Expressway vehicle space-time path prediction method and device

Abstract

The invention discloses an expressway vehicle space-time path prediction method and device, and relates to the technical field of traffic information, and the method comprises the steps: determining a to-be-predicted target expressway section, carrying out the expressway section division of the target expressway section, and obtaining a sub expressway section set; acquiring sub-passing duration when all target vehicles pass through each sub-expressway section, and calculating sub-comprehensive passing duration of each sub-expressway section; and determining an abnormal sub-expressway section based on the sub-comprehensive passing duration, further determining an abnormal influence factor of the abnormal sub-expressway section, and performing vehicle space-time path prediction based on the abnormal influence factor. Therefore, by implementing the method, the determination accuracy of the abnormal sub-expressway section set can be improved, the determination accuracy of the abnormal influence factors of the abnormal sub-expressway sections is further improved, and thus the accuracy and stability of expressway vehicle space-time path prediction are improved.

Description

Technical Field

[0001] The present invention relates to the field of traffic information technology, and in particular to a method and device for predicting the spatiotemporal path of vehicles on a highway. Background Art

[0002] At present, highway construction is gradually transforming into a high-quality and efficient smart highway development stage. As one of the development directions in the field of smart transportation, vehicle spatiotemporal path prediction is a vital link in the construction of smart highways. Vehicle spatiotemporal path prediction relies on the advance perception and simulation calculation of highway traffic status. In the existing technology, the vehicle spatiotemporal path is further predicted by simulating the traffic operation status, which makes it difficult to analyze the actual road driving status and traffic flow rules, such as complex traffic conditions, bad weather (such as rain, snow, fog, hail, sandstorms, etc.), special road conditions (such as complex construction sections, lane line blurred sections, etc.), which leads to inaccurate and unstable prediction of vehicle spatiotemporal paths. Therefore, it is urgent to provide a new highway vehicle spatiotemporal path prediction method to improve the accuracy and stability of the prediction. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for predicting the spatiotemporal path of vehicles on highways, which can improve the accuracy and stability of the spatiotemporal path prediction of vehicles on highways.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for predicting the spatiotemporal path of vehicles on a highway, the method comprising:

[0005] Determine the target highway section to be predicted;

[0006] Dividing the target highway section into highway sections to determine a sub-highway section set including a plurality of sub-highway sections;

[0007] Obtaining the sub-passing time of all target vehicles passing through the target expressway section within a preset first time range when passing through each of the sub-expressway sections;

[0008] Calculating the sub-comprehensive passing time of each sub-highway section based on the sub-passing time of all target vehicles in each sub-highway section;

[0009] Determine an abnormal sub-highway section set including a plurality of abnormal sub-highway sections from the sub-highway section set; the abnormal sub-highway section is the sub-highway section whose corresponding sub-comprehensive passing time is abnormal data;

[0010] For each of the abnormal sub-highway sections in the abnormal sub-highway section set, determining an abnormal impact factor of the abnormal sub-highway section;

[0011] Based on all the abnormal influencing factors of each abnormal sub-highway section, a vehicle space-time path prediction operation is performed on the target highway section, and the vehicle space-time path prediction operation is used to predict the space-time path of vehicles passing through the target highway section within a subsequent preset second time range.

[0012] As an optional implementation manner, in the first aspect of the present invention, before dividing the target highway section into highway sections and determining a sub-highway section set including a plurality of sub-highway sections, the method further includes:

[0013] Obtaining the passing time of all target vehicles passing through the target expressway section within a preset first time range;

[0014] Calculate the comprehensive passing time of the target expressway section based on the passing time of all the target vehicles;

[0015] Determine whether the comprehensive passing time meets a preset passing time threshold, and obtain a comprehensive passing time determination result;

[0016] When the comprehensive passing time judgment result is used to indicate that the comprehensive passing time does not meet the passing time threshold, it triggers the execution of the operation of dividing the target expressway section into expressway sections and determining a sub-expressway section set including multiple sub-expressway sections.

[0017] As an optional implementation, in the first aspect of the present invention, all the target vehicles have corresponding vehicle types;

[0018] And, the comprehensive passing time of the target expressway section is calculated based on the passing time of all the target vehicles, including:

[0019] Based on each of the vehicle types and the number of the target vehicles under the vehicle type, determining a passing time weight of the vehicle type;

[0020] Based on the passing time of each target vehicle under each vehicle type, calculating the average passing time of all target vehicles under the vehicle type;

[0021] Based on the passing time weight of each vehicle type and the average passing time of all the target vehicles of the vehicle type, the comprehensive passing time of the target expressway section is calculated.

[0022] As an optional implementation manner, in the first aspect of the present invention, for each abnormal sub-highway section in the abnormal sub-highway section set, determining the abnormal impact factor of the abnormal sub-highway section includes:

[0023] Determine all target intelligent assisted driving vehicles from all the target vehicles;

[0024] For each of the target intelligent assisted driving vehicles, obtaining a first auxiliary data set, a second auxiliary data set, and a third auxiliary data set of the target intelligent assisted driving vehicle;

[0025] The first auxiliary data set includes at least one of forward collision warning data, rear collision warning data, blind spot monitoring warning data, lane change warning data, lane departure warning data, forward crossing warning data, and rear crossing warning data;

[0026] The second auxiliary data set includes at least one of automatic emergency braking data, automatic emergency avoidance data, lane keeping assistance data, lane departure assistance data, lane centering assistance data, intelligent speed limit assistance data, acceleration and deceleration lane change assistance data and automatic overtaking assistance data;

[0027] The third auxiliary data set includes at least one of the intelligent assisted driving takeover mileage, the intelligent assisted driving takeover ratio, and the number of intelligent assisted driving manual takeovers;

[0028] For each abnormal sub-highway section in the abnormal sub-highway section set, the abnormal impact factor of the abnormal sub-highway section is determined based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set of all the target intelligent assisted driving vehicles in the abnormal sub-highway section.

[0029] As an optional implementation manner, in the first aspect of the present invention, for each abnormal sub-highway section in the abnormal sub-highway section set, based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set of all the target intelligent assisted driving vehicles in the abnormal sub-highway section, determining the abnormal impact factor of the abnormal sub-highway section includes:

[0030] For each abnormal sub-highway section in the abnormal sub-highway section set, determining an edge sub-highway section server corresponding thereto, wherein the edge sub-highway section server is used for data processing of the abnormal sub-highway section;

[0031] For each abnormal sub-highway section in the abnormal sub-highway section set, based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set, a first association analysis and a second association analysis are performed on the abnormal sub-highway section on the edge sub-highway section server corresponding to the abnormal sub-highway section to obtain a first association analysis result and a second association analysis result; the first association analysis is used to analyze the collaborative association between the auxiliary data within each set of the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway; the second association analysis is used to analyze the modal association between the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway;

[0032] For each of the abnormal sub-highway sections in the abnormal sub-highway section set, an abnormal impact factor of the abnormal sub-highway section is determined based on the first association analysis result and the second association analysis result of the abnormal sub-highway section.

[0033] As an optional implementation, in the first aspect of the present invention, the performing of the vehicle spatiotemporal path prediction operation on the target highway section based on all the abnormal impact factors of each abnormal sub-highway section includes:

[0034] Based on the target expressway section, generating an initial full-course road model of the target expressway section;

[0035] Based on all the abnormal influencing factors of each abnormal sub-highway section, generating an abnormal sub-road model of the abnormal sub-highway section;

[0036] Based on the abnormal sub-road models of all the abnormal sub-highway sections, the initial full-course road model is modified to obtain a modified full-course road model of the target high-speed section;

[0037] Based on all the abnormal influencing factors of all the abnormal sub-highway sections, the modified full-course road model and all the vehicle types, a predicted space-time path corresponding to each vehicle type in the target high-speed section is generated.

