A safety path planning method and device

Abstract

The application relates to a safety path planning method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring the collision avoidance safety generation value and the driving generation value of each pose point in an open set. For each pose point in the open set, the total generation value of the pose point is obtained according to the collision avoidance safety generation value corresponding to the pose point, the driving generation value corresponding to the pose point and a weight parameter. The pose point with the minimum total generation value in the open set is determined as an intermediate pose point. In the case that there is a collision-free path from the intermediate pose point to a target pose point, a safety path is generated according to the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point. The safety coefficient of vehicle driving can be improved by using the method.

Description

Technical Field

[0001] This application relates to the field of path planning technology, and in particular to a safe path planning method, apparatus, computer equipment, storage medium, and computer program product. Background Technology

[0002] With the development of autonomous driving technology, path planning technology has emerged. The most widely used path planning technology is the path planning strategy based on the A* algorithm. The path planning strategy based on the hybrid A* algorithm is a further improvement on the path planning strategy based on the A* algorithm, and the planned path is more in line with vehicle kinematics.

[0003] The path planning strategy based on the hybrid A* algorithm selects pose points along the path based on the travel cost of each pose point in the map during path search. The travel cost of a pose point is generally determined by the actual motion cost required to travel from the current pose point to that pose point, or by the potential motion cost from a pose point to the target pose point.

[0004] Because the path planning strategy based on the hybrid A* algorithm only considers the driving cost when selecting each pose point in the path, the planned path is a relatively optimal drivable path, but the safety factor of the vehicle is low when the vehicle actually travels on this path. Summary of the Invention

[0005] Therefore, it is necessary to provide a secure path planning method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the path safety factor in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a safe path planning method. The method includes:

[0007] Obtain the collision avoidance safety value and driving value of each pose point in the open set; the open set includes pose points searched in the path planning.

[0008] For each pose point, the total value of the pose point is obtained based on the collision avoidance safety value, the driving value, and the weighting parameters.

[0009] The pose point with the minimum total algebraic value in the open set is determined as the intermediate pose point.

[0010] If a collision-free path exists between the intermediate pose point and the target pose point, a safe path is generated based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point.

[0011] In one embodiment, the method further includes:

[0012] Determine the obstacle density and / or road state value of the path to be planned; wherein the obstacle density and road state value are both positively correlated with the weight parameters corresponding to the collision avoidance safety cost;

[0013] Based on the obstacle density and / or the road condition value, determine the cost weight parameter corresponding to the collision avoidance safety cost.

[0014] In one embodiment, determining the road state value of the path to be planned includes:

[0015] Obtain the current weather data for the geographical location of the route to be planned;

[0016] Based on the current weather data and the pre-built correspondence between weather data and road status values, the current road status value of the path to be planned is determined.

[0017] In one embodiment, determining the collision avoidance safety cost of each pose point in the open set includes:

[0018] Obtain the shape parameters of the target vehicle;

[0019] Based on the shape parameters of the target vehicle, determine the multiple position points of multiple preset parts of the target vehicle when the target vehicle is at the pose point;

[0020] The collision avoidance safety cost of the first pose point is determined based on the Voronoi potential energy values ​​of the multiple location points.

[0021] In one embodiment, obtaining the Voronoi potential values ​​at the plurality of location points includes:

[0022] Determine the obstacle density of the path to be planned;

[0023] Based on the obstacle density, determine the descent parameters and the maximum obstacle distance; wherein the descent parameters are negatively correlated with the obstacle density; and the maximum obstacle distance is negatively correlated with the obstacle density.

[0024] Based on the descent parameters and the maximum distance to the obstacle, the Voronoi potential energy values ​​of the multiple location points are calculated.

[0025] In one embodiment, the method further includes:

[0026] If there is no collision-free path from the intermediate pose point to the target pose point, determine multiple neighboring pose points of the intermediate pose point.

[0027] Among the plurality of neighboring pose points, one neighboring pose point that has a collision-free path to the intermediate point and is not in the closed set is selected as a candidate pose point; the closed set includes each intermediate pose point.

[0028] If the candidate pose point is not in the open set, determine the collision avoidance safety value and driving value of the candidate pose point, add the candidate pose point to the open set, and use the intermediate pose point as the parent node of the candidate pose point.

[0029] Secondly, this application also provides a safe path planning device. The device includes:

[0030] The cost value acquisition module is used to acquire the collision avoidance safety cost value and driving cost value of each pose point in the open set; the open set includes pose points searched in the path planning.

