Speed ​​Decision-Making Method and Apparatus Based on Probabilistic Grid Map, and Related Products

Abstract

This invention discloses a speed decision method and apparatus based on a probabilistic grid map, as well as related products. The method includes: establishing an ST probabilistic grid map in a Frenet coordinate system with the vehicle's position as the origin; expanding multiple extended grid points that meet preset conditions in the ST probabilistic grid map, using the grid point where the vehicle's position is located as the parent node, according to a search step size; continuing to expand multiple extended grid points that meet preset conditions, using each extended grid point as the parent node; searching round by round until a preset termination condition is met to obtain multiple paths and their corresponding speed decision results; for each path, calculating the total cost of the path based on the occupancy probability of each grid point contained in the path and its relative distance to obstacles; determining multiple candidate speed decision results based on the total cost corresponding to multiple paths; and selecting the target speed decision result from the multiple candidate speed decision results. The embodiments of this invention can better ensure the security of the final speed decision result.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a speed decision-making method and apparatus based on probabilistic grid diagrams, as well as related products. Background Technology

[0002] With the development of artificial intelligence technology, autonomous driving technology is becoming increasingly mature. Autonomous driving technology can be broadly divided into perception, prediction, localization, decision-making, planning, and control. Decision-making and planning are key components of autonomous driving. First, it requires the fusion of information from multiple sensors. Then, it makes task decisions based on driving needs. Next, while avoiding potential obstacles, it plans multiple safe paths between two points under specific constraints, and selects the optimal path as the vehicle's trajectory. Decision-making technology ensures the driving safety of autonomous vehicles and their adherence to traffic rules, while also providing limiting information for the smooth optimization of paths and speeds.

[0003] Vehicles frequently interact with other road users during operation, such as when another vehicle cuts in, pedestrians yield, and vehicles traverse intersections. Autonomous vehicles need to adjust their target speed promptly to avoid conflicts with other dynamic targets. While speed planning can be performed in real-time based on the surrounding environment, it struggles to effectively reference historical information and align with human driving habits. Therefore, speed decisions can be combined with historical data and traffic rules to make speed adjustments or behavioral decisions based on specific obstacles or locations. This provides clear planning objectives for speed planning, improving the smoothness and rationality of vehicle performance.

[0004] Common speed decision-making methods in related technologies include setting virtual stopping points, obstacle projection-based search algorithms, and Markov decision processes. The virtual stopping point method determines the existence of collision risk based on the collision time between the vehicle and other obstacles; if a risk exists, a virtual stopping point is set or updated before the nearest collision point to achieve a yielding function. The obstacle projection-based search algorithm projects the perceived and predicted results of obstacles into the obstacle speed planning space, designs a cost function, and uses a search algorithm to obtain the decision result for conflicting obstacles. The Markov decision process predicts and calculates the benefits of actions over a future period based on the vehicle and obstacle states, and adopts the action with the highest expected benefit as the current speed decision result.

[0005] The inventors discovered that some existing solutions cannot guarantee the security of decision-making results, and furthermore, some solutions cannot guarantee the stability of decision-making results. Summary of the Invention

[0006] The embodiments of the present invention are intended to solve at least one of the above-mentioned technical problems.

[0007] In a first aspect, embodiments of the present invention provide a speed decision method based on a probabilistic grid map, comprising: establishing an ST probabilistic grid map in a Frenet coordinate system with the vehicle's position as the origin; expanding multiple extended grid points that satisfy preset conditions in the ST probabilistic grid map, with the grid point where the vehicle's position is located as the parent node, according to a search step size; continuing to expand multiple extended grid points that satisfy preset conditions with each extended grid point as the parent node; searching round by round until a preset termination condition is met to obtain multiple paths and their corresponding speed decision results; for each path, calculating the total cost of the path based on the occupancy probability of each grid point contained in the path and its relative distance to obstacles; determining multiple candidate speed decision results based on the total cost corresponding to the multiple paths; and selecting a target speed decision result from the multiple candidate speed decision results.

[0008] Secondly, the present invention provides a speed decision device based on a probabilistic grid map, comprising: a probabilistic grid map establishment module, configured to establish an ST probabilistic grid map in a Frenet coordinate system with the vehicle's position as the origin; an expansion module, configured to expand multiple extended grid points in the ST probabilistic grid map, with the grid point where the vehicle's position is located as the parent node, according to a search step size, to satisfy a preset condition; to continue expanding multiple extended grid points according to the search step size, with each extended grid point as the parent node; to search in rounds until a preset termination condition is met, so as to obtain multiple paths and their corresponding speed decision results; a total cost calculation module, configured to calculate the total cost of each path based on the occupancy probability of each grid point contained in the path and its relative distance to obstacles; a candidate determination module, configured to determine multiple candidate speed decision results based on the total cost corresponding to the multiple paths; and a decision module, configured to select a target speed decision result from the multiple candidate speed decision results.

[0009] Thirdly, embodiments of the present invention provide an electronic device comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any of the above-described speed decision methods based on probabilistic grid diagrams of the present invention.

[0010] Fourthly, embodiments of the present invention provide a mobile device, including a main body and an electronic device according to any embodiment of the present invention mounted on the main body.

[0011] Fifthly, embodiments of the present invention provide a storage medium storing one or more programs including execution instructions, the execution instructions being readable and executable by electronic devices (including but not limited to computers, servers, or network devices, etc.) to perform any of the above-described speed decision methods based on probabilistic grid diagrams of the present invention.

[0012] Sixthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program stored on a storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to execute any of the above-mentioned speed decision methods based on probabilistic grid maps.

