Automatic driving speed planning method, electronic device, vehicle and storage medium

Abstract

The application relates to an automatic driving speed planning method, an electronic device, a vehicle and a storage medium. The method comprises the following steps: obtaining a predicted driving position of a neighboring vehicle of a self-vehicle in a specified time period, and obtaining a relative position relationship between the self-vehicle and the neighboring vehicle; obtaining a predicted limit position of the self-vehicle in the specified time period; wherein the predicted limit position corresponds to the relative position relationship, and the predicted limit position is one of a predicted maximum acceleration position and a predicted maximum deceleration position; judging whether the self-vehicle will collide with the neighboring vehicle according to the predicted limit position and the predicted driving position, and if yes, determining that speed planning of the self-vehicle fails. The scheme provided in the embodiment of the application can detect the speed planning failure of the self-vehicle in advance, thereby improving the automatic driving performance, for example, avoiding or reducing the overall delay of the automatic driving link caused by the speed planning failure.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to an autonomous driving speed planning method, electronic device, vehicle, and storage medium. Background Technology

[0002] With the rapid development of automotive technology, driver assistance technologies, especially autonomous driving technology, have become increasingly sophisticated, improving people's travel experience. Speed ​​planning is particularly important in the process of autonomous driving.

[0003] Speed ​​planning in related technologies uses solvers for smooth solutions. However, in many scenarios, under constraints such as vehicle speed and acceleration, the solver spends a long time but fails to complete the smooth speed solution, resulting in a solver smooth solution timeout. Solver smooth solution timeouts lead to a waste of computational resources and increase planning time, thus increasing the overall latency of the autonomous driving pipeline. Summary of the Invention

[0004] To address or partially address the problems existing in related technologies, this application provides an autonomous driving speed planning method, electronic device, vehicle, and storage medium that can detect speed planning failures of a vehicle in advance, thereby improving autonomous driving performance, such as avoiding or reducing the overall delay of the autonomous driving link caused by speed planning failures.

[0005] The first aspect of this application provides an autonomous driving speed planning method, the method comprising:

[0006] Obtain the predicted driving positions of adjacent vehicles of the vehicle within a specified time period, and obtain the relative positional relationship between the vehicle and the adjacent vehicles;

[0007] Obtain the predicted limit position of the vehicle during the specified time period; wherein the predicted limit position corresponds to the relative position relationship, and the predicted limit position is one of the predicted maximum acceleration position and the predicted maximum deceleration position;

[0008] Based on the predicted limit position and the predicted driving position, it is determined whether the vehicle will collide with the adjacent vehicle. If so, the speed planning of the vehicle is determined to have failed.

[0009] In one embodiment, obtaining the predicted limit position of the vehicle in the specified time period includes: if the adjacent vehicle is the vehicle in front of the vehicle, obtaining at least one of the predicted maximum deceleration positions of the vehicle at at least one sampling time in the specified time period.

[0010] Determining whether the vehicle will collide with the adjacent vehicle based on the predicted extreme position and the predicted driving position includes:

[0011] If any of the predicted driving positions of the vehicle ahead is less than the corresponding position in the at least one predicted maximum deceleration position of the vehicle, it is determined that the vehicle will collide with the adjacent vehicle.

[0012] In one embodiment, the predicted maximum deceleration position of the vehicle at the sampling time is obtained through the following steps:

[0013] Obtain the estimated speed of the vehicle at the sampling time;

[0014] Based on the vehicle's estimated speed and predicted maximum deceleration position at the previous moment, the estimated speed at the sampling moment, and the time interval between the sampling moment and the previous moment, the predicted maximum deceleration position of the vehicle at the sampling moment is calculated.

[0015] The process of obtaining the estimated speed of the vehicle at the sampling time includes:

[0016] Based on the vehicle's minimum acceleration value, minimum jerk value, and the vehicle's estimated acceleration at the previous moment, the estimated acceleration of the vehicle at the sampling moment is obtained.

[0017] Determine whether the estimated speed of the vehicle at the previous moment is less than the minimum speed of the vehicle;

[0018] If so, let the estimated speed of the vehicle at the sampling time be equal to the minimum speed;

[0019] Otherwise, the estimated speed of the vehicle at the sampling time is calculated based on the estimated speed of the vehicle at the previous moment, the estimated acceleration at the sampling time, and the time interval between the sampling time and the previous moment.

[0020] In one embodiment, obtaining the estimated acceleration of the vehicle at the sampling time based on the vehicle's minimum acceleration value, minimum jerk value, and the vehicle's estimated acceleration at the previous moment includes:

[0021] Determine whether the estimated acceleration of the vehicle at the previous moment is less than the minimum acceleration value;

[0022] If so, set the estimated acceleration of the vehicle at the sampling time to be equal to the minimum acceleration value;

[0023] Otherwise, the estimated acceleration of the vehicle at the sampling time is calculated based on the estimated acceleration of the vehicle at the previous moment, the minimum acceleration value, and the time interval between the sampling time and the previous moment.

[0024] In one embodiment, obtaining the predicted limit position of the vehicle in the specified time period includes: if the adjacent vehicle is a vehicle behind the vehicle, obtaining at least one of the predicted maximum acceleration positions of the vehicle at at least one sampling time in the specified time period.

[0025] Determining whether the vehicle will collide with the adjacent vehicle based on the predicted extreme position and the predicted driving position includes:

[0026] If any of the predicted driving positions of the vehicles behind is greater than the corresponding position in at least one of the predicted maximum acceleration positions of the vehicle, it is determined that the vehicle will collide with the adjacent vehicle.

[0027] In one embodiment, the predicted maximum acceleration position of the vehicle at the sampling time is obtained through the following steps:

[0028] Obtain the estimated speed of the vehicle at the sampling time;

[0029] Based on the vehicle's estimated speed and predicted maximum acceleration position at the previous moment, the estimated speed at the sampling moment, and the time interval between the sampling moment and the previous moment, the predicted maximum acceleration position of the vehicle at the sampling moment is calculated.