[0038] As an optional implementation manner, in the first aspect of the present invention, dividing the target highway section into highway sections and determining a sub-highway section set including a plurality of sub-highway sections comprises:

[0039] Determine all toll stations, main road ETC gantries and ramp ETC gantries on the target expressway section;

[0040] Divide the target expressway section into expressway sections based on all the toll stations, the main road ETC gantries and the ramp ETC gantries to obtain a shortest sub-expressway section set including multiple shortest sub-expressway sections;

[0041] For each of the shortest sub-highway sections in the shortest sub-highway section set, determining whether the section length of the shortest sub-highway section meets a preset sub-highway section length threshold, and obtaining a length threshold determination result of the shortest sub-highway section;

[0042] For each of the shortest sub-highway sections in the shortest sub-highway section set, when the length threshold judgment result of the shortest sub-highway section is used to indicate that the section length of the shortest sub-highway section does not meet the sub-highway section length threshold, determine the target shortest sub-highway section corresponding to the shortest sub-highway section; splice the shortest sub-highway section with the target shortest sub-highway section corresponding to the shortest sub-highway section to obtain the spliced ​​shortest sub-highway section corresponding to the shortest sub-highway section; the target shortest sub-highway section is the shortest sub-highway section with the shorter length of the two shortest sub-highway sections adjacent to the shortest sub-highway section;

[0043] Based on all the unjoined shortest sub-freeway sections in the shortest sub-freeway section set and all the joined shortest sub-freeway sections, a sub-freeway section set including a plurality of sub-freeway sections is determined.

[0044] The second aspect of the present invention discloses a device for predicting the spatiotemporal path of vehicles on a highway, the device comprising:

[0045] A determination module, used to determine a target highway section to be predicted;

[0046] A division module, used for dividing the target highway section into highway sections and determining a sub-highway section set including a plurality of sub-highway sections;

[0047] A first acquisition module is used to acquire the sub-passing time of all target vehicles passing through the target expressway section within a preset first time range when passing through each sub-expressway section;

[0048] A first calculation module is used to calculate the sub-comprehensive passing time of each sub-highway section based on the sub-passing time of all target vehicles in each sub-highway section;

[0049] The determination module is further used to determine an abnormal sub-highway section set including a plurality of abnormal sub-highway sections from the sub-highway section set; the abnormal sub-highway section is the sub-highway section whose corresponding sub-comprehensive passing time is abnormal data; for each abnormal sub-highway section in the abnormal sub-highway section set, determine the abnormal impact factor of the abnormal sub-highway section;

[0050] A prediction module is used to perform a vehicle space-time path prediction operation on the target highway section based on all the abnormal influencing factors of each abnormal sub-highway section, and the vehicle space-time path prediction operation is used to predict the space-time path of the vehicle passing through the target highway section within a subsequent preset second time range.

[0051] As an optional implementation, in the second aspect of the present invention, before the division module divides the target highway section into highway sections and determines a sub-highway section set including a plurality of sub-highway sections, the device further includes:

[0052] A second acquisition module is used to acquire the passing time of all target vehicles passing through the target expressway section within a preset first time range;

[0053] A second calculation module is used to calculate the comprehensive passing time of the target expressway section based on the passing time of all the target vehicles;

[0054] A judgment module, used to judge whether the comprehensive passing time meets a preset passing time threshold, and obtain a comprehensive passing time judgment result;

[0055] A trigger module is used to trigger the division module to execute the operation of dividing the target expressway section into expressway sections and determining a sub-expressway section set including multiple sub-expressway sections when the comprehensive passing time judgment result is used to indicate that the comprehensive passing time does not meet the passing time threshold.

[0056] As an optional implementation, in the second aspect of the present invention, all the target vehicles have corresponding vehicle types;

[0057] And, the specific manner in which the second calculation module calculates the comprehensive passing time of the target expressway section based on the passing time of all the target vehicles includes:

[0058] Based on each of the vehicle types and the number of the target vehicles under the vehicle type, determining a passing time weight of the vehicle type;

[0059] Based on the passing time of each target vehicle under each vehicle type, calculating the average passing time of all target vehicles under the vehicle type;

[0060] Based on the passing time weight of each vehicle type and the average passing time of all the target vehicles of the vehicle type, the comprehensive passing time of the target expressway section is calculated.

[0061] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the abnormal impact factor of each abnormal sub-highway section in the abnormal sub-highway section set includes:

[0062] Determine all target intelligent assisted driving vehicles from all the target vehicles;

[0063] For each of the target intelligent assisted driving vehicles, obtaining a first auxiliary data set, a second auxiliary data set, and a third auxiliary data set of the target intelligent assisted driving vehicle;

[0064] The first auxiliary data set includes at least one of forward collision warning data, rear collision warning data, blind spot monitoring warning data, lane change warning data, lane departure warning data, forward crossing warning data, and rear crossing warning data;

[0065] The second auxiliary data set includes at least one of automatic emergency braking data, automatic emergency avoidance data, lane keeping assistance data, lane departure assistance data, lane centering assistance data, intelligent speed limit assistance data, acceleration and deceleration lane change assistance data and automatic overtaking assistance data;

[0066] The third auxiliary data set includes at least one of the intelligent assisted driving takeover mileage, the intelligent assisted driving takeover ratio, and the number of intelligent assisted driving manual takeovers;

[0067] For each abnormal sub-highway section in the abnormal sub-highway section set, the abnormal impact factor of the abnormal sub-highway section is determined based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set of all the target intelligent assisted driving vehicles in the abnormal sub-highway section.

[0068] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the abnormal impact factor of the abnormal sub-highway section for each abnormal sub-highway section in the abnormal sub-highway section set based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set of all the target intelligent assisted driving vehicles in the abnormal sub-highway section includes:

[0069] For each abnormal sub-highway section in the abnormal sub-highway section set, determining an edge sub-highway section server corresponding thereto, wherein the edge sub-highway section server is used for data processing of the abnormal sub-highway section;

[0070] For each abnormal sub-highway section in the abnormal sub-highway section set, based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set, a first association analysis and a second association analysis are performed on the abnormal sub-highway section on the edge sub-highway section server corresponding to the abnormal sub-highway section to obtain a first association analysis result and a second association analysis result; the first association analysis is used to analyze the collaborative association between the auxiliary data within each set of the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway; the second association analysis is used to analyze the modal association between the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway;

[0071] For each of the abnormal sub-highway sections in the abnormal sub-highway section set, an abnormal impact factor of the abnormal sub-highway section is determined based on the first association analysis result and the second association analysis result of the abnormal sub-highway section.

[0072] As an optional implementation, in the second aspect of the present invention, the specific manner in which the prediction module performs the vehicle spatiotemporal path prediction operation on the target highway section based on all the abnormal influencing factors of each abnormal sub-highway section includes:

[0073] Based on the target expressway section, generating an initial full-course road model of the target expressway section;

[0074] Based on all the abnormal influencing factors of each abnormal sub-highway section, generating an abnormal sub-road model of the abnormal sub-highway section;

[0075] Based on the abnormal sub-road models of all the abnormal sub-highway sections, the initial full-course road model is modified to obtain a modified full-course road model of the target high-speed section;

[0076] Based on all the abnormal influencing factors of all the abnormal sub-highway sections, the modified full-course road model and all the vehicle types, a predicted space-time path corresponding to each vehicle type in the target high-speed section is generated.

