A target path determination method based on data fusion, electronic device, and medium

Abstract

The present invention provides a method, an electronic device, and a medium for determining a target path based on data fusion, including: obtaining a road network topology map of a to-be-processed area, and thereby obtaining an initial road segment ID list set A = {A1,..., A i ,..., A m} corresponding to a target vehicle; where A i is the initial road segment ID list corresponding to the i-th initial path of the target vehicle, i ∈ [1, m], and m is the number of initial paths of the target vehicle; obtaining a predicted travel duration list B corresponding to the target vehicle traveling according to the initial road segment ID list; obtaining a predicted stop number list C corresponding to the target vehicle traveling according to the initial road segment ID list; performing normalization processing on the predicted travel duration list B and the predicted stop number list C to obtain a path priority list D; and taking the initial path corresponding to the minimum path priority in the path priority list D as the target path.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a target path determination method based on data fusion, electronic equipment, and medium. Background Art

[0002] As cities expand in size, there are more and more roads in the cities. Vehicles can reach their destinations from their starting points via several paths, and the time and length of different paths are different. Users driving vehicles can select a path from several paths as the target path based on their needs, and drive from their starting points to their destination via the target path, so as to save time or minimize driving distance.

[0003] In the prior art, the path with the shortest distance among several paths can be calculated through Dijkstra algorithm, A* search algorithm, Bellman-Ford algorithm, dynamic programming and other algorithms and used as the target path. The path with the shortest time among several paths can also be calculated through real-time dynamic path planning, time-dependent shortest path algorithm and other algorithms and used as the target path.

[0004] However, the above method also has the following technical problems:

[0005] The above method can only obtain the path with the shortest time or the path with the shortest distance. When the purpose of the user driving the vehicle is not the shortest time or the shortest distance, but to reduce the number of stops as much as possible based on the short time or to reduce the time as much as possible based on the few stops, the above method cannot obtain the target path, and cannot provide the user driving the vehicle with more accurate and efficient traffic path planning, thereby reducing travel efficiency. Summary of the invention

[0006] In view of this, the present invention provides a target path determination method based on data fusion, an electronic device, and a medium.

[0007] In view of the above technical problems, the technical solution adopted by the present invention is:

[0008] According to a first aspect of the present invention, a method for determining a target path based on data fusion is provided, the method comprising the following steps:

[0009] Obtain the road network topology map of the area to be processed, and obtain the initial road segment ID list set A={A1, A2, …, A i , …, A m}; Among them, A iis the list of initial road segment IDs corresponding to the i-th initial path corresponding to the target vehicle, i∈[1,m], m is the number of initial paths corresponding to the target vehicle;

[0010] Obtain the predicted travel time list B={B1, B2, …, B i , …, B m}; Among them, B i For the target vehicle, follow the i-th initial road segment ID list A i The predicted travel time of the corresponding initial path from the preset departure point to the preset destination point;

[0011] Get the predicted stop count list C={C1, C2, …, C i , …, C m}; Among them, C i For the target vehicle, follow the i-th initial road segment ID list A i The predicted number of stops corresponding to the initial route from the preset departure point to the preset destination point;

[0012] For the predicted travel time list B = {B1, B2, ..., B i , …, B m}、Predicted parking number list C={C1,C2,…,C i , …, C m} is normalized to obtain the path priority list D = {D1, D2, ..., D i , …, D m};

[0013] The path priority list D = {D1, D2, ..., D i , …, D m The initial path corresponding to the minimum path priority in} is the target path.

[0014] According to a second aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above-mentioned target path determination method based on data fusion.

[0015] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for determining a target path based on data fusion is implemented.

[0016] According to a fourth aspect of the present invention, there is provided a computer program product, comprising a computer program / instruction, which implements the above-mentioned target path determination method based on data fusion when executed by a processor.

[0017] The present invention has at least the following beneficial effects:

[0018] The present invention provides a method, electronic device and storage medium for determining a target path based on data fusion, wherein the method can obtain the predicted travel time and predicted number of stops corresponding to the target vehicle traveling from a preset departure point to a preset destination point according to each initial path, normalize the predicted travel time and the predicted number of stops, and take the average value to obtain the path priority corresponding to each path; the smaller the value obtained by normalizing the predicted travel time, the smaller the predicted travel time; the smaller the value obtained by normalizing the predicted number of stops, the smaller the predicted number of stops; therefore, the smaller the path priority, the more the corresponding path is in line with the requirement of short time consumption and fewer stops. Therefore, by taking the initial path corresponding to the smallest path priority in the path priority list as the target path, a path with short time consumption and fewer stops can be obtained, and a more accurate and efficient travel path planning can be provided for the user driving the target vehicle, which is conducive to improving travel efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of a method for determining a target path based on data fusion provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The present invention provides a method for determining a target path based on data fusion, the method comprising the following steps: Figure 1 As shown:

[0024] Step S1, obtain the road network topology map of the area to be processed, and obtain the initial road section ID list set A={A1, A2, ..., A i , …, A m}, where A i is the list of initial road segment IDs corresponding to the i-th initial path corresponding to the target vehicle, i∈[1,m], and m is the number of initial paths corresponding to the target vehicle.

