Trajectory prediction methods, devices, related equipment and vehicles

Abstract

This application relates to a trajectory prediction method, apparatus, related equipment, and vehicle. It involves acquiring lane line position information and vehicle operation information; determining the centerline prediction trajectory corresponding to the lane line based on the position information; and adjusting the initial prediction trajectory corresponding to the operation information based on the centerline prediction trajectory to obtain the vehicle's target prediction trajectory. By introducing lane line position information, using it to determine the centerline prediction trajectory, and further using the centerline prediction trajectory to adjust the vehicle's initial prediction trajectory to obtain the vehicle's target prediction trajectory, the accuracy of vehicle trajectory prediction is improved, thereby enhancing the user experience.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a trajectory prediction method, device, related equipment and vehicle. Background Technology

[0002] With the development of autonomous driving technology, vehicles are becoming increasingly intelligent. To ensure safe and intelligent driving, predicting vehicle trajectories is crucial. However, existing vehicle trajectory prediction solutions suffer from low accuracy, impacting user experience. Summary of the Invention

[0003] This application provides a trajectory prediction method, device, electronic device, storage medium, computer program product, and vehicle. By introducing lane line position information, the method determines the centerline prediction trajectory using the lane line position information, and further adjusts the initial prediction trajectory of the vehicle using the centerline prediction trajectory to obtain the target prediction trajectory of the vehicle, thereby improving the accuracy of vehicle trajectory prediction and enhancing the user experience.

[0004] This application provides a trajectory prediction method, including:

[0005] Obtain lane line location information and vehicle operation information;

[0006] Based on the location information, determine the predicted trajectory of the centerline corresponding to the lane line;

[0007] Based on the predicted centerline trajectory, the initial predicted trajectory corresponding to the operating information is adjusted to obtain the target predicted trajectory of the vehicle.

[0008] Accordingly, embodiments of this application provide a trajectory prediction device, including:

[0009] The information acquisition module is used to acquire lane line location information and vehicle operation information;

[0010] The centerline prediction trajectory determination module is used to determine the centerline prediction trajectory corresponding to the lane line based on the location information.

[0011] The adjustment module is used to adjust the initial predicted trajectory corresponding to the running information based on the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle.

[0012] Furthermore, this application also provides an electronic device, including one or more processors and a memory, wherein the memory stores a computer program, and the processor is used to run the computer program in the memory to implement the trajectory prediction method provided in this application.

[0013] Furthermore, this application embodiment also provides a storage medium storing a computer program. When the computer program is run on an electronic device, the computer program is used to cause the electronic device to execute any of the trajectory prediction methods provided in this application embodiment.

[0014] Furthermore, embodiments of this application also provide a computer program product, including a computer program or instructions, which, when executed by a processor, implement any trajectory prediction method provided in embodiments of this application.

[0015] In addition, embodiments of this application also provide a vehicle, including the aforementioned electronic device, or the aforementioned storage medium, or the aforementioned computer program product.

[0016] In this embodiment, lane line position information and vehicle operation information are acquired; based on the position information, a centerline prediction trajectory corresponding to the lane line is determined; and based on the centerline prediction trajectory, the initial prediction trajectory corresponding to the operation information is adjusted to obtain the vehicle's target prediction trajectory. Thus, by introducing lane line position information, using it to determine the centerline prediction trajectory, and further using the centerline prediction trajectory to adjust the vehicle's initial prediction trajectory to obtain the vehicle's target prediction trajectory, the accuracy of vehicle trajectory prediction is improved, thereby enhancing the user experience. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an implementation environment scenario for the trajectory prediction method provided in this application embodiment;

[0019] Figure 2 This is a schematic flowchart of a trajectory prediction method provided in one embodiment of this application;

[0020] Figure 3 This is a schematic diagram of a centerline prediction trajectory in a straight-ahead scenario provided by an embodiment of this application;

[0021] Figure 4 This is a schematic diagram of a centerline predicted trajectory in a lane-changing scenario provided by one embodiment of this application;

[0022] Figure 5 This is a schematic diagram of a centerline prediction trajectory in a sharp curve scenario provided in one embodiment of this application;

[0023] Figure 6 This is a schematic diagram of a centerline prediction trajectory in a line-pressing scenario provided by one embodiment of this application;

[0024] Figure 7 This is a schematic diagram of a target prediction trajectory in a sharp curve scenario provided in one embodiment of this application;

[0025] Figure 8 This is a schematic diagram of a target prediction trajectory in a lane-changing scenario provided by an embodiment of this application;

[0026] Figure 9 This is a schematic diagram of the trajectory prediction device provided in one embodiment of this application;

[0027] Figure 10 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] Furthermore, in the embodiments of this application, "multiple" refers to two or more. The terms "first" and "second," etc., in the embodiments of this application are used for distinguishing descriptions and should not be construed as implying relative importance.

[0030] This application provides a trajectory prediction method, apparatus, electronic device, storage medium, computer program product, and vehicle. The trajectory prediction apparatus can be integrated into an electronic device, which may be a server or a terminal, etc.

[0031] The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, network acceleration services (Content Delivery Network, CDN), as well as big data and artificial intelligence platforms.

[0032] The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be connected directly or indirectly through wired or wireless communication, which is not limited herein.

[0033] Please see Figure 1 Taking the integration of trajectory prediction devices into electronic devices as an example, Figure 1 This is a schematic diagram of an implementation scenario for the trajectory prediction method provided in this application. The electronic device can be a terminal device, which obtains the position information of the lane line and the vehicle's operation information; determines the centerline prediction trajectory corresponding to the lane line based on the position information; and adjusts the initial prediction trajectory corresponding to the operation information based on the centerline prediction trajectory to obtain the vehicle's target prediction trajectory.

[0034] It should be noted that, Figure 1 The illustrated scenario of the trajectory prediction method is merely an example. The implementation environment of the trajectory prediction method described in this application is intended to more clearly illustrate the technical solutions of this application and does not constitute a limitation on the technical solutions provided in this application. Those skilled in the art will recognize that, with the evolution of data processing and the emergence of new business scenarios, the technical solutions provided in this application are equally applicable to similar technical problems.

[0035] The solutions provided in this application are specifically illustrated through the following embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments.

[0036] This embodiment will be described from the perspective of a trajectory prediction device, which can be integrated into an electronic device, which can be a terminal and / or a server, and this application does not limit it.

[0037] Please see Figure 2 , Figure 2 This is a schematic flowchart of a trajectory prediction method provided in an embodiment of this application. The trajectory prediction method may include the following steps S101 to S103:

[0038] S101. Obtain lane line location information and vehicle operation information.

[0039] Lane markings are markings used in road traffic to guide and regulate vehicle movement. Lane location information refers to the perceived location of the lane markings.

[0040] There are various ways to obtain lane line location information, and this application embodiment does not impose any limitations. For example, it can be obtained through visual perception technology. For instance, road images can be acquired using camera sensors installed on the vehicle, and lane lines and their location information can be extracted from the road images. Alternatively, navigation data can be acquired, and lane lines and their location information can be determined from the navigation data.

