information, and if yes, a passing envelope range of the emergency vehicle is determined based on a preset three-dimensional dynamic envelope model; and based on the passing envelope range, the real-time position information and the corresponding intersection to be passed, determining conflict phase green wave optimization duration corresponding to the emergency vehicle, so as to adjust signal lamp phase time of the intersection to be passed according to the conflict phase green wave optimization duration, thereby enabling the emergency vehicle to pass preferentially at the intersection to be passed.

Description

Technical Field

[0001] This application relates to the technical field of traffic control, and particularly to a three-dimensional priority passage method, system and device based on edge computing and V2X collaboration. Background Art

[0002] In the current situation of increasingly congested urban traffic, the three-dimensional traffic network is becoming more and more complex, and the priority passage of emergency vehicles (such as ambulances, fire trucks, etc.) faces severe challenges. Existing systems that rely on Radio Frequency Identification (RFID) to identify emergency vehicles have many drawbacks. For example, RFID tags are statically set and cannot obtain the vehicle speed of emergency vehicles in real time. The speed error is often greater than 15 km / h, making it difficult to perform precise traffic signal control based on the vehicle speed; at congested intersections, the occlusion rate of RFID signals exceeds 40%, resulting in frequent failure of vehicle positioning; the RFID system cannot predict the lane-changing intention of emergency vehicles and cannot prepare for traffic flow guidance in advance.

[0003] The traditional traffic signal control system adopts a fixed green wave band, and the matching degree between its speed standard (such as 50 km / h) and the actual average speed of emergency vehicles (65 km / h) is less than 60%, resulting in emergency vehicles frequently braking suddenly when passing through multiple intersections, further increasing the delay time. In terms of multi-intersection collaboration, existing technologies often rely on preset phase differences and cannot be flexibly adjusted in the face of sudden congestion, easily causing secondary delays for emergency vehicles and seriously affecting their passage efficiency. Summary of the Invention

[0004] To solve the above problems, the embodiments of this application provide a three-dimensional priority passage method, system and device based on edge computing and V2X collaboration, which are used to enable emergency vehicles to pass efficiently, flexibly and safely in the three-dimensional traffic network scenario.

[0005] On the one hand, the embodiments of this application provide a three-dimensional priority passage method based on edge computing and V2X collaboration, and the method includes: After receiving the in-transit signal from an emergency vehicle, the edge computing device determines the corresponding DSRC data and C-V2X long-distance path planning data; Based on the DSRC data and the C-V2X long-distance path planning data, determine the real-time position information of the emergency vehicle, so as to judge whether the emergency vehicle enters the overpass driving area according to the real-time position information; When the emergency vehicle enters the overpass driving area, based on a preset three-dimensional dynamic envelope model, determine the passage envelope range of the emergency vehicle; Based on the passing envelope range, the real-time position information, and the corresponding intersection to be passed, determine the green wave optimization duration of the conflict phase corresponding to the emergency vehicle, so as to adjust the signal light phase time of the intersection to be passed according to the green wave optimization duration of the conflict phase, so that the emergency vehicle can pass through the intersection to be passed preferentially.

[0006] In an implementation manner of the present application, the DSRC data at least includes the real-time steering angle information, the first position information, the vehicle acceleration, and the turn signal state of the emergency vehicle; the C-V2X long-distance path planning data at least includes the second position information, the real-time speed, and the predicted timestamps for reaching each of the preset number of intersections in the planned path.

[0007] In an implementation manner of the present application, based on the DSRC data and the C-V2X long-distance path planning data, determining the real-time position information of the emergency vehicle specifically includes: According to the first position information, the second position information, and the multi-source position information, determine the passing distance of the emergency vehicle from the next intersection to be passed; According to the passing distance and the preset distance threshold, determine the first fusion weight corresponding to the first position information and the second fusion weight corresponding to the second position information; wherein, the sum value of the first fusion weight and the second fusion weight is 1; Perform weighted summation processing on the first fusion weight, the first position information, the second fusion weight, and the second position information, so as to determine the real-time position information of the emergency vehicle based on the calculation result of the weighted summation.

[0008] In an implementation manner of the present application, based on the preset three-dimensional dynamic envelope model, determining the passing envelope range of the emergency vehicle specifically includes: Input the real-time position information, the vehicle acceleration, and the real-time speed into the preset three-dimensional dynamic envelope model, calculate the offset range components of the emergency vehicle in the three coordinate axis directions of the preset space coordinate system within a preset time window, so as to construct the passing envelope range of the emergency vehicle according to each of the offset range components; wherein, the sum value of each of the offset range components is less than or equal to 1.