[0077] As an optional implementation, in the second aspect of the present invention, the division module divides the target highway section into highway sections, and a specific method of determining a sub-highway section set including a plurality of sub-highway sections includes:

[0078] Determine all toll stations, main road ETC gantries and ramp ETC gantries on the target expressway section;

[0079] Divide the target expressway section into expressway sections based on all the toll stations, the main road ETC gantries and the ramp ETC gantries to obtain a shortest sub-expressway section set including multiple shortest sub-expressway sections;

[0080] For each of the shortest sub-highway sections in the shortest sub-highway section set, determining whether the section length of the shortest sub-highway section meets a preset sub-highway section length threshold, and obtaining a length threshold determination result of the shortest sub-highway section;

[0081] For each of the shortest sub-highway sections in the shortest sub-highway section set, when the length threshold judgment result of the shortest sub-highway section is used to indicate that the section length of the shortest sub-highway section does not meet the sub-highway section length threshold, determine the target shortest sub-highway section corresponding to the shortest sub-highway section; splice the shortest sub-highway section with the target shortest sub-highway section corresponding to the shortest sub-highway section to obtain the spliced ​​shortest sub-highway section corresponding to the shortest sub-highway section; the target shortest sub-highway section is the shortest sub-highway section with the shorter length of the two shortest sub-highway sections adjacent to the shortest sub-highway section;

[0082] Based on all the unjoined shortest sub-freeway sections in the shortest sub-freeway section set and all the joined shortest sub-freeway sections, a sub-freeway section set including a plurality of sub-freeway sections is determined.

[0083] A third aspect of the present invention discloses another device for predicting the spatiotemporal path of a highway vehicle, the device comprising:

[0084] A memory storing executable program code;

[0085] a processor coupled to the memory;

[0086] The processor calls the executable program code stored in the memory to execute the highway vehicle spatiotemporal path prediction method disclosed in the first aspect of the present invention.

[0087] The fourth aspect of the present invention discloses a computer storage medium, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the highway vehicle spatiotemporal path prediction method disclosed in the first aspect of the present invention.

[0088] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0089] In an embodiment of the present invention, a target highway section to be predicted is determined and the target highway section is divided into highway sections to obtain a sub-highway section set; the sub-passing time of all target vehicles when passing through each sub-highway section is obtained and the sub-comprehensive passing time of each sub-highway section is calculated; based on the sub-comprehensive passing time, an abnormal sub-highway section is determined and an abnormal impact factor of the abnormal sub-highway section is further determined, and a vehicle spatiotemporal path prediction is performed based on the abnormal impact factor; this is conducive to improving the accuracy of determining the abnormal sub-highway section set, and then improving the accuracy of determining the abnormal impact factor of the abnormal sub-highway section, thereby improving the accuracy and stability of the spatiotemporal path prediction of highway vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] 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.

[0091] Figure 1 It is a flow chart of a method for predicting the spatiotemporal path of a highway vehicle disclosed in an embodiment of the present invention;

[0092] Figure 2 It is a flow chart of another method for predicting the spatiotemporal path of a highway vehicle disclosed in an embodiment of the present invention;

[0093] Figure 3 It is a structural schematic diagram of a device for predicting the spatiotemporal path of vehicles on a highway disclosed in an embodiment of the present invention;

[0094] Figure 4 It is a structural schematic diagram of another device for predicting the spatiotemporal path of a highway vehicle disclosed in an embodiment of the present invention;

[0095] Figure 5 It is a structural schematic diagram of another highway vehicle spatiotemporal path prediction device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0096] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0097] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or terminal including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or terminals.

[0098] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0099] The present invention discloses a method and device for predicting the spatiotemporal path of vehicles on a highway. The implementation of the present invention can determine the target highway section to be predicted and divide the target highway section into highway sections to obtain a sub-highway section set; obtain the sub-passing time of all target vehicles when passing through each sub-highway section and calculate the sub-comprehensive passing time of each sub-highway section; determine the abnormal sub-highway section based on the sub-comprehensive passing time and further determine the abnormal impact factor of the abnormal sub-highway section, and perform vehicle spatiotemporal path prediction based on the abnormal impact factor; it is beneficial to improve the determination accuracy of the abnormal sub-highway section set, and then improve the determination accuracy of the abnormal impact factor of the abnormal sub-highway section, thereby improving the accuracy and stability of the spatiotemporal path prediction of vehicles on the highway. The following are detailed descriptions.

[0100] Embodiment 1

[0101] See also Figure 1 , Figure 1 1 is a flow chart of a method for predicting the spatiotemporal path of a highway vehicle disclosed in an embodiment of the present invention. Figure 1 The described method can be applied to any scenario where it is necessary to predict the spatiotemporal paths of vehicles passing through a highway section, and the embodiments of the present invention are not limited thereto. Figure 1 As shown, the highway vehicle spatiotemporal path prediction method includes the following operations:

[0102] 101. Determine the target expressway section to be predicted;

[0103] 102. Divide the target highway section into highway sections, and determine a sub-highway section set including a plurality of sub-highway sections;

[0104] 103. Obtaining the sub-passing time of all target vehicles passing through the target expressway section within a preset first time range when passing through each sub-expressway section;

[0105] In the embodiment of the present invention, the first time range can be freely set according to many factors such as the prediction time accuracy, server computing power, rush hour, holidays, etc., and the embodiment of the present invention does not limit it;

[0106] 104. Calculate the sub-comprehensive passing time of each sub-highway section based on the sub-passing time of all target vehicles in each sub-highway section;

[0107] In the embodiment of the present invention, it can be understood that the sub-passing time of the target vehicle refers to the time it takes for the target vehicle to pass through each sub-highway section; the sub-comprehensive passing time refers to the passing time obtained by comprehensively weighing and analyzing the sub-passing times of all target vehicles in each sub-highway section;

[0108] 105. Determine, from the sub-highway section set, an abnormal sub-highway section set including a plurality of abnormal sub-highway sections;

[0109] In the embodiment of the present invention, the abnormal sub-highway section is a sub-highway section whose corresponding sub-comprehensive passing time is abnormal data; the abnormal data can be understood as the sub-comprehensive passing time is too long, exceeding a preset threshold, or can be considered from the perspective that the sub-comprehensive passing time is too short and there are a large number of speeding behaviors, which is not limited in the embodiment of the present invention;

[0110] 106. For each abnormal sub-highway section in the abnormal sub-highway section set, determine an abnormal impact factor of the abnormal sub-highway section;

[0111] In the embodiment of the present invention, the abnormal impact factor includes many factors such as complex traffic situation impact factor, bad weather impact factor and special road condition impact factor. The abnormal impact factor of the abnormal sub-highway section can be determined by field survey, querying weather forecast and accident information, etc., which is not limited in the embodiment of the present invention.

[0112] 107. Based on all abnormal influencing factors of each abnormal sub-highway section, perform a vehicle spatiotemporal path prediction operation on the target high-speed section;

[0113] In an embodiment of the present invention, the vehicle spatiotemporal path prediction operation is used to predict the spatiotemporal path of the vehicle passing through the target expressway section within a subsequent preset second time range; performing prediction on the target expressway section based on the abnormal influencing factor can be understood as marking the sub-expressway section corresponding to the abnormal influencing factor with special conditions such as congestion, bad weather, accidents, and driving conditions such as speed reduction and stopping, and displaying them by generating a topological map or a model map; spatiotemporal path prediction can be understood as predicting that a vehicle passing through the expressway section can travel to a certain node (position) of the expressway section at a certain time.