[0030] The process of obtaining the estimated speed of the vehicle at the sampling time includes:

[0031] Based on the vehicle's maximum acceleration value, maximum jerk value, and the vehicle's estimated acceleration at the previous moment, the estimated acceleration of the vehicle at the sampling moment is obtained.

[0032] Determine whether the estimated speed of the vehicle at the previous moment is greater than the maximum speed of the vehicle;

[0033] If so, let the estimated speed of the vehicle at the sampling time be equal to the maximum speed;

[0034] Otherwise, the estimated speed of the vehicle at the sampling time is calculated based on the estimated speed of the vehicle at the previous moment, the estimated acceleration at the sampling time, and the time interval between the sampling time and the previous moment.

[0035] In one embodiment, obtaining the estimated acceleration of the vehicle at the sampling time based on the vehicle's maximum acceleration value, maximum jerk value, and the vehicle's estimated acceleration at the previous moment includes:

[0036] Determine whether the estimated acceleration of the vehicle at the previous moment is greater than the maximum acceleration value;

[0037] If so, let the estimated acceleration of the vehicle at the sampling time be equal to the maximum acceleration value;

[0038] Otherwise, the estimated acceleration of the vehicle at the sampling time is calculated based on the estimated acceleration of the vehicle at the previous moment, the maximum acceleration value, and the time interval between the sampling time and the previous moment.

[0039] In one embodiment, the adjacent vehicles include: adjacent vehicles in the same lane as the vehicle, and / or adjacent vehicles in the relevant lane during the vehicle's lane change, and / or adjacent vehicles in the adjacent lane that will change lanes to the lane where the vehicle is located.

[0040] Determining whether the vehicle will collide with the adjacent vehicle, and if so, determining that the vehicle's speed planning has failed, includes: determining whether the vehicle will collide with any of the adjacent vehicles, and if so, determining that the vehicle's speed planning has failed.

[0041] In one embodiment, after determining that the vehicle's speed planning has failed, the method further includes:

[0042] Exit the speed planning task; and / or,

[0043] Output collision warning user notification.

[0044] A second aspect of this application provides an electronic device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method described above.

[0045] A third aspect of this application provides a vehicle including the electronic equipment described above.

[0046] A fourth aspect of this application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method described above.

[0047] The technical solution provided in this application may include the following beneficial effects:

[0048] In this embodiment, by predicting the predicted driving positions of adjacent vehicles and the predicted limit positions of the vehicle within a specified time period, and based on the predicted limit positions and predicted driving positions, if the vehicle is about to collide with an adjacent vehicle within the specified time period, it is determined that the vehicle's speed planning has failed. Thus, the vehicle's speed planning failure can be detected in advance, thereby improving the performance of autonomous driving. For example, it avoids unnecessary speed planning time consumption and computing resource occupation, and avoids or reduces the overall delay of the autonomous driving link caused by speed planning failure.

[0049] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0050] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0051] Figure 1 This is a flowchart illustrating an embodiment of an autonomous driving speed planning method according to this application;

[0052] Figure 2 This is another flowchart illustrating an autonomous driving speed planning method according to another embodiment of this application;

[0053] Figure 3 This is a flowchart illustrating the process of obtaining the predicted maximum deceleration position of a vehicle in an autonomous driving speed planning method according to an embodiment of this application.

[0054] Figure 4 This is a flowchart illustrating the process of obtaining the predicted maximum acceleration position of a vehicle in an autonomous driving speed planning method according to an embodiment of this application.

[0055] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0056] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0057] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0058] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0059] When using solvers to perform speed smoothing calculations, under constraints such as vehicle speed and acceleration, the solvers often fail to complete the smoothing calculation despite consuming a significant amount of time, resulting in a solver smoothing timeout. This timeout wastes computational resources, increases planning time, and raises the overall latency of the autonomous driving pipeline.

[0060] To address the aforementioned issues, this application provides an autonomous driving speed planning method that can detect speed planning failures in advance, thereby improving autonomous driving performance, such as avoiding or reducing the overall delay of the autonomous driving link caused by speed planning failures.

[0061] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0062] Figure 1 This is a flowchart illustrating an embodiment of an autonomous driving speed planning method according to this application.

[0063] See Figure 1 An autonomous driving speed planning method includes:

[0064] In step S11, the predicted driving positions of the vehicle's neighboring vehicles during a specified time period are obtained, and the relative positional relationship between the vehicle and its neighboring vehicles is obtained.

[0065] In one embodiment, the vehicle can obtain the predicted driving positions of its neighboring vehicles within a specified time period through its prediction module; the prediction of the prediction module can be achieved using known related technical means, which will not be described in detail in this application.

[0066] In one embodiment, the relative positional relationship between the vehicle and adjacent vehicles during a specified time period includes the front-to-back positional relationship between the two vehicles. In another embodiment, the relative positional relationship between the vehicle and adjacent vehicles during a specified time period also includes the lane positional relationship between the two vehicles, etc.

[0067] In step S12, the predicted limit position of the vehicle during a specified time period is obtained; wherein the predicted limit position corresponds to the relative position relationship, and the predicted limit position is one of the predicted maximum acceleration position and the predicted maximum deceleration position.

[0068] In one embodiment, the maximum acceleration value acc_max, minimum acceleration value acc_min, maximum jerk_max, and minimum jerk_min of the vehicle can be predefined. Acceleration is the rate of change of velocity with respect to time, representing how fast the velocity changes. Jerk, also known as variable acceleration, is a physical quantity describing how fast acceleration changes; it is determined by the amount of change in acceleration and time, i.e., the rate of change of acceleration. The maximum acceleration value acc_max, minimum acceleration value acc_min, maximum jerk_max, and minimum jerk_min of the vehicle represent its longitudinal driving capability. The maximum acceleration value acc_max and maximum jerk_max characterize the vehicle's maximum acceleration capability, while the minimum acceleration value acc_min and minimum jerk_min characterize the vehicle's maximum deceleration capability.