[0041] Operational information refers to various information related to vehicle operation. Operational information includes, but is not limited to, vehicle speed, acceleration, steering wheel angle, and steering wheel angle rate. The specific content of the operational information can be adjusted according to actual conditions, and this application embodiment does not impose any limitations.

[0042] S102. Based on the location information, determine the predicted trajectory of the centerline corresponding to the lane line.

[0043] Among them, the centerline prediction trajectory is used to indicate the lane centerline trajectory predicted based on the position information of the lane lines.

[0044] It should be noted that the existing method for obtaining the centerline prediction trajectory is as follows: directly sample trajectory points on the two lane lines, add the coordinates of the trajectory points at the lateral position of the two lane lines and average them to obtain the lane centerline trajectory point, and then perform fitting processing on the lane centerline trajectory point to obtain the centerline prediction trajectory, where the vehicle's driving direction is taken as the longitudinal direction and the lane in which the vehicle is traveling is taken as the lateral direction.

[0045] By using the position information of the lane lines, the centerline prediction trajectory is determined. This allows for subsequent integration of vehicle movement trends and the centerline prediction trajectory, thereby improving the accuracy of predicting the trajectory the vehicle will travel.

[0046] S103. Based on the centerline predicted trajectory, adjust the initial predicted trajectory corresponding to the running information to obtain the target predicted trajectory of the vehicle.

[0047] The initial predicted trajectory refers to the vehicle's driving trajectory predicted based on the vehicle's operating information.

[0048] The adjustment process involves fusing the target trajectory points on the centerline prediction trajectory with the prediction trajectory points on the initial prediction trajectory to obtain fused trajectory points, and then fitting these fused trajectory points to obtain the target prediction trajectory.

[0049] It should be noted that the fitting method can be adjusted according to the actual situation, and the embodiments in this application are not limited. For example, the least squares method can be used for fitting. Or, the minimum residual method can be used for fitting.

[0050] In this embodiment, lane line position information and vehicle operation information are acquired; based on the position information, a centerline prediction trajectory corresponding to the lane line is determined; and based on the centerline prediction trajectory, the initial prediction trajectory corresponding to the operation information is adjusted to obtain the vehicle's target prediction trajectory. Thus, by introducing lane line position information, using it to determine the centerline prediction trajectory, and further using the centerline prediction trajectory to adjust the vehicle's initial prediction trajectory to obtain the vehicle's target prediction trajectory, the accuracy of vehicle trajectory prediction is improved, thereby enhancing the user experience.

[0051] In some embodiments, the operational information includes at least one of speed, acceleration, steering wheel angle, and steering wheel angle rate. The process of determining the predicted centerline trajectory corresponding to the lane line based on the location information may include: determining the predicted centerline trajectory corresponding to the lane line based on the location information and the operational information.

[0052] Among them, the centerline prediction trajectory is used to indicate the lane centerline trajectory predicted based on the lane line position information and vehicle operation information.

[0053] By combining lane line location information with vehicle operation information, the centerline prediction trajectory is determined. This allows for the prediction of the centerline trajectory based on vehicle movement trends, thereby improving the accuracy of subsequent predictions of the trajectory the vehicle will travel.

[0054] In some embodiments, the process of determining the centerline prediction trajectory corresponding to the lane line based on the location information may include: determining the vehicle's motion state information based on the operation information, wherein the motion state information includes a straight-ahead state and a lane-changing state; determining a target lane line from the lane lines based on the motion state information; determining multiple target trajectory points based on the trajectory equation corresponding to the location information of the target lane line; and performing fitting processing based on the multiple target trajectory points to obtain the centerline prediction trajectory corresponding to the lane line.

[0055] Among them, motion status information is used to indicate the motion status of the vehicle.

[0056] There are several ways to acquire motion state information. For example, vehicle motion state information can be obtained by analyzing vehicle operation information. This analysis process can involve acquiring the vehicle's historical trajectory and current operation information (e.g., data collected by sensors) to determine the vehicle's motion trend. Another example is obtaining vehicle motion state information from the vehicle's operation information and lane line position information.

[0057] There are various forms of motion status information, which can be set according to the actual situation. For example, motion status information includes straight-ahead status and lane-changing status. Straight-ahead status can include: straight-ahead with lane markings; straight-ahead without lane markings; straight-ahead with lane markings due to lane markings not meeting conditions; and crossing lane markings. Lane-changing status can include: lane changing; turning without lane markings; turning with lane markings due to lane markings not meeting conditions; lane-changing while crossing lane markings; and sharp curve scenarios. As another example, vehicle motion status information includes: straight-ahead with lane markings; lane changing; straight-ahead without lane markings; turning without lane markings; straight-ahead with lane markings due to lane markings not meeting conditions; turning with lane markings due to lane markings not meeting conditions; crossing lane markings; lane-changing while crossing lane markings; and sharp curve scenarios. The motion status information during lane changes can be further subdivided into: lane change in progress (left); lane change in progress (right); lane change successful; lane change failed; lane change has already crossed the lane line, but it has not yet been determined as a successful / cancelled lane change; lane change over the line (left); lane change over the line (right); driving along the left lane line; driving along the right lane line, etc.

[0058] The target lane line refers to the lane line corresponding to the target lane used for lane centerline prediction.

[0059] The target lane markings include a first lane marking and a second lane marking. The first and second lane markings are relative and used to distinguish the two lane markings. For example, when the first lane marking is the target left lane marking, the second lane marking is the target right lane marking. Conversely, when the first lane marking is the target right lane marking, the second lane marking is the target left lane marking.

[0060] Specifically, the process of determining the target lane line from the lane lines based on the motion state information corresponding to the operation information can be as follows: determining the target lane based on the motion state information corresponding to the operation information; and determining the target lane line from the lane lines based on the target lane.

[0061] For example, suppose the first lane line is the target left lane line, and the second lane line is the target right lane line. There are multiple lane lines on the road, such as left-left lane lines, left lane lines, right lane lines, and right-right lane lines. The left-left lane line indicates the lane line to the left of the left lane line, and the right-right lane line indicates the lane line to the right of the right lane line. It should be noted that the current lane is the lane formed by the left lane line and the right lane line. The left lane is the lane formed by the left-left lane line and the left lane line. The naming principle for other lanes is similar and will not be elaborated here.

[0062] In this example, if the vehicle's motion status information is straight or the lane change status is lane change failed / successful, then the target lane is the current lane, the first lane line is the left lane line, and the second lane line is the right lane line.

[0063] In this example, if the vehicle's motion status information is lane change and the lane change status is left lane change, then the target lane is the left lane, the first lane line is the left-left lane line, and the second lane line is the left lane line.

[0064] In this example, if the vehicle's motion status information is lane change and the lane change status is right lane change, then the target lane is the right lane, the first lane line is the right lane line, and the second lane line is the right-right lane line.

[0065] In this example, if the vehicle's motion status information indicates that it has crossed the lane line when changing lanes, but it has not yet been determined whether the lane change was successful or canceled, then the target lane is the current lane, the first lane line is the left lane line, and the second lane line is the right lane line.