[0013] This invention establishes an ST probability grid map and then uses the occupancy probability of the grid points in the ST probability grid map during the expansion and calculation of the total cost. This allows the speed decision result to better avoid grid points with a high occupancy probability and stay as far away as possible from grids with an occupancy probability, so that the vehicle always maintains a safe relative distance from moving obstacles, thereby ensuring the safety of the final speed decision result. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of an embodiment of the velocity decision method based on probabilistic grid diagrams of the present invention;

[0016] Figure 2 This is a flowchart of another embodiment of the velocity decision method based on probabilistic grid diagrams of the present invention;

[0017] Figure 3 This is a flowchart of another embodiment of the velocity decision method based on probabilistic grid diagrams of the present invention;

[0018] Figure 4 This is a flowchart of another embodiment of the velocity decision method based on probabilistic grid diagrams of the present invention;

[0019] Figure 5 This is a flowchart of another embodiment of the velocity decision method based on probabilistic grid diagrams of the present invention;

[0020] Figure 6 A schematic diagram of a feasible decision result provided in an embodiment of the present invention in an ST probability grid diagram;

[0021] Figure 7 A flowchart of a feasible solution search based on a probabilistic grid graph is provided in an embodiment of the present invention.

[0022] Figure 8 This is a block diagram of a velocity decision device based on a probabilistic grid diagram according to an embodiment of the present invention;

[0023] Figure 9 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0026] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, elements, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0027] In this invention, terms such as "module," "device," and "system" refer to relevant entities applied to a computer, such as hardware, combinations of hardware and software, software, or software in execution. More specifically, for example, an element can be, but is not limited to, a process running on a processor, a processor, an object, an executable element, an execution thread, a program, and / or a computer. Furthermore, an application program or script running on a server, and the server itself, can also be an element. One or more elements may be in an execution process and / or thread, and elements may be localized on a single computer and / or distributed across two or more computers, and may be run on various computer-readable media. Elements can also communicate via local and / or remote processes based on signals having one or more data packets, for example, signals from data interacting with another element in a local system, a distributed system, and / or interacting with other systems via signals over a network of the Internet.

[0028] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising" or "including" include not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0029] This invention provides a speed decision method based on probabilistic grid diagrams. This method can be applied to any mobile tool capable of autonomous driving, such as autonomous vehicles (passenger cars, buses, minibuses, trucks, off-road vehicles, sanitation vehicles, sweepers, floor scrubbers, and vacuum cleaners, etc.), and sweeping robots, etc. This invention does not limit the application of this method.

[0030] Please refer to Figure 1 This illustrates a velocity decision method based on a probabilistic grid diagram provided by an embodiment of the present invention.

[0031] like Figure 1 As shown, in step 101, an ST probability grid map is established in the Frenet coordinate system with the vehicle position as the origin;

[0032] In step 102, in the ST probability grid map, the grid point where the vehicle is located is taken as the parent node, and multiple extended grid points that meet the preset conditions are expanded according to the search step size; each extended grid point is taken as the parent node and the multiple extended grid points that meet the preset conditions are expanded according to the search step size; the search is performed round by round until the preset termination condition is met, so as to obtain multiple paths and their corresponding speed decision results.

[0033] In step 103, for each path, the total cost of the path is calculated based on the occupancy probability of each grid point contained in the path and its relative distance to obstacles;

[0034] In step 104, multiple candidate speed decision results are determined based on the total cost corresponding to the multiple paths;

[0035] In step 105, a target speed decision result is selected from the plurality of candidate speed decision results.

[0036] In this embodiment, for step 101, it is necessary to establish an ST probability grid map in the Frenet coordinate system with the vehicle position as the origin. In the ST probability grid map, the vertical coordinate S is the longitudinal distance of the obstacle relative to the reference path after projection at time T.

[0037] In some embodiments, the aforementioned search step size can be set to, for example, 1 second. The preset conditions can be a series of conditions that ensure the vehicle does not violate traffic rules or engage in risky driving, such as the vehicle not exceeding the speed limit of the current road segment or running red lights. This application does not impose any restrictions on these conditions. The preset termination conditions can be reaching the preset search step size or the search time reaching a preset duration. This application does not impose any restrictions on these conditions. Searching under these preset conditions can ensure safe driving while reducing the number of expanded grid points and limiting the number of paths obtained. Each path corresponds to a speed decision result, which can be, for example, overtaking obstacle A and obstacle B, yielding to obstacle A and obstacle B, or overtaking obstacle A and yielding to obstacle B. The number of obstacles can also be one or more, and the speed decision results can also be other possible combinations. This application does not impose any restrictions on these combinations.

[0038] Then, for step 103, for each path, the total cost of the path is calculated based on the occupancy probability of each grid point in the path and its relative distance to obstacles. Each grid point on each path has an occupancy probability of being occupied by one or more obstacles and a relative distance to each obstacle. The total cost of the path can be calculated based on this occupancy probability and relative distance. For example, for grid point 1, the occupancy probability of being occupied by obstacle A in one search step is 0.4, and the occupancy probability of being occupied by another obstacle B is 0. Similarly, the occupancy probability of each grid point on the path being occupied by an obstacle can be obtained. The total cost can be calculated based on the occupancy probability and relative distance using a potential function, such as a repulsive potential function or an attractive potential function, which is not limited in this application. By introducing occupancy probability and relative distance into the speed decision process, the target speed decision result can avoid grid points with high occupancy probability, stay away from grid points with occupancy probability, and ensure that the vehicle always maintains a safe distance from obstacles.

[0039] Next, in step 104, multiple candidate speed decisions are determined based on the total cost corresponding to the multiple paths. Since each path corresponds to one candidate speed decision, there will be multiple candidate speed decisions determined by multiple total costs. Finally, in step 105, the target speed decision is selected from the multiple candidate speed decisions. The final target speed decision can be selected from the multiple candidate decisions using some filtering methods.

