Stereoscopic Priority Passing Method, System and Device Based on Edge Computing and V2X Collaboration

Abstract

The present application provides a three-dimensional priority passing method, system and device based on edge computing and V2X collaboration, belonging to the field of traffic control technology. 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 C-V2X long-distance path planning data, determines the real-time position information of the emergency vehicle, so as to judge whether the emergency vehicle enters the interchange driving area according to the real-time position information. If so, based on a preset three-dimensional dynamic envelope model, determines the passing envelope range of the emergency vehicle; based on the passing envelope range, real-time position information and the corresponding intersection to be passed, determines the conflict phase green wave optimization duration corresponding to the emergency vehicle, 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 can pass through the intersection to be passed preferentially.

Description

Technical Field

[0001] This application relates to the field of traffic control technology, and in particular, to a three-dimensional priority passing 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 passing 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 diversion in advance.

[0003] The traditional traffic signal control system uses a fixed green waveband, 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 passing efficiency. Summary of the Invention

[0004] To solve the above problems, the embodiments of this application provide a three-dimensional priority passing 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 passing method based on edge computing and V2X collaboration, and the method includes:

[0006] 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;

[0007] 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 interchange driving area according to the real-time position information;

[0008] In the case where the emergency vehicle enters the interchange driving area, based on a preset three-dimensional dynamic envelope model, determine the passing envelope range of the emergency vehicle;

[0009] 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.

[0010] 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 a preset number of intersections in the planned path.

[0011] 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:

[0012] 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;

[0013] According to the passing distance and a preset distance threshold, determine 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;

[0014] Perform a weighted sum 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 sum.

[0015] In an implementation manner of the present application, based on a preset three-dimensional dynamic envelope model, determining the passing envelope range of the emergency vehicle specifically includes:

[0016] 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 axes directions of a preset space coordinate system within a preset time window, so as to construct the passing envelope range of the emergency vehicle according to each offset range component; wherein, the sum of each offset range component is less than or equal to 1.

[0017] 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:

[0018] Determine the driving deviation of the emergency vehicle based on the passing envelope range, the real-time position information, and the corresponding intersection to be passed, so as to determine the corresponding passing control phase and its corresponding conflict phase according to the driving deviation;

[0019] Calculate the green wave optimization duration of the conflict 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 conflict phase; wherein, the green wave optimization duration of the conflict phase is less than the initial green light duration.

[0020] In an implementation manner of the present application, the method further includes:

[0021] When it is determined that the number of phases of the conflict phase is multiple, obtain the lane traffic corresponding to each conflict phase respectively;

[0022] Based on the first proportional relationship between the lane traffic flows, match the green light duration compression coefficients corresponding to each conflict phase in the preset green light compression coefficient comparison table; wherein, the second proportional relationship between the green light duration compression coefficients and the first proportional relationship between the lane traffic flows are in an inverse relationship;

[0023] Calculate the green wave optimization duration of the conflict phase according to each 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 conflict phase.

[0024] In an implementation manner of the present application, the method further includes:

[0025] Determine the section passing time, queuing 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 intersection to be passed, 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 conflict phase obstruction duration;

[0026] Construct a passing state transition graph according to each edge weight; the passing state transition graph takes intersections and different phases of intersections as nodes and preset divided sections as edges;

[0027] According to each of the edge weights in the described traffic state transition diagram, starting from the node corresponding to the phase of the intersection where the emergency vehicle is located, determine the estimated arrival time from the starting point to other reachable nodes, and add the path corresponding to the minimum value among the estimated arrival times to the distance table according to a preset rule. Continuously iterate until the path from other reachable nodes to the target node is determined, and obtain the 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.

[0028] In one implementation manner of the present application, the method further includes:

[0029] After the emergency vehicle passes through the intersection to be passed, determine the conflict phase compensation duration according to the conflict phase green wave optimization duration;

[0030] Take the sum of the initial green light duration of the conflict phase and the conflict phase compensation duration as the green light duration of the conflict phase in the next cycle.

[0031] On the other hand, an embodiment of the present application further provides a three-dimensional priority traffic system based on edge computing and V2X collaboration, and the system includes:

[0032] The first determination module is used for the edge computing device to determine the corresponding DSRC data and C-V2X long-distance path planning data after receiving the in-transit signal from the emergency vehicle;

[0033] The second determination module is used 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 judge whether the emergency vehicle enters the overpass driving area according to the real-time position information;

[0034] The third determination module is used to determine the traffic envelope range of the emergency vehicle based on a preset three-dimensional dynamic envelope model when the emergency vehicle enters the overpass driving area;

[0035] The fourth determination module is used to determine the conflict phase green wave optimization duration corresponding to the emergency vehicle based on the traffic envelope range, the real-time position information and the corresponding intersection to be passed, so as to adjust the signal phase time of the intersection to be passed according to the conflict phase green wave optimization duration, so that the emergency vehicle can give priority to passing through the intersection to be passed.