[0066] In this example, when the vehicle's movement status information is a lane change over the line, if the lane change status is a lane change to the left over the line and has not yet crossed the line, then the target lane is the left lane, the first lane line is the left-left lane line, and the second lane line is the left lane line; if the lane change status is a lane change to the right over the line and has not yet crossed the line, then the target lane is the right lane, the first lane line is the right lane line, and the second lane line is the right-right lane line; if the lane change status is a lane change to the left / right over the line and has already crossed the line, then the target lane is the current lane, the first lane line is the left lane line, and the second lane line is the right lane line.

[0067] It should be noted that, in some embodiments, the process of determining the centerline prediction trajectory corresponding to the lane line based on the location information and the operation information may further include: when the motion state information corresponding to the operation information is straight driving over the line, directly calculating the possible driving trajectory of the vehicle based on the lateral distance of the vehicle from the lane line it is driving over and the direction of the lane line, taking points on the trajectory as target trajectory points, and performing fitting processing based on multiple target trajectory points to obtain the centerline prediction trajectory corresponding to the lane line.

[0068] The target lane line can be described using a trajectory equation, specifically a cubic curve equation. That is, after obtaining the lane line's position information, this information is used in the trajectory prediction process in the form of a cubic curve equation.

[0069] For example, the trajectory equation corresponding to the position information of the target lane line can be represented by formula (1):

[0070] y = C0 + C1*x + C2*x 2 +C3*x 3 (1)

[0071] Where y is used to indicate the target lane line. C o C1 is the coefficient of the constant term, C2 is the coefficient of the linear term, C3 is the coefficient of the quadratic term, and C4 is the coefficient of the cubic term. oC1, C2, and C3 are all constant terms. x represents the value of the x-axis in the vehicle coordinate system (e.g., with the direction of the vehicle's front as the x-axis). When x = 0, C... o This indicates the lateral distance from the vehicle's center to the lane line. C1 represents the value of tan(α). α is the angle between the vehicle's X-axis (e.g., the direction of the vehicle's front) and the lane line; left deviation is positive, right deviation is negative. C2 indicates half the curvature of the lane line. C3 indicates one-sixth of the rate of change of curvature of the lane line.

[0072] The target trajectory point refers to the trajectory point used to fit and generate the predicted trajectory of the centerline.

[0073] There are multiple ways to determine the target trajectory point, and the embodiments of this application do not impose any limitations.

[0074] In one example, the target trajectory point is determined as follows: obtain the trajectory point sampled on the first lane line based on the position information of the first lane line, and the trajectory point sampled on the second lane line based on the position information of the second lane line. The coordinates of the corresponding trajectory points are directly added together and averaged to obtain the target trajectory point.

[0075] In another example, the target trajectory points are determined as follows: a first predicted trajectory point is obtained based on a first trajectory point and the road width, wherein the first trajectory point indicates a trajectory point sampled on the first lane line based on the position information of the first lane line, and the road width indicates the lane width determined based on the first lane line and the second lane line; a second predicted trajectory point is obtained based on a second trajectory point and the road width, wherein the second trajectory point indicates a trajectory point sampled on the second lane line based on the position information of the second lane line; and multiple target trajectory points are obtained based on the first predicted trajectory point and the second predicted trajectory point.

[0076] Specifically, based on the ordinate of the sampled points set in the vehicle coordinate system, and combined with the trajectory equation of the first lane line, multiple first trajectory points are obtained by sampling along the first lane line. Similarly, based on the trajectory equation of the second lane line, multiple second trajectory points are obtained by sampling along the second lane line. The number of first and second trajectory points is the same, and the ordinates of both the first and second trajectory points are the set ordinates.

[0077] It should be noted that the vehicle coordinate system is constructed with the direction of the vehicle's front as the x-axis and the direction of the front of the parallel vehicle as the y-axis.

[0078] It should be noted that since the quality of lane lines is related to their effective length, when the longitudinal distance between sampling points is much greater than the effective length of the lane line, the fitting effect is not ideal due to the inherent properties of cubic curves. Therefore, when sampling points are within the effective length of the lane line, points are directly taken on the cubic curve equation of the lane line. For sampling points exceeding the effective length, the equation of the tangent line at the end of the lane line is calculated, and points are taken on the tangent line.

[0079] The above example will be explained below with a specific embodiment. Assume that the first lane line is the target left lane line and the second lane line is the target right lane line.

[0080] Determine the road width according to the following formula (2):

[0081] w = C OL -C OR (2)

[0082] Where w is the road width, C OL C represents the coefficient of the constant term in the trajectory equation corresponding to the target left lane. OR The constant term coefficients are the values ​​of the trajectory equation corresponding to the target right lane.

[0083] When the target lane line is the target left lane line, the coordinates of the first predicted trajectory point are determined according to the following formula (3):

[0084]

[0085] Where, X′ Li Let Y′ be the ordinate of the first predicted trajectory point on the target left lane. Li Let x be the x-coordinate of the first predicted trajectory point on the target left lane. i It refers to the ordinate of the first trajectory point on the left lane of the target, y i This refers to the x-coordinate of the first trajectory point on the target left lane. w is the road width, α0 = atan(C1), and α0 indicates the angle between the current vehicle and the target left lane line. i =α0 + 2*C2*x + 3*C3*x2, α i Used to indicate the location of the i-th first trajectory point, the angle between the vehicle and the target left lane line, C o C1 is the coefficient of the constant term in the trajectory equation corresponding to the target left lane, C2 is the coefficient of the quadratic term in the trajectory equation corresponding to the target left lane, and C3 is the coefficient of the cubic term in the trajectory equation corresponding to the target left lane. The number of the first trajectory points is (1, n), where n is a positive integer.

[0086] When the target lane line is the target right lane line, the coordinates of the second predicted trajectory point are determined according to the following formula (4):

[0087]

[0088] Where, X′ Ri Let Y′ be the ordinate of the second predicted trajectory point on the target right lane. Ri Let x be the x-coordinate of the second predicted trajectory point on the target right lane. i It refers to the ordinate of the second trajectory point on the right lane of the target, y i This refers to the x-coordinate of the second trajectory point on the target right lane. w is the road width, α0 = atan(C1), and α0 indicates the angle between the current vehicle and the target right lane line. i =α0 + 2*C2*x + 3*C3*x 2 α i Used to indicate the location of the i-th second trajectory point, the angle between the vehicle and the target right lane line, C o C1 is the coefficient of the constant term in the trajectory equation corresponding to the target right lane, C2 is the coefficient of the quadratic term in the trajectory equation corresponding to the target right lane, and C3 is the coefficient of the cubic term in the trajectory equation corresponding to the target right lane. The number of second trajectory points is (1, n), where n is a positive integer.

[0089] The process of obtaining multiple target trajectory points based on the first predicted trajectory point and the second predicted trajectory point may include: adding the coordinates of the corresponding first predicted trajectory point and the coordinates of the second predicted trajectory point and averaging them to obtain the target trajectory point.