[0031] The total value determination module is used to obtain the total value of each pose point based on the collision avoidance safety value, the driving value, and weight parameters.

[0032] The intermediate pose point determination module is used to determine the pose point with the minimum total algebraic value in the open set as the intermediate pose point.

[0033] The path generation module is used to generate a safe path based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point, when there is a collision-free path from the intermediate pose point to the target pose point.

[0034] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0035] Obtain the collision avoidance safety value and driving value of each pose point in the open set; the open set includes pose points searched in the path planning.

[0036] For each pose point, the total value of the pose point is obtained based on the collision avoidance safety value, the driving value, and the weighting parameters.

[0037] The pose point with the minimum total algebraic value in the open set is determined as the intermediate pose point.

[0038] If a collision-free path exists between the intermediate pose point and the target pose point, a safe path is generated based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point.

[0039] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0040] Obtain the collision avoidance safety value and driving value of each pose point in the open set; the open set includes pose points searched in the path planning.

[0041] For each pose point, the total value of the pose point is obtained based on the collision avoidance safety value, the driving value, and the weighting parameters.

[0042] The pose point with the minimum total algebraic value in the open set is determined as the intermediate pose point.

[0043] If a collision-free path exists between the intermediate pose point and the target pose point, a safe path is generated based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point.

[0044] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0045] Obtain the collision avoidance safety value and driving value of each pose point in the open set; the open set includes pose points searched in the path planning.

[0046] For each pose point, the total value of the pose point is obtained based on the collision avoidance safety value, the driving value, and the weighting parameters.

[0047] The pose point with the minimum total algebraic value in the open set is determined as the intermediate pose point.

[0048] If a collision-free path exists between the intermediate pose point and the target pose point, a safe path is generated based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point.

[0049] The aforementioned parking path method, apparatus, computer equipment, storage medium, and computer program product obtain the collision avoidance safety value and driving value of each pose point in the open set. For each pose point in the open set, the total value of that pose point is obtained based on its corresponding collision avoidance safety value, driving value, and weight parameters. The pose point with the minimum total value in the open set is determined as the intermediate pose point. If a collision-free path exists between the intermediate pose point and the target pose point, a safe path is generated based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point. Because the collision avoidance safety value is considered when selecting the intermediate pose point during path planning, the vehicle has a higher safety factor when driving on the planned path, making it suitable for scenarios with high safety requirements in path planning. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a path planning strategy based on a hybrid A* algorithm according to an embodiment of this application;

[0051] Figure 2 This is a flowchart illustrating a safe path planning method according to one embodiment of this application;

[0052] Figure 3 This is a flowchart illustrating a safe path planning method based on a hybrid A* algorithm according to a specific embodiment of this application;

[0053] Figure 4 This is a structural block diagram of a safe path planning device according to one embodiment of this application;

[0054] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] In related technologies, the path planning strategy based on the hybrid A* algorithm considers the actual motion constraints of the object, building upon the A*-based algorithm path planning strategy. The basic process of the hybrid A* algorithm path planning strategy is as follows: Figure 1 As shown.

[0057] 1. First, determine the current pose point and target pose point to which the vehicle will travel in the path planning, and initialize the open set to the current pose point and the closed set to be empty.

[0058] 2. Check if the open set is empty. If it is empty, the path search has failed; otherwise, continue to step 3.

[0059] 3. Determine the travel cost f for each pose point in the open set, and select the pose point with the minimum travel cost f in the open set as the intermediate pose point, and add the intermediate pose point to the closed set. The travel cost f for each pose point includes two parts: a motion cost term g and a heuristic cost term h. The motion cost term g represents the actual travel cost from the current pose point to that pose point, and the heuristic cost term h represents the possible travel cost from the current pose point to the target pose point.

[0060] 4. Detect whether there is a collision-free path between the intermediate pose point and the target pose point. If there is, use the planned path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point as the final planned path; if there is no collision-free path, continue to step 5.

[0061] 5. Determine and select multiple neighboring pose points of the intermediate pose point based on the kinematic formula, and select one as a candidate pose point.

[0062] 6. Determine if there is a collision-free path from the candidate pose point to the intermediate pose point. If it exists, proceed to step 7; otherwise, return to step 5.

[0063] 7. Determine if the candidate pose point is in a closed set. If yes, return to step 5; otherwise, continue to step 8.

[0064] 8. Calculate the motion cost g from the current pose point to the intermediate pose point, and the motion cost from the intermediate pose point to the candidate pose point.