[0025] Among them, the initial section ID list includes several initial section IDs. The initial path corresponding to the target vehicle is an ordered sequence consisting of a series of continuous sections that the target vehicle passes through from a preset starting point to a preset destination point. The initial section ID is a unique identity identifier of the initial section, and the initial section is a section in the initial path.

[0026] It should be noted that the road network topology diagram is a graphical tool for representing the road network structure, which mainly focuses on the connection relationship between roads. The road network topology diagram uses the intersections or endpoints of the road as nodes and the roads connecting two nodes as edges.

[0027] Among them, those skilled in the art know that the preset departure point and the preset destination point are set by those skilled in the art according to actual needs, and any method of obtaining the path between the departure point and the destination in the prior art belongs to the protection scope of the present invention and will not be repeated here, for example: depth-first search method, breadth-first search method.

[0028] Step S2, obtain the predicted travel time list B = {B1, B2, ..., B i , …, B m}; Among them, B i For the target vehicle, follow the i-th initial road segment ID list A i The predicted travel time of the corresponding initial path from the preset departure point to the preset destination point.

[0029] Specifically, step S2 includes the following steps:

[0030] Step S201: In this example, the i-th initial road segment ID list A i For example, A i ={A i1 , A i2 , …, A ij , …, A in(i)}, A ij A is the i-th initial road segment ID list iGet the jth initial road segment ID in the list A of the i-th initial road segment ID i Corresponding road intersection ID list , j∈[1,n(i)], n(i) is the i-th initial road segment ID list A i The number of initial segment IDs in; A is the i-th initial road segment ID list i The jth initial segment A in ij The corresponding road intersection ID.

[0031] The road intersection ID is the unique identification of the road intersection.

[0032] Furthermore, the i-th initial road segment ID list A i The jth initial segment A in ij The corresponding road intersection is the i-th initial road section ID list A i The jth initial segment A in ij The corresponding initial road segment and the i-th initial road segment ID list A i The j+1th initial segment A in i(j+1) The crossroad intersections between the corresponding initial road segments.

[0033] Step S202: Get the target vehicle arrival time The corresponding predicted time point T of the road intersection ij and the target vehicle in A 0 ij The predicted waiting time t of the corresponding road intersection ij .

[0034] Further, the target vehicle arrival The corresponding predicted time point T of the road intersection ij The process includes:

[0035] ;

[0036] Where, T 0 is the time point when the target vehicle departs from the preset starting point to the preset destination, S ij A is the i-th initial road segment ID list i The jth initial segment A in ij The corresponding target vehicle travel distance, V is the preset average speed of the target vehicle, T i(j-1) List A of IDs of the target vehicle arriving at the i-th initial road segment i The j-1th initial segment A in i(j-1) Corresponding road intersection ID The corresponding predicted time point of the road intersection, t i(j-1) For the target vehicle The predicted waiting time of the corresponding road intersection;

[0037] Among them, the i-th initial road segment ID list A i The jth initial segment A in ij The corresponding target vehicle travel distance S ij The expression is as follows:

[0038] ;

[0039] In the formula, a i A is the preset departure location and the i-th initial road segment ID list i The first initial segment A in i1 Corresponding road intersection ID The vehicle travel distance between the corresponding designated stop lines; L ij A is the i-th initial road segment ID list i The j-1th initial segment A in i(j-1) Corresponding road intersection ID The corresponding designated stop line and the i-th initial road section ID list A i The jth initial segment A in ij Corresponding road intersection ID The vehicle travel distance between the corresponding designated stop lines; b i A is the i-th initial road segment ID list i The n(i)-1th initial segment A in i[n(i)-1] Corresponding road intersection ID The vehicle travel distance between the corresponding designated stop line and the preset destination point.