[0069] In one embodiment, the predicted maximum acceleration position and predicted maximum deceleration position of the vehicle within a specified time period can be predicted based on the vehicle's minimum acceleration value acc_min, minimum jerk_min, maximum acceleration value acc_max, and maximum jerk_max, as well as the vehicle's current position s0, velocity v0, and acceleration a0. For example, when the vehicle has adjacent vehicles both behind and in front of it, the predicted maximum acceleration position and predicted maximum deceleration position can be predicted to further determine whether the vehicle will collide with the two adjacent vehicles.

[0070] In one embodiment, the predicted maximum acceleration position of the vehicle during a specified time period can be predicted based on the vehicle's maximum acceleration value acc_max and maximum jerk value jerk_max, as well as the vehicle's current position s0, velocity v0, and acceleration a0; for example, the predicted maximum acceleration position of the vehicle can be predicted when the adjacent vehicle is the vehicle in front of the vehicle.

[0071] In one embodiment, the predicted maximum deceleration position of the vehicle over a specified time period can be predicted based on the vehicle's minimum acceleration value acc_min, minimum jerk value jerk_min, and the vehicle's current position s0, velocity v0, and acceleration a0. For example, the predicted maximum deceleration position of the vehicle can be predicted when the adjacent vehicle is behind it.

[0072] In step S13, based on the predicted limit position and the predicted driving position, it is determined whether the vehicle will collide with an adjacent vehicle; if yes, proceed to step S14; if no, proceed to step S15.

[0073] In one embodiment, during a specified time period in the future, based on the predicted limit position of the vehicle and the predicted driving position of the adjacent vehicle, it is determined whether the vehicle will collide with the adjacent vehicle; if the vehicle will collide with the adjacent vehicle, that is, during the specified time period in the future, the vehicle cannot avoid colliding with the adjacent vehicle even under maximum acceleration or maximum deceleration capabilities, then step S14 is executed; if the vehicle will not collide with the adjacent vehicle, then step S15 is executed.

[0074] In step S14, it is determined that the vehicle's speed planning has failed.

[0075] In one embodiment, if it is determined that the vehicle will collide with an adjacent vehicle, and a suitable autonomous driving speed cannot be planned for the vehicle, then the vehicle speed planning is determined to have failed.

[0076] In step S15, the speed planning of the vehicle is determined to continue.

[0077] In one embodiment, if the vehicle is not expected to collide with an adjacent vehicle, the vehicle's speed planning can continue for a specified period of time in the future.

[0078] The autonomous driving speed planning method illustrated in this application predicts the predicted driving positions of adjacent vehicles and the predicted limit positions of the vehicle within a specified time period. Based on the predicted limit positions and predicted driving positions, if the vehicle is expected to collide with an adjacent vehicle within the specified time period, the speed planning of the vehicle is determined to have failed. Thus, the speed planning failure of the vehicle can be detected in advance, thereby improving the performance of autonomous driving. For example, it avoids unnecessary speed planning time consumption and computational resource occupation, and avoids or reduces the overall delay of the autonomous driving link caused by planning failure.

[0079] Figure 2 This is a flowchart illustrating an autonomous driving speed planning method according to another embodiment of this application.

[0080] See Figure 2 An autonomous driving speed planning method includes:

[0081] In step S21, the predicted driving positions of the adjacent vehicles of the vehicle during a specified time period are obtained, as well as the relative positional relationship between the vehicle and the adjacent vehicle; if the adjacent vehicle is the vehicle in front of the vehicle, step S22 is executed; if the adjacent vehicle is the vehicle behind the vehicle, step S24 is executed.

[0082] In one embodiment, adjacent vehicles include vehicles in the same lane as the vehicle, including vehicles in front of and behind the vehicle.

[0083] In one embodiment, it is determined whether the distance between the vehicle and the adjacent vehicle is within a preset range. If so, step S21 is executed; otherwise, the distance between the vehicle and the adjacent vehicle continues to be detected until the distance between the two vehicles reaches the preset range. If the distance between the vehicle and the adjacent vehicle exceeds the preset range, the two vehicles will not collide within a specified time period, and it is not necessary to detect whether speed planning will fail, thus saving computational resources.

[0084] In one embodiment, adjacent vehicles include adjacent vehicles in the relevant lane during the lane change process, such as adjacent vehicles in the same lane as the vehicle, and / or adjacent vehicles in the lane the vehicle passes through during the lane change process, and / or adjacent vehicles in the target lane after the vehicle changes lanes.

[0085] In one embodiment, adjacent vehicles include adjacent vehicles in adjacent lanes that will change lanes to the lane where the vehicle is located.

[0086] In step S22, the predicted maximum deceleration position of the vehicle during the specified time period is obtained.

[0087] In this embodiment, if the adjacent vehicle is the vehicle in front of the vehicle, the predicted maximum deceleration position of the vehicle within a specified time period is obtained.

[0088] For ease of description, in the embodiments of this application, the time interval between predicting the maximum deceleration position and the maximum acceleration position is set to dt.

[0089] In one embodiment, the predicted maximum deceleration position of the vehicle within a specified time period is predicted based on the predefined minimum acceleration value acc_min, minimum jerk_min, and the real-time position s0, velocity v0, and acceleration a0 of the vehicle.

[0090] In one embodiment, the specified time period can be, for example, N seconds (e.g., 2 seconds or 4 seconds) after the current time. The duration of the specified time period can be equal to the prediction time interval dt; or, the duration of the specified time period can be greater than the time interval dt, such that the obtained predicted limit position of the vehicle within the specified time period is a set of multiple predicted limit positions corresponding to multiple sampling times spaced dt apart.