[0065] 9. Determine whether the candidate pose point is in an open set. If yes, proceed to step 10; otherwise, proceed to step 12.

[0066] 10. Determine the magnitude of the motion cost g of the candidate pose point compared to the motion cost g of the candidate pose point stored in the open set. If the motion cost g of the candidate pose point is not less than the motion cost g of the candidate pose point stored in the open set, return directly to step 2. If the motion cost g of the candidate pose point is less than the motion cost g of the candidate pose point stored in the open set, proceed to step 11.

[0067] 11. Update the motion cost g of the candidate pose points in the open set, the parent node of the candidate pose points, and the total cost f of the candidate pose points, and return to step 2.

[0068] 12. Calculate the heuristic cost h of the candidate pose point, and based on the motion cost g and heuristic cost h of the candidate pose point, calculate the total cost f of the candidate pose point, denote its parent node as the intermediate pose point, and add the candidate pose point to the open set. Return to step 2.

[0069] As can be seen from the basic process of the path planning strategy based on the hybrid A* algorithm, when updating intermediate pose points, the strategy calculates the travel cost based on the motion cost and heuristic cost. Therefore, it can plan a better path faster, that is, it takes into account the travel cost and planning speed more.

[0070] However, the actual conditions of the planned route are ever-changing. For example, on rainy or snowy days, the roads are slippery, and the planned route needs to be further away from obstacles to prevent skidding and scratches to the vehicle body; or in a planned route with dense obstacles, vehicles are more likely to collide.

[0071] Based on this, this application proposes a safe path planning method to obtain the collision avoidance safety value and driving value of each pose point in an open set. The open set serves the same purpose as in related technologies, containing pose points searched during path planning. For each pose point, the total value of that pose point is obtained based on its corresponding collision avoidance safety value, driving value, and their respective value weight parameters. The pose point with the minimum total value in the open set is selected as the intermediate pose point. If a collision-free path exists between the intermediate pose point and the target pose point, a safe path is generated based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point.

[0072] The safety path planning method of this application considers the collision avoidance safety cost between each pose point when selecting intermediate pose points, so that the generated path is not only a drivable path, but also has a higher safety factor for each pose point in the path, thus the path is relatively safe.

[0073] First, the parking path planning method shown in this application will be described in detail. The parking path planning method provided in the embodiments of this application can be applied to, but is not limited to, personal computers, laptops, smartphones, tablets, IoT devices and portable wearable devices, independent servers, or server sets composed of multiple servers.

[0074] like Figure 2 The diagram shown is a flowchart illustrating a parking path planning method provided by this application according to an embodiment, including the following steps:

[0075] Step 201: Obtain the collision avoidance safety value and driving value of each attitude point in the open set.

[0076] The open set includes pose points searched during path planning, serving the same purpose as the open set in the path planning strategy based on the hybrid A* algorithm. As the path is searched, the pose points in the open set are updated accordingly. When the open set is empty, it means that all searchable pose points in the map have been added to the closed set, and there is no traversable path between the current pose point and the target pose point.

[0077] The collision avoidance safety cost at a certain pose point is used to characterize the safety costs that the vehicle may encounter when it is in that pose point. When the vehicle is in that pose point, the farther away from the obstacle, the safer the vehicle is, and the smaller the potential safety costs (the lower the probability of a collision), and the lower the collision avoidance safety cost at that pose point. Conversely, when the vehicle is in that pose point, the closer to the obstacle, the less safe the vehicle is, and the greater the potential safety costs (the higher the probability of a collision), and the higher the collision avoidance safety cost at that pose point.

[0078] In one embodiment, the device can calculate the collision avoidance safety cost at a given pose by using the Voronoi potential energy values ​​of the locations of various parts of the vehicle. Alternatively, the device can calculate the collision avoidance safety cost at a given pose by using other potential energy values ​​at the locations of various parts of the vehicle that reflect the vehicle's collision avoidance safety.

[0079] The driving cost of a certain pose point is used to characterize the driving cost required to use that pose point as a pose point in the path. The driving cost of a certain pose point can be the motion cost or initiation cost mentioned above, that is, the actual driving cost of the current pose point to that pose point, or the possible driving cost of the pose point to the target pose point (this cost is generally obtained from the straight-line distance between the pose point and the target pose point; the closer the straight-line distance, the smaller the possible cost), or it can be the driving cost obtained based on the motion cost or heuristic cost.