[0040] It should be noted that the designated stop line corresponding to the road intersection ID is the stop line encountered by the target vehicle when entering the road intersection corresponding to the road intersection ID. For example, if the target vehicle enters the road intersection from the south side of the road intersection corresponding to the road intersection ID, then the designated stop line corresponding to the road intersection ID is the south stop line of the road intersection corresponding to the road intersection ID, that is, the stop line set at the south entrance of the road intersection corresponding to the road intersection ID.

[0041] Further, the target vehicle is obtained The predicted waiting time t of the corresponding road intersection ij The process includes:

[0042] Get the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID Corresponding key time period list ;in, for The corresponding r-th key time period, r∈[1,s(ij)], s(ij) is the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID The number of corresponding key time periods, The starting time point is , The end time is , The corresponding key time period list is a single time slice where the target vehicle departs from the preset departure point to the preset destination. The corresponding traffic light faces The period of time when the green light signal in the corresponding target driving direction is continuously open;

[0043] Traverse the key time period list , predict waiting time t ij The expression is as follows:

[0044] ;

[0045] In the formula, A is the i-th initial road segment ID list i The jth initial segment A in ij Corresponding road intersection ID The corresponding r+1th key time period The starting time point.

[0046] It should be noted that the key time period list middle, The corresponding target traffic light is set at The corresponding road intersection can be controlled Whether the vehicle coming from the corresponding target direction can pass The corresponding traffic lights at the road intersection.

[0047] The corresponding target direction is the target vehicle entering The direction of the corresponding road intersection, for example: if the target vehicle comes from If you drive into the intersection from the south side of the intersection, then The corresponding target traffic light is facing The traffic light on the south side of the corresponding road intersection is located at Corresponding to the traffic light on the north side of the road intersection, facing south.

[0048] The corresponding target driving direction is from the target vehicle entering The direction of the corresponding road intersection to the target vehicle leaving The direction of the corresponding road intersection, for example: if the target vehicle enters The direction of the corresponding road intersection is south, and the target vehicle leaves The direction of the corresponding road intersection is west, that is, the target vehicle enters A from the south 0 ij The corresponding road intersection leaves from the west The corresponding road intersection, then The corresponding specific driving direction is from south to west.

[0049] Optionally, the period during which the green light signal is continuously open can be understood as the period during which the signal light displays green.

[0050] Optional, The corresponding target traffic light faces The green light signal in the corresponding target driving direction is continuously open, which can be understood as: the traffic flow can follow The corresponding target driving direction is The corresponding road intersections are, for example: The corresponding green light signal for the target driving direction is from south to north, that is, go straight, then The corresponding target traffic light faces The green light signal in the corresponding target driving direction is continuously open. The corresponding target traffic light indicates that vehicles or pedestrians can go straight through The signal at the corresponding road intersection is green.

[0051] Optionally, the starting time point of a single time slice is 00:00:00, and the ending time point of a single time slice is 23:59:59. For example, if the target vehicle departs from the preset departure point to the preset destination at 12:05 on December 1, 2024, then the starting time point of the target time slice is 00:00:00 on December 1, 2024, and the ending time point is 23:59:59 on December 1, 2024.

[0052] Among them, get the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID Corresponding key time period list The process includes:

[0053] Step S100, obtaining the i-th initial road segment ID list Ai The jth initial segment A in ij Corresponding road intersection ID The traffic light ID of the corresponding target traffic light , get the traffic light ID Corresponding signal light status video .

[0054] Specifically, the traffic light status video corresponding to the traffic light ID is a video of the traffic light corresponding to the traffic light ID collected by the video acquisition device corresponding to the traffic light ID in the historical time slice, the historical time slice is a single time slice at a time point 24 hours earlier than the current time point, and the video acquisition device corresponding to the traffic light ID is set opposite to the traffic light corresponding to the traffic light ID, and is used to collect the video of the traffic light corresponding to the traffic light ID.

[0055] Step S200, according to the signal light status video , get the traffic light ID The corresponding signal light status data list collection , e∈[1,f(ij)], for The signal light state data list of the e-th signal light state of the corresponding target traffic light, e∈[1,f(ij)], f(ij) is The number of signal light states of the corresponding target traffic light.

[0056] in, , for The signal light status label used to represent the signal light status. is the start time of the e-th signal light state, is the end time of the e-th signal light state. The signal light states include: red light, green light, yellow light, left turn arrow green light, left turn arrow red light, right turn arrow green light, right turn arrow red light, all red light, flashing red light, flashing yellow light, straight green light, etc. Exemplarily, if the signal light state is red light, then the signal light state label corresponding to the signal light state is also red light.