[0090] The process of obtaining multiple target trajectory points based on the first predicted trajectory point and the second predicted trajectory point may further include: determining a first weight for the first lane line and a second weight for the second lane line based on the effective length of the first lane line and the effective length of the second lane line, wherein the effective length is used to indicate the perceived length of the corresponding lane line; and performing weighted processing on the coordinates of the first predicted trajectory point and the coordinates of the second predicted trajectory point based on the first weight and the second weight to obtain multiple target trajectory points.

[0091] The first weight is used to indicate the weight of the first predicted trajectory point on the first lane line that participates in the determination of the target trajectory, and the second weight is used to indicate the weight of the second predicted trajectory point on the second lane line that participates in the determination of the target trajectory.

[0092] Specifically, the difference between the effective length of the first lane line and the effective length of the second lane line can be calculated. This difference is then compared with a preset difference. Based on the comparison result, a first weight is set for the first lane line, and a second weight is set for the second lane line. For example, if the comparison result shows a difference less than the preset difference, the first weight and the second weight are set to be equal. Alternatively, if the comparison result shows a difference greater than the preset difference, and the effective length of the first lane line is longer, a first weight is set based on the difference between the effective lengths of the first and second lane lines, and the effective length of the first lane line. A second weight is determined based on the sum of the weights and the first weight. Similarly, if the comparison result shows a difference greater than the preset difference, and the effective length of the second lane line is longer, a second weight is set based on the difference between the effective lengths of the first and second lane lines, and the effective length of the second lane line. A first weight is determined based on the sum of the weights and the second weight.

[0093] Following the previous specific embodiment, assuming the preset difference is 2 meters, the process of setting the first weight and the second weight in this step will be explained:

[0094] In Example A, assuming the difference between the effective length of the target left lane line and the effective length of the target right lane line is less than the preset difference of 2 meters, the coordinates of the target trajectory point are determined according to the following formula (5):

[0095]

[0096] Where the first and second weights are both 0.5, X′ i Y′ is the ordinate of the target trajectory point. i is the x-coordinate of the target trajectory point.

[0097] In Example B, assuming the difference between the effective length of the target left lane line and the effective length of the target right lane line is greater than the preset difference of 2 meters, and the effective length of the target left lane line is greater, the coordinates of the target trajectory point are determined according to the following formula (6):

[0098] diff = D L -D R

[0099]

[0100] Where, X′ i Y′ is the ordinate of the target trajectory point. i Let w be the x-coordinate of the target trajectory point, and diff be the difference between the effective lengths of the target left lane line and the target right lane line. lane As the first weight, (1-w lane ) is the second weight.

[0101] In Example C, assuming the difference between the effective length of the target left lane line and the effective length of the target right lane line is greater than the preset difference of 2 meters, and the effective length of the target right lane line is greater, then the coordinates of the target trajectory point are determined according to the following formula (7):

[0102] diff = D R D L

[0103]

[0104] Where, X′ i Y′ is the ordinate of the target trajectory point. i Let w be the x-coordinate of the target trajectory point, and diff be the difference between the effective lengths of the target left lane line and the target right lane line. lane As the second weight, (1-w lane ) is the first weight.

[0105] like Figures 3 to 6 As shown, in different scenarios, it can stably and accurately determine the target trajectory points and generate a predicted centerline trajectory based on the target trajectory points. Among these, Figure 3 This is a schematic diagram of a centerline predicted trajectory in a straight-line scenario provided in an embodiment of this application. Figure 4 This is a schematic diagram of a centerline predicted trajectory in a lane-changing scenario provided in an embodiment of this application. Figure 5 This is a schematic diagram of a centerline prediction trajectory in a sharp curve scenario provided in an embodiment of this application. Figure 6 This is a schematic diagram of a centerline prediction trajectory in a line-pressing scenario provided in an embodiment of this application.

[0106] In some embodiments, the method further includes: updating the included angle parameter according to the first weight and the second weight, wherein the included angle parameter is used to indicate the angle between the lane where the vehicle is located and the predicted trajectory of the centerline; and updating the road width based on the updated included angle parameter, the second lane line of the first lane line, and the second lane line of the first lane line.

[0107] Following examples A, B, and C in the previous specific embodiment, the process of updating the included angle parameter in this step will be explained:

[0108] In Example A above, the included angle parameter is updated according to the following formula (8):

[0109] α=0.5*atan(C 1L )+0.5*atan(C 1R (8)

[0110] Where α is the updated included angle parameter, the first weight and the second weight are both 0.5, and C1L C represents the coefficient of the first-order term in the trajectory equation corresponding to the target left lane. 1R The coefficient of the first-order term in the trajectory equation corresponding to the target right lane.

[0111] In Example B above, the included angle parameter is updated according to the following formula (9):

[0112] α = w lane *abs(atan(C 1L ))+(1-w lane )*abs(atan(C 1R (9)

[0113] Where α is the updated included angle parameter, w lane As the first weight, (1-w lane ) is the second weight, C 1L C represents the coefficient of the first-order term in the trajectory equation corresponding to the target left lane. 1R The coefficient of the first-order term in the trajectory equation corresponding to the target right lane.

[0114] In Example C above, the included angle parameter is updated according to the following formula (10):

[0115] α=(1-w lane )*abs(atan(C 1L ))+w lane *abs(atan(C 1R (10)

[0116] Where α is the updated included angle parameter, w lane As the second weight, (1-w lane ) is the first weight, C 1L C represents the coefficient of the first-order term in the trajectory equation corresponding to the target left lane. 1R The coefficient of the first-order term in the trajectory equation corresponding to the target right lane.

[0117] Based on the above embodiments, the road width is updated according to the following formula (11):

[0118] w′=(C 0L -C 0R )*cos(α) (11)

[0119] Where w′ refers to the updated road width, α is the updated included angle parameter, and C 0L C represents the coefficient of the constant term in the trajectory equation corresponding to the target left lane. 0R The constant term coefficients in the trajectory equation corresponding to the target right lane.

[0120] It's important to note that the perception accuracy of lane lines directly impacts the quality of lane centerline points. Generally, when the effective length of lane lines is short, especially in curved scenarios, the calculated coordinate error of the target trajectory point is significant. Therefore, we calculate the weights of the corresponding lane lines by combining the effective lengths of the first and second lane lines. These weights are then used to fuse the first and second lane lines and update the road width, thereby improving the accuracy of vehicle trajectory prediction and enhancing the user experience.

[0121] In some embodiments, before determining multiple target trajectory points based on the trajectory equation corresponding to the position information of the target lane line, the method further includes: obtaining the position information of the roadside; and determining the first lane line and the second lane line among the target lane lines based on the position information of the roadside and the position information of the lane line.

[0122] Among them, curbs are objects that have traffic guidance and isolation functions, used to guide vehicles to travel along the road and prevent vehicles from deviating from the road.

[0123] Road edges can be described using trajectory equations. These trajectory equations refer to cubic curve equations. That is, after obtaining the location information of the road edge, this information is used in the trajectory prediction process in the form of a cubic curve equation.

[0124] It should be noted that when two lane lines are obtained, there may be cases where only one lane line is valid, or where both lane lines are invalid. In either case, the target lane line needs to be repaired.