[0091] See Figure 3 In one embodiment, step S22 includes:

[0092] Step S221: Based on the vehicle's acceleration a0 at the current moment, calculate the estimated acceleration a of the vehicle at time t after a time interval dt, with the minimum acceleration value jerk_min. t .

[0093] The following formula can be used to calculate the estimated acceleration *a* of the vehicle at time *t* after a time interval *dt*, based on the vehicle's current acceleration *a*0. t :

[0094] a t = a0 + jerk_min * dt.

[0095] Step S222: Determine the estimated acceleration a of the vehicle at time t. t Is it less than the minimum acceleration value acc_min? If yes, proceed to step S223; if no, proceed to step S224.

[0096] Step S223, let the estimated acceleration a of the vehicle at time t+1 be... t+1 If the minimum acceleration value acc_min is equal, proceed to step S225.

[0097] If the estimated acceleration of the vehicle at time t is a t If the acceleration is less than the vehicle's minimum acceleration value acc_min, then let the estimated acceleration a of the vehicle at time t+1 be... t+1 It equals the minimum acceleration value acc_min.

[0098] Step S224, based on the estimated acceleration a of the vehicle at time t t Calculate the acceleration a of the vehicle at time t+1. t+1 .

[0099] If the estimated acceleration of the vehicle at time t is a t If the acceleration is greater than or equal to the minimum acceleration value acc_min, the estimated acceleration a of the vehicle at time t+1 can be calculated using the following formula. t+1 :

[0100] a t+1 =a t +jerk_min*dt, where a t = a0 + jerk_min * dt.

[0101] Step S225: Determine the estimated speed v of the vehicle at time t. tIs it less than the vehicle's minimum speed? If yes, proceed to step S226; if no, proceed to step S227.

[0102] In some embodiments, the minimum speed v of the vehicle min This refers to the predefined minimum speed at which a vehicle can operate autonomously, for example, the minimum speed v. min A value of 0 indicates that reversing is not allowed.

[0103] Step S226, let the estimated speed v of the vehicle at time t+1 be... t+1 If the speed is equal to the vehicle's minimum speed, proceed to step S228.

[0104] If the estimated speed v of the vehicle at time t t Less than the vehicle's minimum speed v min Let the estimated velocity v at time t+1 be... t+1 =v min Based on this estimated speed, the predicted maximum deceleration position of the vehicle at time t+1 is determined.

[0105] Step S227, based on the estimated speed v of the vehicle at time t t Calculate the estimated speed v of the vehicle at time t+1. t+1 .

[0106] If the estimated speed v of the vehicle at time t t Greater than or equal to the vehicle's minimum speed v min The estimated speed v of the vehicle at time t can be calculated using the following formula. t and the estimated acceleration a at time t+1 t+1 Calculate the estimated speed v of the vehicle at time t+1. t+1 :

[0107] v t+1 =v t +a t+1 *dt, where v t =v0+a t *dt,a t = a0 + jerk_min * dt.

[0108] Obtain the estimated speed v of the vehicle at time t+1. t+1 Then, based on the estimated speed, the predicted maximum deceleration position of the vehicle at time t+1 can be predicted.

[0109] Step S228: Based on the vehicle's current speed v0 and the estimated speed v at time t... t The estimated velocity v at time t+1 t+1 Predict the predicted maximum deceleration position s of the vehicle at time t. t_deacc and the predicted maximum deceleration position s at time t+1 t+1 _deacc.

[0110] In one embodiment, the predicted maximum deceleration position s of the vehicle at time t can be calculated using the following formula. t _deacc and the predicted maximum deceleration position s at time t+1 t+1 _deacc:

[0111] s t _deacc=s0+(v t +v0)*0.5*dt;

[0112] s t+1 _deacc=s t +(v t+1 +v t )*0.5*dt.

[0113] In the above formula, using v t and v t+1 The average of the estimated velocities at two times is used to calculate the predicted maximum deceleration position s at time t+1. t+1 _deacc, understandably, this application is not limited to this; for example, different weights can be assigned to the estimated velocities at two times, and s can be calculated by weighted average. t+1 _deacc.

[0114] Understandably, for other sampling times t+i (where i is an integer greater than or equal to 2) after t+1 within a specified time period, a similar process can be used, based on the estimated velocity v at that sampling time t+i. t+i The estimated velocity v at the previous time t+i-1 t+i-1 And the predicted maximum deceleration position s at the previous time t+i-1. t+i-1 _deacc calculates the predicted maximum deceleration position s at time t+i. t+i _deacc.

[0115] In one embodiment, a similar process to that described above can be used to obtain the estimated velocity v of the vehicle at other sampling times t+i. t+i ,include:

[0116] Based on the vehicle's minimum acceleration value acc_min, minimum jerk_min, and the vehicle's estimated acceleration a at the previous time t+i-1. t+i-1 Obtain the estimated acceleration a of the vehicle at sampling time t+i. t+i ;as well as

[0117] Determine the estimated velocity v of the vehicle at the previous time t+i-1.t+i-1 Is it less than the vehicle's minimum speed v? min If so, let the estimated velocity v of the vehicle at sampling time t+i be... t+i Equal to minimum speed v min Otherwise, based on the estimated speed v of the vehicle at the previous time t+i-1... t+i-1 The estimated acceleration a at sampling time t+i t+i Given the time interval between sampling time t+i and the previous time t+i-1, calculate the estimated speed v of the vehicle at sampling time t+i. t+i .