[0040] The method in this embodiment establishes an ST probability grid map, and then uses the occupancy probability and the relative distance between the vehicle and the obstacle in the ST probability grid map during the calculation of the total cost in the expansion process. This allows the speed decision result to better avoid grids with a high occupancy probability and stay as far away from grids with an occupancy probability as possible, so that the vehicle always maintains a safe relative distance from the moving obstacle, thereby ensuring the safety of the final target speed decision result.

[0041] In a specific example, the process of establishing the ST probability grid map based on the Frenet coordinate system is as follows: establish the obstacle grid map of the current frame in the Frenet coordinate system with the current position of the vehicle as the origin; determine the current frame occupied area of ​​the obstacle in the current frame obstacle grid map based on the current frame perception data of the obstacle; calculate and update the current frame occupancy probability of each grid in the current frame occupied area of ​​each obstacle in the current frame obstacle grid map to obtain the obstacle risk field of the current frame.

[0042] For example, the obstacles include dynamic obstacles. A current frame obstacle raster map is established in the Frenet coordinate system with the current position of the vehicle as the origin. Specifically, this includes: establishing a current frame ST raster map corresponding to the dynamic obstacles in the Frenet coordinate system with the current position of the vehicle as the origin.

[0043] For example, determining the current occupied area of ​​the obstacle in the current frame obstacle grid based on the obstacle's current frame perception data specifically includes: determining the ST region where the dynamic obstacle collides with the vehicle based on the current frame predicted trajectory of the dynamic obstacle and the vehicle's reference line; discretizing the ST region grid onto the current frame ST grid to obtain a first estimated occupied area; projecting the previous frame occupied area of ​​the dynamic obstacle onto the current frame ST grid to obtain a second estimated occupied area; and determining the current frame occupied area of ​​the dynamic obstacle in the current frame ST grid based on the first estimated occupied area and the second estimated occupied area.

[0044] For example, projecting the area occupied by the dynamic obstacle in the previous frame onto the ST grid of the current frame to obtain the second estimated area specifically includes: displacing the area occupied by the dynamic obstacle in the previous frame according to the time change and the change in the vehicle position between the previous and current frames to obtain the second estimated area of ​​the dynamic obstacle in the ST grid of the current frame.

[0045] For example, the current frame occupancy probability of each obstacle in the current frame occupied area of ​​the obstacle grid map in the current frame is calculated and updated to obtain the obstacle risk field in the current frame. Specifically, this includes: calculating and updating the current frame predicted trajectory probability of each dynamic obstacle in the current frame occupied area of ​​the obstacle grid map in the current frame; determining the current frame occupancy probability of each occupied grid in the current frame ST grid map based on the current frame predicted trajectory probability of each dynamic obstacle in the current frame occupied area of ​​the obstacle grid map in the current frame, to obtain the current frame ST risk field (the current frame ST risk field is the current frame ST probability grid map).

[0046] For example, determining the current frame occupancy probability of each occupied grid in the current frame ST grid based on the current frame predicted trajectory probability of each dynamic obstacle in the current frame occupied area of ​​each grid in the current frame ST grid specifically includes: for each occupied grid in the current frame ST grid, performing the following steps: determining at least one target dynamic obstacle corresponding to each occupied grid based on the current frame occupied area of ​​each dynamic obstacle in the current frame ST grid; determining the current frame estimated occupancy probability of the occupied grid based on the current frame predicted trajectory probability of each target dynamic obstacle occupying the occupied grid and a preset maximum occupancy probability; calculating the current frame occupancy probability of the occupied grid based on the current frame estimated occupancy probability of the occupied grid and the previous frame occupancy probability of the occupied grid.

[0047] For example, the occupancy probability of each grid point in the second occupied region is updated using a Bayesian filtering method, as follows:

[0048] For example, in this embodiment, a Bayesian filtering algorithm can be used to update the ST risk field probability, and the process is as follows:

[0049] For dynamic obstacles perceived in the input, the risk field of each occupied grid point P(s,t) is updated and calculated, where obstacle Id is represented by i, grid point number is represented by j, and the probability of occupancy of a single obstacle is calculated. i P m = i k m No probability of occupancy i P f =1- i k m Since the same grid point may be occupied by multiple obstacle ST regions simultaneously, the probability of a grid point being occupied is:

[0050] P m =min(max_p, ∑ i k m (1)

[0051] ini k m Let Id[] be the predicted trajectory probability of a dynamic obstacle with value i, max_p be the maximum occupancy probability, and L be an intermediate variable. According to the Bayesian filtering formula, we can obtain:

[0052]

[0053]

[0054]

[0055] At the same time, based on the physical meaning represented by obstacle st, when t m m-1 When the value is less than 0, the obstacle P(S,T) risk field is cleared. A high probability value of P(s,t) indicates high stability of the prediction result in the time dimension and high repeatability in the spatial dimension. This can be used to define the threshold range and determine the risk level of the obstacle's intention and the corresponding predicted trajectory.

[0056] Repeat the above steps to continuously update the obstacle risk field at the current moment.

[0057] The updated ST risk field consists of two parts: accurate location information—the location of occupied grid points—and the occupancy probability P(s,t) for each grid point. This enables the establishment of an obstacle risk field environment model, allowing the autonomous driving system to make decisions and plans based on the obstacle risk {s,t,P(s,t)}. Furthermore, based on this occupancy probability, speed decision-making and planning can be performed.

[0058] Furthermore, the origin of the Frenet coordinate system is the projection point of the vehicle on the structured road, and the ordinate S is the longitudinal distance of the obstacle projected relative to the reference path of the vehicle at time T.

[0059] It should be noted that the specific process for establishing the ST probability grid above is only to illustrate one method of establishing an ST probability grid and is not intended to limit the scope of protection of this application. Any existing or future methods for establishing ST probability grids are within the scope of protection of this application.