[0125] Among them, the case where a single lane line is valid means that only one side of the lane line exists, or a lane consisting of two lane lines is invalid but it is determined that the lane line is invalid when driving along one of the lane lines.

[0126] In some embodiments, the target lane lines can be repaired based on curb information.

[0127] In cases where a single lane line is valid, the target lane line can be repaired based on the location information of the lane line on one side, the location information of the road edge, and the road width.

[0128] It should be noted that existing methods for obtaining centerline prediction trajectories only achieve good results when both lane lines exist and their effective distances are similar. However, when only one lane line exists, or when one lane line has a large error, the coordinates of the lane centerline trajectory points obtained by this method have significant errors and cannot accurately predict the centerline trajectory. This application, however, combines lane line position information and vehicle operation information to predict the lane centerline trajectory, effectively reducing the errors caused by only one lane line existing, or only one lane line, thereby improving the accuracy of centerline prediction.

[0129] For example, assuming the target left lane line is the left lane line, and only the left lane line is valid, then the system checks if the right curb is a valid curb. If the right curb's position information is valid, then the right curb is used as the target right lane line. A valid right curb position information means that the right curb exists and the road width formed by the right lane line and the left curb is less than a preset road width threshold. If the right curb's position information is invalid, then the target right lane line is determined based on the left lane line's position information and the road width. Similarly, the right lane line is repaired in the same way.

[0130] In cases where both lane markings are invalid, the position information of the road edges on both sides can be obtained. It also checks whether the left and right road edges are valid road edges. If the right and left road edges exist, and the road width formed by the right and left road edges is less than a preset road width threshold, the left road edge is designated as the target left lane, and the right road edge is designated as the target right lane.

[0131] It should be noted that after the target lane lines are determined, the road width is determined based on the two lane lines or the curb within the target lane lines. The curb is determined based on the difference between the constant term coefficients in the trajectory equation corresponding to the target left lane and the constant term coefficients in the trajectory equation corresponding to the target right lane.

[0132] In some embodiments, the target lane lines can also be repaired based on navigation information. The process of repairing the target lane lines based on navigation information may include: obtaining navigation information through the vehicle's navigation module; determining the road surface information on which the vehicle is traveling based on the navigation information; and repairing the target lane lines based on the road surface information.

[0133] In some embodiments, the target lane line can also be repaired based solely on the road width. The process of repairing the target lane line based on the road width may include: collecting road surface information during vehicle travel using sensors; analyzing the road surface information to obtain multiple road widths; selecting a target road width from the multiple road widths based on a preset road width threshold, wherein the target road width is close to and smaller than the preset road width threshold; and using the lane line corresponding to the target road width as the target lane line to achieve the repair process for the target lane line.

[0134] In some embodiments, before adjusting the initial predicted trajectory corresponding to the operating information based on the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle, the method further includes: processing the operating information based on a preset vehicle dynamics model to obtain the initial predicted trajectory.

[0135] The preset vehicle dynamics model refers to a digital model established by analyzing the vehicle's motion. This preset vehicle dynamics model treats the vehicle as a rigid body and, combined with the vehicle's motion information, determines the predicted trajectory of the vehicle. It can be understood that the more accurate the preset vehicle dynamics model, the more accurate the description of the vehicle's motion, and the higher the accuracy of the trajectory prediction.

[0136] It should be noted that the initial predicted trajectory, generated by the preset vehicle dynamics model, has high confidence and is relatively stable when the vehicle is traveling in a straight line. However, due to the limited range of data used, the predicted trajectory for long distances differs significantly from the actual trajectory. For example, when a user in the vehicle quickly turns the steering wheel, the predicted trajectory will frequently change, making it impossible to consistently output an accurate predicted trajectory.

[0137] This application combines lane line position information and vehicle operation information to first predict the lane centerline trajectory, and then uses the predicted centerline trajectory to adjust the initial predicted trajectory predicted by the preset vehicle dynamics model to obtain the vehicle's target predicted trajectory. This can effectively solve the trajectory instability problem and the error problem in long-distance prediction.

[0138] Based on this embodiment, the process of adjusting the initial predicted trajectory corresponding to the running information according to the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle further includes: determining the third weight of the centerline predicted trajectory and the fourth weight of the initial predicted trajectory; adjusting the initial predicted trajectory using the centerline predicted trajectory according to the third weight and the fourth weight to obtain the target predicted trajectory of the vehicle.

[0139] The third weight of the centerline prediction trajectory is determined based on at least one of the following: the vehicle's motion state information, the angle between the vehicle and the lane tangent direction, the lateral distance between the vehicle and the centerline prediction trajectory, and the longitudinal distance between the sampling point on the initial prediction trajectory and the vehicle.

[0140] In some embodiments, the process of determining the third weight of the centerline predicted trajectory and the fourth weight of the initial predicted trajectory may include: determining the third weight of each target trajectory point in the centerline predicted trajectory based on the difference in longitudinal distance between the target trajectory point on the centerline predicted trajectory and the corresponding sampling point on the initial predicted trajectory; and determining the fourth weight of the sampling point corresponding to the target trajectory point in the initial predicted trajectory based on the third weight of each target trajectory point and a preset weight.

[0141] It should be noted that while vehicle trajectory points calculated based on vehicle dynamics models or historical trajectories are relatively accurate and reflect the vehicle's true trajectory well at close range, their reliability is lower at longer distances. Therefore, this study assigns different weights to lane sampling points at different distances based on the longitudinal distance between the lane sampling point and the vehicle; that is, the farther the lane sampling point is from the vehicle, the greater its weight, and vice versa.

[0142] In some embodiments, the process of determining the third weight of the centerline predicted trajectory and the fourth weight of the initial predicted trajectory may further include: determining the third weight of each target trajectory point in the centerline predicted trajectory based on the motion state information; and determining the fourth weight of the sampling point corresponding to the target trajectory point in the initial predicted trajectory based on the third weight of each target trajectory point and a preset weight.

[0143] It should be noted that, since the sources of trajectory points are different and their credibility is different, the third weight and the fourth weight mentioned above are determined based on the points with different credibility.

[0144] For example, when the motion status information is a straight-ahead state, the third and fourth weights mentioned above can be determined based on the credibility of the straight-ahead state. As another example, when the motion status information is a straight-ahead state with the line in sight, the third and fourth weights mentioned above can be determined based on the credibility of the straight-ahead state with the line in sight.

[0145] The following is an explanation using a specific embodiment:

[0146] The third weight is determined according to the following formula (12):

[0147]

[0148] Among them, V iP1 refers to the weight of the i-th target trajectory point, L is the ordinate (x) of the target trajectory point farthest from the vehicle in the vehicle coordinate system, P1 is the weight of the target trajectory point at x=0, and P2 is the weight of the target trajectory point at x=L.

[0149] When the vehicle's motion status information is "driving straight with the line on", P2 = 1.0, P1 = 0.1.