[0057] Step S300: according to the signal light status data list set , get the traffic light ID Corresponding signal light status label list And a list of their display durations Among them, the signal light status label list , for The corresponding g-th key signal light state label, g∈[1, h(ij)], h(ij) is The number of corresponding key signal light status labels, , for The corresponding display duration.

[0058] Specifically, step S300 includes the following steps:

[0059] Step S3001, obtain the traffic light status video Corresponding first intermediate signal light status label list ,in, for The corresponding state label of the e-th intermediate signal light, Meet the following conditions:

[0060]

[0061] In the formula, for The signal light status label used to represent the signal light status.

[0062] Step S3002: List the first intermediate signal light status label Perform deduplication processing to obtain the traffic light status video The corresponding second intermediate signal light status label list, The corresponding second intermediate signal label list includes h(ij) second intermediate signal labels; among them, those skilled in the art know that any deduplication method in the prior art belongs to the protection scope of the present invention and will not be described in detail here.

[0063] Step S3003: Sort all the second intermediate signal labels in the corresponding second intermediate signal label list and obtain , where, when g=e, .

[0064] Step S3004, when When Corresponding As The starting time point of a display time period in the corresponding display time period list is Corresponding As the end time point of the display time period, obtain Corresponding display time period list ,in, for The yth display time period in y∈[1,q(ij)], q(ij) is The number of time periods in the display time period list corresponding to the corresponding key signal light status label.

[0065] Step S3005: display the time period Get the corresponding display duration ; Traverse all display time periods to obtain a list of display durations ;in, Meet the following conditions:

[0066]

[0067] In the formula, for Length of time.

[0068] It should be noted that, through the above steps, the first intermediate signal light status label list is obtained according to the signal light status data list set, all the first intermediate signal light status labels in the first intermediate signal light status label list are deduplicated to obtain the second intermediate signal label list, and the second intermediate signal label list is sorted to obtain the key signal light status label list. It can be understood that the signal light states that appear in sequence from the front to the back in a signal control cycle of the corresponding traffic light are obtained, and the key time period corresponding to the road intersection ID is obtained based on the key signal light status label list and the display duration corresponding to the key signal light status label, which is conducive to improving the accuracy of obtaining the said key time period.

[0069] Step S3006, similarly, obtain Corresponding key signal light status label list and Corresponding display duration list ; Corresponding key signal light status label list and Corresponding display duration list ; Corresponding key signal light status label list and Corresponding display duration list .

[0070] Further, To set in the traffic light ID corresponding to the traffic light at the corresponding road intersection and opposite to the target traffic light, , for The corresponding g-th key signal light state label, , for The corresponding display duration.

[0071] Further, To set in The traffic light ID corresponding to the traffic light at the corresponding road intersection and adjacent to the target traffic light, , for The corresponding g-th key signal light state label, , for The corresponding display duration.

[0072] To set in The corresponding road intersection and The corresponding traffic light is the traffic light ID corresponding to the traffic light. , for The corresponding g-th key signal light state label, , for The corresponding display duration.

[0073] Specifically, if The corresponding target traffic light is set at The corresponding traffic light on the south side of the intersection, then The corresponding traffic lights are set at The corresponding traffic light on the north side of the road intersection, The corresponding traffic lights are set at The traffic lights on the west or east side of the corresponding road intersection, if The corresponding traffic lights are set at The corresponding traffic light on the west side of the intersection is The corresponding traffic lights are set at The corresponding traffic light on the east side of the road intersection.

[0074] Step S400: according to the signal light status label list And a list of their display durations , get the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID The starting time point of the corresponding rth key time period , end time point .

[0075] Specifically, step S400 includes the following sub-steps S4001-S4002:

[0076] Step S4001, obtain a list of traffic light status tags Corresponding key duration list ,in, The signal light status label The corresponding critical duration.

[0077] Specifically, step S4001 includes the following sub-steps:

[0078] Step S400101, obtain the signal light status tag The corresponding first waiting time , .

[0079] Step S400102, obtain The corresponding target traffic light is When the corresponding signal light state, The preset signal state label of the preset signal state of the corresponding target signal light .

[0080] The preset signal light status label can be understood as: The corresponding target traffic light is The corresponding signal light status The signal light status label of the signal light status that the corresponding target signal light should display, for example: The corresponding target traffic light is The corresponding traffic light is green, indicating that traffic in the north-south direction can pass. The corresponding target signal light should show a red light, indicating that traffic in the east-west direction or the west-east direction cannot pass. Otherwise, traffic in the north-south direction and traffic in the east-west direction or the west-east direction may collide and cause a traffic accident. Therefore, the preset signal light state is a red light, and the preset signal light state label is a red light.