[0036] On yet another aspect, an embodiment of the present application further provides a three-dimensional priority traffic device based on edge computing and V2X collaboration, and the device includes:

[0037] 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 passing method based on edge computing and V2X cooperation as described above.

[0038] Compared with the prior art, the significant effects of this application are as follows:

[0039] Through the above technical solution, by combining DSRC and C-V2X technologies, high-precision positioning of emergency vehicles can be effectively carried out, and signal lamp adaptation control can be dynamically performed, improving the three-dimensional passing efficiency of emergency vehicles and reducing delays. It can effectively solve the technical problems that currently, it is difficult to accurately obtain the positioning and vehicle signals of emergency vehicles in complex three-dimensional traffic scenarios, and priority passing cannot be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of this application, form a part of this application, and the illustrative embodiments and descriptions of this application are used to explain this application, and do not constitute an improper limitation of this application. In the drawings:

[0041] Figure 1 is a schematic flowchart of a three-dimensional priority passing method based on edge computing and V2X cooperation in an embodiment of this application;

[0042] Figure 2 is a schematic structural diagram of a three-dimensional priority passing system based on edge computing and V2X cooperation in an embodiment of this application;

[0043] Figure 3 is a schematic structural diagram of a three-dimensional priority passing device based on edge computing and V2X cooperation in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments of this application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0045] The embodiments of this application provide a three-dimensional priority passing method, system, and device based on edge computing and V2X cooperation to solve the technical problems that currently, it is difficult to accurately obtain the positioning and vehicle signals of emergency vehicles in complex three-dimensional traffic scenarios, and priority passing cannot be guaranteed.

[0046] The following describes each embodiment of the present application in detail with reference to the accompanying drawings.

[0047] An embodiment of the present application provides a three-dimensional priority passing method based on edge computing and V2X collaboration. As Figure 1 shown, the method may include steps S101 - S104:

[0048] 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.

[0049] 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 can be a core edge computing device in the edge computing node network set on the road, or the edge computing node network. The present application does not make specific limitations on this. The present application can deploy edge computing nodes in roadside units, and can collect data collected by dedicated short - range communication technology (DSRC), cellular vehicle - to - everything (C - V2X), and image acquisition devices on the road.

[0050] In the embodiment of the present application, the emergency vehicle can actively send the in - transit signal to the edge computing device through its corresponding user terminal, or the edge computing device can identify the emergency vehicle by analyzing the collected data, and obtain the in - transit signal after sending a confirmation signal to the user terminal corresponding to the emergency vehicle. Herein, the user terminal can be understood as the terminal device bound to the emergency vehicle, which can be an in - vehicle terminal, or a pre - bound mobile phone or other terminal devices of the driver. The present application does not make specific limitations on this.

[0051] The above - mentioned 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 a 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.

[0052] 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.

[0053] In the embodiment of the present application, the determining 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:

[0054] 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 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.

[0055] That is to say, the present application combines DSRC, C-V2X and multi-source data to determine the distance between the emergency vehicle and the upcoming intersection to be passed, and determines the fusion weights corresponding to DSRC and C-V2X respectively through a calculation rule, and uses the fusion weights to fuse the two parts of position information 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.

[0056] Specifically, in the present application, through the stereo perception layer in the edge computing device, the passing distance of the emergency vehicle from the next intersection to be passed can be calculated by combining the fusion data of multiple sensors, that is, the present application can respectively extract the positioning coordinates of DSRC, C-V2X and multi-source position information for the position of the emergency vehicle, and perform averaging 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:

[0057] ;

[0058] Among them, is the position coordinate of the emergency vehicle corresponding to the real-time position information at moment; represents the first fusion weight, Indicates The passing distance at a moment Indicates a preset distance threshold, such as 500 meters; Indicates at The first position coordinates corresponding to the first position information at the moment; Indicates the second fusion weight; Indicates at The second position coordinates corresponding to the second position information at the moment; Indicates a preset compensation item, which can be set by the user according to the acquisition error of the image acquisition device, and the present application does not make specific limitations on this.