[0009] In an implementation manner of the present application, based on the passing envelope range, the real-time position information, and the corresponding intersection to be passed, determining the green wave optimization duration of the conflict phase corresponding to the emergency vehicle specifically includes: According to the passing envelope range, the real-time position information, and the corresponding intersection to be passed, determine the driving bias of the emergency vehicle, so as to determine the corresponding passing control phase and its corresponding conflict phase according to the driving bias; Calculate the green wave optimization duration of the conflicting phase according to the real-time position information, the intersection position coordinates of the intersection to be passed, the real-time speed, and the initial green light duration of the conflicting phase; wherein, the green wave optimization duration of the conflicting phase is less than the initial green light duration.

[0010] In an implementation manner of the present application, the method further includes: When it is determined that the number of phases of the conflicting phase is multiple, obtain the lane traffic corresponding to each of the conflicting phases; Based on the first proportional relationship between the lane traffic volumes, match the green light duration compression coefficients corresponding to each of the conflicting phases in the preset green light compression coefficient comparison table; wherein, the second proportional relationship between the green light duration compression coefficients is inversely proportional to the first proportional relationship between the lane traffic volumes; Calculate the green wave optimization duration of the conflicting phase according to the green light duration compression coefficients, the real-time position information, the intersection position coordinates of the intersection to be passed, the real-time speed, and the initial green light duration of the conflicting phase.

[0011] In an implementation manner of the present application, the method further includes: According to the real-time speed, the preset divided section, and the real-time intersection information of the intersection to be passed, determine the section passing time, the queuing delay time, and the conflicting phase blocking duration of the emergency vehicle, so as to obtain the edge weight between two intersection nodes corresponding to the preset divided section according to the sum value of the section passing time, the queuing delay time, and the conflicting phase blocking duration; Construct a traffic state transition graph according to the edge weights; the traffic state transition graph takes intersections and different phases of intersections as nodes and the preset divided section as edges; According to the edge weights in the traffic state transition graph, taking the node corresponding to the phase of the intersection where the emergency vehicle is located as the starting point, determine the estimated arrival time from the starting point to other reachable nodes, and add the path corresponding to the minimum value in each of the estimated arrival times to the distance table according to the preset rule, and continuously iterate until the path from other reachable nodes to the target node is determined, so as to obtain the distance table including the planned path from the starting point to the target node, and send the path corresponding to the distance table to the user terminal of the emergency vehicle in real time according to the real-time position information.

[0012] In an implementation manner of the present application, the method further includes: After the emergency vehicle passes through the intersection to be passed, determine the conflicting phase compensation duration according to the green wave optimization duration of the conflicting phase; Use the sum of the initial green light duration of the conflicting phase and the compensation duration of the conflicting phase as the green light duration of the conflicting phase in the next cycle.

[0013] On the other hand, an embodiment of the present application also provides a three-dimensional priority traffic system based on edge computing and V2X collaboration. The system includes: A first determination module, configured to, after an edge computing device receives an in-transit signal from an emergency vehicle, determine corresponding DSRC data and C-V2X long-distance path planning data; A second determination module, configured to determine real-time position information of the emergency vehicle based on the DSRC data and the C-V2X long-distance path planning data, so as to determine whether the emergency vehicle enters the interchange driving area according to the real-time position information; A third determination module, configured to, when the emergency vehicle enters the interchange driving area, determine a traffic envelope range of the emergency vehicle based on a preset three-dimensional dynamic envelope model; A fourth determination module, configured to determine an optimized green wave duration of a conflicting phase corresponding to the emergency vehicle based on the traffic envelope range, the real-time position information, and corresponding intersections to be passed, so as to adjust the signal phase time of the intersections to be passed according to the optimized green wave duration of the conflicting phase, so that the emergency vehicle can have priority in passing through the intersections to be passed.

[0014] On yet another aspect, an embodiment of the present application also provides a three-dimensional priority traffic device based on edge computing and V2X collaboration. The device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a three-dimensional priority traffic method based on edge computing and V2X collaboration as described above.

[0015] Compared with the prior art, the present application has the following remarkable effects: Through the above technical solutions, by combining DSRC and C-V2X technologies, high-precision positioning of emergency vehicles can be effectively performed, and signal lamp adaptation control can be dynamically performed, improving the three-dimensional traffic efficiency of emergency vehicles and reducing delays. It can effectively solve the technical problems that it is difficult to accurately obtain the positioning and vehicle signals of emergency vehicles in complex three-dimensional traffic scenarios at present, and priority passage cannot be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation to the present application. In the drawings: Figure 1 It is a schematic flowchart of a three-dimensional priority passing method based on edge computing and V2X collaboration in an embodiment of the present application; Figure 2 It is a schematic structural diagram of a three-dimensional priority passing system based on edge computing and V2X collaboration in an embodiment of the present application; Figure 3 It is a schematic structural diagram of a three-dimensional priority passing device based on edge computing and V2X collaboration in an embodiment of the present application. Specific embodiments

[0017] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0018] The embodiments of the present application provide a three-dimensional priority passing method, system, and device based on edge computing and V2X collaboration to solve the technical problems that it is difficult to accurately obtain the positioning and vehicle signals of emergency vehicles in complex three-dimensional traffic scenarios currently, and the priority passing cannot be guaranteed.