[0080] In one embodiment, each pose point includes three pieces of information: its x-coordinate on the map, its y-coordinate on the map, and the vehicle's orientation. That is, the same x-coordinate and y-coordinate can be represented by multiple pose points. For example, a pose point can contain three dimensions (x, y, θ), representing its position (x, y) in the xOy coordinate system and its vehicle orientation angle θ.

[0081] Step 203: For each pose point, obtain the total cost of the pose point based on the collision avoidance safety cost, driving cost, and weight parameters corresponding to that pose point.

[0082] The weighting parameters include the weighting parameters of the collision avoidance safety cost value and the weighting parameters of the driving cost value. Generally, the sum of the weighting parameters of the collision avoidance safety cost value and the weighting parameters of the driving cost value is 1.

[0083] In one embodiment, weight parameters can be pre-set on the device, namely, the weight parameter for the collision avoidance safety cost and the weight parameter for the driving cost. If vehicle safety is prioritized, the weight parameter for the collision avoidance safety cost is set greater than the weight parameter for the driving cost; for example, the weight parameter for the collision avoidance safety cost is 0.6, and the weight parameter for the driving cost is 0.4. If path search efficiency is prioritized, the weight parameter for the collision avoidance safety cost is set less than the weight parameter for the driving cost; for example, the weight parameter for the collision avoidance safety cost is 0.2, and the weight parameter for the driving cost is 0.8.

[0084] In one embodiment, the device can also determine the weight parameters of collision avoidance safety value and driving value based on the obstacle density and / or road state value of the planned path, so as to determine whether the vehicle safety factor needs to be increased according to different environments.

[0085] In one embodiment, after acquiring the collision avoidance safety value and the driving value of a pose point, the device performs a weighted calculation on the collision avoidance safety value and the driving value according to the corresponding weight parameters, to obtain the total value of the pose point. Before performing the weighted calculation on the collision avoidance safety value and the driving value, the device can normalize the collision avoidance safety value and the driving value to ensure they are on the same order of magnitude.

[0086] Step 205: Determine the pose point with the minimum total value in the open set as the intermediate pose point.

[0087] In one embodiment, after calculating the total cost of each pose point in the open set, the device can compare the total cost of each pose point in the open set pairwise and select the pose point with the minimum total cost as the intermediate pose point. Alternatively, after calculating the total cost of each pose point in the open set, the device can sort the total cost of each pose point and select the pose point with the minimum total cost as the intermediate pose point.

[0088] In one embodiment, the device may determine multiple pose points with the minimum total cost, and the device may select one of them as the intermediate pose point.

[0089] It should be noted that, apart from the current pose point, each pose point in the open set has a parent node. By tracing back to the parent node from a certain pose point, it is possible to trace back to the current pose point. The corresponding path is the path from the current pose point to that pose point planned by the device. There is already quite detailed information in the relevant technology, so it will not be elaborated here.

[0090] Step 207: If there is a collision-free path from the intermediate pose point to the target pose point, generate a safe path based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point.

[0091] In one embodiment, the device stores map data containing obstacle information, determines the current and target parking pose points for parking path planning, and determines the basic parameters of the actual vehicle being driven, such as the vehicle's length and width, and vehicle kinematic parameters, such as wheelbase, distance between the rear axle and the front axle, and minimum turning radius. Therefore, when determining whether a collision-free path exists between two pose points, the device can generate a driving path conforming to vehicle kinematics based on a preset path generation strategy and the vehicle's kinematic parameters (such as minimum turning radius). Then, based on the vehicle's basic parameters, such as length and width, it determines whether a collision will occur when the vehicle travels along the generated path. If a collision is likely, the device determines that no collision-free path exists between the two pose points; if no collision occurs, the device determines that a collision-free path exists between the two pose points.

[0092] The preset path generation strategy can be either the RS (Reeds-Shepp) curve path generation strategy or the Dubins curve path generation strategy. The RS curve is similar to the Dubins curve, both being paths consisting of a circle and a straight line. The difference is that the RS curve allows vehicles to reverse, while the Dubins curve only allows vehicles to move forward.

[0093] In one embodiment, the safe path planned by the device from the current pose point to the target pose point includes two parts. The first part is the path from the current pose point to the intermediate pose point planned by the device according to the intermediate pose point selection method described above. The second part is the collision-free path from the intermediate pose point to the target pose point generated according to the preset path generation strategy. By merging the paths of these two parts, the device can generate a safe path from the current pose point to the target pose point.