[0114] It can be seen that the implementation of the method described in the embodiment of the present invention can determine the target expressway section to be predicted and divide the target expressway section into expressway sections to obtain a sub-expressway section set; obtain the sub-passing time of all target vehicles when passing through each sub-expressway section and calculate the sub-comprehensive passing time of each sub-expressway section; determine the abnormal sub-expressway section based on the sub-comprehensive passing time and further determine the abnormal influencing factor of the abnormal sub-expressway section, and perform vehicle spatiotemporal path prediction based on the abnormal influencing factor; it is beneficial to improve the determination accuracy of the abnormal sub-expressway section set, and then improve the determination accuracy of the abnormal influencing factor of the abnormal sub-expressway section, thereby improving the accuracy and stability of the spatiotemporal path prediction of highway vehicles.

[0115] In an optional embodiment, in the above step 106, for each abnormal sub-highway section in the abnormal sub-highway section set, determining the abnormal impact factor of the abnormal sub-highway section may include:

[0116] Determine all target intelligent assisted driving vehicles from all target vehicles;

[0117] For each target intelligent assisted driving vehicle, obtaining a first auxiliary data set, a second auxiliary data set, and a third auxiliary data set of the target intelligent assisted driving vehicle;

[0118] The first auxiliary data set includes at least one of forward collision warning data, rear collision warning data, blind spot monitoring warning data, lane change warning data, lane departure warning data, forward crossing warning data, and rear crossing warning data;

[0119] The second auxiliary data set includes at least one of automatic emergency braking data, automatic emergency avoidance data, lane keeping assistance data, lane departure assistance data, lane centering assistance data, intelligent speed limit assistance data, acceleration and deceleration lane change assistance data, and automatic overtaking assistance data;

[0120] The third auxiliary data set includes at least one of the intelligent assisted driving takeover mileage, the intelligent assisted driving takeover ratio, and the number of intelligent assisted driving manual takeovers;

[0121] For each abnormal sub-highway section in the abnormal sub-highway section set, the abnormal impact factor of the abnormal sub-highway section is determined based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set of all target intelligent assisted driving vehicles in the abnormal sub-highway section.

[0122] In this optional embodiment, the first auxiliary data set, the second auxiliary data set and the third auxiliary data set can be obtained from an intelligent assisted driving vehicle that has an authorized relationship or a cooperative relationship with the highway service unit, and the embodiment of the present invention does not limit this; it can be understood that the data contained in the first auxiliary data set, the second auxiliary data set and the third auxiliary data set can be used as indirect auxiliary data to infer that there are sections of the highway that affect normal traffic. For example, when there are more complex road conditions on the highway, the automatic emergency avoidance data and the number of manual takeovers of intelligent assisted driving will increase significantly.

[0123] It can be seen that this optional embodiment is able to determine all target intelligent assisted driving vehicles from all target vehicles, obtain multiple auxiliary data sets of the target intelligent assisted driving vehicles, determine the abnormal influencing factors of the abnormal sub-highway sections based on the auxiliary data, thereby improving the utilization rate of the target vehicle driving data, and further improving the accuracy of determining the abnormal influencing factors of the abnormal sub-highway sections, thereby improving the accuracy and stability of the spatiotemporal path prediction of highway vehicles.

[0124] In another optional embodiment, for each abnormal sub-highway section in the abnormal sub-highway section set, determining the abnormal impact factor of the abnormal sub-highway section based on the first auxiliary data set, the second auxiliary data set, and the third auxiliary data set of all target intelligent assisted driving vehicles in the abnormal sub-highway section in the above step may include:

[0125] For each abnormal sub-highway section in the abnormal sub-highway section set, determining an edge sub-highway section server corresponding thereto, the edge sub-highway section server being used for data processing of the abnormal sub-highway section;

[0126] For each abnormal sub-highway section in the abnormal sub-highway section set, based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set, a first association analysis and a second association analysis are performed on the abnormal sub-highway section on the edge sub-highway section server corresponding to the abnormal sub-highway section to obtain a first association analysis result and a second association analysis result; the first association analysis is used to analyze the collaborative association between the auxiliary data within each set of the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway; the second association analysis is used to analyze the modal association between the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway;

[0127] For each abnormal sub-freeway section in the abnormal sub-freeway section set, an abnormal impact factor of the abnormal sub-freeway section is determined based on the first association analysis result and the second association analysis result of the abnormal sub-freeway section.

[0128] In this optional embodiment, the edge sub-section server can be established at a location such as a toll station with server storage and computing conditions to meet the needs of edge computing. At the same time, while completing the edge computing of each abnormal sub-highway section, all abnormal sub-highway sections can also be jointly learned to train relevant models for highway vehicle spatiotemporal path prediction. This embodiment of the present invention is not limited to this.

[0129] It can be seen that this optional embodiment can determine the edge sub-section server corresponding to each abnormal sub-highway section, and perform association analysis on the abnormal sub-highway section on the edge sub-section server, which is beneficial to reducing the load pressure of cloud (assembly) computing through the edge computing capability of the edge sub-section server and improving the data processing efficiency of the abnormal sub-highway section; further, determining the abnormal influencing factor of the abnormal sub-highway section based on the association analysis is beneficial to improving the accuracy of determining the abnormal influencing factor of the abnormal sub-highway section, thereby improving the accuracy of the spatiotemporal path prediction of highway vehicles.

[0130] In yet another optional embodiment, dividing the target highway section into highway sections in step 102 to determine a sub-highway section set including a plurality of sub-highway sections may include:

[0131] Identify all toll stations, main road ETC gantries and ramp ETC gantries on the target expressway section;

[0132] Divide the target expressway section into expressway sections based on all toll stations, main road ETC gantries and ramp ETC gantries, and obtain a shortest sub-expressway section set containing multiple shortest sub-expressway sections;

[0133] For each shortest sub-highway section in the shortest sub-highway section set, determine whether the section length of the shortest sub-highway section meets a preset sub-highway section length threshold, and obtain a length threshold determination result of the shortest sub-highway section;

[0134] For each shortest sub-highway section in the shortest sub-highway section set, when the length threshold judgment result of the shortest sub-highway section is used to indicate that the section length of the shortest sub-highway section does not meet the sub-highway section length threshold, determine the target shortest sub-highway section corresponding to the shortest sub-highway section; splice the shortest sub-highway section with the target shortest sub-highway section corresponding to the shortest sub-highway section to obtain the spliced ​​shortest sub-highway section corresponding to the shortest sub-highway section; the target shortest sub-highway section is the shortest sub-highway section with the shorter length of the two adjacent shortest sub-highway sections of the shortest sub-highway section;

[0135] Based on all unjoined shortest sub-freeway sections and all joined shortest sub-freeway sections in the shortest sub-freeway section set, a sub-freeway section set including a plurality of sub-freeway sections is determined.

[0136] In this optional embodiment, it can be understood that when the spliced ​​shortest sub-highway section still does not meet the sub-highway section length threshold, it is necessary to continue splicing until the spliced ​​shortest sub-highway section meets the highway section length threshold; based on all the unspliced ​​shortest sub-highway sections and all the spliced ​​shortest sub-highway sections in the shortest sub-highway section set, determining the sub-highway section set containing multiple sub-highway sections can be understood as taking all the unspliced ​​shortest sub-highway sections and all the spliced ​​shortest sub-highway sections as sub-highway sections.