[0019] The following will describe each embodiment of the present application in detail with reference to the drawings.

[0020] The embodiments of the present application provide a three-dimensional priority passing method based on edge computing and V2X collaboration. As Figure 1 shown, the method may include steps S101 - S104: S101, after the edge computing device receives the in - transit signal from the emergency vehicle, determine the corresponding DSRC data and C - V2X long - distance path planning data.

[0021] It should be noted that the edge computing device, as the execution subject of the three - dimensional priority passing method based on edge computing and V2X collaboration, is only an exemplary existence. The execution subject may be a core edge computing device in an edge computing node network set on the road, or an edge computing node network. The present application does not make specific limitations in this regard. The present application may deploy edge computing nodes in roadside units to collect data from dedicated short - range communication technology (DSRC), cellular vehicle - to - everything (C - V2X), and image acquisition devices on the road.

[0022] In the embodiment of the present application, an emergency vehicle can actively send a on-the-way driving signal to an edge computing device through its corresponding user terminal, or it can also be a on-the-way driving signal obtained by the edge computing device analyzing the collected data, identifying the emergency vehicle, and sending a confirmation signal to the user terminal corresponding to the emergency vehicle. Among them, the user terminal can be understood as the terminal device bound to the emergency vehicle, which can be an in-vehicle terminal, or a mobile phone or other terminal devices pre-bound to the driver. The present application does not make specific limitations on this.

[0023] The above DSRC data can be that the DSRC module set on the road interacts with the vehicle through radio frequency signals to collect relevant data of the emergency vehicle. The DSRC data at least includes the real-time steering angle information, the first position information, the vehicle acceleration, and the turn signal state of the emergency vehicle. The C-V2X communication module is set on the emergency vehicle and can communicate with the C-V2X network infrastructure such as a base station set on the road, so as to upload the basic information of the vehicle and relevant vehicle tracking data. Among them, the C-V2X long-distance path planning data at least includes the second position information, the real-time speed, and the predicted timestamps for reaching each of the preset number of intersections in the planned path. The planned path can be obtained through a traffic state transition diagram, and the preset number can be preset by the user or an expert, such as 3. The present application does not make specific limitations on this.

[0024] S102. The edge computing device determines the real-time position information of the emergency vehicle based on the DSRC data and the C-V2X long-distance path planning data, so as to judge whether the emergency vehicle enters the interchange driving area according to the real-time position information.

[0025] In the embodiment of the present application, the determination of the real-time position information of the emergency vehicle based on the DSRC data and the C-V2X long-distance path planning data specifically includes: Determine the passing distance of the emergency vehicle from the next intersection to be passed according to the first position information, the second position information, and the multi-source position information. According to the passing distance and the preset distance threshold, determine the first fusion weight corresponding to the first position information and the second fusion weight corresponding to the second position information. Among them, the sum of the first fusion weight and the second fusion weight is 1. Perform a weighted summation process on the first fusion weight, the first position information, the second fusion weight, and the second position information, so as to determine the real-time position information of the emergency vehicle based on the calculation result of the weighted summation.

[0026] That is to say, this application combines DSRC, C-V2X, and multi-source data to determine the distance between an emergency vehicle and the upcoming intersection to be passed, and partially determines the fusion weights corresponding to DSRC and C-V2X respectively through a calculation rule, and uses the fusion weights to fuse the position information of the two parts of DSRC and C-V2X to obtain more accurate real-time position information. Among them, the edge computing device can pre-store the position coordinates of each intersection.

[0027] Specifically, this application can calculate the passing distance of the emergency vehicle from the next intersection to be passed through the stereo perception layer in the edge computing device by combining multi-sensor fusion data. That is, this application can respectively extract the positioning coordinates of DSRC, C-V2X, and multi-source position information for the position of the emergency vehicle, and take the average to obtain the measured position coordinates of the emergency vehicle. The multi-source position information can be obtained through visual sensors, etc., such as the position of the emergency vehicle collected by certain cameras set on the road. The edge computing device can also determine the position coordinates of the next intersection to be passed by the emergency vehicle according to the planned path. By calculating the Euclidean distance between the measured position coordinates and the position coordinates of the intersection, the above passing distance can be obtained. Subsequently, through the following formula, the calculation of the fusion weight and the output of the real-time position information are carried out. The formula is as follows: ; Wherein, is the position coordinate of the emergency vehicle corresponding to the real-time position information at time; represents the first fusion weight, represents the passing distance at time, represents a preset distance threshold, such as 500 meters; represents the first position coordinate corresponding to the first position information at time; represents the second fusion weight; represents the second position coordinate corresponding to the second position information at time;