[0060] In some optional embodiments, the method further includes: during the round-by-round expansion process, if at least two parent nodes expand to the same expanded grid point, retaining the parent node with the lowest cost. This allows for the removal of some schemes with higher expansion costs during the grid point expansion process, while retaining schemes with lower expansion costs, significantly reducing computational load and improving computational efficiency.

[0061] In some optional embodiments, the preset conditions include speed constraints and collision constraints. This can effectively prevent speeding and collisions with other obstacles. Further optionally, the speed constraint is a maximum speed constraint and / or a maximum acceleration constraint; thus, by constraining the vehicle's maximum speed and maximum acceleration, driving safety can be better guaranteed. For example, the maximum speed constraint and / or maximum acceleration constraint can be set according to the current road speed limit regulations. In other optional embodiments, the collision constraint is prohibiting crossing of risk grid points, where a risk grid point is a grid point with an occupancy probability greater than a preset threshold. For example, the preset threshold can be set to 0.2; when the occupancy probability of a certain grid point is greater than or equal to 0.2, then that grid point is a risk grid point. The preset threshold can also be set to other values, which are not limited in this application.

[0062] Please continue to refer to this. Figure 2 This document illustrates a flowchart of another velocity decision-making method based on a probabilistic grid map, provided by an embodiment of the present invention. This flowchart is primarily aimed at… Figure 1 The flowchart further defines step 103, "Calculate the total cost of the path based on the occupancy probability of each grid point contained in the path and its relative distance to the obstacle".

[0063] like Figure 2 As shown, in step 201, the cost value of each expanded grid point obtained in each round of expansion is calculated according to the preset first cost function;

[0064] In step 202, the cost of the last grid point in the path is determined as the total cost of the path.

[0065] In this embodiment, after each round of expansion, the cost of the expanded grid point can be calculated according to a preset first cost function. The first cost function is configured such that the cost of the expanded grid point is the sum of the maximum value of the repulsive potential function of each grid point traversed from the parent node of the expanded grid point to the expanded grid point, and the cost of the parent node of the expanded grid point. The repulsive potential function is related to the occupancy probability of the grid point and its relative distance to obstacles. The cost of subsequent grid points is obtained by superimposing the costs of previous grid points, so that the cost of the last grid point can represent the total cost of the path. Therefore, the cost of each currently expanded grid point can directly represent the total cost of the current path. Specifically, after each round of expansion, the following steps are performed for each expanded grid point in that round: calculating the repulsive potential function value of each grid point traversed between the expanded grid point and its parent node; and using the sum of the maximum repulsive potential function value and the cost value of the parent node of the expanded grid point as the cost of the expanded grid point.

[0066] Continue to refer to Figure 3This document illustrates a flowchart of another velocity decision-making method based on a probabilistic grid map, provided by an embodiment of the present invention. This flowchart primarily addresses... Figure 1 The flowchart further defines step 105, "selecting the target speed decision result from the plurality of candidate speed decision results".

[0067] In step 301, for each candidate speed decision result, a penalty value is calculated according to a preset penalty function. The penalty function is related to whether the candidate speed decision result violates the right-of-way of an obstacle and / or disrupts the consistency of historical speed decision results. For example, the more times the right-of-way of an obstacle is violated, the higher the penalty value is; the more times the consistency of historical speed decision results is disrupted, the higher the penalty value is.

[0068] In step 302, the target velocity decision result is selected from the multiple candidate velocity decision results based on the penalty values ​​of the multiple candidate velocity decision results and the magnitude of s of the corresponding path projection in the ST probability grid.

[0069] In this embodiment, after the speed decision-making device based on the probabilistic grid map calculates the cost of expanding the grid points according to the aforementioned repulsive potential function and obtains multiple candidate speed decision results based on the continuous expansion results, it is still necessary to select the target speed decision result from the multiple feasible decision results. Since the calculation process of the aforementioned multiple candidate speed decision results has already taken into account driving safety, this embodiment mainly considers driving stability and decision consistency, and can conform to safe and civilized driving habits. Therefore, this embodiment mainly calculates the penalty value of the candidate speed decision result according to a preset penalty function, wherein the penalty function is related to whether the candidate speed decision result violates the right-of-way of obstacles and / or disrupts the consistency of historical speed decision results. Finally, by comprehensively considering the penalty value and the magnitude of the vertical coordinate s corresponding to multiple paths in the ST probabilistic grid map of the feasible speed decision result, the final speed decision result is determined.

[0070] In this context, right-of-way is primarily determined by traffic rules requiring vehicles to yield to obstacles with higher right-of-way as much as possible. Consistency determination mainly involves judging whether the current speed decision is consistent with historical speed decisions in the decision pool (which consists of previously executed speed decisions or previous behaviors). For example, for the same obstacle, the decision must be consistent with the most recent speed decision to avoid hesitation and instability in the vehicle's overall performance. Alternatively, it can be set to be consistent with the most recent N decision results for the same obstacle. For instance, if the decision pool shows that the most recent three decisions for obstacle A were to yield, then if the current decision is to yield, it is consistent with previous decisions; if it is to overtake, it is inconsistent with previous decisions. In such cases, a penalty function needs to be used to calculate the penalty value. This application does not impose any restrictions on this.

[0071] In a specific example, regarding consistency, if a historical speed decision is to yield to both obstacles, then if the current decision is to yield to one and then overtake the other, this can be recorded as a violation of consistency, with a penalty value of 1. Overtaking two obstacles can be recorded as a violation of consistency twice. Each change in the decision outcome for each obstacle can be recorded as a violation of consistency, and this applies to multiple obstacles; further details will not be elaborated here. For right-of-way, a violation of right-of-way has a penalty value of 1, which will also not be elaborated here. Right-of-way and consistency can also have corresponding coefficients to make the calculation results more reliable; the determination of these coefficients will not be elaborated here.