[0081] Step S400103, obtain the signal light status tag The corresponding second waiting time .

[0082] Specifically, when , and When ;when , and When Otherwise, confirm ;in, for The corresponding g-1th key signal light status label, for The corresponding g+1th key signal light status label, for The corresponding display duration.

[0083] Step S400104, obtain The corresponding target traffic light is The corresponding signal light status The preset signal state label of the preset signal state of the corresponding target signal light .

[0084] The preset signal light status label can be understood as: The corresponding target traffic light is The corresponding signal light status The signal light status label of the signal light status that the corresponding target signal light should display, for example: The corresponding target traffic light is The corresponding traffic light is green, indicating that traffic in the north-south direction can pass. The corresponding target signal light should show a red light, indicating that traffic in the east-west direction or the west-east direction cannot pass. Otherwise, traffic in the north-south direction and traffic in the east-west direction or the west-east direction may collide and cause a traffic accident. Therefore, the preset signal light state is a red light, and the preset signal light state label is a red light.

[0085] Step S400105, obtain The corresponding third waiting time .

[0086] Specifically, when , and When ;when , and When Otherwise, confirm ;in, for The corresponding g-1th key signal light status label, for The corresponding g+1th key signal light status label, for The corresponding display duration.

[0087] Step S400106, traffic light ID Corresponding signal light status label list List of display durations , First waiting time , Second waiting time , the third waiting time Find the average value to obtain the key duration .

[0088] Step S4002, traverse traffic light IDs Corresponding signal light status label list , if in The traffic flows through the corresponding signal light state The direction of the corresponding road intersection The corresponding specific driving direction is the same, then determine α(ij)=g and according to Get the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID The starting time point of the corresponding rth key time period , End time point , where α(ij) is The corresponding preset mark value, starting time point and end time point Meet the following conditions:

[0089] When r=1, , , where MB is the starting time point of the target time slice, for The corresponding key duration, for The corresponding α(ij)-1th key signal light state label;

[0090] When r≠1, determine , ,in, for The starting time point, for The end time point, for The corresponding r-1th key time period.

[0091] Specifically, The traffic flows through the corresponding signal light state The direction of the corresponding road intersection The corresponding specific driving directions are the same, which can be understood as: The traffic flows through the corresponding signal light state The direction of the corresponding road intersection is from south to north, then The corresponding specific driving direction is also from south to north, for example: the target traffic light is set at The north side of the corresponding road intersection, facing On the south side of the corresponding road intersection, if The corresponding specific driving direction is the same as from north to south, so the The corresponding signal light state should be green, indicating that traffic can enter the road intersection from the north side of the road intersection and exit the road intersection from the south side of the road intersection.

[0092] Through the above steps, the key signal light status label list corresponding to the four traffic lights corresponding to the road intersection and the display duration list corresponding to the key signal light status label list are obtained ( , , , , , , , ), according to the key signal light state labels in the key signal light state label list and their corresponding display durations, a key duration list corresponding to the key signal light state label list corresponding to the target traffic light corresponding to the road intersection is obtained, which can be understood as proofreading the display duration of each signal light state that appears in sequence from the front to the back in a signal control cycle of the target traffic light, and obtaining the final display duration of each signal light state that appears in sequence from the front to the back in a signal control cycle of the target traffic light. Based on the key duration list corresponding to the key signal light state label list corresponding to the target traffic light corresponding to the road intersection, based on the key duration list, the start time point and end time point of the key time period corresponding to the road intersection ID can be obtained, which is conducive to improving the accuracy of obtaining the key time period.

[0093] Step S500, traverse each key time period to obtain the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID Corresponding key time period list .

[0094] Step S203: according to the predicted time point T ij and the predicted waiting time t ij Get the predicted travel time B i , the expression is as follows:

[0095]

[0096] Where V is the preset average speed of the target vehicle.

[0097] Step S204, traverse each initial road section ID list to obtain a corresponding predicted travel time list B = {B1, B2, ..., B i , …, B m}.

[0098] It should be noted that the road intersection ID corresponding to the initial road section ID is obtained, and a list of key time periods corresponding to the road intersection ID is obtained. The key time period corresponding to the road intersection ID is the time period during which the target traffic light corresponding to the road intersection within the target time slice continuously opens the green light signal facing the specific driving direction corresponding to the road intersection. This means that the target vehicle does not need to wait during the key time period and can directly pass through the road intersection. The target vehicle cannot directly pass through the road intersection during the non-critical time period and needs to wait. Therefore, the predicted time point of the vehicle's arrival at each road intersection corresponding to the initial path and the predicted waiting time of the target vehicle at the road intersection are obtained. The predicted passing time is obtained based on the key time period, the predicted time point and the predicted waiting time. This can reduce the error caused by the target vehicle waiting for passage at the road intersection, and is conducive to improving the accuracy of obtaining the predicted passing time.