[0028] Through the above scheme, when the travel distance is less than the preset distance threshold, DSRC data can play a key role to obtain more accurate location information. When the travel distance is greater than the preset distance threshold, the positioning error of DSRC is large, and the advantages of C-V2X data can be used to obtain macro-path guidance and make driving plans in advance. Combining DSRC and C-V2X data can achieve accurate, efficient and stable positioning, and the vehicle positioning will not be unable to be performed due to signal obstruction.

[0029] After obtaining the real-time location information of the emergency vehicle through the above solution, it is possible to continuously determine whether the emergency vehicle has reached the overpass driving area. The area corresponding to the overpass driving area is pre-stored in the edge computing device. According to the judgment result, the traffic control of the emergency vehicle driving to the complex driving scene of the overpass is flexibly executed to ensure that the emergency vehicle has priority.

[0030] S103, when an emergency vehicle enters the overpass driving area, the edge computing device determines the passage envelope range of the emergency vehicle based on a preset three-dimensional dynamic envelope model.

[0031] In the embodiment of the present application, the above-mentioned determination of the passage envelope range of the emergency vehicle based on the preset three-dimensional dynamic envelope model specifically includes: The real-time position information, vehicle acceleration, and real-time speed are input into a preset three-dimensional dynamic envelope model, and the offset range components of the emergency vehicle in the three coordinate axis directions of the preset spatial coordinate system within the preset time window are calculated, so as to construct the passage envelope range of the emergency vehicle according to each offset range component. The sum of each offset range component is less than or equal to 1.

[0032] In other words, when the edge computing device determines that an emergency vehicle enters the overpass driving area, it further generates the emergency vehicle's passage envelope range, thereby more accurately monitoring the emergency vehicle's driving and accurately obtaining the range within which the emergency vehicle can pass.

[0033] More specifically, the edge computing device inputs the real-time location information, vehicle acceleration, real-time speed and other information of the emergency vehicle into a preset three-dimensional dynamic envelope model in order to construct the passage envelope range of the emergency vehicle itself. Among them, the real-time location information, vehicle acceleration and real-time speed can be obtained through the above steps, and the formula of the preset three-dimensional dynamic envelope model is as follows: ; in, The real-time location information of the emergency vehicle corresponds to the location coordinates. is the coordinate in the direction of the horizontal axis, is the coordinate in the direction of the ordinate axis, is the vertical axis coordinate, represents the real-time speed, is the time, is the preset lane-changing compensation deviation; is the preset time window, 0.5 is the preset positioning error compensation coefficient, and is the preset empirical value; is the preset maximum vehicle acceleration, and through the possible driving position range of the emergency vehicle in the axis direction can be calculated; is the standard deviation of the first preset lane offset in the longitudinal coordinate axis direction, corresponding to the possible driving position range of the emergency vehicle in the axis direction; is the preset initial height of the emergency vehicle, is the standard deviation of the second preset lane offset in the vertical coordinate axis direction, corresponding to the possible driving position range of the emergency vehicle in the axis direction. The above preset lane-changing compensation deviation, preset time window, preset maximum vehicle acceleration, first preset lane offset standard deviation, preset initial height, and second preset lane offset standard deviation can be set by users or experts in actual usage scenarios, and this application does not make specific limitations.

[0034] Among them, for the preset three-dimensional dynamic envelope model, when it is necessary to ensure that the left side of the above formula is less than or equal to 1, the corresponding coordinate interval, so as to construct the passing envelope range according to the coordinate interval that meets the condition of being less than or equal to 1.

[0035] Through the formula of the above preset three-dimensional dynamic envelope model, the dynamic driving state, lane offset, and multi-layer structure of the overpass of the emergency vehicle can be comprehensively considered from the horizontal and vertical directions, accurately defining the passing range of the emergency vehicle in the three-dimensional space of the overpass and reducing resource waste. In addition, establishing the passing envelope range can more effectively plan the exclusive passing area in advance. For example, the fire truck has a higher ladder, and establishing its envelope range can coordinate its passing on roads at different height levels.

[0036] S104. The edge computing device determines the green wave optimization duration of the conflict phase corresponding to the emergency vehicle based on the passing envelope range, real-time position information, and the corresponding intersection to be passed, so as to adjust the signal light phase time of the intersection to be passed according to the green wave optimization duration of the conflict phase, so that the emergency vehicle can pass through the intersection to be passed preferentially.