[0118] In one embodiment, a similar process to that described above can be employed, based on the vehicle's minimum acceleration value acc_min, minimum jerk_min, and the vehicle's estimated acceleration a at the previous time t+i-1. t+i Obtain the estimated acceleration a of the vehicle at sampling time t+i. t+i ,include:

[0119] Determine the estimated acceleration a of the vehicle at the previous time t+i-1. t+i-1 Is it less than the minimum acceleration value acc_min? If so, let the estimated acceleration a of the vehicle at sampling time t+i be... t+i It equals the minimum acceleration value acc_min; otherwise, it is based on the estimated acceleration a of the vehicle at the previous time t+i-1. t+i-1 Given the minimum jerk_min value and the time interval between sampling time t+i and the previous time t+i-1, calculate the estimated acceleration a of the vehicle at sampling time t+i. t+i .

[0120] In step S23, based on the predicted maximum deceleration position of the vehicle within a specified time period and the predicted driving position of the vehicle in front within the specified time period, it is determined whether the vehicle will collide with the vehicle in front; if yes, proceed to step S26; if no, proceed to step S27.

[0121] In one embodiment, if any position of the predicted driving position of the vehicle in front is smaller than the corresponding position of the predicted maximum deceleration position of the vehicle, it is determined that the vehicle will collide with the vehicle in front, and step S26 is executed.

[0122] In one embodiment, it is determined whether the predicted driving position of the vehicle ahead at each sampling time within a specified time period is less than the predicted maximum deceleration position of the vehicle at the same sampling time. If the predicted driving position s of the vehicle ahead at any sampling time t+i is less than the predicted maximum deceleration position of the vehicle itself at the same sampling time, then... t+i The predicted maximum deceleration position s of the vehicle at the same sampling time is less than the previous obj. t+i _deacc, i.e., s t+i_former obj t+i _deacc indicates that, under the constraints of the predetermined minimum acceleration values ​​acc_min and jerk_min, as well as the vehicle's current position s0, speed v0, and acceleration a0, even with maximum deceleration, a collision with the vehicle ahead cannot be avoided. In this case, a suitable autonomous driving speed cannot be planned for the vehicle, and step S26 is executed. If the predicted driving position s of the vehicle ahead is within each sampling time in the specified time period... t+i The preceding obj values ​​are all greater than or equal to the predicted maximum deceleration position s of the vehicle at the same sampling time. t+i _deacc, i.e., s t+i _previous obj≥s t+i _deacc indicates that the vehicle will not collide with the vehicle in front under the constraints of the predetermined minimum acceleration value acc_min, minimum jerk_min, and the vehicle's current position s0, speed v0, and acceleration a0. Then, step S27 is executed.

[0123] In step S24, the predicted maximum acceleration position of the vehicle during the specified time period is obtained.

[0124] If the adjacent vehicle is the vehicle behind the vehicle, obtain the predicted maximum acceleration position of the vehicle within the specified time period.

[0125] In one embodiment, the predicted maximum acceleration position of the vehicle within a specified time period is predicted based on the predefined maximum acceleration value acc_max, maximum jerk_max of the vehicle, and the real-time position s0, velocity v0, and acceleration a0 of the vehicle.

[0126] See Figure 4 In one embodiment, step S24 includes:

[0127] Step S241: Based on the vehicle's current acceleration a0, calculate the estimated acceleration a of the vehicle at time t after a time interval dt, where the vehicle has the maximum acceleration value jerk_max. t .

[0128] The following formula can be used to calculate the estimated acceleration *a* of the vehicle at time *t* after a time interval *dt*, based on the vehicle's current acceleration *a*0. t :

[0129] a t = a0 + jerk_max * dt.

[0130] Step S242: Determine the estimated acceleration a of the vehicle at time t. t ​Is it greater than the maximum acceleration value acc_max? If yes, proceed to step S243; if no, proceed to step S244.

[0131] Step S243, let the estimated acceleration a of the vehicle at time t+1 be... t+1 If the value equals the maximum acceleration value acc_max, proceed to step S245.

[0132] If the estimated acceleration of the vehicle at time t is a t If the acceleration is greater than the vehicle's maximum acceleration value acc_max, then let the estimated acceleration a of the vehicle at time t+1 be... t+1 It equals the maximum acceleration value acc_max.

[0133] Step S244: Based on the estimated acceleration a of the vehicle at time t t Calculate the acceleration a of the vehicle at time t+1. t+1 .

[0134] If the estimated acceleration of the vehicle at time t is a t If the acceleration is less than or equal to the maximum acceleration value acc_max, the estimated acceleration a of the vehicle at time t+1 can be calculated using the following formula. t+1 :

[0135] a t+1 =a t +jerk_max*dt, where a t = a0 + jerk_max * dt.

[0136] Step S245: Determine the estimated speed v of the vehicle at time t. t Is the speed greater than the vehicle's maximum speed? If yes, proceed to step S246; if no, proceed to step S247.

[0137] In some embodiments, the maximum speed v of the vehicle max This refers to the maximum speed of the vehicle when it is driving autonomously, such as 140 kph (kilometers per hour).

[0138] Step S246, let the estimated speed v of the vehicle at time t+1 be... t+1 If the speed is equal to the maximum speed of the vehicle, proceed to step S248.

[0139] If the estimated speed v of the vehicle at time t t Greater than the vehicle's maximum speed v max Let the estimated speed v of the vehicle at time t+1 be... t+1 =v max Based on this estimated speed, the predicted maximum acceleration position of the vehicle at time t+1 is determined.

[0140] Step S247, based on the estimated speed v of the vehicle at time t t Calculate the estimated speed v of the vehicle at time t+1. t+1 .

[0141] If the estimated speed v of the vehicle at time t t Less than or equal to the vehicle's maximum speed v max The estimated speed v of the vehicle at time t can be calculated using the following formula. t and the estimated acceleration a at time t+1 t+1 Calculate the estimated speed v of the vehicle at time t+1. t+1 :

[0142] v t+1 =v t +a t+1 *dt, where v t =v0+a t *dt,a t = a0 + jerk_max * dt.