[0094] For example, if the current pose point is A, the current intermediate pose point is D, and the target pose point is Y, the device backtracks on the current intermediate pose point D according to the method for selecting the intermediate pose point mentioned above, and obtains the path from the current pose point A to the intermediate pose point D as ABCD. Therefore, the device plans the path from the current pose point A to the intermediate pose point D as ABCD. It is determined that there is a collision-free path D~Y between the intermediate pose point D and the target pose point Y. Then the path generated by the device is ABCD~Y.

[0095] The safety path planning method illustrated in the above embodiments selects intermediate pose points during the path search process that minimize the combined safety cost and driving cost of collision avoidance. This approach considers both the efficiency of path search and the collision avoidance safety of the vehicle during travel. Compared to path planning methods in related technologies, this method results in a higher vehicle safety coefficient.

[0096] In one embodiment, the device can further determine the weight parameters corresponding to the collision avoidance safety cost and the driving cost based on the obstacle density and / or road state value of the path to be planned, and determine the level of vehicle safety factor requirement according to different environments. In this case, the above-mentioned safety planning method may further include:

[0097] Step 209: Determine the obstacle density and / or road condition values ​​of the path to be planned.

[0098] Both obstacle density and road condition value are positively correlated with the weighting parameters corresponding to the collision avoidance safety cost. A higher road condition value indicates a worse road condition. In other words, a higher obstacle density corresponds to a higher weighting parameter for the collision avoidance safety cost, and thus a greater impact of the collision avoidance safety cost on the planned path; a higher road condition value indicates a worse road condition, and a higher weighting parameter for the collision avoidance safety cost, and thus a greater impact of the collision avoidance safety cost on the planned path.

[0099] In one embodiment, the device can determine the ratio of the area occupied by obstacles to the area of ​​the entire map based on a map containing obstacle information (i.e., the map on which the planned path is based), and thus obtain the obstacle density of the planned path.

[0100] In one embodiment, the device can determine road condition values ​​based on weather data of the geographical location of the route to be planned. For example, on sunny days, the road is relatively dry, the chance of vehicles skidding is small, and the road conditions are good; while on rainy or snowy days, the road is relatively slippery, the chance of vehicles skidding is large, and the road conditions are poor.

[0101] In one embodiment, the device can acquire road images or videos of the route to be planned, and determine the road state value of the route by recognizing and analyzing the images or videos. For example, after acquiring road images or videos of the route to be planned, the device identifies the acquired image or video frames to determine the area occupied by slippery road, and then determines the road state value of the route to be planned based on the ratio of the area occupied by slippery road to the total area of ​​the road. This ratio is positively correlated with the road state value.

[0102] Step 2011: Determine the weight parameters corresponding to the collision avoidance safety cost based on obstacle density and / or road condition values.

[0103] In one embodiment, the device can pre-set the correspondence between obstacle density and the weight parameter corresponding to the collision avoidance safety cost, or pre-set the correspondence between road state value and the weight parameter corresponding to the collision avoidance safety cost, or pre-set the correspondence between obstacle density, road state value, and the weight parameter corresponding to the collision avoidance safety cost. Then, upon obtaining the obstacle density and / or road state value, the device can obtain the weight parameter corresponding to the collision avoidance safety cost.

[0104] In one embodiment, the device can determine the cost-value weighting parameter of the collision avoidance safety cost based on obstacle density and a first mapping function, wherein the first mapping function reflects the mapping relationship between obstacle density values ​​and the cost-value weighting parameter values ​​of the collision avoidance safety cost. Similarly, the device can determine the cost-value weighting parameter of the collision avoidance safety cost based on road state values ​​and a second mapping function, wherein the second mapping function reflects the mapping relationship between road state values ​​and the cost-value weighting parameter values ​​of the collision avoidance safety cost. The device can also determine the cost-value weighting parameter of the collision avoidance safety cost based on obstacle density, road state values, and a third mapping function, wherein the third mapping function reflects the mapping relationship between obstacle density, road state values, and the cost-value weighting parameter values ​​corresponding to the collision avoidance safety cost.

[0105] Among them, the first mapping function, the second mapping function, and the third mapping function can be linear functions. Taking the first mapping function as an example, the cost weight parameter of the collision avoidance safety cost value = a * obstacle density value + b, where a is a positive constant and b is a real number. It can be set according to experience or obtained by training and fitting multiple actual samples.