[0099] Step S3, obtaining a list of predicted stop times corresponding to the target vehicle traveling according to the initial road segment ID list C = {C1, C2, ..., C i , …, C m}; Among them, C i For the target vehicle, follow the i-th initial road segment ID list A i The corresponding initial path travels from the preset starting point to the preset destination point and the corresponding predicted number of stops, that is, the target vehicle follows A i The total number of stops predicted during the corresponding initial path from the preset departure point to the preset destination point.

[0100] For example, if the target vehicle follows A i The corresponding initial path is predicted to stop 5 times during the process of driving from the preset starting point to the preset destination point, so C i =5.

[0101] Specifically, step S3 includes the following sub-steps:

[0102] Step S301, obtain the i-th initial road segment ID list A i Corresponding road intersection ID list , j∈[1, n(i)], n(i) is the i-th initial road segment ID list A i The number of initial segment IDs in; A is the i-th initial road segment ID list i The jth initial segment A in ij The corresponding road intersection ID.

[0103] Step S302, obtaining the target vehicle The predicted waiting time t of the corresponding road intersection ij .

[0104] It should be noted that in this example, the target vehicle is The predicted waiting time t of the corresponding road intersection ij The process is the same as that in the above step S202 and will not be repeated here.

[0105] Step S303, when the predicted waiting time t ij When is equal to 0, the target vehicle is The number of stops at the corresponding road intersection is 0; when the predicted waiting time t ij When is not equal to 0, the target vehicle is The number of stops at the corresponding road intersection is 1; the expression is as follows:

[0106]

[0107] In the formula, For the target vehicle The number of stops corresponding to the corresponding road intersection.

[0108] Step S304, accumulate the target vehicle The number of stops at the corresponding road intersection , get the target vehicle according to the i-th initial road section ID list A i The predicted number of stops C corresponding to the initial path from the preset starting point to the preset destination point i ; The expression is as follows:

[0109]

[0110] Step S305, traverse each initial road segment ID list, so that the target vehicle travels according to the initial road segment ID list and the corresponding predicted stop number list C = {C1, C2, ..., C i , …, C m}.

[0111] It should be noted that, when the predicted waiting time of the target vehicle at a road intersection is 0, it means that the target vehicle did not wait for passage at the road intersection but directly passed through the road intersection without stopping at the road intersection; when the predicted waiting time of the target vehicle at the road intersection is not 0, it means that the target vehicle waited at the road intersection instead of directly passing through the road intersection, and stopped once at the road intersection; therefore, the number of stops can be accurately obtained based on the predicted waiting time, and the predicted number of stops is obtained based on the number of stops, which is conducive to improving the accuracy of obtaining the predicted number of stops.

[0112] S4, for the predicted travel time list B={B1, B2, ..., B i , …, B m}、Predicted parking number list C={C1,C2,…,C i , …, C m} is normalized to obtain the path priority list D = {D1, D2, ..., D i , …, D m}; The expression is as follows:

[0113]

[0114] Where λ is the predicted parking number list C = {C1, C2, …, C i , …, C m} corresponding to the preset weight list, λ i C i The corresponding preset weights, min() is the minimum value function, and max() is the maximum value function.

[0115] Optionally, the value of the preset weight may be 1, wherein those skilled in the art know that the specific value of the preset weight may also be set by those skilled in the art according to actual needs, which will not be described in detail here.

[0116] S5. Set the path priority list D = {D1, D2, ..., D i , …, D m The initial path corresponding to the minimum path priority in} is the target path.

[0117] It should be noted that for the predicted travel time list B={B1, B2, …, B i , …, B m} and the predicted parking number list C = {C1, C2, ..., C i , …, C m}Perform normalization processing and take the average value to obtain the path priority corresponding to each path. The smaller the value obtained by normalizing the predicted travel time, the smaller the predicted travel time is. The smaller the value obtained by normalizing the predicted number of stops, the smaller the predicted number of stops is. Therefore, the smaller the path priority, the more consistent the corresponding path is with shorter time consumption and fewer stops. Therefore, the initial path corresponding to the smallest path priority in the path priority list is taken as the target path. A path with shorter time consumption and fewer stops can be obtained, which can provide users driving the target vehicle with more accurate and efficient travel path planning, which is conducive to improving travel efficiency.