[0143] Obtain the estimated speed v of the vehicle at time t+1. t+1 Then, based on this estimated speed, the predicted maximum acceleration position of the vehicle at time t+1 can be predicted.

[0144] Step S248: Based on the vehicle's current speed v0 and the estimated speed v at time t... t The estimated velocity v at time t+1 t+1 Predict the predicted maximum acceleration position s of the vehicle at time t. t _acc and the predicted maximum acceleration position s at time t+1 t+1 _acc.

[0145] In one embodiment, the predicted maximum acceleration position s of the vehicle at time t can be calculated using the following formula. t _acc and the predicted maximum acceleration position s at time t+1 t+1 _acc:

[0146] s t _acc=s0+(v t +v0)*0.5*dt;

[0147] s t+1 _acc=s t +(v t+1 +v t )*0.5*dt.

[0148] In the above formula, using v t and v t+1The average of the estimated velocities at two moments is used to calculate the predicted maximum acceleration position s at time t+1. t+1 _acc, understandably, this application is not limited to this; for example, different weights can be assigned to the estimated velocities at two times, and s can be calculated by weighted average. t+1 _acc.

[0149] Understandably, for other sampling times t+i (where i is an integer greater than or equal to 2) after t+1 within a specified time period, a similar process can be used, based on the estimated velocity v at that sampling time t+i. t+i The estimated velocity v at the previous time t+i-1 t+i-1 And the predicted maximum acceleration position s at the previous time t+i-1. t+i-1 _acc, calculate the predicted maximum acceleration position s at time t+i. t+i _acc.

[0150] In one embodiment, a similar process to that described above can be used to obtain the estimated velocity v of the vehicle at other sampling times t+i. t+i ,include:

[0151] Based on the vehicle's maximum acceleration value acc_max, maximum jerk_max, and the vehicle's estimated acceleration a at the previous time t+i-1. t+i-1 Obtain the estimated acceleration a of the vehicle at sampling time t+i. t+i ;as well as

[0152] Determine the estimated velocity v of the vehicle at the previous time t+i-1. t+i-1 Is it greater than the vehicle's maximum speed v? max If so, let the estimated velocity v of the vehicle at sampling time t+i be... t+i Equal to maximum speed v max Otherwise, based on the estimated speed v of the vehicle at the previous time t+i-1... t+i-1 The estimated acceleration a at sampling time t+i t+i Given the time interval between sampling time t+i and the previous time t+i-1, calculate the estimated speed v of the vehicle at sampling time t+i. t+i .

[0153] In one embodiment, a similar process to that described above can be employed, based on the vehicle's maximum acceleration value acc_max, maximum jerk_max, and the estimated acceleration a of the vehicle at the previous time t+i-1. t+i Obtain the estimated acceleration a of the vehicle at sampling time t+i. t+i ,include:

[0154] Determine the estimated acceleration a of the vehicle at the previous time t+i-1. t+i-1 Is it greater than the maximum acceleration value acc_max? If so, let the estimated acceleration a of the vehicle at sampling time t+i be... t+i It equals the maximum acceleration value acc_max; otherwise, it is based on the estimated acceleration a of the vehicle at the previous time t+i-1. t+i-1 Given the maximum jerk_max and the time interval between sampling time t+i and the previous time t+i-1, calculate the estimated acceleration a of the vehicle at sampling time t+i. t+i .

[0155] In step S25, based on the predicted maximum acceleration position of the vehicle during the specified time period and the predicted driving position of the vehicle behind during the specified time period, it is determined whether the vehicle will collide with the vehicle behind; if yes, proceed to step S26; if no, proceed to step S27.

[0156] In one embodiment, if any of the predicted driving positions of the following vehicles is greater than the corresponding position in the predicted maximum acceleration position of the vehicle, it is determined that the vehicle will collide with the following vehicles, and step S26 is executed.

[0157] In one embodiment, it is determined whether the predicted driving position of the vehicle behind at each sampling time within a specified time period is greater than the predicted maximum acceleration position of the vehicle at the same sampling time. If the predicted driving position of the vehicle behind at any sampling time t+i is greater than the predicted maximum acceleration position of the vehicle itself at the same sampling time, then... t+i The predicted maximum acceleration position s of the vehicle at the same sampling time is greater than the value of obj after the vehicle. t+i _acc, i.e., s t+i _afterobj>s t+i _acc indicates that, under the constraints of the predetermined maximum acceleration values ​​acc_max and jerk_max, as well as the vehicle's current position s0, velocity v0, and acceleration a0, even with maximum acceleration, a collision with the vehicle behind cannot be avoided. In this case, a suitable autonomous driving speed cannot be planned for the vehicle, and step S26 is executed. If the predicted driving position s of the vehicle behind is within each sampling time in the specified time period... t+i After _, obj is less than or equal to the predicted maximum acceleration position s of the vehicle at the same sampling time. t+i _acc, i.e., s t+i _after obj≤s t+i _acc indicates that the vehicle will not collide with the vehicle behind it under the constraints of the predetermined maximum acceleration value acc_max, maximum jerk_max, and the vehicle's current position s0, speed v0, and acceleration a0. Then, step S27 is executed.

[0158] In step S26, it is determined that the vehicle's speed planning has failed.

[0159] If it is determined that the vehicle will collide with the vehicle in front or behind, and a suitable autonomous driving speed cannot be planned for the vehicle, then the speed planning of the vehicle has failed.

[0160] In one embodiment, if it is determined that the vehicle's speed planning has failed, the speed solver is exited.

[0161] The vehicle's speed planning task can be performed by the speed solver. If it is determined that the vehicle will collide with the vehicle in front or behind, the speed planning of the vehicle can be considered to have failed. The speed solver will not be able to complete the optimization solution for the vehicle's speed. Therefore, the optimization solution can be stopped and the speed solver can be exited.

