A vehicle trajectory completion system

Abstract

The present application relates to the field of data processing, and particularly relates to a vehicle driving track completion system, when a computer program is executed by a processor, the following steps are implemented: when a target vehicle actually arrives at a first intersection A i γ0 and a predicted first intersection A i γ1 are consistent, a candidate predicted driving route set corresponding to the predicted first intersection A is selected from a predicted driving route set as a target driving track of a γth segment, and the consistency of A i γ0 and A i γ1 is inconsistent, a drivable path set, a corresponding drivable path length set and a corresponding driving possibility degree set from a γ-1th first intersection to a γth first intersection are obtained according to the drivable path set and the path length set, and then the target driving track of the γth segment is determined, and the consistency of A i γ0 and A i γ1 is judged, and the vehicle driving track is completed in combination with the predicted driving route and the actual driving route, so that the completeness and accuracy of the vehicle driving track are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a vehicle trajectory completion system. Background Technology

[0002] With the widespread use of car driving in daily life, road congestion is becoming increasingly serious. Maintaining smooth traffic flow is of great significance to the economy, environment, society, and individuals. Therefore, predicting the driving routes of vehicles on the road can provide real-time information on traffic conditions, which plays an important role in optimizing traffic organization, signal control, and traffic signal coordination.

[0003] For intersections with checkpoints, vehicle traffic data can be collected through the checkpoints to obtain vehicle passage information at the intersection. However, when vehicles pass through intersections without checkpoints, it is impossible to obtain the corresponding vehicle passage data, and therefore it is impossible to directly know the vehicle's passage time, passage status, turning, and other information at the intersection. As a result, it is also impossible to accurately obtain the traffic status of the intersection, and further optimize the signal control and traffic organization of the intersection to alleviate traffic congestion, optimize traffic, and improve vehicle passage efficiency.

[0004] Therefore, how to complete the vehicle's full driving trajectory based on existing checkpoint data, while improving the accuracy of the predicted driving trajectory, has become an urgent problem to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention provides a vehicle trajectory completion system, comprising: a first intersection set A = {A1, A2, ..., A...} i , ..., A m The second intersection set B = {B1, B2, ..., B} j , ..., B n The checkpoint set corresponding to A is K = {K1, K2, ..., K}. i , ..., K m The set of drivable paths C = {C1, C2, ..., C} i , ..., C m}, Path length set D = {D1, D2, ..., D} i , ..., D m The predicted driving route set of the target vehicle is MX = {MX1, MX2, ..., MX...} i , ..., MX m}, processor and memory storing computer programs;

[0006] Among them, A i This refers to the i-th first intersection with a checkpoint, B. jThis refers to the j-th second intersection without a checkpoint, K. i Refers to A i The corresponding bayonet, C i Refers to A i The corresponding set of drivable paths, C i ={C i1 C i2 , ..., C i(i -1), C i(i+1) , ..., C im}, C i(i+1) Refers to A i To A i+1 The set of drivable paths, C i(i+1) ={C i(i+1)1 C i(i+1)2 , ..., C i(i+1)ki , ..., C i(i+1)si}, C i(i+1)ki Refers to A i To A i+1 The ki-th drivable path, D i It refers to C i The corresponding set of path lengths, D i ={D i1 D i2 , ..., D i(i-1) D i(i+1) , ..., D im}, D i(i+1) It refers to C i(i+1) The corresponding set of path lengths, D i(i+1) ={D i(i+1)1 D i(i+1)2 , ..., D i(i+1)ki , ..., D i(i+1)si}, D i(i+1)ki It refers to C i(i+1)ki The corresponding path length, MX i This refers to the target vehicle using A i For the set of predicted driving routes corresponding to the starting point, MX i ={MX i1 MX i2 , ..., MX ix , ..., MX iyi}, MX ix This refers to the predicted target vehicle using A i Let x be the x-th intersection encountered while traveling from the starting point, i = 1, 2, ..., m, where m represents the total number of the first intersection; j = 1, 2, ..., n, where n represents the total number of the second intersection; and ki = 1, 2, ..., si, where si represents the intersection A. i To A i+1 The total number of drivable paths, x = 1, 2, ..., yi, where yi refers to MX.i The total number of intersections passed through during the journey;

[0007] When a computer program is executed by a processor, the following steps are performed:

[0008] S1, based on K, obtain the actual target vehicle and A i The γth intersection A passed by the starting point i γ0 A i γ0 The corresponding average driving speed V i γ and actual driving time T i γ , where A i γ0 ∈A, γ=1,2,……,δ, where δ refers to the target vehicle starting from A i Let δ represent the total number of the first intersections passed by the starting point, where δ≤m.

[0009] S2, according to MX i Obtain the predicted target vehicle with A i The γth intersection A passed by the starting point i γ1 , where A i γ1 ∈(MX i ∩A).

[0010] S3, if A i γ1 =A i γ0 From MX i Select A from i γ1 The corresponding candidate predicted driving route set MX i γ1 ={MX i1 γ MX i2 γ , ..., MX iαγ γ , ..., MX iβγ γ A i γ1 The target vehicle's γ-th segment of the target trajectory GJ γ , among which, MX i γ1 ∈C,MX iαγ γ ∈B,MX iαγ γ This refers to the target vehicle passing through A. i(γ-1)1 Afterwards, and while driving through A i γ1 The αγth second intersection previously passed, where αγ = 1, 2, ..., βγ, and βγ refers to MX i γ1 The total number of intersections in the middle.

[0011] S4, if A i 0 ≠A i 1 A is obtained from C. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, C γ0 2, ..., C γ0 uγ , ..., C γ0 hγ}, where C γ0 ∈C, C γ0 uγ Refers to A i (γ-1)1 To A i γ1 The uγth drivable path, uγ = 1, 2, ..., hγ, where hγ refers to C γ0 The total number of drivable paths.

[0012] S5, obtain C based on D. γ0 The corresponding set of drivable path lengths D γ0 ={D γ0 1, D γ0 2, ..., D γ0 uγ , ..., D γ0 hγ}, where D γ0 ∈D, D γ0 uγ It refers to C γ0 uγ The corresponding path length.

[0013] S6, according to V i γ T i γ and D γ0 , obtain C γ0 The corresponding driving probability set E γ0 ={E γ0 1, E γ0 2, ..., E γ0 uγ, ..., E γ0 hγ}, where E γ0 uγ It refers to C γ0 uγ The corresponding driving probability level, E γ0 uγ The following conditions must be met:

[0014] E γ0 uγ =e^(-|(D) γ0 uγ / V i γ )-(T i γ -T i γ-1 )|).