[0037] That is to say, this application can determine the green wave optimization duration of the conflict phase and adjust the signal light phase time according to the passing envelope range, etc., so as to effectively ensure that the emergency vehicle passes through the intersection to be passed preferentially, improve the passing efficiency, and reduce delays.

[0038] In the embodiment of the present application, determining the green wave optimization duration of the conflict phase corresponding to the emergency vehicle based on the passing envelope range, real-time position information, and the corresponding intersection to be passed specifically includes: Based on the passing envelope range, real-time position information, and the corresponding intersection to be passed, determine the driving bias of the emergency vehicle, so as to determine the corresponding traffic control phase and its corresponding conflict phase according to the driving bias. Calculate the green wave optimization duration of the conflict phase based on the real-time position information, the intersection position coordinates of the intersection to be passed, the real-time speed, and the initial green light duration of the conflict phase. Wherein, the green wave optimization duration of the conflict phase is less than the initial green light duration.

[0039] That is to say, after constructing the passing envelope range of the emergency vehicle, the spatial displacement of the passing envelope range at the intersection to be passed can be located according to the real-time position information, so as to judge the path direction of the emergency vehicle driving towards the intersection to be passed, and then determine the driving bias of the emergency vehicle. For example, if the vehicle is biased to the right within the passing envelope range and the intersection to be passed is in its front right, it is determined that the driving bias is to drive towards the front right. The edge computing device will further determine the traffic control phase corresponding to the driving bias by combining the driving bias with the current phase setting rule of the intersection to be passed. For example, if the driving bias is to drive towards the front right, the right turn or straight-ahead phase of the corresponding intersection may be the traffic control phase. According to the traffic control phase, further look up the signal light phase corresponding rule in the preset database to determine the phase that conflicts with the traffic control phase. For example, if the traffic control phase is the right turn phase, then during the same period, the oncoming left turn phase is usually the conflict phase; if the traffic control phase is the straight-ahead phase, the oncoming straight-ahead phase and some left turn phases may be the conflict phases.

[0040] Subsequently, calculate the green wave optimization duration of the conflict phase based on the current real-time position information of the emergency vehicle, the intersection position coordinates of the intersection to be passed, the real-time speed, and the initial green light duration of the above conflict phase, so as to continuously adjust the green light duration before the emergency vehicle arrives, thereby ensuring the passing efficiency of the emergency vehicle. The calculation formula for calculating the green wave optimization duration of the conflict phase is as follows: ; Wherein, represents the green wave optimization duration of the conflict phase before the emergency vehicle passes through the intersection to be passed, represents the initial green light duration; is a preset empirical coefficient, such as 0.75, which can be set by users or experts through experiments, and the present application does not make specific limitations on this.

[0041] In the embodiment of the present application, a traffic control phase may include multiple conflict phases at the same intersection to be passed. At this time, the present application also provides the following embodiments, including: When it is determined that the number of phases of the conflicting phases is multiple, obtain the lane traffic flows corresponding to each of the conflicting phases respectively. Based on the first proportional relationship among the lane traffic flows, match the green light duration compression coefficients corresponding to each of the conflicting phases in the preset green light compression coefficient look-up table. Among them, the second proportional relationship among the green light duration compression coefficients is an inverse proportional relationship with the first proportional relationship among the lane traffic flows. Calculate the green wave optimization duration of the conflicting phases according to the green light duration compression coefficients, the real-time position information, the intersection position coordinates of the intersection to be passed, the real-time speed, and the initial green light duration of the conflicting phases.

[0042] That is to say, the present application can use devices such as millimeter-wave radars and geomagnetic sensors deployed at intersections to obtain the lane traffic flows of each of the conflicting phases in real time. Subsequently, the edge computing device can calculate the first proportional relationship among the lane traffic flows of the conflicting phases. For example, the lane traffic flows of multiple conflicting phases are respectively: , , ... , and is in the ratio of , and is in the ratio of etc. Subsequently, according to the inverse proportional relationship corresponding to the first proportional relationship, such as , , obtain the second proportional relationship used to match the specific green light duration compression coefficients in the preset green light compression coefficient look-up table. Such as , , By looking up the preset green light compression coefficient look-up table, find the specific green light duration compression coefficients that can satisfy , such as , . The preset green light compression coefficient look-up table contains the preset several lane traffic flows and the green light duration compression coefficients corresponding to the first proportional relationship. The specific content of the look-up table can be set by the user and will not be specifically limited here. Then, use the green light duration compression coefficients corresponding to the above different conflicting phases as the above-mentioned preset empirical coefficients of the conflicting phases respectively, and use the above-mentioned calculation formula for the green wave optimization duration of the conflicting phases to calculate the green wave optimization durations of the respective conflicting phases respectively, so as to adjust the green light durations of the respective conflicting phases respectively, so that emergency vehicles can pass through the intersection first while ensuring normal road traffic.