[0059] Through the above solution, when the passing distance is less than the preset distance threshold, the DSRC data can play a key role to obtain more accurate position information. When the passing distance is greater than the preset distance threshold, at this time the positioning error of DSRC is relatively large, and the advantages of C-V2X data are utilized to obtain a macroscopic path guidance and perform driving planning in advance. Combining the 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 occlusion.

[0060] After obtaining the real-time position information of the emergency vehicle through the above solution, it is possible to continuously judge whether the emergency vehicle has reached the overpass driving area, and the corresponding area range of the overpass driving area is pre-stored in the edge computing device. According to the judgment result, flexible implementation of the traffic control for the emergency vehicle driving in the complex driving scenario of the overpass is performed to ensure the priority passage of the emergency vehicle.

[0061] S103. When the edge computing device determines that the emergency vehicle enters the overpass driving area, based on the preset three-dimensional dynamic envelope model, determine the passing envelope range of the emergency vehicle.

[0062] In the embodiment of the present application, the above-mentioned determination of the passing envelope range of the emergency vehicle based on the preset three-dimensional dynamic envelope model specifically includes:

[0063] Input the real-time position information, vehicle acceleration, and real-time speed into the preset three-dimensional dynamic envelope model, calculate the offset range components of the emergency vehicle in the three coordinate axes 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 offset range component. Among them, the sum value of each offset range component is less than or equal to 1.

[0064] In other words, when the edge computing device determines that the emergency vehicle enters the overpass driving area, it further executes the generation of the passing envelope range of the emergency vehicle, so as to more accurately monitor the driving of the emergency vehicle and accurately obtain the passable range of the emergency vehicle.

[0065] More specifically, the edge computing device inputs information such as the real-time position information, vehicle acceleration, and real-time speed of the emergency vehicle into a preset three-dimensional dynamic envelope model to construct a passing envelope range for the emergency vehicle itself. Among them, the real-time position information, vehicle acceleration, and real-time speed can be obtained through the above steps. The formula for the preset three-dimensional dynamic envelope model is as follows:

[0066] ;

[0067] Among them, is the position coordinate corresponding to the real-time position information of the emergency vehicle, is the coordinate in the x-axis direction, is the coordinate in the y-axis direction, is the coordinate in the z-axis direction, 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 y-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 z-axis direction, corresponding to the possible driving position range of the emergency vehicle in the axis direction. The above-mentioned 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 the actual usage scenario, and the present application does not make specific limitations on this.

[0068] Among them, when the preset three-dimensional dynamic envelope model needs 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 a passing envelope range according to the coordinate interval that satisfies the condition of being less than or equal to 1.

[0069] Through the formula of the above-mentioned preset three-dimensional dynamic envelope model, the dynamic driving state, lane deviation of the emergency vehicle, and the multi-layer structure of the overpass can be comprehensively considered in 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.

[0070] S104. The edge computing device determines the optimized green wave 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 optimized green wave duration of the conflict phase, so that the emergency vehicle can pass through the intersection to be passed preferentially.

[0071] That is to say, the present application can determine the optimized green wave duration of the conflict phase and adjust the signal light phase time according to the passing envelope range and the like, so as to effectively ensure that the emergency vehicle passes through the intersection to be passed preferentially, improve the passing efficiency, and reduce the delay.

[0072] In the embodiment of the present application, the determining the optimized green wave 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:

[0073] According to the passing envelope range, real-time position information, and the corresponding intersection to be passed, determine the driving bias of the emergency vehicle, and determine the corresponding passing control phase and its corresponding conflict phase according to the driving bias. Calculate the optimized green wave duration of the conflict 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 conflict phase. Among them, the optimized green wave duration of the conflict phase is less than the initial green light duration.

[0074] 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 deviation 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 right front, it is determined that the driving deviation is to drive towards the right front. The edge computing device will further determine the passing control phase corresponding to the driving deviation by combining the driving deviation with the current phase setting rule of the intersection to be passed. For example, if the driving deviation is to drive towards the right front, the right-turn or straight-through phase of the corresponding intersection may be the passing control phase. According to the passing control phase, further search for the signal light phase corresponding rule in the preset database to determine the phase conflicting with the passing control phase. For example, if the passing control phase is the right-turn phase, usually the oncoming left-turn phase is the conflicting phase within the same time period; if the passing control phase is the straight-through phase, the oncoming straight-through phase and some left-turn phases may be the conflicting phases.