[0150] Since the target trajectory point is more trusted at a distance, its weight is increased when the distance between the target trajectory point and the vehicle exceeds ε. In this case, the third weight is determined according to the following formula (13):

[0151]

[0152] Where P2 = 1.0, P1 = 0.7, L is the ordinate (x) of the distance to the target trajectory point in the vehicle coordinate system, and ε is the threshold set according to different vehicle scenarios.

[0153] For example, when a vehicle is traveling straight, ε = 40. When a vehicle is changing lanes, ε = 50. When a vehicle is traveling on a sharp curve, ε = 20.

[0154] It should be noted that when a vehicle is driving on a sharp curve and the absolute value of the curvature of one lane line is greater than 0.03, P1 = 0.9, and it is not necessary to judge the magnitude of P2 and P1.

[0155] When the distance between the target trajectory point and the vehicle is less than ε, the magnitudes of P1 and P2 are determined according to the following method:

[0156] Based on the vehicle's motion state information, determine the initial values ​​of P1 and P2. When the vehicle is moving straight, the default values ​​for P1 and P2 are P1 and P2, respectively. min (0.5) and P max (1). When the vehicle's motion state is changing lanes, the default values ​​for P1 and P2 are P1 and P2, respectively. min (0.3) and P max (1).

[0157] When a vehicle has a certain angle with the target lane, it is considered that the vehicle is not traveling entirely along the lane direction. The larger the absolute value of the angle, the greater the tendency to deviate from the lane direction, and vice versa. Therefore, the values ​​of P1 and P2 are optimized based on the size of the angle between the vehicle and the target lane. α is the maximum angle value that the vehicle will reach with the lane during driving, given empirically. If the absolute value of the current angle x is less than or equal to α, the optimized P1 and optimized P2 are determined according to the following formula (14):

[0158]

[0159] In this context, Yaw_P1 defaults to 0.1, and Yaw_P2 defaults to 0.3. P′1 is the optimized P1, and P′2 is the optimized P2.

[0160] If x is greater than α, then P′1 = 0 and P′2 = 0.

[0161] Since it is impossible to accurately predict the driver's driving behavior and there is no guarantee that the vehicle will travel along the lane centerline, the maximum weight P2 and minimum weight P1 of the lane sampling points are optimized based on the lateral distance of the vehicle from the target lane centerline to more accurately predict the vehicle's trajectory. The closer the vehicle is to the target lane centerline, the closer P1 and P2 are to the default values ​​P′1 and P′2, and vice versa. To reduce trajectory jumps caused by vehicle state switching, a minimum weight lower bound Yaw_P1 (0.1) and a maximum weight lower bound Yaw_P2 (0.3) are introduced during lane changes.

[0162] When the vehicle is moving straight and the lateral distance between the vehicle and the effective lane line is x, the optimized P1 and optimized P2 are determined according to the following formula (15):

[0163]

[0164] Where d is half the width of the target lane.

[0165] When the vehicle is changing lanes or changing lanes over the line, and the lateral distance between the vehicle and the center line of the target lane is x, the optimized P1 and optimized P2 are determined according to the following formula (16):

[0166]

[0167] Where d is the lane width of the target lane.

[0168] like Figures 7 to 8 As shown, Figure 7 This image shows the fusion result of vehicle trajectory points in a sharp curve scenario. Blue trajectory points represent those on the initial predicted trajectory calculated using vehicle dynamics formulas; orange trajectory points represent those on the centerline predicted trajectory calculated using the trajectory prediction method provided in this application; and green points represent those on the fused target predicted trajectory calculated based on the initial predicted trajectory, the centerline predicted trajectory, and the third and fourth weights. It can be seen that in sharp curve scenarios, the trajectory calculated solely based on steering wheel information has a large error, deviating from the lane and inconsistent with reality. Adding the centerline predicted trajectory can significantly optimize the vehicle's trajectory.

[0169] Figure 8The fusion results of vehicle trajectory points in lane changing scenarios show that the trajectory calculated using information such as the steering wheel during lane changing can well represent the vehicle trajectory at short distances, but it cannot predict the vehicle trajectory well at long distances. By adding lane information and vehicle status information, the problem of unusable trajectory points at long distances can be solved. Since each trajectory point is weighted, the vehicle trajectory will not completely overlap with the lane sampling points, and the vehicle trajectory can be predicted more ideally.

[0170] To facilitate better implementation of the trajectory prediction method provided in the embodiments of this application, the embodiments of this application also provide an apparatus based on the above trajectory prediction method. The meanings of the terms used are the same as in the trajectory prediction method described above, and specific implementation details can be found in the descriptions in the method embodiments.

[0171] For example, such as Figure 9 As shown, the trajectory prediction device may include an information acquisition unit 201, a centerline predicted trajectory determination unit 202, and an adjustment unit 203, as detailed below:

[0172] The information acquisition unit 201 is used to acquire lane line location information and vehicle operation information;

[0173] The centerline prediction trajectory determination unit 202 is used to determine the centerline prediction trajectory corresponding to the lane line based on the location information.

[0174] The adjustment unit 203 is used to adjust the initial predicted trajectory corresponding to the running information based on the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle.

[0175] In one embodiment of this application, the running information includes at least one of speed, acceleration, steering wheel angle, and steering wheel angular rate; the aforementioned centerline prediction trajectory determination unit 202 includes:

[0176] The centerline prediction trajectory determination subunit is used to determine the centerline prediction trajectory corresponding to the lane line based on location information and operation information.

[0177] In one embodiment of this application, the aforementioned centerline prediction trajectory determination subunit includes:

[0178] The motion state information determination module is used to determine the motion state information of the vehicle based on the operation information, wherein the motion state information includes straight-going state and lane-changing state;

[0179] The target lane line determination module is used to determine the target lane line from the lane lines based on the motion state information;

[0180] The target trajectory point determination module is used to determine multiple target trajectory points based on the trajectory equation corresponding to the position information of the target lane line.

[0181] The centerline prediction trajectory determination module is used to perform fitting processing based on multiple target trajectory points to obtain the centerline prediction trajectory corresponding to the lane line.

[0182] In one embodiment of this application, the target lane line includes a first lane line and a second lane line, and the target trajectory point determination module includes:

[0183] The first predicted trajectory point determination submodule is used to obtain the first predicted trajectory point based on the first trajectory point and the road width. The first trajectory point is used to indicate the trajectory point sampled on the first lane line based on the position information of the first lane line, and the road width is used to indicate the lane width determined based on the first lane line and the second lane line.

[0184] The second predicted trajectory point determination submodule is used to obtain the second predicted trajectory point based on the second trajectory point and the road width. The second trajectory point is used to indicate the trajectory point sampled on the second lane line based on the position information of the second lane line.

[0185] The target trajectory point determination submodule is used to obtain multiple target trajectory points based on the first predicted trajectory point and the second predicted trajectory point.

[0186] In this embodiment of the application, the target trajectory point determination submodule is used for:

[0187] Based on the effective length of the first lane line and the effective length of the second lane line, a first weight for the first lane line and a second weight for the second lane line are determined, wherein the effective length is used to indicate the perceived length of the corresponding lane line.

[0188] Based on the first weight and the second weight, the coordinates of the first predicted trajectory point and the second predicted trajectory point are weighted to obtain multiple target trajectory points.