[0043] In an embodiment of the present application, it is also possible to provide accurate path planning for emergency vehicles, which specifically includes the following steps: Based on the real-time speed, the preset divided road sections, and the real-time intersection information of the intersection to be passed, determine the road section passing time, queuing delay time, and conflict phase obstruction duration of the emergency vehicle, so as to obtain the edge weight between two intersection nodes corresponding to the preset divided road section according to the sum of the road section passing time, queuing delay time, and conflict phase obstruction duration. According to each edge weight, construct a traffic state transition graph. The traffic state transition graph uses intersections and different phases of intersections as nodes and preset divided road sections as edges. According to the edge weights in the traffic state transition graph, with the node corresponding to the phase of the intersection where the emergency vehicle is located as the starting point, determine the estimated arrival time from the starting point to other reachable nodes, and add the path corresponding to the minimum value in each estimated arrival time to the distance table according to the preset rules, and continuously iterate until the path from other reachable nodes to the target node is determined, and obtain a distance table including the planned path from the starting point to the target node, so as to send the path corresponding to the distance table to the user terminal of the emergency vehicle in real time according to the real-time position information.

[0044] In other words, the present application can combine the real-time speed 、the preset divided road sections and the real-time intersection information to calculate the road section passing time 、queuing delay time and conflict phase obstruction duration of the emergency vehicle. Let be the road section length from intersection to intersection ; let be the number of queuing vehicles at intersection ; let be the preset average vehicle length; let be the preset saturation flow value at intersection ; let be the preset conflict phase compensation coefficient; let be the green light time difference between the conflict phase and the traffic control phase. Take the sum of the three as the edge weight:

[0045] . The larger the edge weight, the greater the difficulty for the emergency vehicle to pass through the corresponding preset divided road section and the longer the potential delay time. Based on the calculated edge weights, construct a traffic state transition graph with each intersection and different phases of intersections as nodes and the preset divided road sections as the edges connecting the nodes, so as to present the traffic relationship of the emergency vehicle between different intersections and phases.Subsequently, the edge computing device can, based on the edge weights in the traffic state transition diagram, use the node corresponding to the intersection phase where the emergency vehicle is currently located as the starting point, and utilize a path search algorithm such as Dijkstra's Algorithm to calculate the feasible path with the minimum edge weight. The edge weight can be used to represent the estimated arrival time. There can be multiple paths from the starting point to the target node. The edge computing device can calculate the estimated arrival time between each adjacent node of multiple paths, compare all the estimated arrival times from the same starting point to the next reachable node, select the minimum value among the estimated arrival times, and then add it to the distance table. From the starting point to node A, it is necessary to pass through the edges ( ), ( ), ……, and the estimated arrival time is . In this way, the influence of various factors on the travel time is comprehensively considered, so as to plan the travel path with the minimum delay for the emergency vehicle. The distance table records the intersection nodes that the emergency vehicle is going to, and can be displayed to the user terminal for the user to view.

[0046] In addition, the above-mentioned edge weight can also add a delay factor related to driving on the overpass. This delay factor can be set by the user according to data such as the slope of the overpass that affect the travel time. This application does not make specific limitations on this.

[0047] In another embodiment of this application, after the emergency vehicle passes through the intersection to be passed, the conflict phase compensation duration is determined according to the green wave optimization duration of the conflict phase. The sum of the initial green light duration of the conflict phase and the conflict phase compensation duration is used as the green light duration of the conflict phase in the next cycle.

[0048] In other words, after the emergency vehicle passes through the intersection to be passed, it can restore the optimized green light duration of the original conflict phase, and can match the conflict phase step duration corresponding to the difference value from the preset compensation duration list according to the difference between the green wave optimization duration of the conflict phase and the original initial green light duration, and use the sum of the initial green light duration and the conflict phase compensation duration as the green light duration of the conflict phase in the next cycle. Among them, the preset step duration list can be set by the user according to the actual used intersection. This application does not make specific limitations on this. For example, if the initial green light duration is 40 seconds, the green wave optimization duration of the conflict phase is 35 seconds, and the conflict phase compensation duration corresponding to the difference value of 5 seconds is 2 seconds, then the green light duration in the next cycle is 42 seconds. Furthermore, it can well relieve the traffic pressure originally brought to the conflict phase, improve traffic efficiency and reduce the traffic impact caused by the priority passage of emergency vehicles.

[0049] Through the above technical solutions, combining DSRC and C-V2X technologies can effectively perform high-precision positioning of emergency vehicles, and can dynamically perform signal lamp adaptation control, improve the three-dimensional traffic efficiency of emergency vehicles, and reduce delays. It can effectively solve the technical problems that it is difficult to accurately obtain the positioning and vehicle signals of emergency vehicles in complex three-dimensional traffic scenarios and the priority passage cannot be guaranteed currently.