[0015] S7, max{E γ0 The corresponding drivable path is determined as the γth segment of the target vehicle's target trajectory GJ. γ .

[0016] S8, iterate through γ = 1, 2, ..., δ to obtain the target vehicle's target trajectory GJ = {GJ1, GJ2, ..., GJ}. γ ..., GJ δ}

[0017] This invention has at least the following beneficial effects: Based on K, the actual target vehicle is obtained using A. i The γth intersection A passed by the starting point i γ0 A i γ0 The corresponding average driving speed V i γ and actual driving time T i γ According to MX i Obtain the predicted target vehicle with A i The γth intersection A passed by the starting point i γ1 If A i γ1 =A i γ0 From MX i Select A from i γ1 The corresponding candidate predicted driving route set MX i γ1 ={MX i1 γ MX i2γ , ..., MX iαγ γ , ..., MX iβγ γ A i γ1 The target vehicle's γ-th segment of the target trajectory GJ γ If A i 0 ≠A i 1 A is obtained from C. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, C γ0 2, ..., C γ0 uγ , ..., C γ0 hγ}, obtain C based on D γ0 The corresponding set of drivable path lengths D γ0 ={D γ0 1, D γ0 2, ..., D γ0 uγ , ..., D γ0 hγ According to V i γ T i γ and D γ0 , obtain C γ0 The corresponding driving probability set E γ0 ={E γ0 1, E γ0 2, ..., E γ0 uγ , ..., E γ0 hγ}, max{E γ0 The corresponding drivable path is determined as the γth segment of the target vehicle's target trajectory GJ. γ Iterate through γ = 1, 2, ..., δ to obtain the target vehicle's target trajectory GJ = {GJ1, GJ2, ..., GJ}. γ ..., GJ δ}, through A i γ1 =A i γ0 Select MX i γ1 As GJ γ In A i 0 ≠Ai 1 Time based on target vehicle with T i γ In D γ0 uγ The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i (γ-1)1 Drive to A i γ0 Actual driving time T i γ-1 -T i γ The difference is used to characterize C. γ0 uγ Corresponding driving probability E γ0 uγ , and thus max{E γ0 The corresponding drivable path is determined to be GJ. γ By combining the predicted driving route with the actual driving route to complete the vehicle's driving trajectory, the completeness and accuracy of the vehicle's driving trajectory are improved. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the computer program executing a vehicle trajectory completion system provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0022] like Figure 1 As shown, this embodiment provides a vehicle trajectory completion system, including: a first intersection set A = {A1, A2, ..., A...} i , ..., A m The second intersection set B = {B1, B2, ..., B} j , ..., B n The checkpoint set corresponding to A is K = {K1, K2, ..., K}. i , ..., K m The set of drivable paths C = {C1, C2, ..., C} i , ..., C m}, Path length set D = {D1, D2, ..., D} i , ..., D m The predicted driving route set of the target vehicle is MX = {MX1, MX2, ..., MX...} i , ..., MX m}, processor and memory storing computer programs;

[0023] Among them, A i This refers to the i-th first intersection with a checkpoint, B. j This refers to the j-th second intersection without a checkpoint, K. i Refers to A i The corresponding bayonet, C i Refers to A i The corresponding set of drivable paths, C i ={C i1 C i2 , ..., C i(i-1) C i(i+1) , ..., C im}, C i(i+1) Refers to A i To A i+1 The set of drivable paths, C i(i+1) ={Ci(i+1)1 C i(i+1)2 , ..., C i(i+1)ki , ..., C i(i+1)si}, C i(i+1)ki Refers to A i To A i+1 The ki-th drivable path, D i It refers to C i The corresponding set of path lengths, D i ={D i1 D i2 , ..., D i(i-1) D i(i+1) , ..., D im}, D i(i+1) It refers to C i(i+1) The corresponding set of path lengths, D i(i+1) ={D i(i+1)1 D i(i+1)2 , ..., D i(i+1)ki , ..., D i(i+1)si}, D i(i+1)ki It refers to C i(i+1)ki The corresponding path length, MX i This refers to the target vehicle using A i For the set of predicted driving routes corresponding to the starting point, MX i ={MX i1 MX i2 , ..., MX ix , ..., MX iyi}, MX ix This refers to the predicted target vehicle using A i Let x be the x-th intersection encountered while traveling from the starting point, i = 1, 2, ..., m, where m represents the total number of the first intersection; j = 1, 2, ..., n, where n represents the total number of the second intersection; and ki = 1, 2, ..., si, where si represents the intersection A. i To A i+1 The total number of drivable paths, x = 1, 2, ..., yi, where yi refers to MX. i The total number of intersections passed through during the journey;

[0024] When a computer program is executed by a processor, the following steps are performed:

[0025] S1, based on K, obtain the actual target vehicle and A i The γth intersection A passed by the starting point i γ0 A i γ0 The corresponding average driving speed V i γ and actual driving time T i γ, where A i γ0 ∈A, γ=1,2,……,δ, where δ refers to the target vehicle starting from A i Let δ represent the total number of the first intersections passed by the starting point, where δ≤m.

[0026] Among them, checkpoints can refer to monitoring and capture equipment, which can use advanced optoelectronic, computer, image processing, pattern recognition, data access and other technologies to continuously capture, record and process the front feature images of vehicles, panoramic images of vehicles and real-time video streams of the road surface. The first intersection refers to the intersection with a checkpoint, and the second intersection refers to the intersection without a checkpoint.

[0027] C i(i+1) Refers to A i To A i+1 The set of drivable paths, C i(i+1) ={C i(i+1)1 C i(i+1)2 , ..., C i(i+1)ki , ..., C i(i+1)si}, C i(i+1)ki Refers to A i To A i+1 The ki-th drivable path, which can be followed by C i(i+1)ki From A i Depart and drive to A i+1 C i(i+1)ki This includes the corresponding first and second intersections.

[0028] The predicted driving route set MX of the target vehicle can be obtained based on a preset trajectory prediction model, where MX ix This refers to the predicted target vehicle using A i MX is the xth intersection passed by the starting point. ix It can belong to the first intersection or the second intersection. Those skilled in the art will know that any existing trajectory prediction model falls within the protection scope of this invention, and will not be elaborated further here.