[0189] In this embodiment of the application, the trajectory prediction device further includes:

[0190] Angle parameter update unit is used to update the angle parameter according to the first weight and the second weight, wherein the angle parameter is used to indicate the angle between the lane where the vehicle is located and the predicted trajectory of the center line;

[0191] The road width update unit is used to update the road width based on the updated included angle parameters and the second lane line of the first lane line.

[0192] In this embodiment of the application, before the target trajectory point determination module described above, the following is also included:

[0193] The curb acquisition module is used to acquire the location information of the curb;

[0194] The lane line determination module is used to determine the first lane line and the second lane line in the target lane line based on the location information of the road edge and the location information of the lane line.

[0195] In this embodiment of the application, the target lane line determination module includes:

[0196] The target lane determination submodule is used to determine the target lane based on the motion status information corresponding to the operation information;

[0197] The target lane line determination submodule is used to determine the target lane line from the lane lines based on the target lane.

[0198] In this embodiment of the application, before the aforementioned adjustment unit 203, the following is included:

[0199] The initial predicted trajectory determination unit is used to process the running information based on a preset vehicle dynamics model to obtain the initial predicted trajectory;

[0200] The aforementioned adjustment unit 203 includes:

[0201] The weight determination module is used to determine the third weight of the centerline prediction trajectory and the fourth weight of the initial prediction trajectory.

[0202] The adjustment module is used to adjust the initial predicted trajectory based on the third and fourth weights and the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle.

[0203] In this embodiment of the application, the weight determination module includes:

[0204] The third weight first determination submodule is used to determine the third weight of each target trajectory point in the centerline prediction trajectory based on the difference between the longitudinal distance between the target trajectory point on the centerline prediction trajectory and the corresponding sampling point on the initial prediction trajectory;

[0205] The fourth weight first determination submodule is used to determine the fourth weight of the sampling point corresponding to the target trajectory point in the initial predicted trajectory based on the third weight and the preset weight of each target trajectory point.

[0206] In this embodiment of the application, the weight determination module includes:

[0207] The third weight second determination submodule is used to determine the third weight of each target trajectory point in the centerline prediction trajectory based on the motion state information.

[0208] The fourth weight second determination submodule is used to determine the fourth weight of the sampling point corresponding to the target trajectory point in the initial predicted trajectory based on the third weight and the preset weight of each target trajectory point.

[0209] In this embodiment, the information acquisition unit 201 acquires the position information of the lane lines and the vehicle's operating information; the centerline prediction trajectory determination unit 202 determines the centerline prediction trajectory corresponding to the lane lines based on the position information; and the adjustment unit 203 adjusts the initial prediction trajectory corresponding to the operating information based on the centerline prediction trajectory to obtain the vehicle's target prediction trajectory. Thus, by introducing the position information of the lane lines, using this information to determine the centerline prediction trajectory, and further using the centerline prediction trajectory to adjust the vehicle's initial prediction trajectory to obtain the vehicle's target prediction trajectory, the accuracy of vehicle trajectory prediction is improved, thereby enhancing the user experience.

[0210] In practice, each of the above modules can be implemented as an independent entity or can be combined arbitrarily to be implemented as the same or several entities. For the specific implementation methods and corresponding beneficial effects of each of the above modules, please refer to the previous method embodiments, which will not be repeated here.

[0211] This application also provides an electronic device, the operating system of which includes a first operating system and a second operating system, such as... Figure 10 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically:

[0212] The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 10 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0213] in:

[0214] The processor 301 is the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes computer programs and / or modules stored in the memory 302, and calls data stored in the memory 302 to perform various functions and process data. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0215] The memory 302 can be used to store computer programs and modules. The processor 301 executes various functional applications and trajectory predictions by running the computer programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, computer programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0216] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0217] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0218] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more computer programs into the memory 302 according to the following instructions, and the processor 301 runs the computer programs stored in the memory 302 to realize various functions, such as:

[0219] Obtain lane line location information and vehicle operation information;

[0220] Based on the location information, determine the predicted trajectory of the centerline corresponding to the lane line;

[0221] Based on the centerline predicted trajectory, the initial predicted trajectory corresponding to the running information is adjusted to obtain the vehicle's target predicted trajectory.

[0222] Therefore, the electronic device provided in this application obtains lane line position information and vehicle operation information; determines the centerline prediction trajectory corresponding to the lane line based on the position information; and adjusts the initial prediction trajectory corresponding to the operation information based on the centerline prediction trajectory to obtain the vehicle's target prediction trajectory. Thus, by introducing lane line position information, using it to determine the centerline prediction trajectory, and further using the centerline prediction trajectory to adjust the vehicle's initial prediction trajectory to obtain the vehicle's target prediction trajectory, the accuracy of vehicle trajectory prediction is improved, thereby enhancing the user experience.

[0223] For details on the specific implementation methods and corresponding beneficial effects of each of the above operations, please refer to the detailed description of the trajectory prediction method above, which will not be repeated here.

[0224] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by a computer program, or by a computer program controlling related hardware. The computer program can be stored in a storage medium and loaded and executed by a processor.

[0225] Therefore, embodiments of this application provide a storage medium storing a computer program that can be loaded by a processor to execute the steps of any trajectory prediction method provided in embodiments of this application. For example, the computer program can execute the following steps:

[0226] Obtain lane line location information and vehicle operation information;

[0227] Based on the location information, determine the predicted trajectory of the centerline corresponding to the lane line;

[0228] Based on the centerline predicted trajectory, the initial predicted trajectory corresponding to the running information is adjusted to obtain the vehicle's target predicted trajectory.

[0229] Therefore, the storage medium provided in the embodiments of this application,

[0230] By acquiring lane line location information and vehicle operation information, the centerline prediction trajectory corresponding to the lane line is determined based on the location information. The initial prediction trajectory corresponding to the operation information is then adjusted based on the centerline prediction trajectory to obtain the vehicle's target prediction trajectory. In this way, by introducing lane line location information, using it to determine the centerline prediction trajectory, and further using the centerline prediction trajectory to adjust the vehicle's initial prediction trajectory to obtain the vehicle's target prediction trajectory, the accuracy of vehicle trajectory prediction is improved, thus enhancing the user experience.

[0231] For details on the specific implementation methods and corresponding beneficial effects of the above operations, please refer to the previous embodiments, which will not be repeated here.

[0232] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0233] Since the computer program stored in the storage medium can execute the steps in any of the trajectory prediction methods provided in the embodiments of this application, the beneficial effects that any of the trajectory prediction methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0234] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a storage medium. A processor of a computer device reads the computer instructions from the storage medium and executes the computer instructions, causing the computer device to perform the trajectory prediction method described above.

[0235] This application also provides a vehicle that includes the aforementioned trajectory prediction device, electronic device, or computer program product. The specific structure of the vehicle is not limited in this application. The specific implementation methods and corresponding beneficial effects of the various operations of the electronic device described above are also applicable to this vehicle; please refer to the detailed description of the trajectory prediction method above, which will not be repeated here.