[0050] Figure 2 The following is a schematic structural diagram of a three-dimensional priority passage system based on edge computing and V2X collaboration provided by an embodiment of the present application. As Figure 2 shown, the three-dimensional priority passage system 200 based on edge computing and V2X collaboration includes: A first determination module 201, configured to, after the edge computing device receives the in-transit signal from the emergency vehicle, determine the corresponding DSRC data and C-V2X long-distance path planning data. A second determination module 202, configured to determine the real-time position information of the emergency vehicle based on the DSRC data and the C-V2X long-distance path planning data, so as to determine whether the emergency vehicle enters the interchange driving area according to the real-time position information. A third determination module 203, configured to, when the emergency vehicle enters the interchange driving area, determine the passage envelope range of the emergency vehicle based on a preset three-dimensional dynamic envelope model. A fourth determination module 204, configured to determine the conflict phase green wave optimization duration corresponding to the emergency vehicle based on the passage envelope range, the real-time position information, and the corresponding intersection to be passed, so as to adjust the signal lamp phase time of the intersection to be passed according to the conflict phase green wave optimization duration, so that the emergency vehicle can pass through the intersection to be passed first.

[0051] Figure 3 The following is a schematic structural diagram of a three-dimensional priority passage device based on edge computing and V2X collaboration provided by an embodiment of the present application. As Figure 3 shown, the device includes: At least one processor; and a memory communicatively connected to the at least one processor. Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can: After receiving the in-transit signal from an emergency vehicle, the edge computing device determines the corresponding DSRC data and C-V2X long-distance path planning data. Based on the DSRC data and C-V2X long-distance path planning data, it determines the real-time position information of the emergency vehicle to judge whether the emergency vehicle enters the interchange driving area according to the real-time position information. In the case where the emergency vehicle enters the interchange driving area, based on a preset three-dimensional dynamic envelope model, it determines the passing envelope range of the emergency vehicle. Based on the passing envelope range, the real-time position information, and the corresponding intersection to be passed, it determines the conflict phase green wave optimization duration corresponding to the emergency vehicle to adjust the signal light phase time of the intersection to be passed according to the conflict phase green wave optimization duration, so that the emergency vehicle can pass through the intersection to be passed preferentially.

[0052] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system and device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0053] The systems and devices provided in the embodiments of this application correspond one by one to the methods. Therefore, the systems and devices also have beneficial technical effects similar to those of the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and devices will not be elaborated here.

[0054] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the said element.

[0055] The above description is only for the embodiments of this application and is not intended to limit this application. For those skilled in the art, this application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the scope of the claims of this application.

Claims

1. A three-dimensional priority passage method based on edge computing and V2X collaboration, characterized in that: The method comprises: After receiving the on-the-way driving signal from the emergency vehicle, the edge computing device determines the corresponding DSRC data and C-V2X long-distance path planning data; Determine the real-time location information of the emergency vehicle based on the DSRC data and the C-V2X long-distance path planning data, so as to judge whether the emergency vehicle enters an interchange driving area according to the real-time location information; When the emergency vehicle enters the interchange driving area, determining the passage envelope range of the emergency vehicle based on a preset three-dimensional dynamic envelope model; Based on the passage envelope range, the real-time location information and the corresponding intersection to be passed, the conflict phase green wave optimization duration corresponding to the emergency vehicle is determined, so as to adjust the signal light phase time of the intersection to be passed according to the conflict phase green wave optimization duration, so that the emergency vehicle has priority at the intersection to be passed.

2. According to claim 1, a three-dimensional priority passage method based on edge computing and V2X collaboration is characterized in that: The DSRC data includes at least the real-time steering angle information, first position information, vehicle acceleration and turn signal status of the emergency vehicle; the C-V2X long-distance path planning data includes at least the second position information, real-time speed and predicted timestamps for arriving at a preset number of intersections in the planned path.

3. According to claim 2, a three-dimensional priority passage method based on edge computing and V2X collaboration is characterized in that: Determining the real-time location information of the emergency vehicle based on the DSRC data and the C-V2X long-distance path planning data, specifically including: Determine the travel distance of the emergency vehicle from the next intersection to be passed according to the first location information, the second location information and the multi-source location information; Determine, according to the travel distance and the preset distance threshold, a first fusion weight corresponding to the first position information and a second fusion weight corresponding to the second position information; wherein the sum of the first fusion weight and the second fusion weight is 1; The first fusion weight, the first position information, the second fusion weight and the second position information are weighted-summed to determine the real-time position information of the emergency vehicle based on a calculation result of the weighted-summed calculation.