[0029] Due to the influence of the prediction accuracy of MX, the prediction results of some intersections in MX may be incorrect. Therefore, it is necessary to determine whether the target vehicle has passed through each intersection in MX, so as to retain the intersections with a high probability of the target vehicle passing through as part of the target vehicle's driving trajectory. Combined with the first intersection passed by the target vehicle in actual driving, the driving trajectory of part of the vehicle is completed to obtain the final vehicle driving trajectory.

[0030] Specifically, during the actual driving process of the target vehicle, the actual target vehicle information is obtained based on K and A. i The γth intersection A passed by the starting point iγ0 A i γ0 The corresponding average driving speed V i γ and actual driving time T i γ , among which, T i γ According to the target vehicle, via K i and through A i γ0 The time difference at the corresponding checkpoint is obtained, V i γ Based on the target vehicle from A i Drive to A i γ0 The ratio of distance to time difference is obtained.

[0031] A i (γ-1)0 This refers to the actual target vehicle using A i If the journey starts from point γ-1 and passes through the first intersection, then according to A... i γ0 The corresponding checkpoint can determine the target vehicle's travel trajectory, including from A. i (γ-1)1 Drive to A i γ0 However, it is impossible to determine from A i (γ-1)1 Drive to A i γ0 The specific second intersection passed through during the process; therefore, this embodiment can be combined with A. i γ0 V i γ T i γ And MX to determine from A i (γ-1)0 Drive to A i γ0 Based on the second intersection specifically passed through during the process, the vehicle's trajectory was completed, including A. i 00 =A i .

[0032] In this embodiment, the actual target vehicle is obtained based on K and A. i The γth intersection A passed by the starting point i γ0 V i γ and T i γ As a combination of MX to determine from A i(γ-1)0 Drive to A i γ0 Based on the specific second intersection passed through during the process, the integrity and accuracy of the vehicle's driving trajectory were improved.

[0033] S2, according to MX i Obtain the predicted target vehicle with A i The γth intersection A passed by the starting point i γ1 , where A i γ1 ∈(MX i ∩A).

[0034] Among them, MX ix It can belong to the first intersection or the second intersection, and is related to A. i γ0 Correspondingly, this embodiment is based on MX i Obtain the predicted target vehicle with A i The γth intersection A passed by the starting point i γ1 If A i γ1 =A i γ0 Then, in MX, the target vehicle is driving past A. i (γ-1)1 Afterwards, and while driving through A i γ1 All the second intersections passed previously can be used to confirm that the target vehicle started from A. i (γ-1)1 Drive to A i γ1 The vehicle's trajectory; if A i 0 ≠A i 1 Therefore, it can be assumed that the target vehicle in MX is traveling through A. i (γ-1)1 Afterwards, and while driving through A i γ1 All the second intersections passed previously were not part of the target vehicle's journey from A. i (γ-1)1 Drive to A i γ1 The vehicle's trajectory needs to be determined based on the information from A. i (γ-1)1 To A i γ1 The likelihood of a drivable trajectory is used to determine the target vehicle's trajectory from point A. i (γ-1)1 Drive to A iγ1 This improves the integrity and accuracy of vehicle trajectory tracking.

[0035] This embodiment is based on MX. i Obtain the predicted target vehicle with A i The γth intersection A passed by the starting point i γ1 Based on A i γ1 and A i γ0 The consistency judgment result determines A i (γ-1)1 To A i γ1 The drivable trajectory takes into account the target vehicle's travel through A in MX. i (γ-1)1 Afterwards, and while driving through A i γ1 The accuracy of predictions for all previously passed second intersections improves the completeness and accuracy of vehicle trajectories.

[0036] S3, if A i γ1 =A i γ0 From MX i Select A from i γ1 The corresponding candidate predicted driving route set MX i γ1 ={MX i1 γ MX i2 γ , ..., MX iαγ γ , ..., MX iβγ γ A i γ1 The target vehicle's γ-th segment of the target trajectory GJ γ , among which, MX i γ1 ∈C,MX iαγ γ ∈B,MX iαγ γ This refers to the target vehicle passing through A. i (γ-1)1 Afterwards, and while driving through A i γ1 The αγth second intersection previously passed, where αγ = 1, 2, ..., βγ, and βγ refers to MX i γ1 The total number of intersections in the middle.

[0037] Where, if A i γ1 =A i γ0 Then from MX i Select A from i γ1 The corresponding candidate predicted driving route set MX i γ1 ={MX i1 γ MX i2 γ , ..., MX iαγ γ , ..., MX iβγ γ A i γ1 The target vehicle's γ-th segment of the target trajectory GJ γ , among which, MX iαγ γ This refers to the target vehicle passing through A. i (γ-1)1 Afterwards, and while driving through A i γ1 The second intersection we passed earlier.

[0038] This embodiment is in A i γ1 =A i γ0 MX i γ1 The target vehicle's γ-segment driving trajectory GJ γ This improves the integrity of the vehicle's driving trajectory.

[0039] In one specific embodiment, S3 specifically includes the following steps:

[0040] S31, obtain MX based on D. i γ1 The corresponding path length D i γ1 , where D i γ1 ∈D;

[0041] S32, according to D i γ1 V i γ and T i γ , obtain MX i γ1 The corresponding prediction reliability F i γ1, of which F i γ1 The following conditions must be met:

[0042] F i γ1 =e^(-|1-((T) i γ -T i γ-1 V i γ / D i γ1 )|), T i γ-1 This refers to the actual target vehicle using A i The starting point is the (γ-1)th intersection A that the vehicle passes through. i (γ-1)0 The corresponding actual driving time;

[0043] S33, if F i γ1 ≥F0, MX i γ1 The target vehicle's γ-th segment of the target trajectory GJ was determined. γ Where F0 refers to the preset prediction reliability threshold.

[0044] In this embodiment, the target vehicle can be selected based on V. i γ In D i γ1 The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i (γ-1)1 Drive to A i γ0 Actual driving time T i γ-1 -T i γ Comparison to characterize MX i γ1 The corresponding prediction reliability F i γ1 Correspondingly, the target vehicle uses V i γ In D i γ1 The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i (γ-1)1 Drive to A i γ0 Actual driving time T i γ-1 -T i γ The greater the difference, the better for MX iγ1 The lower the reliability of the corresponding prediction, therefore, F i γ1 =e^(-|1-((T) i γ -T i γ-1 V i γ / D i γ1 )|).