[0236] The above provides a detailed description of a trajectory prediction method, apparatus, electronic device, storage medium, and vehicle provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A trajectory prediction method, characterized in that, include: Obtain lane line location information and vehicle operation information; Based on the location information, determine the predicted trajectory of the centerline corresponding to the lane line; Based on the predicted centerline trajectory, the initial predicted trajectory corresponding to the running information is adjusted to obtain the target predicted trajectory of the vehicle; The centerline prediction trajectory is obtained by fitting multiple target trajectory points determined by the trajectory equation corresponding to the position information of the target lane line in the lane line. The target lane line includes a first lane line and a second lane line. The target trajectory point is determined based on a first weight and a first predicted trajectory point of the first lane line, a second weight and a second predicted trajectory point of the second lane line. The first predicted trajectory point is determined based on the first lane line and the road width, and the second predicted trajectory point is determined based on the second lane line and the road width. The first weight and the second weight are determined based on the effective length of the first lane line and the effective length of the second lane line. The effective length is used to indicate the perceived length of the corresponding lane line. The trajectory prediction process also includes: updating the included angle parameter according to the first weight and the second weight, wherein the included angle parameter is used to indicate the angle between the lane where the vehicle is located and the predicted trajectory of the center line; and updating the road width based on the updated included angle parameter, the first lane line and the second lane line.

2. The trajectory prediction method according to claim 1, characterized in that, The operational information includes at least one of speed, acceleration, steering wheel angle, and steering wheel angle rate; determining the predicted centerline trajectory corresponding to the lane line based on the position information includes: Based on the location information and the operation information, the predicted centerline trajectory corresponding to the lane line is determined.

3. The trajectory prediction method according to claim 2, characterized in that, Determining the predicted centerline trajectory corresponding to the lane line based on the location information and the operation information includes: Based on the operational information, the vehicle's motion state information is determined, wherein the motion state information includes straight-going state and lane-changing state; The target lane line is determined from the lane lines based on the motion state information; Based on the trajectory equation corresponding to the position information of the target lane line, multiple target trajectory points are determined; The centerline prediction trajectory corresponding to the lane line is obtained by fitting multiple target trajectory points.

4. The trajectory prediction method according to claim 3, characterized in that, The step of determining multiple target trajectory points based on the trajectory equation corresponding to the position information of the target lane line includes: The first predicted trajectory point is obtained based on the first trajectory point and the road width, wherein the first trajectory point is used to indicate the trajectory point sampled on the first lane line based on the position information of the first lane line, and the road width is used to indicate the lane width determined based on the first lane line and the second lane line. The second predicted trajectory point is obtained based on the second trajectory point and the road width, wherein the second trajectory point is used to indicate the trajectory point sampled on the second lane line based on the position information of the second lane line; Based on the first predicted trajectory point and the second predicted trajectory point, multiple target trajectory points are obtained.

5. The trajectory prediction method according to claim 4, characterized in that, The process of obtaining multiple target trajectory points based on the first predicted trajectory point and the second predicted trajectory point includes: Based on the first weight and the second weight, the coordinates of the first predicted trajectory point and the coordinates of the second predicted trajectory point are weighted to obtain multiple target trajectory points.

6. The trajectory prediction method according to claim 4, characterized in that, Before determining multiple target trajectory points based on the trajectory equation corresponding to the position information of the target lane line, the method further includes: Obtain the location information of the curb; Based on the location information of the curb and the location information of the lane lines, the first lane line and the second lane line in the target lane lines are determined.

7. The trajectory prediction method according to claim 3, characterized in that, Determining the target lane line from the lane lines based on the motion state information includes: Based on the motion status information, the target lane is determined; Based on the target lane, the target lane line is determined from the lane lines.

8. The trajectory prediction method according to any one of claims 1 to 7, characterized in that, Before adjusting the initial predicted trajectory corresponding to the operating information based on the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle, the following steps are included: Based on a preset vehicle dynamics model, the operating information is processed to obtain an initial predicted trajectory; The step of adjusting the initial predicted trajectory corresponding to the operating information based on the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle includes: Determine the third weight of the predicted midline trajectory and the fourth weight of the initial predicted trajectory; Based on the third and fourth weights, the initial predicted trajectory is adjusted using the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle.

9. The trajectory prediction method according to claim 8, characterized in that, The determination of the third weight of the predicted centerline trajectory and the fourth weight of the initial predicted trajectory includes: Based on the difference in longitudinal distance between the target trajectory points on the predicted centerline trajectory and the corresponding sampling points on the initial predicted trajectory, the third weight of each target trajectory point in the predicted centerline trajectory is determined. Based on the third weight and preset weight of each target trajectory point, the fourth weight of the sampling point corresponding to the target trajectory point in the initial predicted trajectory is determined.

10. The trajectory prediction method according to claim 8, characterized in that, The determination of the third weight of the predicted centerline trajectory and the fourth weight of the initial predicted trajectory includes: Based on the motion state information, determine the third weight of each target trajectory point in the predicted centerline trajectory; Based on the third weight and preset weight of each target trajectory point, the fourth weight of the sampling point corresponding to the target trajectory point in the initial predicted trajectory is determined.

11. A trajectory prediction device, characterized in that, include: The information acquisition unit is used to acquire lane line location information and vehicle operation information; The centerline prediction trajectory determination unit is used to determine the centerline prediction trajectory corresponding to the lane line based on the location information. The adjustment unit is used to adjust the initial predicted trajectory corresponding to the running information according to the centerline predicted trajectory to obtain the target predicted trajectory of the vehicle. The centerline prediction trajectory is obtained by fitting multiple target trajectory points determined by the trajectory equation corresponding to the position information of the target lane line in the lane line. The target lane line includes a first lane line and a second lane line. The target trajectory point is determined based on a first weight and a first predicted trajectory point of the first lane line, a second weight and a second predicted trajectory point of the second lane line. The first predicted trajectory point is determined based on the first lane line and the road width, and the second predicted trajectory point is determined based on the second lane line and the road width. The first weight and the second weight are determined based on the effective length of the first lane line and the effective length of the second lane line. The effective length is used to indicate the perceived length of the corresponding lane line. The trajectory prediction process also includes: updating the included angle parameter according to the first weight and the second weight, wherein the included angle parameter is used to indicate the angle between the lane where the vehicle is located and the predicted trajectory of the center line; and updating the road width based on the updated included angle parameter, the first lane line and the second lane line.

12. An electronic device, characterized in that, It includes one or more processors and a memory, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the trajectory prediction method according to any one of claims 1 to 10.

13. A storage medium, characterized in that, Includes a computer program, which, when run on an electronic device, causes the electronic device to perform the steps of the trajectory prediction method according to any one of claims 1 to 10.

14. A computer program product, characterized in that, Includes a computer program or instructions that, when executed by a processor, implement the steps of the trajectory prediction method according to any one of claims 1 to 10.

15. A vehicle, characterized in that, The vehicle includes the trajectory prediction device as claimed in claim 11, or the electronic device as claimed in claim 12, or the storage medium as claimed in claim 13, or the computer program product as claimed in claim 14.

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