[0072] For further options, please refer to Figure 4 It shows a flowchart of a step that further defines the above step 302, "selecting a target speed decision result from the multiple candidate speed decision results based on the penalty value of the multiple candidate speed decision results and the magnitude of s of the corresponding path projected in the ST probability grid."

[0073] like Figure 4 As shown, in step 401, the candidate speed decision result with the lowest penalty value is selected;

[0074] In step 402, if there is only one candidate speed decision result with the lowest penalty value, then the candidate speed decision result with the lowest penalty value is determined as the target speed decision result.

[0075] In step 403, if there are multiple candidate speed decision results with the lowest penalty value, the candidate speed decision result corresponding to the path with the largest vertical coordinate s in the ST probability grid is selected as the target speed decision result.

[0076] The method in this embodiment mainly represents how to obtain the final target speed decision result based on the penalty value. When there is only one minimum penalty value, the candidate speed decision result corresponding to the minimum penalty value can be directly determined as the final target decision result. When there are multiple minimum penalty values, it is necessary to further consider the size of the vertical axis s in the ST probability grid. The larger the vertical axis s is, the higher the driving efficiency is, and the higher the driving efficiency is, the safer it is.

[0077] Please refer to Figure 5 This document illustrates a flowchart of another velocity decision method based on a probabilistic grid map, provided by an embodiment of the present invention. This flowchart is primarily aimed at… Figure 1 The flowchart further defines step 103, "Calculate the total cost of the path based on the occupancy probability of each grid point contained in the path and its relative distance to the obstacle".

[0078] like Figure 5 As shown, in step 501, the cost of each expanded grid point obtained in each round of expansion is calculated according to a preset second cost function; wherein, the second cost function is configured to be a cost function obtained by comprehensively weighting the repulsion potential function, the penalty function for violating the right-of-way of obstacles and / or disrupting consistency, and the vehicle acceleration; the repulsion potential function is related to the occupancy probability of the grid point and its relative distance to the obstacle. That is, after each round of expansion is completed, for each expanded grid point in that round of expansion, the following steps are performed: calculate the repulsion potential function value of each grid point traversed between the expanded grid point and its parent node; calculate the penalty value for violating the right-of-way of obstacles and / or disrupting consistency of the path where the expanded grid point is located according to the preset penalty function; and use the maximum value of the repulsion potential function, the penalty value, and the weighted sum of the vehicle acceleration as the cost of the expanded grid point.

[0079] In step 502, the cost of the last grid point on the path is determined as the total cost of the path.

[0080] The second cost function in this embodiment differs from the first cost function in that it is a cost function obtained by comprehensively weighting the repulsive potential function, the right-of-way function for violating obstacles and / or the penalty function for disrupting consistency, and the vehicle acceleration. Therefore, it eliminates the need for separate repulsive potential functions and penalty functions, and instead integrates both functions into the second cost function. The final speed decision result can be obtained using a single cost function, resulting in higher computational efficiency.

[0081] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of combined actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, as some steps can be performed in other orders or simultaneously according to the present invention. Secondly, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention. In the above embodiments, the descriptions of each embodiment have their own emphasis; for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0082] Furthermore, one formula for calculating the repulsive potential function mentioned in the above embodiments is as follows:

[0083]

[0084] Among them, U req (q) represents the repulsive potential of grid point q in the ST probability grid diagram caused by surrounding obstacles, where η is the repulsive gain, and Q * The effective distance threshold for the obstacle, the effective distance threshold Q *Related to the vehicle's speed, p represents the occupancy probability of grid point q, and d(q) represents the relative distance of grid point q to the vehicle. Therefore, the repulsive potential function is improved by incorporating the probabilities in the ST probability grid. When the relative distance is greater than the effective distance threshold, it indicates that the vehicle maintains a safe driving distance from the obstacle, and the repulsive potential is 0. When the relative distance is less than or equal to the effective distance threshold, a repulsive potential is generated. This repulsive potential is negatively correlated with the relative distance, meaning the smaller the relative distance, the greater the repulsive potential. It is also positively correlated with the occupancy probability, meaning the higher the occupancy probability, the greater the repulsive potential. This allows for better characterization and handling of the repulsive forces between moving obstacles, enabling paths with expansion potential to avoid grids with high occupancy probabilities and to move as far away as possible from grids with occupancy probabilities, thus selecting a safer path for the final speed decision.

[0085] It should be noted that when calculating the repulsive potential function of each grid point, since a grid point may be occupied by different obstacles, meaning the calculated repulsive potential function for that grid point will differ depending on the obstacle, the maximum value of the repulsive potential function at that grid point can be taken when calculating the repulsive potential function. Thus, the value of that grid point as an extended grid point is the maximum value of the repulsive potential function of all obstacles, thereby better characterizing the danger of the grid point and allowing the vehicle to better avoid potentially dangerous grid points. In other solutions, multiple obstacles (all satisfying a relative distance greater than Q) can also be considered. * The value of the repulsive potential function of a grid point is obtained by weighted calculation of the repulsive potential functions of each grid point. This application does not limit this.

[0086] To help those skilled in the art better understand the improvements to the repulsive potential function in this application, the original repulsive potential function is provided for reference: The basic idea of ​​the artificial potential field method is to simulate the repulsive force generated by obstacles on the vehicle's motion. Guided by the potential field, the vehicle avoids the obstacles. The traditional repulsive potential function is:

[0087]

[0088] Where D(q) is the distance between point q and its nearest obstacle, η is the repulsive force gain, and Q * This is the threshold distance at which obstacles exert their influence; obstacles beyond this distance will not have a repulsive effect.