[0137] It can be seen that this optional embodiment can divide the expressway section based on all toll stations, main road ETC gantries and ramp ETC gantries, obtain the shortest sub-expressway section and determine whether the section length of each shortest sub-expressway section meets the conditions, and when the conditions are met, determine the corresponding target shortest sub-expressway section and splice the shortest sub-expressway section with the corresponding target shortest sub-expressway section, and determine the sub-expressway section set after all splicing is completed; it is beneficial to improve the accuracy of expressway section division, and then improve the accuracy of determining abnormal sub-expressway sections, thereby improving the accuracy and stability of highway vehicle spatiotemporal path prediction.

[0138] Embodiment 2

[0139] See also Figure 2 , Figure 2 1 is a flow chart of a method for predicting the spatiotemporal path of a highway vehicle disclosed in an embodiment of the present invention. Figure 2 The described method can be applied to any scenario where it is necessary to predict the spatiotemporal paths of vehicles passing through a highway section, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the highway vehicle spatiotemporal path prediction method includes the following operations:

[0140] 201. Determine a target highway section to be predicted;

[0141] 202. Obtaining the passing time of all target vehicles passing through the target expressway section within a preset first time range;

[0142] 203. Based on the passing time of all target vehicles, calculate the comprehensive passing time of the target expressway section;

[0143] 204. Determine whether the comprehensive passing time meets a preset passing time threshold, and obtain a comprehensive passing time determination result;

[0144] 205. When the comprehensive passing time judgment result indicates that the comprehensive passing time does not meet the passing time threshold, the target expressway section is divided into expressway sections to determine a sub-expressway section set including a plurality of sub-expressway sections;

[0145] 206. Obtaining the sub-passing time of all target vehicles passing through the target expressway section within a preset first time range when passing through each sub-expressway section;

[0146] 207. Calculate the sub-comprehensive passing time of each sub-highway section based on the sub-passing time of all target vehicles in each sub-highway section;

[0147] 208. Determine, from the sub-highway section set, an abnormal sub-highway section set including a plurality of abnormal sub-highway sections;

[0148] In the embodiment of the present invention, the abnormal sub-highway section is a sub-highway section whose corresponding sub-comprehensive passing time is abnormal data;

[0149] 209. For each abnormal sub-highway section in the abnormal sub-highway section set, determine an abnormal impact factor of the abnormal sub-highway section;

[0150] 210. Based on all abnormal influencing factors of each abnormal sub-highway section, perform a vehicle spatiotemporal path prediction operation on the target high-speed section;

[0151] In an embodiment of the present invention, the vehicle spatiotemporal path prediction operation is used to predict the spatiotemporal path of the vehicle passing through the target expressway section within a subsequent preset second time range; it can be understood that when the comprehensive passing time judgment result is used to indicate that the comprehensive passing time meets the passing time threshold, then from the perspective of the entire target expressway section, it can be determined that there is no need to divide it and further determine abnormal sub-sections.

[0152] In the embodiment of the present invention, for other descriptions of step 201, step 205, step 206, step 207, step 208, step 200 and step 210, please refer to the detailed description of step 101, step 102, step 103, step 104, step 105, step 106 and step 107 in embodiment 1, and the embodiment of the present invention will not be repeated here.

[0153] It can be seen that the implementation of the method described in the embodiment of the present invention can determine the target expressway section to be predicted, obtain the passing time of all target vehicles passing through the target expressway section and calculate the comprehensive passing time of the target expressway section. When the comprehensive passing time does not meet the passing time threshold, the target expressway section is divided into expressway sections to obtain a sub-expressway section set; the sub-passing time of all target vehicles when passing through each sub-expressway section is obtained and the sub-comprehensive passing time of each sub-expressway section is calculated; the abnormal sub-expressway section is determined based on the sub-comprehensive passing time and the abnormal impact factor of the abnormal sub-expressway section is further determined, and the vehicle spatiotemporal path prediction is performed based on the abnormal impact factor; it is beneficial to improve the determination accuracy of the expressway section division, reduce the cost of dividing the target expressway section and subsequent calculation when the comprehensive passing time reaches the standard, and improve the efficiency of the spatiotemporal path prediction of highway vehicles.

[0154] In an optional embodiment, all target vehicles have corresponding vehicle types;

[0155] In the embodiment of the present invention, the vehicle types can be three categories: passenger cars, trucks and special vehicles; passenger cars, trucks and special vehicles can also be further classified, such as passenger cars include Class 1 passenger cars, Class 2 passenger cars, Class 3 passenger cars and Class 4 passenger cars. The approved number of passengers for Class 1 passenger cars should be ≤9, and the length of the car should be less than 6000mm; the approved number of passengers for Class 2 passenger cars is 10-19, and the length of the car should be less than 6000mm; the approved number of passengers for Class 3 passenger cars should be ≤39, and the length of the car should not be less than 6000mm; the approved number of passengers for Class 4 passenger cars should be ≥40, and the length of the car should not be less than 6000mm. The classification and determination of vehicle types are not limited in the embodiment of the present invention.

[0156] Furthermore, the calculation of the comprehensive passing time of the target expressway section based on the passing time of all target vehicles in step 203 may include:

[0157] Based on each vehicle type and the number of target vehicles under the vehicle type, determine the passing time weight of the vehicle type;

[0158] Based on the passing time of each target vehicle under each vehicle type, calculate the average passing time of all target vehicles under the vehicle type;

[0159] Based on the passing time weight of each vehicle type and the average passing time of all target vehicles under this vehicle type, the comprehensive passing time of the target expressway section is calculated.

[0160] In the embodiment of the present invention, it can be understood that the passing time weight of the vehicle type is determined based on each vehicle type and the number of target vehicles under the vehicle type. The passing time weight can be flexibly adjusted based on the passing time weight of the preset vehicle type and the number of target vehicles under the vehicle type, and the embodiment of the present invention does not limit this.

[0161] It can be seen that this optional embodiment can determine the passing time weight of each vehicle type and calculate the average passing time of all target vehicles under each vehicle type, and calculate the comprehensive passing time of the target expressway section based on the passing time weight and the average passing time of each vehicle type; it is beneficial to improve the accuracy of determining the comprehensive passing time, and then improve the accuracy of judging whether the comprehensive passing time meets the threshold.

[0162] In another optional embodiment, the step 210 in the above step of performing a vehicle spatiotemporal path prediction operation on the target highway section based on all abnormal impact factors of each abnormal sub-highway section may include:

[0163] Based on the target expressway section, an initial full-course road model of the target expressway section is generated;

[0164] Based on all abnormal influencing factors of each abnormal sub-highway section, an abnormal sub-road model of the abnormal sub-highway section is generated;

[0165] Based on the abnormal sub-road models of all abnormal sub-highway sections, the initial full-course road model is modified to obtain a modified full-course road model of the target high-speed section;

[0166] Based on all abnormal influencing factors of all abnormal sub-highway sections, the modified full-course road model and all vehicle types, a predicted space-time path corresponding to each vehicle type in the target high-speed section is generated.

[0167] In the embodiment of the present invention, it can be understood that the correction of the initial whole road model based on the abnormal sub-road models of all abnormal sub-highway sections can be understood as replacing the corresponding sub-section models in the initial whole road model with the abnormal sub-road models of all abnormal sub-highway sections; based on all abnormal influencing factors of all abnormal sub-highway sections, the corrected whole road model and all vehicle types, the predicted space-time path corresponding to each vehicle type in the target highway section is generated, which can be understood as marking the sub-highway sections corresponding to the abnormal influencing factors with special conditions such as congestion, bad weather, accidents, and marking the driving conditions such as speed reduction and stopping corresponding to all vehicle types, and reflecting them in the corrected whole road model, and displaying them by generating a topological map or a model map.