[0162] In one embodiment, after determining that the vehicle's speed planning has failed, a collision warning user notification is output to remind the driver that a collision may occur. The driver can then take over the vehicle and switch to manual driving in a timely manner.

[0163] In step S27, the speed planning of the vehicle is determined to continue.

[0164] If it is determined that the vehicle will not collide with the vehicle in front or behind, the speed solver can be used to perform speed planning and optimize the speed of the vehicle.

[0165] The autonomous driving speed planning method of this application predicts the predicted driving positions of adjacent vehicles and the predicted limit positions of the vehicle within a specified time period in advance. Based on the predicted limit positions and predicted driving positions, if the vehicle is expected to collide with an adjacent vehicle within the specified time period, the speed planning of the vehicle is determined to have failed. Thus, the speed planning failure of the vehicle can be detected in advance, thereby improving the performance of autonomous driving. For example, it avoids unnecessary speed planning time consumption and computing resource occupation, and avoids or reduces the overall delay of the autonomous driving link caused by speed planning failure.

[0166] Furthermore, in an embodiment of the autonomous driving speed planning method of this application, the following steps are taken: (1) determining whether the estimated acceleration of the vehicle at the previous moment is less than the minimum acceleration value; if so, setting the estimated acceleration of the vehicle at the sampling moment equal to the minimum acceleration value; otherwise, calculating the estimated acceleration of the vehicle at the sampling moment based on the estimated acceleration of the vehicle at the previous moment, the minimum acceleration value, and the time interval between the sampling moment and the previous moment; (2) determining whether the estimated speed of the vehicle at the previous moment is less than the minimum speed of the vehicle; if so, setting the estimated speed of the vehicle at the sampling moment equal to the minimum speed; otherwise, calculating the estimated speed of the vehicle at the sampling moment based on the estimated speed of the vehicle at the previous moment, the estimated acceleration at the sampling moment, and the time interval between the sampling moment and the previous moment. This allows for the calculation of the predicted maximum deceleration position of the vehicle at the sampling time based on the vehicle's estimated speed and predicted maximum deceleration position at the previous moment, the estimated speed at the sampling time, and the time interval between the sampling time and the previous moment. By improving the accuracy of the estimated speed and estimated acceleration, the accuracy of the predicted maximum deceleration position can be improved, thereby enhancing the reliability of speed planning failure detection.

[0167] Furthermore, an embodiment of the autonomous driving speed planning method of this application involves: (1) determining whether the estimated acceleration of the vehicle at the previous moment is greater than the maximum acceleration value; if so, setting the estimated acceleration of the vehicle at the sampling moment to be equal to the maximum acceleration value; otherwise, calculating the estimated acceleration of the vehicle at the sampling moment based on the estimated acceleration of the vehicle at the previous moment, the maximum acceleration value, and the time interval between the sampling moment and the previous moment; (2) determining whether the estimated speed of the vehicle at the previous moment is greater than the maximum speed of the vehicle; if so, setting the estimated speed of the vehicle at the sampling moment to be equal to the maximum speed; otherwise, calculating the estimated speed of the vehicle at the sampling moment based on the estimated speed of the vehicle at the previous moment, the estimated acceleration at the sampling moment, and the time interval between the sampling moment and the previous moment. This allows for the calculation of the predicted maximum acceleration position of the vehicle at the sampling time based on the vehicle's estimated speed and predicted maximum acceleration position at the previous moment, the estimated speed at the sampling time, and the time interval between the sampling time and the previous moment. By improving the accuracy of the estimated speed and estimated acceleration, the accuracy of the predicted maximum acceleration position of the vehicle can be improved, thereby enhancing the reliability of speed planning failure detection.

[0168] Furthermore, in one embodiment of the autonomous driving speed planning method of this application, after determining that the vehicle's speed planning has failed, the speed solver exits the speed planning task. By exiting the speed solver in advance, unnecessary time consumption and computational resource occupation caused by the speed solver continuing to perform optimization and solving when no solution result can be obtained are avoided, and the overall delay of the autonomous driving link is avoided due to the speed solver timeout.

[0169] Furthermore, the autonomous driving speed planning method shown in this application embodiment outputs a collision warning user notification after determining that the vehicle's speed planning has failed. This can issue a warning signal in advance to remind the driver to manually drive the vehicle and ensure the vehicle's driving safety.

[0170] Corresponding to the aforementioned application function implementation method embodiments, this application also provides an electronic device and corresponding embodiments.

[0171] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. It will be understood that the electronic device in this embodiment may be, for example, but not limited to, a vehicle's electronic control unit, an autonomous driving system controller, or a mobile device such as a smart navigation device, a smartphone, or a smart tablet.

[0172] See Figure 5 The electronic device 500 includes a memory 510 and a processor 520.

[0173] The processor 520 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0174] Memory 510 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by the processor 520 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 510 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, memory 510 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-high-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0175] The memory 510 stores executable code, which, when processed by the processor 520, can cause the processor 520 to execute part or all of the methods described above.

[0176] According to another embodiment of this application, this application also provides a vehicle having the electronic equipment described above.

[0177] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0178] Alternatively, this application may be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0179] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. An automated driving speed planning method, characterized by, The method comprises: obtaining a predicted driving position of a neighboring vehicle of the ego vehicle in a specified time period, and obtaining a relative position relationship between the ego vehicle and the neighboring vehicle; obtaining a predicted limit position of the ego vehicle in the specified time period; wherein the predicted limit position corresponds to the relative position relationship, and the predicted limit position is one of a predicted maximum acceleration position and a predicted maximum deceleration position; the predicted maximum acceleration position is determined according to a maximum acceleration value, a maximum jerk value, a position at a current time, a speed at the current time, and an acceleration at the current time of the ego vehicle, and the predicted maximum deceleration position is determined according to a minimum acceleration value, a minimum jerk value, the position at the current time, the speed at the current time, and the acceleration at the current time of the ego vehicle; judging whether the ego vehicle will collide with the neighboring vehicle according to the predicted limit position and the predicted driving position, and determining that a speed planning of the ego vehicle fails if the ego vehicle will collide with the neighboring vehicle.