[0089] In the process of developing this application, the inventors discovered that since the probabilistic grid does not provide the precise location of obstacles, but does provide the occupancy probability, the repulsive potential function of the grid point can be calculated from the relative distance to other grid points and the occupancy probability.

[0090] Please refer to Figure 6This diagram illustrates a speed decision result in an ST probability grid diagram according to an embodiment of the present invention. The following describes the process of calculating the penalty value based on consistency and right-of-way, and determining the final speed decision result, based on this diagram. Figure 6 The horizontal axis in the graph represents time T, and the vertical axis represents time S. The values ​​of 0.2, 0.4, and 0.6 marked within the grid points in the graph represent the probability that an obstacle occupies that grid point in the ST probability grid.

[0091] The multiple feasible decision results generated through the aforementioned expansion steps may contain various topologies, meaning that there are multiple permutations and combinations of decision results for each obstacle. For example... Figure 6 There are three topology choices: ① overtake two obstacles, ② and ③ overtake the first obstacle and yield to the second, and ④ yield to both obstacles. During driving, vehicles typically make the same decision about the same obstacle; otherwise, the vehicle's overall performance will be hesitant and unstable. At the same time, traffic rules require yielding to obstacles with higher right-of-way whenever possible. Therefore, from all feasible solutions, we need to select the speed decision that best matches historical decisions and conforms to right-of-way requirements. A penalty term for the decision result is designed as follows:

[0092] E = αm + βn,

[0093] Where m and n represent the number of times consistency and right-of-way are violated, respectively, and α and β are the coefficients of the two penalty terms. For example, if the first obstacle has a higher right-of-way than the vehicle and the second obstacle has a lower right-of-way, the decision at the previous time step would be to overtake the first obstacle and yield to the second obstacle. With α = 2 and β = 1, the penalty value for feasible solution ① is 3, the penalty values ​​for ② and ③ are 1, and the penalty value for ④ is 2. Therefore, the topologies corresponding to ② and ③ are selected. Considering that a larger topology ultimately leads to higher driving efficiency, feasible solution ② is chosen as the final speed decision.

[0094] As a variation, the cost function for calculating the total cost in this embodiment of the invention can be configured to be related to more parameters. For example, it can be related to one or more parameters such as the probability that each grid point is occupied by an obstacle at each time, the relative distance between the vehicle and the obstacle, the vehicle's speed acceleration, whether the vehicle's current speed decision result is consistent with the historical decision results in the decision pool, and the violation of right-of-way. It is understood that the more parameters it is related to, the faster the final speed decision result can be obtained.

[0095] In some optional embodiments, the cost function is a weighted sum of the repulsive potential function, the penalty function for violating obstacle rights and / or consistency, and acceleration, wherein the cost function is positively correlated with the repulsive potential function, the penalty function, and the acceleration. This allows various parameters to be weighted together to form the cost function, which is then used to derive the final decision result.

[0096] Continue to refer to Figure 7 It shows a flowchart of the overall feasible solution search based on a probabilistic grid graph provided by an embodiment of the present invention.

[0097] The ST probability grid reflects the probability that each location on the planned path of the vehicle is occupied at each time point. Grid points with an occupancy probability greater than a certain threshold are considered prohibited grid points. Speed ​​decision needs to avoid all prohibited grid points while staying as far away as possible from grid points with an occupancy probability. Therefore, the aforementioned potential function is used as the cost function to search for feasible solutions for speed decision.

[0098] The specific process for searching feasible solutions based on speed decision is as follows: Figure 7 As shown. The search step size is a fixed time, such as 1 second. The initial grid point, i.e., the parent node, is (0, 0). Expanding grid points must satisfy the constraints of speed limit and maximum acceleration, and cannot pass through prohibited risk grid points with the parent node. The cost of expanding a grid point is the maximum value of the repulsive potential function among the grid points traversed. After completing the first step of the search, the grid points that can be reached in 1 second can be obtained. Then, these are successively used as parent nodes to continue expanding, which also needs to satisfy kinematic constraints and collision constraints. At this time, the total cost of expanding a grid point is the sum of the cost of the parent node and the maximum value of the potential function among the grid points traversed. If there are other parent nodes that expand to the same grid point and have a smaller total cost, the parent node and total cost of the expanding grid point are updated. This process continues until the search length reaches the planned time, at which point the search stops, and several feasible search results are obtained by reverse searching for parent nodes.

[0099] In developing this application, the inventors discovered that since speed decisions and planning heavily rely on environmental perception and prediction, it is necessary to obtain the true and stable distribution and intentions of other traffic participants. Probabilistic grid maps can effectively combine temporal and spatial information. Therefore, converting the measured location of obstacles into the probability of each location being occupied mitigates the impact of input disturbances to some extent. Consequently, the inventors proposed a speed decision-making method based on probabilistic grid maps. This method searches for several feasible decision results in the probabilistic grid map based on a repulsive field function, reducing the impact of uncertainties in perception and prediction information. Simultaneously, by referencing historical decision results and relative right-of-way relationships, the stability and rationality of the decision results are ensured.

[0100] Please refer to Figure 8The diagram shows a block diagram of a velocity decision device based on a probabilistic grid diagram according to an embodiment of the present invention.

[0101] like Figure 8 As shown, the speed decision-making device 800 based on probabilistic grid diagrams includes a probabilistic grid diagram establishment module 810, an expansion module 820, a total cost calculation module 830, a candidate determination module 840, and a decision module 850.