[0106] The collision avoidance safety cost weight parameter is determined by the obstacle density and / or road condition value of the path to be planned. This allows for different focuses when planning paths in different environments. In environments with high obstacle density and poor road conditions that make vehicles prone to skidding, the focus can be on vehicle collision avoidance safety. In environments with low obstacle density and good road conditions that make vehicles less prone to skidding, the focus can be on planning efficiency.

[0107] In one embodiment, step 209, determining the road state value of the path to be planned, may specifically include:

[0108] Step a: Obtain the current weather data for the geographical location of the route to be planned.

[0109] In one embodiment, the device can obtain current weather data of the geographical location of the planned route to determine the current road conditions at that geographical location.

[0110] The weather data can be weather conditions, such as cloudy, sunny, rainy, snowy, etc., or it can be multi-dimensional data, such as air humidity, visibility, wind force, etc.

[0111] Step b: Determine the current road status value of the planned path based on the current weather data and the pre-built correspondence between weather data and road status values.

[0112] In one embodiment, the device stores the relationship between weather data and road condition values. For example, on sunny days, roads are relatively dry, and vehicles are less likely to skid, indicating good road conditions; on rainy or snowy days, roads are relatively slippery, and vehicles are more likely to skid, indicating poor road conditions. Therefore, the road condition values ​​corresponding to sunny and cloudy days can be good, or Level I, etc.; the road condition values ​​corresponding to rainy and snowy days can be poor, or Level II, etc.

[0113] In one embodiment, the device can use the acquired weather data to obtain the current road state value of the planned path using a pre-set road state value conversion strategy. The road state value conversion strategy can be a model obtained through model training or a mapping function that can reflect the mapping relationship between weather data and road state values.

[0114] In this embodiment, the current road condition of the planned path is inferred by obtaining the current weather data of the geographical location of the planned path, and then the weight parameters corresponding to the collision avoidance safety cost are further determined to determine whether the vehicle needs safety or planning efficiency more at present.

[0115] In one embodiment, determining the collision avoidance safety cost of each pose point in the open set in step 201 may specifically include:

[0116] Step 201a: Obtain the shape parameters of the target vehicle.

[0117] The target vehicle is the vehicle that actually travels along the planned path. Different brands of vehicles have different shape parameters, including shape, size, and outer contour.

[0118] In one embodiment, the device can find the shape parameters corresponding to the target vehicle based on the brand and model of the target vehicle.

[0119] Step 201b: Based on the shape parameters of the target vehicle, determine the multiple position points of multiple preset parts of the target vehicle when the target vehicle is at the pose point.

[0120] When the vehicle is at a pose point, the center of the vehicle generally coincides with the pose point, and the vehicle's orientation is consistent with the orientation of the pose point.

[0121] In one embodiment, after acquiring the shape parameters of the target vehicle, the device determines the relative position of the target vehicle's center position with the relative positions of the vehicle's various contour points and components. Then, based on the relative position of the center position with the relative positions of the vehicle's various contour points and components, and the position points of the pose points on the map, the device obtains the position points of the vehicle's various contour points and components on the map. The device can distinguish different position points using position coordinates.

[0122] Among them, the target vehicle can be several key parts of the vehicle, such as the front of the car or the engine, or the outer contour of the vehicle that is most likely to be involved in a collision. Generally, it is the outer contour of the top view of the target vehicle.

[0123] Step 201c: Determine the collision avoidance safety cost of the first pose point based on the Voronoi potential energy values ​​of multiple position points.

[0124] Voronoi potential energy ρ v The formula for calculating (x,y) at the location point (x,y) is:

[0125]

[0126] Where, d o Let d be the shortest distance between the point (x, y) and the obstacle. v Let be the shortest distance between the location (x,y) and the edge of the generalized Voronoi polygon (the edge of the generalized Voronoi polygon can be directly calculated using the MATLAB function voronoi(x,y)). α is a positive constant and is the rate of decrease parameter of the Voronoi potential energy. The maximum distance to the obstacle, when At that time, the potential energy value ρ v(x,y)=0. It can also be seen from the calculation formula that when the position point (x,y) lies on the edge of the generalized Voronoi polygon (i.e., d...),... v (x,y)=0), potential energy value ρ v (x,y)=0. Voronoi potential energy ρ v The range of (x,y) is [0,1]. The Voronoi potential energy is maximum at the obstacle point, i.e., ρ v (x,y)=1. Determine the specific parameters α, Then, the Voronoi potential energy value can be calculated.