[0045] Therefore, if F i γ1 ≥F0, MX i γ1 The target vehicle's γ-th segment of the target trajectory GJ was determined. γ F0 refers to the preset prediction reliability threshold. The specific value of F0 can be set according to the actual situation. For example, in this embodiment, F0 is set to 0.8.

[0046] This embodiment further considers the target vehicle as V i γ In D i γ1 The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i (γ-1)1 Drive to A i γ0 Actual driving time T i γ-1 -T i γ The difference between them is used to characterize MX i γ1 The corresponding prediction reliability F i γ and in F i γ When ≥F0, MX i γ1 The target vehicle's γ-th segment of the target trajectory GJ was determined. γ It improved GJ γ This improves the accuracy of the target vehicle's trajectory.

[0047] S4, if A i 0 ≠A i 1 A is obtained from C. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, Cγ0 2, ..., C γ0 uγ , ..., C γ0 hγ}, where C γ0 ∈C, C γ0 uγ Refers to A i (γ-1)1 To A i γ1 The uγth drivable path, uγ = 1, 2, ..., hγ, where hγ refers to C γ0 The total number of drivable paths.

[0048] Where, if A i 0 ≠A i 1 Therefore, it can be assumed that the target vehicle in MX is traveling through A. i (γ-1)1 Afterwards, and while driving through A i γ1 All the second intersections passed previously were not part of the target vehicle's journey from A. i (γ-1)1 Drive to A i γ1 The vehicle's driving trajectory is obtained from C in this embodiment. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, C γ0 2, ..., C γ0 uγ , ..., C γ0 hγ}, to be from C γ0 The most likely drivable path is selected from A to complete the route for the target vehicle. i (γ-1)1 Drive to A i γ1 To improve the completeness and accuracy of the vehicle's driving trajectory.

[0049] In one specific embodiment, S4 specifically includes the following steps:

[0050] S41, if A i 0 ≠A i 1 , or F i γ1 <F0, then MX i γ1This is determined to be a non-target driving trajectory;

[0051] S42, obtain A based on C. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, C γ0 2, ..., C γ0 uγ , ..., C γ0 hγ}

[0052] Where, if A i 0 ≠A i 1 Then, in MX, the target vehicle is driving past A. i (γ-1)1 Afterwards, and while driving through A i γ1 All the second intersections passed previously were not part of the target vehicle's journey from A. i (γ-1)1 Drive to A i γ1 The vehicle's trajectory, if F i γ1 When <F0, MX i γ1 The corresponding prediction reliability is low; therefore, MX i γ1 The trajectory was determined to be a non-target driving trajectory, and A was obtained based on C. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, C γ0 2, ..., C γ0 uγ , ..., C γ0 hγ}, to complete the target vehicle from A i (γ-1)1 Drive to A i γ1 To improve the completeness and accuracy of the vehicle's driving trajectory.

[0053] This embodiment is in A i 0 ≠A i 1 Or F i γ1 <F0 indicates that A is obtained from C. i(γ-1)1 To A i γ1 The set of drivable paths C γ0 As a supplement to the target vehicle from A i (γ-1)1 Drive to A i γ1 Based on the vehicle's driving trajectory, it improves the completeness and accuracy of the target vehicle's driving trajectory.

[0054] S5, obtain C based on D. γ0 The corresponding set of drivable path lengths D γ0 ={D γ0 1, D γ0 2, ..., D γ0 uγ , ..., D γ0 hγ}, where D γ0 ∈D, D γ0 uγ It refers to C γ0 uγ The corresponding path length.

[0055] Among them, C is obtained from D. γ0 The corresponding set of drivable path lengths D γ0 ={D γ0 1, D γ0 2, ..., D γ0 uγ , ..., D γ0 hγ}, to characterize C γ0 The data provides a basis for the likelihood of driving along drivable paths.

[0056] S6, according to V i γ T i γ and D γ0 , obtain C γ0 The corresponding driving probability set E γ0 ={E γ0 1, E γ0 2, ..., E γ0 uγ , ..., E γ0 hγ}, where E γ0 uγ It refers to C γ0 uγ The corresponding driving probability level, E γ0 uγ The following conditions must be met:

[0057] Eγ0 uγ =e^(-|(D) γ0 uγ / V i γ )-(T i γ -T i γ-1 )|).

[0058] Among them, the target vehicle is T i γ In D γ0 uγ The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i (γ-1)1 Drive to A i γ0 Actual driving time T i γ-1 -T i γ Comparison to characterize C γ0 uγ Corresponding driving probability E γ0 uγ Correspondingly, the target vehicle is in T... i γ In D γ0 uγ The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i (γ-1)1 Drive to A i γ0 Actual driving time T i γ-1 -T i γ The greater the difference, the more E γ0 uγ The smaller, therefore, E γ0 uγ =e^(-|(D) γ0 uγ / V i γ )-(T i γ -T i γ-1 )|).

[0059] This embodiment is based on the target vehicle in T i γ In D γ0 uγ The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i (γ-1)1 Drive to A iγ0 Actual driving time T i γ-1 -T i γ The difference is used to characterize C. γ0 uγ Corresponding driving probability E γ0 uγ As a supplement to the target vehicle from A i (γ-1)1 Drive to A i γ1 Based on the vehicle's driving trajectory, it improves the completeness and accuracy of the target vehicle's driving trajectory.

[0060] S7, max{E γ0 The corresponding drivable path is determined as the γth segment of the target vehicle's target trajectory GJ. γ .

[0061] Among them, E γ0 uγ The larger the value, the more it represents C. γ0 uγ As the target vehicle from A i (γ-1)1 Drive to A i γ1 The higher the probability of the vehicle's trajectory, the more likely it is to be determined in this embodiment. γ0 The corresponding drivable path is determined as the γth segment of the target vehicle's target trajectory GJ. γ .

[0062] In one specific embodiment, S8 specifically includes the following steps:

[0063] S81, obtain the target vehicle using GJ based on MX. γ The predicted driving route set MX, starting from the last intersection. η ;

[0064] S82, based on K, obtain the actual target vehicle with A i The γth point traveled from the starting point +1 The first intersection A i (γ+1)0 A i0 (γ+1)0 The corresponding average driving speed V i γ+1 and actual driving time T i γ+1 , where A i (γ+1)0 ∈A;

[0065] S83, according to MX ηObtain the predicted target vehicle with A i The first intersection A, which is the γ+1th intersection encountered by the starting point. i (γ+1)1 ;

[0066] S84, Compare A i (γ+1)0 and A i0 (γ+1)1 Based on the comparison results, the target vehicle's (γ+1)th segment of the target trajectory GJ is obtained. γ + 1.