[0102] The system includes: a probability grid map establishment module 810, used to establish an ST probability grid map in the Frenet coordinate system with the vehicle's position as the origin; an expansion module 820, used to expand the ST probability grid map with the grid point where the vehicle's position is located as the parent node, according to the search step size, to multiple extended grid points that meet preset conditions; using each extended grid point as the parent node, it continues to expand multiple extended grid points that meet preset conditions according to the search step size; and it searches round by round until a preset termination condition is met to obtain multiple paths and their corresponding speed decision results; a total cost calculation module 830, used to calculate the total cost of each path based on the occupancy probability of each grid point contained in the path and its relative distance to obstacles; a candidate determination module 840, used to determine multiple candidate speed decision results based on the total cost corresponding to the multiple paths; and a decision module 850, used to select a target speed decision result from the multiple candidate speed decision results.

[0103] In some optional embodiments, the above-described velocity decision device 800 based on probabilistic grid graphs further includes a retention module (not shown in the figure) for retaining the parent node with the lowest cost when at least two parent nodes extend to the same extended grid point during the round-by-round expansion process.

[0104] Optionally, the preset conditions include speed constraints and collision constraints.

[0105] Further optionally, the speed constraint is a maximum speed constraint and / or a maximum acceleration constraint; the collision constraint is to prohibit crossing risk grid points, where risk grid points refer to grid points with an occupancy probability greater than a preset threshold.

[0106] In some optional embodiments, the total cost calculation module 830 specifically includes: calculating the cost value of each expanded grid point obtained in each round of expansion according to a preset first cost function; wherein the first cost function is configured such that: the cost of the expanded grid point is the sum of the maximum value of the repulsive potential function among the grid points traversed from the parent node of the expanded grid point to the expanded grid point and the cost value of the parent node of the expanded grid point, the repulsive potential function being related to the occupancy probability of the grid point and its relative distance to obstacles; and determining the cost of the last grid point in the path as the total cost of the path.

[0107] In some optional embodiments, the decision module 850 specifically includes: for each candidate speed decision result, calculating a penalty value for the candidate speed decision result according to a preset penalty function, wherein the penalty function is related to whether the candidate speed decision result violates the right-of-way of an obstacle and / or disrupts the consistency of historical speed decision results; and selecting a target speed decision result from the multiple candidate speed decision results based on the penalty values ​​of multiple candidate speed decision results and the magnitude of the projection s of their corresponding paths in the ST probability grid.

[0108] Further optionally, based on the penalty values ​​of multiple candidate speed decision results and the magnitude of their projection s in the ST probability grid, a target speed decision result is selected from the multiple candidate speed decision results. Specifically, this includes: selecting the candidate speed decision result with the lowest penalty value; if there is only one candidate speed decision result with the lowest penalty value, then the candidate speed decision result with the lowest penalty value is determined as the target speed decision result; and if there are multiple candidate speed decision results with the lowest penalty value, the candidate speed decision result corresponding to the path with the largest vertical coordinate s in the ST probability grid is selected as the target speed decision result.

[0109] In some alternative embodiments, the total cost calculation module 830 specifically includes: calculating the cost of each expanded grid point obtained in each round of expansion according to a preset second cost function; wherein the second cost function is configured as a cost function obtained by comprehensively weighting a repulsion potential function, a penalty function for violating the right-of-way of obstacles and / or disrupting consistency, and the vehicle acceleration; the repulsion potential function is related to the occupancy probability of the grid point and its relative distance to the obstacle; and determining the cost of the last grid point on the path as the total cost of the path.

[0110] In some embodiments, the present invention provides a non-volatile computer-readable storage medium storing one or more programs including execution instructions, which can be read and executed by an electronic device (including but not limited to a computer, server, or network device, etc.) to perform any of the above-described probabilistic grid-based speed decision methods of the present invention.

[0111] In some embodiments, the present invention also provides a computer program product, the computer program product including a computer program stored on a non-volatile computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform any of the above-described speed decision methods based on probabilistic grid maps.

[0112] In some embodiments, the present invention also provides an electronic device comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute a speed decision method based on a probabilistic grid map.

[0113] In some embodiments, the present invention also provides a mobile device, including a mobile body and the electronic device described in the foregoing embodiments. The mobile device may be a vehicle, a robot, etc. The vehicle may be an unmanned vehicle, such as an unmanned sweeper, an unmanned floor scrubber, an unmanned logistics vehicle, or an unmanned taxi.

[0114] Figure 9 This is a schematic diagram of the hardware structure of an electronic device that performs a speed decision method based on a probabilistic grid map, according to another embodiment of this application. Figure 9 As shown, the device includes:

[0115] One or more processors 910 and memory 920, Figure 9 Take the 910 processor as an example.

[0116] The device for executing the speed decision method based on probabilistic grid diagrams may further include an input device 930 and an output device 940.

[0117] The processor 910, memory 920, input device 930, and output device 940 can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0118] The memory 920, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the speed decision method based on probabilistic grid maps in the embodiments of this application. The processor 910 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 920, thereby implementing the speed decision method based on probabilistic grid maps in the above-described method embodiments.

[0119] The memory 920 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the probability grid-based speed decision-making device. Furthermore, the memory 920 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 non-volatile solid-state storage device. In some embodiments, the memory 920 may optionally include memory remotely located relative to the processor 910, and these remote memories may be connected to the probability grid-based speed decision-making device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0120] The input device 930 can receive input digital or character information and generate signals related to user settings and function control of the probability grid-based speed decision device. The output device 940 may include a display device such as a display screen.

[0121] The one or more modules are stored in the memory 920, and when executed by the one or more processors 910, they execute the speed decision method based on probabilistic grid diagrams in any of the above method embodiments.

[0122] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0123] The electronic devices in this application embodiments exist in various forms, including but not limited to:

[0124] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include smartphones, multimedia phones, feature phones, and low-end phones.

[0125] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include: PDAs, MIDs, and UMPCs, etc.