[0127] In one embodiment, after the device determines multiple location points of the target vehicle, that is, multiple location coordinates of the target vehicle in the subway, the Voronoi potential energy values ​​of these multiple location points can be obtained through the Voronoi potential energy formula mentioned above.

[0128] In one embodiment, the device can average the Voronoi potential energy values ​​at multiple locations of the target vehicle to obtain the collision avoidance safety cost when the target vehicle is in a first attitude point. Alternatively, the device can assign weights to the components corresponding to these multiple locations based on their importance to the target vehicle, and perform a weighted calculation of the Voronoi potential energy values ​​at multiple locations of the target vehicle to obtain the collision avoidance safety cost when the target vehicle is in a first attitude point.

[0129] In one embodiment, the device can grid the map, calculate and save the Voronoi potential energy value of the center point of each grid as the Voronoi potential energy value of that grid. The device determines the Voronoi potential energy values ​​of multiple location points of the target vehicle by acquiring the Voronoi potential energy values ​​of multiple grids to which multiple location points of the target vehicle belong.

[0130] In this embodiment, the distance between the vehicle's outline and the obstacle when the vehicle is in its pose is determined by the Voronoi potential energy value of the vehicle's outline point, thus obtaining the collision avoidance safety cost of the vehicle in its first pose point. The closer the vehicle's outline is to the obstacle, the greater the probability of a collision.

[0131] In one embodiment, step 201c may specifically include:

[0132] Step c1: Determine the obstacle density of the path to be planned.

[0133] In one embodiment, the device can determine the ratio of the area occupied by obstacles to the area of ​​the entire map based on a map containing obstacle information (i.e., the map on which the planned path is based), and thus obtain the obstacle density of the planned path.

[0134] Step c3: Determine the descent parameters and the maximum distance to obstacles based on the obstacle density of the path to be planned.

[0135] The descent parameter is negatively correlated with obstacle density; the maximum obstacle distance is also negatively correlated with obstacle density. The descent parameter is the descent rate parameter α in the Voronoi potential energy formula, and the maximum obstacle distance is the maximum obstacle distance in the Voronoi potential energy formula.

[0136] α is the Voronoi potential energy decrease rate parameter, reflecting the rate at which the potential energy decreases. The closer α is to 0, the faster the potential energy decreases from the obstacle point to the surrounding non-obstacle points. When α approaches 0, the potential field is more like a step function, with the potential energy at the obstacle being 1 and the potential energy at the surrounding non-obstacle points immediately becoming 0. Conversely, the larger α is, the smoother the Voronoi potential energy decreases from the obstacle point to the non-obstacle points. A properly smooth decrease in potential energy can allow the vehicle to move away from the obstacle appropriately, but an excessively slow decrease in potential energy may result in a relatively large potential energy between 0 and 1 in an otherwise safe area, affecting the search for a safe path. Generally, it is possible to... When trying different values ​​in the vicinity, if the map has a high obstacle density, α can be taken as a slightly smaller value to avoid the potential energy around the obstacles decreasing too slowly, resulting in excessive potential energy at the passable path. Conversely, if the map has a low obstacle density, α can be taken as a slightly larger value.

[0137] The maximum distance to the obstacle is set, when the actual distance is d. o (That is, the shortest distance between the location (x, y) and the obstacle) is greater than When the Voronoi potential energy is zero, this parameter is a zero potential energy transition threshold related to obstacle distance. This parameter can be determined based on the obstacle distance characteristics of the actual environment map. When the obstacle density is high and adjacent obstacles are mostly close together, this parameter can be decreased; when the obstacle density is low, this parameter can be appropriately increased.

[0138] In one embodiment, the correspondence between descent parameters and obstacle density, as well as the correspondence between maximum obstacle distance and obstacle density, can be pre-set on the device. Then, the device can determine the descent parameters and maximum obstacle distance based on the obstacle density.

[0139] Step c5: Calculate the Voronoi potential energy values ​​at multiple locations based on the descent parameters and the maximum distance to the obstacle.

[0140] In one embodiment, the device determines the descent parameter α and the maximum distance to the obstacle. Substituting the Voronoi potential energy formula above, and then for each of the multiple location points, obtaining the relevant parameters in the formula, we can obtain the Voronoi potential energy at that location point.

[0141] In this embodiment, the descent parameters and the maximum obstacle distance are determined based on the obstacle density of the path to be planned, thereby affecting the Voronoi potential energy value of each pose point. A higher Voronoi potential energy value is positively correlated with the collision avoidance safety cost; therefore, influencing the Voronoi potential energy value of each pose point directly affects its collision avoidance safety cost. This allows for path planning under different obstacle density environments...