[0067] Among them, in determining the γth segment of the target vehicle's trajectory GJ γ Subsequently, the target vehicle was further obtained based on MX using GJ. γ The predicted driving route set MX, starting from the last intersection. η And according to MX η Obtain the predicted target vehicle with A i The first intersection A, which is the γ+1th intersection encountered by the starting point. i (γ+1)1 And based on K, the actual target vehicle is obtained using A. i The γth point traveled from the starting point +1 The first intersection A i (γ+1)0 A i0 (γ+1)0 The corresponding average driving speed V i γ+1 and actual driving time T i γ+1 According to A i (γ+1)0 and A i0 (γ+1)1 The comparison results yielded the target vehicle's (γ+1)th segment of its trajectory GJ. γ+1 .

[0068] Correspondingly, if A i (γ+1)1 =A i (γ+1)0 Then, in MX, the target vehicle is driving past A. i γ1 Afterwards, and while driving through A i (γ+1)1 All the second intersections passed previously can be used to confirm that the target vehicle started from A. i γ1 Drive to A i (γ+1)1 The vehicle's trajectory; if A i (γ+1)1 ≠Ai (γ+1)0 Therefore, it can be assumed that the target vehicle in MX is traveling through A. i γ1 Afterwards, and while driving through A i (γ+1)1 All the second intersections passed previously were not part of the target vehicle's journey from A. i γ1 Drive to A i (γ+1)1 The vehicle's trajectory needs to be determined based on the information from A. i γ1 To A i (γ+1)1 The likelihood of a drivable trajectory is used to determine the target vehicle's trajectory from point A. i γ1 Drive to A i (γ+1)1 This improves the integrity and accuracy of vehicle trajectory tracking.

[0069] In one specific embodiment, S84 specifically includes the following steps:

[0070] S841, if A i (γ+1)1 =A i (γ+1)0 From MX i Select A from i (γ+1)1 The corresponding candidate predicted driving route set MX i (γ+1)1 ={MX i1 γ+1 MX i2 γ+1 , ..., MX iα(γ+1) γ+1 , ..., MX iβ(γ+1) γ+1 A i (γ+1)1 The target vehicle's (γ+1)th segment of the target trajectory GJ γ+1 , among which, MX i (γ+1)1 ∈C,MX iα(γ+1) (γ+1) ∈B,MX iα(γ+1) (γ+1) This refers to the target vehicle passing through A. i γ1 Afterwards, and while driving through A i (γ+1)1 The α(γ+1)th second intersection previously passed, α(γ+1) = 1, 2, ..., β(γ+1), where β(γ+1) refers to MX i α(γ+1)1 The total number of intersections in the middle.

[0071] Where, if A i (γ+1)1 =A i (γ+1)0 Then from MX i Select A from i (γ+1)1 The corresponding candidate predicted driving route set MX i (γ+1)1 ={MX i1 γ+1 MX i2 γ+1 , ..., MX iα(γ+1) γ+1 , ..., MX iβ(γ+1) γ+1 A i (γ+1)1 The target vehicle's (γ+1)th segment of the target trajectory GJ γ+1 , among which, MX iα(γ+1) γ+1 This refers to the target vehicle passing through A. i γ1 Afterwards, and while driving through A i (γ+1)1 The α(γ+1)th second intersection that we passed before.

[0072] This embodiment is in A i (γ+1)1 =A i (γ+1)0 MX i (γ+1)1 The target vehicle's γ+1 segment of the target trajectory GJ γ+1 This improves the integrity of the vehicle's driving trajectory.

[0073] In one specific embodiment, S84 specifically includes the following steps:

[0074] S842, if A i (γ+1)1 ≠A i (γ+1)0 A is obtained from C. i γ1 To A i (γ+1)1 The set of drivable paths C (γ+1)0 ={C (γ+1)0 1, C (γ+1)0 2, ..., C (γ+1)0 u(γ+1) , ..., C (γ+1)0 h(γ+1)}, where C (γ+1)0∈C, C (γ+1)0 u(γ+1) Refers to A i γ1 To A i (γ+1)1 The u(γ+1)th drivable path, u(γ+1) = 1, 2, ..., h(γ+1), where h(γ+1) refers to C. (γ+1)0 The total number of drivable paths in the system;

[0075] S843, obtain C based on D. (γ+1)0 The corresponding set of drivable path lengths D (γ+1)0 ={D (γ+1)0 1, D (γ+1)0 2, ..., D (γ+1)0 uγ , ..., D (γ+1)0 h(γ+1)}, where D (γ+1)0 ∈D, D (γ+1)0 u(γ+1) It refers to C (γ+1)0 u(γ+1) The corresponding path length;

[0076] S844, according to V i γ+1 T i γ+1 and D (γ+1)0 , obtain C (γ+1)0 The corresponding driving probability set E (γ+1)0 ={E (γ+1)0 1, E (γ+1)0 2, ..., E (γ+1)0 u(γ+1) , ..., E (γ+1)0 h(γ+1)}, where E (γ+1)0 u(γ+1) It refers to C (γ+1)0 u(γ+1) The corresponding driving probability level, E (γ+1)0 u(γ+1) The following conditions must be met:

[0077] E (γ+1)0 u(γ+1) =e^(-|(D) (γ+1)0 u(γ+1) / V i γ+1 )-(T i γ-1 -T i γ )|);

[0078] S845, max{E (γ+1)0The corresponding drivable path is determined as the (γ+1)th segment of the target vehicle's target trajectory GJ. γ+1 .

[0079] Where, if A i (γ+1)1 ≠A i (γ+1)0 Therefore, it can be assumed that the target vehicle in MX is traveling through A. i γ1 Afterwards, and while driving through A i (γ+1)1 All the second intersections passed previously were not part of the target vehicle's journey from A. i γ1 Drive to A i (γ+1)1 The vehicle's driving trajectory is obtained from C in this embodiment. i γ1 To A i (γ+1)1 The set of drivable paths C (γ+1)0 And obtain C based on D. (γ+1)0 The corresponding set of drivable path lengths D (γ+1)0 Then according to V i γ+1 T i γ+1 and D (γ+1)0 For the target vehicle, T i γ+1 In D (γ+1)0 u(γ+1) The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i γ1 Drive to A i (γ+1)1 Actual driving time T i γ -T i γ+1 Comparison to characterize C (γ+1)0 u(γ+1) Corresponding driving probability E (γ+1)0 u(γ+1) .