4. According to claim 2, a three-dimensional priority passage method based on edge computing and V2X collaboration is characterized in that: Based on the preset three-dimensional dynamic envelope model, determining the passage envelope range of the emergency vehicle specifically includes: The real-time position information, the vehicle acceleration, and the real-time speed are input into the preset three-dimensional dynamic envelope model, and the offset range components of the emergency vehicle in the three coordinate axis directions of the preset spatial coordinate system within a preset time window are calculated, so as to construct the passage envelope range of the emergency vehicle according to each of the offset range components; wherein the sum of each of the offset range components is less than or equal to 1.

5. According to claim 2, a three-dimensional priority passage method based on edge computing and V2X collaboration is characterized in that: Based on the passage envelope range, the real-time location information and the corresponding intersection to be passed, determining the conflict phase green wave optimization duration corresponding to the emergency vehicle specifically includes: Determine the driving direction of the emergency vehicle according to the passage envelope range, the real-time position information and the corresponding intersection to be passed, so as to determine the corresponding passage control phase and its corresponding conflict phase according to the driving direction; The optimized green wave duration of the conflicting phase is calculated according to the real-time position information, the intersection position coordinates of the intersection to be passed, the real-time speed and the initial green light duration of the conflicting phase; wherein the optimized green wave duration of the conflicting phase is less than the initial green light duration.

6. A three-dimensional priority passage method based on edge computing and V2X collaboration according to claim 5, characterized in that: The method further comprises: When it is determined that there are multiple conflicting phases, acquiring lane flows corresponding to the conflicting phases respectively; Based on the first proportional relationship between the traffic flows of each lane, the green light duration compression coefficient corresponding to each conflicting phase in the preset green light compression coefficient comparison table is matched; wherein the second proportional relationship between the green light duration compression coefficients is inversely proportional to the first proportional relationship between the traffic flows of each lane; The optimized green light duration of the conflicting phase is calculated according to the green light duration compression coefficient, the real-time position information, the intersection position coordinates of the intersection to be passed, the real-time speed and the initial green light duration of the conflicting phase.

7. The three-dimensional priority passage method based on edge computing and V2X collaboration according to claim 2 is characterized in that: The method further comprises: Determine the section passage time, queue delay time and conflict phase obstruction duration of the emergency vehicle according to the real-time speed, the preset divided section and the real-time intersection information of the to-be-passed intersection, so as to obtain the edge weight between two intersection nodes corresponding to the preset divided section according to the sum of the section passage time, the queue delay time and the conflict phase obstruction duration; According to each of the edge weights, a traffic state transition diagram is constructed; the traffic state transition diagram has intersections and different phases of intersections as nodes and preset divided sections as edges; According to the edge weights in the traffic state transition diagram, the node corresponding to the phase of the intersection where the emergency vehicle is located is taken as the starting point, and the estimated arrival time from the starting point to other reachable nodes is determined, and the path corresponding to the minimum value of each estimated arrival time is added to the distance table according to the preset rules, and the iteration is continued until the path from other reachable nodes to the target node is determined, and the distance table containing the planned path from the starting point to the target node is obtained, so that the path corresponding to the distance table is sent in real time to the user terminal of the emergency vehicle according to the real-time location information.

8. The three-dimensional priority passage method based on edge computing and V2X collaboration according to claim 1 is characterized in that: The method further comprises: After the emergency vehicle passes through the intersection to be passed, determining the conflict phase compensation duration according to the conflict phase green wave optimization duration; The sum of the initial green light duration of the conflicting phase and the compensation duration of the conflicting phase is used as the green light duration of the conflicting phase in the next cycle.

9. A three-dimensional priority passage system based on edge computing and V2X collaboration, characterized in that: The system comprises: A first determination module is used for the edge computing device to determine corresponding DSRC data and C-V2X long-distance path planning data after receiving an on-the-way driving signal from an emergency vehicle; A second determination module is used to determine the real-time location information of the emergency vehicle based on the DSRC data and the C-V2X long-distance path planning data, so as to judge whether the emergency vehicle enters the interchange driving area according to the real-time location information; A third determination module is used to determine the passage envelope range of the emergency vehicle based on a preset three-dimensional dynamic envelope model when the emergency vehicle enters the interchange driving area; The fourth determination module is used to determine the conflict phase green wave optimization duration corresponding to the emergency vehicle based on the passage envelope range, the real-time location information and the corresponding intersection to be passed, so as to adjust the signal light phase time of the intersection to be passed according to the conflict phase green wave optimization duration, so that the emergency vehicle has priority at the intersection to be passed.

10. A three-dimensional priority passage device based on edge computing and V2X collaboration, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a three-dimensional priority passage method based on edge computing and V2X collaboration as described in any one of claims 1 to 8 above.

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