[0168] It can be seen that this optional embodiment can generate an initial full-length road model of the target expressway section, and generate an abnormal sub-road model based on abnormal influencing factors, and correct the initial full-length road model to obtain a corrected full-length road model of the target expressway section, which is beneficial to improving the correction accuracy of the corrected initial full-length road model; further, a predicted space-time path is generated based on all abnormal influencing factors, the corrected full-length road model and all vehicle types, which is beneficial to improving the accuracy of the space-time path prediction of highway vehicles.

[0169] Embodiment 3

[0170] See also Figure 3 , Figure 3 1 is a schematic diagram of the structure of a device for predicting the spatiotemporal path of a highway vehicle disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to any scenario where it is necessary to predict the spatiotemporal path of vehicles passing through a highway section, and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the highway vehicle spatiotemporal path prediction device may include:

[0171] A determination module 301 is used to determine a target highway section to be predicted;

[0172] A division module 302 is used to divide the target highway section into highway sections and determine a sub-highway section set including a plurality of sub-highway sections;

[0173] The first acquisition module 303 is used to obtain the sub-passing time of all target vehicles passing through the target expressway section within a preset first time range when passing through each sub-expressway section;

[0174] A first calculation module 304 is used to calculate the sub-comprehensive passing time of each sub-highway section based on the sub-passing time of all target vehicles in each sub-highway section;

[0175] The determination module 301 is further used to determine an abnormal sub-highway section set including multiple abnormal sub-highway sections from the sub-highway section set; for each abnormal sub-highway section in the abnormal sub-highway section set, determine an abnormal impact factor of the abnormal sub-highway section;

[0176] In the embodiment of the present invention, the abnormal sub-highway section is a sub-highway section whose corresponding sub-comprehensive passing time is abnormal data;

[0177] The prediction module 305 is used to perform a vehicle spatiotemporal path prediction operation on the target highway section based on all abnormal influencing factors of each abnormal sub-highway section. The vehicle spatiotemporal path prediction operation is used to predict the spatiotemporal path of vehicles passing through the target highway section within a subsequent preset second time range.

[0178] It can be seen that the device described in the embodiment of the present invention is capable of determining the target highway section to be predicted and dividing the target highway section into highway sections to obtain a sub-highway section set; obtaining the sub-passing time of all target vehicles when passing through each sub-highway section and calculating the sub-comprehensive passing time of each sub-highway section; determining the abnormal sub-highway section based on the sub-comprehensive passing time and further determining the abnormal influencing factor of the abnormal sub-highway section, and performing vehicle spatiotemporal path prediction based on the abnormal influencing factor; it is beneficial to improve the determination accuracy of the abnormal sub-highway section set, and then improve the determination accuracy of the abnormal influencing factor of the abnormal sub-highway section, thereby improving the accuracy and stability of the spatiotemporal path prediction of highway vehicles.

[0179] In an optional embodiment, if Figure 4 As shown, the device also includes:

[0180] A second acquisition module 306 is used to obtain the passing time of all target vehicles passing through the target highway section within a preset first time range before the division module 302 divides the target highway section into highway sections and determines a sub-highway section set including multiple sub-highway sections;

[0181] The second calculation module 307 is used to calculate the comprehensive passing time of the target expressway section based on the passing time of all target vehicles;

[0182] A judgment module 308 is used to judge whether the comprehensive passing time meets a preset passing time threshold, and obtain a comprehensive passing time judgment result;

[0183] The trigger module 309 is used to trigger the division module to perform the operation of dividing the target expressway section into expressway sections and determining a sub-expressway section set including multiple sub-expressway sections when the comprehensive passing time judgment result is used to indicate that the comprehensive passing time does not meet the passing time threshold.

[0184] It can be seen that the device described in the implementation of this optional embodiment is capable of determining the target expressway section to be predicted, obtaining the passing time of all target vehicles passing through the target expressway section and calculating the comprehensive passing time of the target expressway section; when the comprehensive passing time does not meet the passing time threshold, the target expressway section is divided into expressway sections to obtain a sub-expressway section set; the sub-passing time of all target vehicles when passing through each sub-expressway section is obtained and the sub-comprehensive passing time of each sub-expressway section is calculated; the abnormal sub-expressway section is determined based on the sub-comprehensive passing time and the abnormal impact factor of the abnormal sub-expressway section is further determined, and the vehicle spatiotemporal path prediction is performed based on the abnormal impact factor; it is beneficial to improve the accuracy of the determination of expressway section division, reduce the cost of dividing the target expressway section and subsequent calculations when the comprehensive passing time exceeds the standard, and improve the efficiency of highway vehicle spatiotemporal path prediction.

[0185] In another optional embodiment, all target vehicles have corresponding vehicle types;

[0186] Furthermore, the second calculation module 307 calculates the comprehensive passing time of the target expressway section based on the passing time of all target vehicles in a specific manner including:

[0187] Based on each vehicle type and the number of target vehicles under the vehicle type, determine the passing time weight of the vehicle type;

[0188] Based on the passing time of each target vehicle under each vehicle type, calculate the average passing time of all target vehicles under the vehicle type;

[0189] Based on the passing time weight of each vehicle type and the average passing time of all target vehicles under this vehicle type, the comprehensive passing time of the target expressway section is calculated.

[0190] It can be seen that the device described in the implementation of this optional embodiment is able to determine the passing time weight of each vehicle type and calculate the average passing time of all target vehicles under each vehicle type, and calculate the comprehensive passing time of the target expressway section based on the passing time weight of each vehicle type and the average passing time; it is beneficial to improve the accuracy of determining the comprehensive passing time, and then improve the accuracy of judging whether the comprehensive passing time meets the threshold.

[0191] In yet another optional embodiment, the specific manner in which the determination module 301 determines the abnormal impact factor of each abnormal sub-highway section in the abnormal sub-highway section set includes:

[0192] Determine all target intelligent assisted driving vehicles from all target vehicles;

[0193] For each target intelligent assisted driving vehicle, obtaining a first auxiliary data set, a second auxiliary data set, and a third auxiliary data set of the target intelligent assisted driving vehicle;

[0194] The first auxiliary data set includes at least one of forward collision warning data, rear collision warning data, blind spot monitoring warning data, lane change warning data, lane departure warning data, forward crossing warning data, and rear crossing warning data;

[0195] The second auxiliary data set includes at least one of automatic emergency braking data, automatic emergency avoidance data, lane keeping assistance data, lane departure assistance data, lane centering assistance data, intelligent speed limit assistance data, acceleration and deceleration lane change assistance data, and automatic overtaking assistance data;

[0196] The third auxiliary data set includes at least one of the intelligent assisted driving takeover mileage, the intelligent assisted driving takeover ratio, and the number of intelligent assisted driving manual takeovers;

[0197] For each abnormal sub-highway section in the abnormal sub-highway section set, the abnormal impact factor of the abnormal sub-highway section is determined based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set of all target intelligent assisted driving vehicles in the abnormal sub-highway section.

[0198] It can be seen that the device described in the implementation of this optional embodiment is able to determine all target intelligent assisted driving vehicles from all target vehicles, obtain multiple auxiliary data sets of the target intelligent assisted driving vehicles, and determine the abnormal influencing factors of the abnormal sub-highway sections based on the auxiliary data, thereby improving the utilization rate of the target vehicle driving data, and further improving the accuracy of determining the abnormal influencing factors of the abnormal sub-highway sections, thereby improving the accuracy and stability of the spatiotemporal path prediction of highway vehicles.