[0080] Correspondingly, the target vehicle is T i γ+1 In D (γ+1)0 u(γ+1) The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i γ1 Drive to A i (γ+1)1 Actual driving time T i γ -T iγ+1 The greater the difference, the more E γ0 uγ The smaller, therefore, E (γ+1)0 u(γ+1) =e^(-|(D) (γ+1)0 u(γ+1) / V i γ+1 )-(T i γ-1 -T i γ )|).

[0081] E (γ+1)0 u(γ+1) The larger the value, the more it represents C. (γ+1)0 u(γ+1) As the target vehicle from A i γ1 Drive to A i (γ+1)1 The higher the probability of the vehicle's trajectory, the more likely it is to be determined in this embodiment. (γ+1)0 The corresponding drivable path is determined as the (γ+1)th segment of the target vehicle's target trajectory GJ. γ+1 .

[0082] This embodiment is based on the target vehicle in T i γ+1 In D (γ+1)0 u(γ+1) The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i γ1 Drive to A i (γ+1)0 Actual driving time T i γ -T i γ+1 The difference is used to characterize C. (γ+1)0 u(γ+1) Corresponding driving probability E (γ+1)0 u(γ+1) As a supplement to the target vehicle from A i γ1 Drive to A i (γ+1)1 Based on the vehicle's driving trajectory, it improves the completeness and accuracy of the target vehicle's driving trajectory.

[0083] In one specific embodiment, S841 specifically includes the following steps:

[0084] S8411, obtain MX based on D i (γ+1)1 The corresponding path length D i (γ+1)1 , where D i(γ+1)1 ∈D;

[0085] S8412, according to D i (γ+1)1 V i γ+1 and T i γ+1 , obtain MX i (γ+1)1 The corresponding prediction reliability F i (γ+1)1 , of which F i (γ+1)1 The following conditions must be met:

[0086] F i (γ+1)1 =e^-(1 / |1-((T) i γ-1 -T i γ V i γ+1 / D i (γ+1)1 )|);

[0087] S8413, if F i (γ+1)1 ≥F0, MX i (γ+1)1 The target driving trajectory GJ of segment γ+1 is determined to be the target vehicle. γ+1 Where F0 refers to the preset prediction reliability threshold.

[0088] In this embodiment, the target vehicle can be selected based on V. i γ+1 In D i (γ+1)1 The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i γ1 Drive to A i (γ+1)0 Actual driving time T i γ -T i γ+1 Comparison to characterize MX i (γ+1)1 The corresponding prediction reliability F i (γ+1)1 Correspondingly, the target vehicle uses V i γ+1 In D i (γ+1)1 The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i γ1 Drive to Ai (γ+1)0 Actual driving time T i γ -T i γ+1 The greater the difference, the better for MX i (γ+1)1 The lower the reliability of the corresponding prediction, therefore, F i (γ+1)1 =e^-(1 / |1-((T) i γ-1 -T i γ V i γ+1 / D i (γ+1)1 )|).

[0089] Therefore, if F i (γ+1)1 ≥F0, MX i (γ+1)1 The target driving trajectory GJ of segment γ+1 is determined to be the target vehicle. γ+1 .

[0090] This embodiment further considers the target vehicle as V i γ+1 In D i (γ+1)1 The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i γ1 Drive to A i (γ+1)0 Actual driving time T i γ -T i γ+1 The difference between them is used to characterize MX i (γ+1)1 The corresponding prediction reliability F i (γ+1)1 and in F i (γ+1)1 When ≥F0, MX i (γ+1)1 The target driving trajectory GJ of segment γ+1 is determined to be the target vehicle. γ+1 It improved GJ γ+1 This improves the accuracy of the target vehicle's trajectory.

[0091] In one specific embodiment, S842 specifically includes the following steps:

[0092] S8421, if A i (γ+1)1 ≠A i (γ+1)0, or F i (γ+1)1 <F0, then MX i (γ+1)1 This is determined to be a non-target driving trajectory;

[0093] S8422, obtain A based on C. i γ1 To A i (γ+1)1 The set of drivable paths C (γ+1)0 ={C (γ+1)0 1, C (γ+1)0 2, ..., C (γ+1)0 u(γ+1) , ..., C (γ+1)0 h(γ+1)}

[0094] Where, if A i (γ+1)1 ≠A i (γ+1)0 Then, in MX, the target vehicle is driving past A. i γ1 Afterwards, and while driving through A i (γ+1)1 All the second intersections passed previously were not part of the target vehicle's journey from A. i γ1 Drive to A i (γ+1)0 The vehicle's trajectory, if F i (γ+1)1 When <F0, MX i (γ+1)1 The corresponding prediction reliability is low; therefore, MX i (γ+1)1 The trajectory was determined to be a non-target driving trajectory, and A was obtained based on C. i γ1 To A i (γ+1)1 The set of drivable paths C (γ+1)0 ={C (γ+1)0 1, C (γ+1)0 2, ..., C (γ+1)0 u(γ+1) , ..., C (γ+1)0 h(γ+1)}, to complete the target vehicle from A i γ1 To A i (γ+1)1 To improve the completeness and accuracy of the vehicle's driving trajectory.

[0095] This embodiment is in A i (γ+1)1 ≠A i(γ+1) Or F i (γ+1)1 <F0 0 A is obtained from C. i γ1 To A i (γ+1)1 The set of drivable paths C (γ+1)0 As a supplement to the target vehicle from A i γ1 Drive to A i (γ+1)1 Based on the vehicle's driving trajectory, it improves the completeness and accuracy of the target vehicle's driving trajectory.

[0096] S8, iterate through γ = 1, 2, ..., δ to obtain the target vehicle's target trajectory GJ = {GJ1, GJ2, ..., GJ}. γ ..., GJ δ}

[0097] By iterating through γ = 1, 2, ..., δ, the target vehicle's trajectory GJ = {GJ1, GJ2, ..., GJ} can be obtained. γ ..., GJ δ}, to complete the vehicle trajectory of the target vehicle.