[0126] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes audio and video players, handheld game consoles, e-book readers, as well as smart toys and portable car navigation devices.

[0127] (4) Other airborne electronic devices with data interaction capabilities, such as vehicle-mounted systems installed on vehicles.

[0128] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A velocity decision-making method based on probabilistic grid diagrams, characterized in that, The method includes: Establish an ST probability raster map in the Frenet coordinate system with the vehicle's position as the origin; In the ST probability grid diagram, the grid point where the vehicle is located is taken as the parent node, and multiple extended grid points that meet the preset conditions are expanded according to the search step size; each extended grid point is taken as the parent node and the multiple extended grid points that meet the preset conditions are expanded according to the search step size; the search is performed round by round until the preset termination condition is met, so as to obtain multiple paths and their corresponding speed decision results; For each path, the total cost of the path is calculated based on the occupancy probability of each grid point included in the path and its relative distance to obstacles. Calculating the total cost of the path includes calculating the cost of each expanded grid point obtained in each round of expansion according to a preset first cost function. The first cost function is configured such that the cost of an expanded grid point is the sum of the maximum value of the repulsive potential function among the grid points traversed from the parent node of the expanded grid point to the expanded grid point and the cost of the parent node of the expanded grid point. The repulsive potential function is related to the occupancy probability of the grid point and its relative distance to obstacles. The cost of the last grid point in the path is determined as the total cost of the path. Multiple candidate speed decision results are determined based on the total cost corresponding to the multiple paths; Select the target speed decision result from the multiple candidate speed decision results.

2. The method according to claim 1, characterized in that, The method further includes: During the round-by-round expansion process, if at least two parent nodes expand to the same expanded grid point, the parent node with the lowest cost is retained.

3. The method according to claim 1, characterized in that, The preset conditions include speed constraints and collision constraints.

4. The method according to claim 3, characterized in that, The velocity constraint is a maximum velocity constraint and / or a maximum acceleration constraint; The collision constraint prohibits crossing risk grid points, which are grid points with an occupancy probability greater than a preset threshold.

5. The method according to any one of claims 1-4, characterized in that, Selecting the target velocity decision result from the multiple candidate velocity decision results specifically includes: For each candidate speed decision result, a penalty value is calculated based on a preset penalty function, wherein the penalty function is related to whether the candidate speed decision result violates the right-of-way of an obstacle and / or disrupts the consistency of historical speed decision results; The target velocity decision result is selected from the multiple candidate velocity decision results based on the penalty values ​​of the multiple candidate velocity decision results and the magnitude of the projection s of the corresponding paths in the ST probability grid.

6. The method according to claim 5, characterized in that, Based on the penalty values ​​of multiple candidate velocity decision results and the magnitude of their projection s onto the ST probability grid, a target velocity decision result is selected from the multiple candidate velocity decision results, specifically including: Select the candidate speed decision result with the lowest penalty value; If there is only one candidate speed decision result with the lowest penalty value, then the candidate speed decision result with the lowest penalty value is determined as the target speed decision result; If there are multiple candidate speed decisions with the lowest penalty value, the candidate speed decision corresponding to the path with the largest vertical coordinate s in the ST probability grid is selected as the target speed decision.

7. The method according to claim 1, characterized in that, The total cost of the path is calculated based on the occupancy probability of each grid point contained in the path and its relative distance to obstacles, specifically including: The cost of each expanded grid point obtained in each round of expansion is calculated according to a preset second cost function; wherein, the second cost function is configured to be a cost function obtained by comprehensively weighting the repulsion potential function, the penalty function for violating the right-of-way of obstacles and / or disrupting the consistency of historical speed decision results, and the vehicle acceleration; the repulsion potential function is related to the occupation probability of the grid point and its relative distance to the obstacle; The cost of the last grid point on the path is determined as the total cost of the path.

8. The method according to claim 1 or 7, wherein, The formula for calculating the repulsive potential function is as follows: ， Among them, U req (q) represents the repulsive potential of the surrounding obstacles to grid point q in the ST probability grid diagram. For repulsive force gain, The effective distance threshold for the obstacle, the effective distance threshold Related to the vehicle speed, Let q be the occupancy probability of grid point q. The distance between the vehicle and the grid point q is the relative distance between them.

9. A velocity decision-making device based on a probabilistic grid diagram, characterized in that, include: The probability grid map creation module is used to create an ST probability grid map in the Frenet coordinate system with the vehicle's position as the origin. An extension module is used to expand multiple extended grid points that meet preset conditions in the ST probability grid map, with the grid point where the vehicle position is located as the parent node, according to the search step size; and to continue to expand multiple extended grid points that meet preset conditions with each extended grid point as the parent node, according to the search step size; and to search in rounds until the preset termination condition is met, so as to obtain multiple paths and their corresponding speed decision results. The total cost calculation module is used to calculate the total cost of each path based on the occupancy probability of each grid point in the path and its relative distance to obstacles. Calculating the total cost of the path includes calculating the cost of each expanded grid point obtained in each round of expansion according to a preset first cost function. The first cost function is configured such that the cost of an expanded grid point is the sum of the maximum value of the repulsive potential function among the grid points traversed from the parent node of the expanded grid point to the expanded grid point and the cost of the parent node of the expanded grid point. The repulsive potential function is related to the occupancy probability of the grid point and its relative distance to obstacles. The cost of the last grid point in the path is determined as the total cost of the path. The candidate determination module is used to determine multiple candidate speed decision results based on the total cost corresponding to the multiple paths; The decision module is used to select the target speed decision result from the multiple candidate speed decision results.

10. An electronic device comprising: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method according to any one of claims 1-8.

11. A mobile device comprising a body and an electronic device according to claim 10 mounted on the body.

12. A storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-8.

13. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-8.

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