[0199] In another optional embodiment, the determination module 301 determines, for each abnormal sub-highway section in the abnormal sub-highway section set, the abnormal impact factor of the abnormal sub-highway section based on the first auxiliary data set, the second auxiliary data set, and the third auxiliary data set of all target intelligent assisted driving vehicles in the abnormal sub-highway section. The specific method includes:

[0200] For each abnormal sub-highway section in the abnormal sub-highway section set, determining an edge sub-highway section server corresponding thereto, the edge sub-highway section server being used for data processing of the abnormal sub-highway section;

[0201] For each abnormal sub-highway section in the abnormal sub-highway section set, based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set, a first association analysis and a second association analysis are performed on the abnormal sub-highway section on the edge sub-highway section server corresponding to the abnormal sub-highway section to obtain a first association analysis result and a second association analysis result; the first association analysis is used to analyze the collaborative association between the auxiliary data within each set of the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway; the second association analysis is used to analyze the modal association between the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway;

[0202] For each abnormal sub-freeway section in the abnormal sub-freeway section set, an abnormal impact factor of the abnormal sub-freeway section is determined based on the first association analysis result and the second association analysis result of the abnormal sub-freeway section.

[0203] It can be seen that the device described in implementing this optional embodiment is able to determine the edge sub-section server corresponding to each abnormal sub-highway section, and perform association analysis on the abnormal sub-highway section on the edge sub-section server, which is beneficial to reducing the load pressure of cloud (assembly) computing through the edge computing capability of the edge sub-section server and improving the data processing efficiency of the abnormal sub-highway section; further, determining the abnormal influencing factor of the abnormal sub-highway section based on the association analysis is beneficial to improving the accuracy of determining the abnormal influencing factor of the abnormal sub-highway section, thereby improving the accuracy of the spatiotemporal path prediction of highway vehicles.

[0204] In yet another optional embodiment, the prediction module 305 performs the vehicle spatiotemporal path prediction operation on the target highway section based on all abnormal impact factors of each abnormal sub-highway section in a specific manner including:

[0205] Based on the target expressway section, an initial full-course road model of the target expressway section is generated;

[0206] Based on all abnormal influencing factors of each abnormal sub-highway section, an abnormal sub-road model of the abnormal sub-highway section is generated;

[0207] Based on the abnormal sub-road models of all abnormal sub-highway sections, the initial full-course road model is modified to obtain a modified full-course road model of the target high-speed section;

[0208] Based on all abnormal influencing factors of all abnormal sub-highway sections, the modified full-course road model and all vehicle types, a predicted space-time path corresponding to each vehicle type in the target high-speed section is generated.

[0209] It can be seen that the device described in the implementation of this optional embodiment is capable of generating an initial full-length road model of the target expressway section, and generating an abnormal sub-road model based on abnormal influencing factors, and correcting the initial full-length road model to obtain a corrected full-length road model of the target expressway section, which is beneficial to improving the correction accuracy of the corrected initial full-length road model; further, a predicted space-time path is generated based on all abnormal influencing factors, the corrected full-length road model and all vehicle types, which is beneficial to improving the accuracy of the prediction of the space-time path of highway vehicles.

[0210] In yet another optional embodiment, the division module 302 divides the target highway section into highway sections, and a specific method of determining a sub-highway section set including a plurality of sub-highway sections includes:

[0211] Identify all toll stations, main road ETC gantries and ramp ETC gantries on the target expressway section;

[0212] Divide the target expressway section into expressway sections based on all toll stations, main road ETC gantries and ramp ETC gantries, and obtain a shortest sub-expressway section set containing multiple shortest sub-expressway sections;

[0213] For each shortest sub-highway section in the shortest sub-highway section set, determine whether the section length of the shortest sub-highway section meets a preset sub-highway section length threshold, and obtain a length threshold determination result of the shortest sub-highway section;

[0214] For each shortest sub-highway section in the shortest sub-highway section set, when the length threshold judgment result of the shortest sub-highway section is used to indicate that the section length of the shortest sub-highway section does not meet the sub-highway section length threshold, determine the target shortest sub-highway section corresponding to the shortest sub-highway section; splice the shortest sub-highway section with the target shortest sub-highway section corresponding to the shortest sub-highway section to obtain the spliced ​​shortest sub-highway section corresponding to the shortest sub-highway section; the target shortest sub-highway section is the shortest sub-highway section with the shorter length of the two adjacent shortest sub-highway sections of the shortest sub-highway section;

[0215] Based on all unjoined shortest sub-freeway sections and all joined shortest sub-freeway sections in the shortest sub-freeway section set, a sub-freeway section set including a plurality of sub-freeway sections is determined.

[0216] It can be seen that the device described in the implementation of this optional embodiment can divide the expressway section based on all toll stations, main road ETC gantries and ramp ETC gantries, obtain the shortest sub-expressway section and determine whether the section length of each shortest sub-expressway section meets the conditions, and when the conditions are met, determine the corresponding target shortest sub-expressway section and splice the shortest sub-expressway section with the corresponding target shortest sub-expressway section, and determine the sub-expressway section set after completing all splicing; it is beneficial to improve the accuracy of expressway section division, and then improve the accuracy of determining abnormal sub-expressway sections, thereby improving the accuracy and stability of highway vehicle spatiotemporal path prediction.

[0217] Embodiment 4

[0218] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another device for predicting the spatiotemporal path of a highway vehicle disclosed in an embodiment of the present invention. Figure 5 The highway vehicle spatiotemporal path prediction device shown may include:

[0219] A memory 401 storing executable program codes;

[0220] a processor 402 coupled to the memory 401;

[0221] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the method for predicting the spatiotemporal path of a highway vehicle described in the first embodiment of the present invention or the second embodiment of the present invention.

[0222] Embodiment 5

[0223] An embodiment of the present invention discloses a computer storage medium storing computer instructions. When the computer instructions are called, they are used to execute the steps in the method for predicting the spatiotemporal path of highway vehicles described in the first embodiment of the present invention or the second embodiment of the present invention.

[0224] Embodiment 6

[0225] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the highway vehicle spatiotemporal path prediction method described in embodiment one or embodiment two.

[0226] The system embodiment described above is only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without paying creative labor.

[0227] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0228] Finally, it should be noted that the method and device for predicting the spatiotemporal path of vehicles on highways disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for predicting the spatiotemporal path of vehicles on a highway, characterized in that: The method comprises: Determine the target highway section to be predicted; Dividing the target highway section into highway sections to determine a sub-highway section set including a plurality of sub-highway sections; Obtaining the sub-passing time of all target vehicles passing through the target expressway section within a preset first time range when passing through each of the sub-expressway sections; Calculating the sub-comprehensive passing time of each sub-highway section based on the sub-passing time of all target vehicles in each sub-highway section; Determine an abnormal sub-highway section set including a plurality of abnormal sub-highway sections from the sub-highway section set; the abnormal sub-highway section is the sub-highway section whose corresponding sub-comprehensive passing time is abnormal data; For each of the abnormal sub-highway sections in the abnormal sub-highway section set, determining an abnormal impact factor of the abnormal sub-highway section; Based on all the abnormal influencing factors of each abnormal sub-highway section, a vehicle space-time path prediction operation is performed on the target highway section, and the vehicle space-time path prediction operation is used to predict the space-time path of vehicles passing through the target highway section within a subsequent preset second time range.