[0098] In this embodiment, the actual target vehicle is obtained based on K and A. i The γth intersection A passed by the starting point i γ0 A i γ0 The corresponding average driving speed V i γ and actual driving time T i γ According to MX i Obtain the predicted target vehicle with A i The γth intersection A passed by the starting point i γ1 If A i γ1 =A i γ0 From MX i Select A from i γ1 The corresponding candidate predicted driving route set MX i γ1 ={MX i1 γ MX i2 γ , ..., MX iαγ γ , ..., MXiβγ γ A i γ1 The target vehicle's γ-th segment of the target trajectory GJ γ If A i 0 ≠A i 1 A is obtained from C. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, C γ0 2, ..., C γ0 uγ , ..., C γ0 hγ}, obtain C based on D γ0 The corresponding set of drivable path lengths D γ0 ={D γ0 1, D γ0 2, ..., D γ0 uγ , ..., D γ0 hγ According to V i γ T i γ and D γ0 , obtain C γ0 The corresponding driving probability set E γ0 ={E γ0 1, E γ0 2, ..., E γ0 uγ , ..., E γ0 hγ}, max{E γ0 The corresponding drivable path is determined as the γth segment of the target vehicle's target trajectory GJ. γ Iterate through γ = 1, 2, ..., δ to obtain the target vehicle's target trajectory GJ = {GJ1, GJ2, ..., GJ}. γ ..., GJ δ}, through A i γ1 =A i γ0 Select MX i γ1 As GJ γ In A i 0 ≠A i 1 Time based on target vehicle with T iγ In D γ0 uγ The total travel time required to travel uphill is compared with the time required for the target vehicle to travel from A. i (γ-1)1 Drive to A i γ0 Actual driving time T i γ-1 -T i γ The difference is used to characterize C. γ0 uγ Corresponding driving probability E γ0 uγ , and thus max{E γ0 The corresponding drivable path is determined to be GJ. γ By combining the predicted driving route with the actual driving route to complete the vehicle's driving trajectory, the completeness and accuracy of the vehicle's driving trajectory are improved.

[0099] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A vehicle trajectory completion system, characterized in that, The system includes: a first set of intersections A = {A1, A2, ..., A...} i , ..., A m The second intersection set B = {B1, B2, ..., B} j , ..., B n The checkpoint set corresponding to A is K = {K1, K2, ..., K}. i , ..., K m The set of drivable paths is C = {C1, C2, ..., C}. i , ..., C m The path length set D = {D1, D2, ..., D} i , ..., D m The predicted driving route set of the target vehicle is MX = {MX1, MX2, ..., MX...} i , ..., MX m }, processor and memory storing computer programs; Among them, A i This refers to the i-th first intersection with a checkpoint, B. j This refers to the j-th second intersection without a checkpoint, K. i Refers to A i The corresponding bayonet, C i Refers to A i The corresponding set of drivable paths, C i ={C i1 C i2 , ..., C i(i-1) C i(i+1) , ..., C im }, C i(i+1) Refers to A i To A i+1 The set of drivable paths, C i(i+1) ={C i(i+1)1 C i(i+1)2 , ..., C i(i+1)ki , ..., C i(i+1)si }, C i(i+1)ki Refers to A i To A i+1 The ki-th drivable path, D i It refers to C i The corresponding set of path lengths, D i ={D i1 D i2 , ..., D i(i-1) D i(i+1) , ..., D im }, D i(i+1) It refers to C i(i+1) The corresponding set of path lengths, D i(i+1) ={D i(i+1)1 D i(i+1)2 , ..., D i(i+1)ki , ..., D i(i+1)si }, D i(i+1)ki It refers to C i(i+1)ki The corresponding path length, MX i This refers to the target vehicle using A i For the set of predicted driving routes corresponding to the starting point, MX i ={MX i1 MX i2 , ..., MX ix , ..., MX iyi }, MX ix This refers to the predicted target vehicle using A i Let x be the x-th intersection encountered while traveling from the starting point, i = 1, 2, ..., m, where m represents the total number of the first intersection; j = 1, 2, ..., n, where n represents the total number of the second intersection; and ki = 1, 2, ..., si, where si represents the intersection A. i To A i+1 The total number of drivable paths, x = 1, 2, ..., yi, where yi refers to MX. i The total number of intersections passed through during the journey; When the computer program is executed by the processor, the following steps are performed: S1, based on K, obtain the actual target vehicle in A i The γth intersection A passed by the starting point i γ0 A i γ0 The corresponding average driving speed V i γ and actual driving time T i γ , where A i γ0 ∈A, γ=1,2,……,δ, where δ refers to the target vehicle at A i The total number of the first intersections passed by the starting point, δ≤m; S2, according to MX i The predicted target vehicle was obtained using A i The γth intersection A passed by the starting point i γ1 , where A i γ1 ∈(MX i ∩A); S3, if A i γ1 =A i γ0 From MX i Select A from i γ1 The corresponding candidate predicted driving route set MX i γ1 ={MX i1 γ MX i2 γ , ..., MX iαγ γ , ..., MX iβγ γ A i γ1 } represents the γth segment of the target vehicle's target trajectory GJ γ , among which, MX i γ1 ∈C,MX iαγ γ ∈B,MX iαγ γ This refers to the target vehicle driving through A. i (γ-1)1 Afterwards, and while driving through A i γ1 The αγth second intersection previously passed, where αγ = 1, 2, ..., βγ, and βγ refers to MX i γ1 The total number of the second intersection in the middle; S4, if A i γ1 ≠A i γ0 A is obtained from C. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, C γ0 2, ..., C γ0 uγ , ..., C γ0 hγ }, where C γ0 ∈C, C γ0 uγ Refers to A i (γ-1)1 To A i γ1 The uγth drivable path, uγ=1,2,...,hγ, where hγ refers to C γ0 The total number of drivable paths in the system; S5, obtain C based on D. γ0 The corresponding set of drivable path lengths D γ0 ={D γ0 1, D γ0 2, ..., D γ0 uγ , ..., D γ0 hγ }, where D γ0 ∈D, D γ0 uγ It refers to C γ0 uγ The corresponding path length; S6, according to V i γ T i γ and D γ0 , obtain C γ0 The corresponding driving probability set E γ0 ={E γ0 1, E γ0 2, ..., E γ0 uγ , ..., E γ0 hγ }, where E γ0 uγ It refers to C γ0 uγ The corresponding driving probability level, E γ0 uγ The following conditions must be met: E γ0 uγ =e^(-|(D γ0 uγ / V i γ )-(T i γ -T i γ-1 )|); S7, max{E γ0 The corresponding drivable path is determined as the γth segment of the target vehicle's target trajectory GJ. γ ; S8, iterate through γ=1, 2, ..., δ to obtain the target driving trajectory GJ={GJ1, GJ2, ..., GJ...} of the target vehicle. γ ..., GJ δ } 2. The system according to claim 1, characterized in that, S3 specifically includes the following steps: S31, obtain MX based on D. i γ1 The corresponding path length D i γ1 , where D i γ1 ∈D; S32, according to D i γ1 V i γ and T i γ , obtain MX i γ1 The corresponding prediction reliability F i γ1 , of which F i γ1 The following conditions must be met: F i γ1 =e^(-|1-((T i γ -T i γ-1 V i γ / D i γ1 )|), T i γ-1 This refers to the actual target vehicle with A i The starting point is the (γ-1)th intersection A that the vehicle passes through. i (γ-1)0 The corresponding actual driving time; S33, if F i γ1 ≥F0, MX i γ1 The target driving trajectory GJ of the target vehicle is determined to be the γth segment. γ Where F0 refers to the preset prediction reliability threshold.