[0075] Subsequently, according to 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-mentioned conflicting phase, calculate the green wave optimization duration of the conflicting phase, so as to continuously adjust the green light duration before the emergency vehicle arrives, thereby ensuring the passing efficiency of the emergency vehicle. Among them, the calculation formula for the green wave optimization duration of the conflicting phase is as follows:

[0076] ;

[0077] Among them, represents the green wave optimization duration of the conflicting 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 this application does not make specific limitations on this.

[0078] In the embodiment of the present application, a passing control phase may include multiple conflicting phases at the same intersection to be passed. At this time, the present application also provides the following embodiments, including:

[0079] When it is determined that the number of phases of the conflicting phase is multiple, obtain the lane traffic corresponding to each conflicting phase respectively. Based on the first proportional relationship between the lane traffic flows, match the green light duration compression coefficients corresponding to each conflicting phase in the preset green light compression coefficient comparison table. Among them, the second proportional relationship between the green light duration compression coefficients and the first proportional relationship between the lane traffic flows are in an inverse relationship. According to each green light duration compression coefficient, real-time position information, intersection position coordinates of the intersection to be passed, real-time speed, and the initial green light duration of the conflicting phase, calculate the green wave optimization duration of the conflicting phase.

[0080] 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 of each conflicting phase in real time. Subsequently, the edge computing device can calculate the first proportional relationship between the lane traffic of each conflicting phase. For example, the lane traffic of multiple conflicting phases is 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 , , the second proportional relationship for matching the specific green light duration compression coefficient in the preset green light compression coefficient comparison table is obtained. Such as , , By looking up the preset green light compression coefficient comparison table, the specific green light duration compression coefficient that can satisfy is found, such as , . The preset green light compression coefficient comparison table contains the preset lane traffic and the green light duration compression coefficients corresponding to the first proportional relationship. The specific content of the comparison table can be set by the user and will not be specifically limited here. Then, the green light duration compression coefficients corresponding to the above different conflicting phases are respectively used as the above-mentioned preset empirical coefficients of the conflicting phases, and the conflicting phase green wave optimization durations of each conflicting phase are respectively calculated by using the above-mentioned calculation formula for the conflicting phase green wave optimization duration, so as to adjust the green light durations of each conflicting phase respectively, so that emergency vehicles can give priority to passing through the intersection while ensuring normal road traffic.

[0081] In an embodiment of the present application, an accurate path can also be planned for emergency vehicles, which specifically includes the following steps:

[0082] Based on the real-time speed, the preset divided road segments, and the real-time intersection information of the intersection to be passed, determine the road segment passing time, queuing delay time, and conflict phase blocking duration of the emergency vehicle, so as to obtain the edge weight between two intersection nodes corresponding to the preset divided road segments according to the sum of the road segment passing time, queuing delay time, and conflict phase blocking duration. According to each edge weight, construct a traffic state transition graph. The traffic state transition graph takes intersections and different phases of intersections as nodes and preset divided road segments 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 containing 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.

[0083] In other words, the present application can combine the real-time speed , the preset divided road segments and the real-time intersection information to calculate the road segment passing time , queuing delay time and conflict phase blocking duration of the emergency vehicle. Let be the road segment length from intersection to intersection , be the number of queuing vehicles at intersection , be the preset average vehicle length, be the preset saturation flow value at intersection , be the preset conflict phase compensation coefficient, 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:

[0084] 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 currently arrives 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. Here, 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 times 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, thereby planning the travel path with the minimum delay for the emergency vehicle. The distance table records the intersection nodes that the emergency vehicle is heading to and can be displayed to the user terminal for the user to view.

[0085] In addition, a delay factor related to driving on the overpass can be added to the above-mentioned edge weight. This delay factor can be set by the user according to data such as the slope of the overpass that affects the travel time, and this application does not make specific limitations on this.

[0086] 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.

[0087] 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 according to the difference between the green wave optimization duration of the conflict phase and the original initial green light duration, match the conflict phase step duration corresponding to this difference from the preset compensation duration list, 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 intersection used, and 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 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 brought by the priority passage of emergency vehicles.

[0088] Through the above technical solutions, by combining DSRC and C-V2X technologies, the high-precision positioning of emergency vehicles can be effectively carried out, and the 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 and the priority passage cannot be guaranteed at present.

[0089] 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:

[0090] A first determination module 201, 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 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 passing 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 passing 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 preferentially.

[0091] 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:

[0092] 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:

[0093] 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, the real-time position information of the emergency vehicle is determined 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 the preset three-dimensional dynamic envelope model, the passing envelope range of the emergency vehicle is determined. Based on the passing envelope range, the real-time position information and the corresponding intersection to be passed, the conflict phase green wave optimization duration corresponding to the emergency vehicle is determined 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.