[0118] In summary, the present invention provides a method, electronic device and storage medium for determining a target path. The method can obtain the predicted travel time and predicted number of stops corresponding to the target vehicle traveling from a preset starting point to a preset destination point according to each initial path, normalize the predicted travel time and the predicted number of stops, and take the average value to obtain the path priority corresponding to each path. The smaller the value obtained by normalizing the predicted travel time, the smaller the predicted travel time is, and the smaller the value obtained by normalizing the predicted number of stops is, the smaller the predicted number of stops is. Therefore, the smaller the path priority is, the more the corresponding path is in line with the requirement of short time consumption and fewer stops. Therefore, the initial path corresponding to the smallest path priority in the path priority list is used as the target path, so that a path with short time consumption and fewer stops can be obtained, and a user driving the target vehicle can be provided with more accurate and efficient travel path planning, which is conducive to improving travel efficiency.

[0119] According to an embodiment of the present invention, the present invention also provides an electronic device and a readable storage medium.

[0120] Figure 2 A schematic block diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0121] The electronic device includes a computing unit 101, which can perform various appropriate actions and processes according to a computer program stored in a ROM 102 or a computer program loaded from a storage unit 108 into a RAM 103. In the RAM 103, various programs and data required for the operation of the electronic device can also be stored. The computing unit 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An I / O interface 105 is also connected to the bus 104.

[0122] A number of components in the electronic device are connected to the I / O interface 105, including: an input unit 106, such as a keyboard, a mouse, etc.; an output unit 107, such as various types of displays, speakers, etc.; a storage unit 108, such as a disk, an optical disk, etc.; and a communication unit 109, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 109 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0123] The computing unit 101 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 101 performs the various methods and processes described above. For example, in some embodiments, the method in the multidimensional early warning system for pressure injuries may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 108. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 102 and / or a communication unit 109. When the computer program is loaded into RAM 103 and executed by the computing unit 101, one or more steps of the method in the multidimensional early warning system for pressure injuries described above may be executed. Alternatively, in other embodiments, the computing unit 101 may be configured to execute the method in the multidimensional early warning system for pressure injuries in any other appropriate manner (e.g., by means of firmware).

[0124] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of the present invention, a readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. A readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0128] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0129] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0130] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are only for illustration, not for limiting the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A target path determination method based on data fusion, characterized in that: The method comprises the following steps: Obtain the road network topology map of the area to be processed, and obtain the initial road segment ID list set A={A1, A2, …, A i , …, A m }; Among them, A i is the list of initial road segment IDs corresponding to the i-th initial path corresponding to the target vehicle, i∈[1,m], m is the number of initial paths corresponding to the target vehicle; Obtain the predicted travel time list B={B1, B2, …, B i , …, B m }; Among them, B i For the target vehicle, follow the i-th initial road segment ID list A i The predicted travel time of the corresponding initial path from the preset departure point to the preset destination point; Get the predicted stop count list C={C1, C2, …, C i , …, C m }; Among them, C i For the target vehicle, follow the i-th initial road segment ID list A i The predicted number of stops corresponding to the initial route from the preset departure point to the preset destination point; For the predicted travel time list B = {B1, B2, ..., B i , …, B m }、Predicted parking number list C={C1,C2,…,C i , …, C m } is normalized to obtain the path priority list D = {D1, D2, ..., D i , …, D m }; The path priority list D = {D1, D2, ..., D i , …, D m The initial path corresponding to the minimum path priority in} is the target path.

2. The target path determination method based on data fusion according to claim 1, characterized in that: The process of obtaining the predicted travel time list corresponding to the target vehicle traveling according to the initial road section ID list includes: Get the i-th initial road segment ID list A i Corresponding road intersection ID list , j∈[1,n(i)], n(i) is the i-th initial road segment ID list A i The number of initial segment IDs in; A is the i-th initial road segment ID list i The jth initial segment A in ij The corresponding road intersection ID; Get the target vehicle arrival The corresponding predicted time point T of the road intersection ij and the target vehicle in A 0 ij The predicted waiting time t of the corresponding road intersection ij ; According to the predicted time point T ij and the predicted waiting time t ij Get the predicted travel time B i , the expression is as follows: ； Where V is the preset average speed of the target vehicle; Traverse each initial road section ID list to obtain the predicted travel time list B={B1, B2, …, B i , …, B m }.