3. The system according to claim 2, characterized in that, S4 specifically includes the following steps: S41, if A i γ1 ≠A i γ0 , or F i γ1 <F0, then MX i γ1 This is determined to be a non-target driving trajectory; S42, obtain A based on C. i (γ-1)1 To A i γ1 The set of drivable paths C γ0 ={C γ0 1, C γ0 2, ..., C γ0 uγ , ..., C γ0 hγ } 4. The system according to claim 1, characterized in that, S8 specifically includes the following steps: S81, Obtain the target vehicle using GJ according to MX. γ The predicted driving route set MX, starting from the last intersection. η ; S82, based on K, obtain the actual target vehicle in A i The γth point traveled from the starting point +1 The first intersection A i (γ+1)0 A i (γ+1)0 The corresponding average driving speed V i γ+1 and actual driving time T i γ+1 , where A i (γ+1)0 ∈A; S83, according to MX η The predicted target vehicle was obtained using A i The first intersection A, which is the γ+1th intersection encountered by the starting point. i (γ+1)1 ; S84, Compare A i (γ+1)1 and A i (γ+1)0 Based on the comparison results, the (γ+1)th segment of the target vehicle's trajectory GJ is obtained. γ+1 .

5. The system according to claim 4, characterized in that, S84 specifically includes the following steps: S841, if A i (γ+1)1 =A i (γ+1)0 From MX i Select A from i (γ+1)1 The corresponding candidate predicted driving route set MX i (γ+1)1 ={MX i1 γ+1 MX i2 γ+1 , ..., MX iα(γ+1) γ+1 , ..., MX iβ(γ+1) γ+1 A i (γ+1)1 } is the (γ+1)th segment of the target vehicle's target trajectory GJ γ+1 , among which, MX i (γ+1)1 ∈C,MX iα(γ+1) (γ+1) ∈B,MX iα(γ+1) (γ+1) This refers to the target vehicle driving through A. i γ1 Afterwards, and while driving through A i (γ+1)1 The α(γ+1)th second intersection previously passed, α(γ+1) = 1, 2, ..., β(γ+1), where β(γ+1) refers to MX i α (γ+1)1 The total number of intersections in the middle.

6. The system according to claim 5, characterized in that, S84 specifically includes the following steps: S842, if A i (γ+1)1 ≠A i (γ+1)0 A is obtained from C. i γ1 To A i (γ+1)1 The set of drivable paths C (γ+1)0 ={C (γ+1)0 1, C (γ+1)0 2, ..., C (γ+1)0 u(γ+1) , ..., C (γ+1)0 h(γ+1) }, where C (γ+1)0 ∈C, C (γ+1)0 u(γ+1) Refers to A i γ1 To A i (γ+1)1 The u(γ+1)th drivable path, u(γ+1) = 1, 2, ..., h(γ+1), where h(γ+1) refers to C. (γ+1)0 The total number of drivable paths in the system; S843, obtain C based on D. (γ+1)0 The corresponding set of drivable path lengths D (γ+1)0 ={D (γ+1)0 1, D (γ+1)0 2, ..., D (γ+1)0 uγ , ..., D (γ+1)0 h(γ+1) }, where D (γ+1)0 ∈D, D (γ+1)0 u(γ+1) It refers to C (γ+1)0 u(γ+1) The corresponding path length; S844, according to V i γ+1 T i γ+1 and D (γ+1)0 , obtain C (γ+1)0 The corresponding driving probability set E (γ+1)0 ={E (γ+1)0 1, E (γ+1)0 2, ..., E (γ+1)0 u(γ+1) , ..., E (γ+1)0 h(γ+1) }, where E (γ+1)0 u(γ+1) It refers to C (γ+1)0 u(γ+1) The corresponding driving probability level, E (γ+1)0 u(γ+1) The following conditions must be met: E (γ+1)0 u(γ+1) =e^(-|(D (γ+1)0 u(γ+1) / V i γ+1 )-(T i γ+1 -T i γ )|); S845, max{E (γ+1)0 The corresponding drivable path is determined as the (γ+1)th segment of the target vehicle's target trajectory GJ. γ+1 .

7. The system according to claim 6, characterized in that, S841 specifically includes the following steps: S8411, obtain MX based on D i (γ+1)1 The corresponding path length D i (γ+1)1 , where D i (γ+1)1 ∈D; S8412, according to D i (γ+1)1 V i γ+1 and T i γ+1 , obtain MX i (γ+1)1 The corresponding prediction reliability F i (γ+1)1 , of which F i (γ+1)1 The following conditions must be met: F i (γ+1)1 =e^-(1 / |1-((T i γ+1 -T i γ )V i γ+1 / D i (γ+1)1 )|); S8413, if F i (γ+1)1 ≥F0, MX i (γ+1)1 The target driving trajectory GJ of the target vehicle is determined to be the (γ+1)th segment. γ+1 Where F0 refers to the preset prediction reliability threshold.

8. The system according to claim 6, characterized in that, S842 specifically includes the following steps: S8421, if A i (γ+1)1 ≠A i (γ+1)0 , or F i (γ+1)1 <F0, then MX i (γ+1)1 This is determined to be a non-target driving trajectory; S8422, obtain A based on C. i γ1 To A i (γ+1)1 The set of drivable paths C (γ+1)0 ={C (γ+1)0 1, C (γ+1)0 2, ..., C (γ+1)0 u(γ+1) , ..., C (γ+1)0 h(γ+1) }

Images