2. The method for predicting the spatiotemporal path of a highway vehicle according to claim 1, characterized in that: Before dividing the target highway section into highway sections and determining a sub-highway section set including a plurality of sub-highway sections, the method further includes: Obtaining the passing time of all target vehicles passing through the target expressway section within a preset first time range; Calculate the comprehensive passing time of the target expressway section based on the passing time of all the target vehicles; Determine whether the comprehensive passing time meets a preset passing time threshold, and obtain a comprehensive passing time determination result; When the comprehensive passing time judgment result is used to indicate that the comprehensive passing time does not meet the passing time threshold, it triggers the execution of the operation of dividing the target expressway section into expressway sections and determining a sub-expressway section set including multiple sub-expressway sections.

3. The method for predicting the spatiotemporal path of a highway vehicle according to claim 2, characterized in that: All the target vehicles have corresponding vehicle types; And, the comprehensive passing time of the target expressway section is calculated based on the passing time of all the target vehicles, including: Based on each of the vehicle types and the number of the target vehicles under the vehicle type, determining a passing time weight of the vehicle type; Based on the passing time of each target vehicle under each vehicle type, calculating the average passing time of all target vehicles under the vehicle type; Based on the passing time weight of each vehicle type and the average passing time of all the target vehicles of the vehicle type, the comprehensive passing time of the target expressway section is calculated.

4. The method for predicting the spatiotemporal path of a highway vehicle according to claim 1, characterized in that: The step of determining, for each of the abnormal sub-highway sections in the set of abnormal sub-highway sections, an abnormal impact factor of the abnormal sub-highway section comprises: Determine all target intelligent assisted driving vehicles from all the target vehicles; For each of the target intelligent assisted driving vehicles, obtaining a first auxiliary data set, a second auxiliary data set, and a third auxiliary data set of the target intelligent assisted driving vehicle; The first auxiliary data set includes at least one of forward collision warning data, rear collision warning data, blind spot monitoring warning data, lane change warning data, lane departure warning data, forward crossing warning data, and rear crossing warning data; The second auxiliary data set includes at least one of automatic emergency braking data, automatic emergency avoidance data, lane keeping assistance data, lane departure assistance data, lane centering assistance data, intelligent speed limit assistance data, acceleration and deceleration lane change assistance data and automatic overtaking assistance data; The third auxiliary data set includes at least one of the intelligent assisted driving takeover mileage, the intelligent assisted driving takeover ratio, and the number of intelligent assisted driving manual takeovers; For each abnormal sub-highway section in the abnormal sub-highway section set, the abnormal impact factor of the abnormal sub-highway section is determined based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set of all the target intelligent assisted driving vehicles in the abnormal sub-highway section.

5. The method for predicting the spatiotemporal path of a highway vehicle as claimed in claim 4, characterized in that: For each abnormal sub-highway section in the abnormal sub-highway section set, determining the abnormal impact factor of the abnormal sub-highway section based on the first auxiliary data set, the second auxiliary data set, and the third auxiliary data set of all the target intelligent assisted driving vehicles in the abnormal sub-highway section, comprises: For each abnormal sub-highway section in the abnormal sub-highway section set, determining an edge sub-highway section server corresponding thereto, wherein the edge sub-highway section server is used for data processing of the abnormal sub-highway section; For each abnormal sub-highway section in the abnormal sub-highway section set, based on the first auxiliary data set, the second auxiliary data set and the third auxiliary data set, a first association analysis and a second association analysis are performed on the abnormal sub-highway section on the edge sub-highway section server corresponding to the abnormal sub-highway section to obtain a first association analysis result and a second association analysis result; the first association analysis is used to analyze the collaborative association between the auxiliary data within each set of the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway; the second association analysis is used to analyze the modal association between the first auxiliary data set, the second auxiliary data set and the third auxiliary data set based on the perspective of the abnormal condition of the highway; For each of the abnormal sub-highway sections in the abnormal sub-highway section set, an abnormal impact factor of the abnormal sub-highway section is determined based on the first association analysis result and the second association analysis result of the abnormal sub-highway section.

6. The method for predicting the spatiotemporal path of a highway vehicle as claimed in claim 3, characterized in that: The performing of a vehicle spatiotemporal path prediction operation on the target highway section based on all the abnormal impact factors of each abnormal sub-highway section includes: Based on the target expressway section, generating an initial full-course road model of the target expressway section; Based on all the abnormal influencing factors of each abnormal sub-highway section, generating an abnormal sub-road model of the abnormal sub-highway section; Based on the abnormal sub-road models of all the abnormal sub-highway sections, the initial full-course road model is modified to obtain a modified full-course road model of the target high-speed section; Based on all the abnormal influencing factors of all the abnormal sub-highway sections, the modified full-course road model and all the vehicle types, a predicted space-time path corresponding to each vehicle type in the target high-speed section is generated.

7. The method for predicting the spatiotemporal path of a highway vehicle according to any one of claims 1 to 6, characterized in that: The step of dividing the target highway section into highway sections and determining a sub-highway section set including a plurality of sub-highway sections includes: Determine all toll stations, main road ETC gantries and ramp ETC gantries on the target expressway section; Divide the target expressway section into expressway sections based on all the toll stations, the main road ETC gantries and the ramp ETC gantries to obtain a shortest sub-expressway section set including multiple shortest sub-expressway sections; For each of the shortest sub-highway sections in the shortest sub-highway section set, determining whether the section length of the shortest sub-highway section meets a preset sub-highway section length threshold, and obtaining a length threshold determination result of the shortest sub-highway section; For each of the shortest sub-highway sections in the shortest sub-highway section set, when the length threshold judgment result of the shortest sub-highway section is used to indicate that the section length of the shortest sub-highway section does not meet the sub-highway section length threshold, determine the target shortest sub-highway section corresponding to the shortest sub-highway section; splice the shortest sub-highway section with the target shortest sub-highway section corresponding to the shortest sub-highway section to obtain the spliced ​​shortest sub-highway section corresponding to the shortest sub-highway section; the target shortest sub-highway section is the shortest sub-highway section with the shorter length of the two shortest sub-highway sections adjacent to the shortest sub-highway section; Based on all the unjoined shortest sub-freeway sections in the shortest sub-freeway section set and all the joined shortest sub-freeway sections, a sub-freeway section set including a plurality of sub-freeway sections is determined.

8. A device for predicting the spatiotemporal path of vehicles on a highway, characterized in that: The device comprises: A determination module, used to determine a target highway section to be predicted; A division module, used for dividing the target highway section into highway sections and determining a sub-highway section set including a plurality of sub-highway sections; An acquisition module, used for acquiring the sub-passing time of all target vehicles passing through the target expressway section within a preset first time range when passing through each sub-expressway section; A calculation module, used for calculating the sub-comprehensive passing time of each sub-highway section based on the sub-passing time of all target vehicles in each sub-highway section; The determination module is further used to determine an abnormal sub-highway section set including a plurality of abnormal sub-highway sections from the sub-highway section set; the abnormal sub-highway section is the sub-highway section whose corresponding sub-comprehensive passing time is abnormal data; for each abnormal sub-highway section in the abnormal sub-highway section set, determine the abnormal impact factor of the abnormal sub-highway section; A prediction module is used to perform a vehicle space-time path prediction operation on the target highway section based on all the abnormal influencing factors of each abnormal sub-highway section, and the vehicle space-time path prediction operation is used to predict the space-time path of the vehicle passing through the target highway section within a subsequent preset second time range.

9. A device for predicting the spatiotemporal path of vehicles on a highway, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the steps in the highway vehicle spatiotemporal path prediction method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, the steps in the highway vehicle spatiotemporal path prediction method according to any one of claims 1 to 7 are executed.

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