3. The target path determination method based on data fusion according to claim 2 is characterized in that: Get the target vehicle arrival The corresponding predicted time point T of the road intersection ij The process includes: ； Where, T 0 is the time point when the target vehicle departs from the preset starting point to the preset destination, S ij A is the i-th initial road segment ID list i The jth initial segment A in ij The corresponding target vehicle travel distance, V is the preset average speed of the target vehicle, T i(j-1) List A of IDs of the target vehicle arriving at the i-th initial road segment i The j-1th initial segment A in i(j-1) Corresponding road intersection ID The corresponding predicted time point of the road intersection, t i(j-1) For the target vehicle The predicted waiting time of the corresponding road intersection; Among them, the i-th initial road segment ID list A i The jth initial segment A in ij The corresponding target vehicle travel distance S ij The expression is as follows: ； In the formula, a i A is the preset departure location and the i-th initial road segment ID list i The first initial segment A in i1 Corresponding road intersection ID The vehicle travel distance between the corresponding designated stop lines; L ij A is the i-th initial road segment ID list i The j-1th initial segment A in i(j-1) Corresponding road intersection ID The corresponding designated stop line and the i-th initial road section ID list A i The jth initial segment A in ij Corresponding road intersection ID The vehicle travel distance between the corresponding designated stop lines; b i A is the i-th initial road segment ID list i The n(i)-1th initial segment A in i[n(i)-1] Corresponding road intersection ID The vehicle travel distance between the corresponding designated stop line and the preset destination point.

4. The target path determination method based on data fusion according to claim 2 is characterized in that: Get the target vehicle The predicted waiting time t of the corresponding road intersection ij The process includes: Get the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID Corresponding key time period list ;in, for The corresponding r-th key time period, r∈[1,s(ij)], s(ij) is the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID The number of corresponding key time periods, The starting time point is , The end time is , The corresponding key time period list is a single time slice where the target vehicle departs from the preset departure point to the preset destination. The corresponding traffic light faces The period of time when the green light signal for the corresponding driving direction is continuously on; Traverse the key time period list , predict waiting time t ij The expression is as follows: ； In the formula, A is the i-th initial road segment ID list i The jth initial segment A in ij Corresponding road intersection ID The corresponding r+1th key time period The starting time point.

5. The target path determination method based on data fusion according to claim 4 is characterized in that: Get the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID Corresponding key time period list The process includes: Get the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID The traffic light ID of the corresponding target traffic light , get the traffic light ID Corresponding signal light status video ; According to the status of the signal light video , get the traffic light ID The corresponding signal light status data list collection , for The signal light state data list of the e-th signal light state of the corresponding target traffic light, e∈[1, f(ij)], f(ij) is The number of signal light states of the corresponding target traffic light; According to the signal light status data list collection , get the traffic light ID Corresponding signal light status label list And a list of their display durations ; Tag list based on traffic light status And a list of their display durations , get the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID The starting time point of the corresponding rth key time period , end time point ; Traverse each key time period to obtain the i-th initial road segment ID list A i The jth initial segment A in ij Corresponding road intersection ID Corresponding key time period list .

6. The target path determination method based on data fusion according to claim 1, characterized in that: Get the predicted stop count list C={C1, C2, …, C i , …, C m The process of} is: Get the i-th initial road segment ID list A i Corresponding road intersection ID list , j∈[1, n(i)], n(i) is the i-th initial road segment ID list A i The number of initial segment IDs in; A is the i-th initial road segment ID list i The jth initial segment A in ij The corresponding road intersection ID; Get the target vehicle The predicted waiting time t of the corresponding road intersection ij ; When the predicted waiting time t ij When is equal to 0, the target vehicle is The number of stops at the corresponding road intersection is 0; when the predicted waiting time t ij When is not equal to 0, the target vehicle is The number of stops at the corresponding road intersection is 1; Accumulate the target vehicle The number of stops at the corresponding road intersection is obtained by the target vehicle according to the i-th initial road section ID list A i The predicted number of stops C corresponding to the initial path from the preset starting point to the preset destination point i ; Traverse each initial road segment ID list, so that the target vehicle travels according to the initial road segment ID list and the corresponding predicted stop number list C = {C1, C2, ..., C i , …, C m }.

7. The method for determining a target path based on data fusion according to claim 1, characterized in that: Path priority list D = {D1, D2, ..., D i , …, D m The expression of} is as follows: ； In the formula, λ i C i The corresponding preset weights, min() is the minimum value function, and max() is the maximum value function.

8. An electronic device, comprising a memory and a processor, characterized in that: The memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the target path determination method based on data fusion as described in any one of claims 1-7 above.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a target path determination method based on data fusion as described in any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the target path determination method based on data fusion described in any one of claims 1 to 7 is implemented.

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