[0094] 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.

[0095] 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.

[0096] It should also be noted that the term "including", "comprising" or any other variation 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 further includes elements inherent to such process, method, commodity or device. Without further limitation, the element defined by the phrase "including one..." does not exclude the existence of additional identical elements in the process, method, commodity or device including the said element.

[0097] The above are only the embodiments of this application and are not intended to limit this application. For those skilled in the art, various changes and modifications can be made to this application. 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 includes: After receiving the in - transit signal from an emergency vehicle, an edge computing device determines 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 interchange driving area according to the real - time position information; When the emergency vehicle enters the interchange driving area, based on a preset three - dimensional dynamic envelope model, determine 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, determine the conflict - phase green - wave optimization duration corresponding to the emergency vehicle, 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 can pass through the intersection to be passed preferentially; Wherein, 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 a preset number of intersections in the planned path respectively; Wherein, based on the passing envelope range, the real - time position information and the corresponding intersection to be passed, determining the conflict - phase green - wave optimization duration 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; 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 conflict phase, calculate the conflict - phase green - wave optimization duration; wherein, the conflict - phase green - wave optimization duration is less than the initial green - light duration; Wherein, the method further includes: When it is determined that the number of phases of the conflict phase is multiple, obtain the lane traffic flow corresponding to each conflict phase respectively; Based on the first proportional relationship between the lane traffic flows, match the green - light duration compression coefficients corresponding to each conflict phase in a preset green - light compression coefficient comparison table; wherein, the second proportional relationship between the green - light duration compression coefficients is an inverse - proportional relationship with the first proportional relationship between the lane traffic flows; According to each 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 conflict phase, calculate the conflict - phase green - wave optimization duration.

2. The three-dimensional priority passage method based on edge computing and V2X cooperation according to claim 1, characterized in that, 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 multi - source position information, determine the passing distance of the emergency vehicle from the next intersection to be passed; Determine a first fusion weight corresponding to the first position information and a second fusion weight corresponding to the second position information according to the passing distance and a preset distance threshold; wherein, the sum 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 to determine the real-time position information of the emergency vehicle based on the calculation result of the weighted summation.

3. The three-dimensional priority passage method based on edge computing and V2X collaboration according to claim 1, wherein Based on a preset three-dimensional dynamic envelope model, determine the passing envelope range of the emergency vehicle, specifically including: Input the real-time position information, the vehicle acceleration, and the real-time speed into the preset three-dimensional dynamic envelope model, and calculate the offset range components of the emergency vehicle in the three coordinate axes 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 offset range component; wherein, the sum of each offset range component is less than or equal to 1.

4. A three-dimensional priority passing method based on edge computing and V2X collaboration according to claim 1, characterized in that, The method further includes: Determine the section passing time, queuing delay time, and conflict phase blocking duration of the emergency vehicle according to the real-time speed, preset divided sections, and real-time intersection information of the intersection to be passed, and obtain the edge weight between two intersection nodes corresponding to the preset divided section according to the sum of the section passing time, the queuing delay time, and the conflict phase blocking duration. Construct a passing state transition graph according to each edge weight; the passing state transition graph takes intersections and different phases of intersections as nodes and preset divided sections as edges; According to each edge weight in the passing 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 a preset rule, and continuously iterate until the path from other reachable nodes to the target node is determined, and obtain the 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.

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

6. A three-dimensional priority traffic system based on edge computing and V2X collaboration, characterized in that, The system adopts a three-dimensional priority passing method based on edge computing and V2X collaboration as described in any one of claims 1-5 above; the system includes: A first determination module, configured to, when an in-route driving signal from an emergency vehicle is received by an edge computing device, determine corresponding DSRC data and C-V2X long-distance path planning data; A second determination module, 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, and determine whether the emergency vehicle enters the overpass driving area according to the real-time position information; A third determination module, configured to determine a passing envelope range of the emergency vehicle based on a preset three-dimensional dynamic envelope model when the emergency vehicle enters the interchange driving area; A fourth determination module, configured to determine an optimized green wave duration for a conflict phase corresponding to the emergency vehicle based on the passing envelope range, the 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 optimized green wave duration for the conflict phase, so that the emergency vehicle can pass through the intersection to be passed preferentially.

7. A three-dimensional priority passage device based on edge computing and V2X collaboration, characterized in that, 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 execute a three-dimensional priority passing method based on edge computing and V2X cooperation as described in any one of claims 1-5 above.

Images