2. The method of claim 1, wherein obtaining a predicted limit position of the ego vehicle in the specified time period comprises: if the neighboring vehicle is a front vehicle of the ego vehicle, obtaining at least one predicted maximum deceleration position of the ego vehicle at at least one sampling time in the specified time period; judging whether the ego vehicle will collide with the neighboring vehicle according to the predicted limit position and the predicted driving position comprises: if any position in the predicted driving position of the front vehicle is less than a corresponding position in the at least one predicted maximum deceleration position of the ego vehicle, determining that the ego vehicle will collide with the neighboring vehicle.

3. The method of claim 2, wherein, The predicted maximum deceleration position of the ego vehicle at a sampling time is obtained by the following steps: obtaining an estimated speed of the ego vehicle at the sampling time; calculating the predicted maximum deceleration position of the ego vehicle at the sampling time according to an estimated speed of the ego vehicle at a previous time and a predicted maximum deceleration position, an estimated speed of the ego vehicle at the sampling time, and a time interval between the sampling time and the previous time; wherein obtaining the estimated speed of the ego vehicle at the sampling time comprises: obtaining an estimated acceleration of the ego vehicle at the sampling time according to a minimum acceleration value, a minimum jerk value, and an estimated acceleration of the ego vehicle at the previous time; judging whether the estimated speed of the ego vehicle at the previous time is less than a minimum speed of the ego vehicle; if yes, setting the estimated speed of the ego vehicle at the sampling time to be equal to the minimum speed; otherwise, calculating the estimated speed of the ego vehicle at the sampling time according to the estimated speed of the ego vehicle at the previous time, the estimated acceleration of the ego vehicle at the sampling time, and the time interval between the sampling time and the previous time.

4. The method of claim 3, wherein, The obtaining of the estimated acceleration of the ego vehicle at the sampling time according to the minimum acceleration value, the minimum jerk value, and the estimated acceleration of the ego vehicle at the previous time comprises: judging whether the estimated acceleration of the ego vehicle at the previous time is less than the minimum acceleration value; if yes, let the estimated acceleration of the ego vehicle at the sampling time be equal to the minimum acceleration value; otherwise, calculate the estimated acceleration of the ego vehicle at the sampling time according to the estimated acceleration of the ego vehicle at the previous time, the minimum jerk value, and the time interval between the sampling time and the previous time.

5. The method of claim 1, wherein obtaining the predicted extreme position of the ego vehicle within the specified time period comprises, if the adjacent vehicle is a rear vehicle of the ego vehicle, obtaining at least one predicted maximum acceleration position of the ego vehicle at at least one sampling time within the specified time period; determining whether the ego vehicle will collide with the adjacent vehicle according to the predicted extreme position and the predicted travel position comprises: if any position in the predicted travel position of the rear vehicle is greater than a corresponding position in the at least one predicted maximum acceleration position of the ego vehicle, determining that the ego vehicle will collide with the adjacent vehicle.

6. The method of claim 5, wherein, obtaining the predicted maximum acceleration position of the ego vehicle at a sampling time comprises: obtaining an estimated speed of the ego vehicle at the sampling time; calculating the predicted maximum acceleration position of the ego vehicle at the sampling time according to the estimated speed and predicted maximum acceleration position of the ego vehicle at a previous time, the estimated speed at the sampling time, and the time interval between the sampling time and the previous time; wherein obtaining the estimated speed of the ego vehicle at the sampling time comprises: obtaining an estimated acceleration of the ego vehicle at the sampling time according to a maximum acceleration value, a maximum jerk value, and the estimated acceleration of the ego vehicle at the previous time; determining whether the estimated speed of the ego vehicle at the previous time is greater than a maximum speed of the ego vehicle; if yes, letting the estimated speed of the ego vehicle at the sampling time be equal to the maximum speed; otherwise, calculating the estimated speed of the ego vehicle at the sampling time according to the estimated speed of the ego vehicle at the previous time, the estimated acceleration at the sampling time, and the time interval between the sampling time and the previous time.

7. The method of claim 6, wherein, the obtaining of the estimated acceleration of the ego vehicle at the sampling time according to the maximum acceleration value, the maximum jerk value, and the estimated acceleration of the ego vehicle at the previous time comprises: determining whether the estimated acceleration of the ego vehicle at the previous time is greater than the maximum acceleration value; if yes, letting the estimated acceleration of the ego vehicle at the sampling time be equal to the maximum acceleration value; otherwise, calculating the estimated acceleration of the ego vehicle at the sampling time according to the estimated acceleration of the ego vehicle at the previous time, the maximum jerk value, and the time interval between the sampling time and the previous time.

8. The method of any one of claims 1 to 7, wherein: the adjacent vehicle comprises an adjacent vehicle in the same lane as the ego vehicle, and / or an adjacent vehicle in a relevant lane during a lane-changing process of the ego vehicle, and / or an adjacent vehicle on an adjacent lane that will change lane to the lane of the ego vehicle. If yes, determining that the speed planning of the ego vehicle fails comprises: determining whether the ego vehicle will collide with any of the adjacent vehicles, and if yes, determining that the speed planning of the ego vehicle fails.

9. The method of claim 8, wherein, After the determination that the speed planning of the ego vehicle fails, the method further comprises: quitting the speed planning task; and / or, outputting a collision warning user notification.

10. An electronic device, comprising: An electronic device comprising a processor, a memory; and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implements the method of any one of claims 1 to 9.

11. A vehicle characterized by comprising: An electronic device as claimed in claim 10.

12. A computer-readable storage medium, characterized in that: An electronic device comprising a processor, a memory; and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implements the method of any one of claims 1 to 9.

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