[0142] In one embodiment, the method further includes:

[0143] Step 2013: If there is no collision-free path from the intermediate pose point to the target pose point, determine multiple neighboring pose points of the intermediate pose point.

[0144] If there is no collision-free path from the intermediate pose point to the target pose point, it means that more pose points need to be searched forward. A preset path search strategy can be used to search for more pose points based on the intermediate pose point.

[0145] Step 2015: Among multiple neighboring pose points, select a neighboring pose point that has a collision-free path to the intermediate point and is not in the closed set as a candidate pose point.

[0146] The closed set includes all intermediate pose points. It serves the same purpose as the closed set in the path planning strategy based on the hybrid A* algorithm. After selecting a pose point from the open set as an intermediate pose point, the pose point is deleted from the open set and added to the closed set.

[0147] In one embodiment, if the device determines that there is no collision-free path between the adjacent pose point and the intermediate pose point, it means that the vehicle cannot travel from the intermediate pose point to the adjacent pose point, and the adjacent pose point is directly discarded.

[0148] In one embodiment, if the device determines that the neighboring pose points are in a closed set, it means that during the path search process, the neighboring pose point is treated as a passing intermediate pose point, and there is no collision-free path between the neighboring pose point and the target pose point, so the neighboring pose point is directly discarded.

[0149] In one embodiment, if the device determines that there is a collision-free path between the neighboring pose point and the intermediate pose point and that they are not in a closed set, it means that the device cannot determine whether there is a collision-free path between the neighboring pose point and the target pose point, and the neighboring pose point can be used as a candidate pose point.

[0150] Step 2017: If the candidate pose point is not in the open set, determine the collision avoidance safety value and driving value of the candidate pose point, and add the candidate pose point to the open set.

[0151] The collision avoidance safety cost at each pose point is fixed; that is, the collision avoidance safety cost of a vehicle at a certain pose point does not change with path planning. Therefore, in one embodiment, when the device determines that a candidate pose point is in an open set, the device has already calculated the collision avoidance safety cost of the candidate pose point when it is added to the open set. It only needs to calculate the driving cost when the current pose point travels to the candidate pose point via an intermediate pose point. Then, the calculated driving cost is compared with the driving cost of the candidate pose point stored in the open set. If the calculated driving cost is less than the driving cost of the candidate pose point stored in the open set, the driving cost of the candidate pose point stored in the open set is updated to the calculated driving cost, and the parent node of the candidate pose point in the open set is updated to an intermediate pose point.

[0152] In one embodiment, if the device determines that the candidate pose point is not in the open set, the device needs to determine the collision avoidance safety value and driving value of the candidate pose point, then add the candidate pose point to the open set, and use the middle pose point as the parent node of the candidate pose point.

[0153] In this embodiment, the collision avoidance safety value and driving value of the candidate pose points are calculated without adding any pose points to the open set. This allows the collision avoidance safety value and driving value of each pose point in the open set to be obtained directly, reducing redundant calculations.

[0154] The following is a detailed description of a specific embodiment shown in this application, such as... Figure 3 The diagram shown is a flowchart illustrating a secure path planning method according to a specific embodiment of this application.

[0155] 1. First, determine the current pose point and target pose point to which the vehicle will travel in the path planning, and initialize the open set to the current pose point and the closed set to be empty.

[0156] 2. Check if the open set is empty. If it is empty, the path search has failed; otherwise, continue to step 3.

[0157] 3. Determine the driving cost f for each pose point in the open set, and select the pose point with the minimum driving cost f in the open set as the intermediate pose point, and add the intermediate pose point to the closed set. The driving cost f for each pose point includes three parts: motion cost g, heuristic cost h, and collision avoidance safety term m. The motion cost g represents the actual driving cost from the current pose point to that pose point, the heuristic cost h represents the possible driving cost from the current pose point to the target pose point, and the collision avoidance safety term m represents the collision avoidance safety cost when the vehicle is at that pose point.

[0158] 4. Detect whether there is a collision-free path between the intermediate pose point and the target pose point. If there is, use the planned path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point as the final planned path; if there is no collision-free path, continue to step 5.

[0159] 5. Determine and select multiple neighboring pose points of the intermediate pose point based on the kinematic formula, and select one as a candidate pose point.

[0160] 6. Determine if there is a collision-free path from the candidate pose point to the intermediate pose point. If it exists, proceed to step 7; otherwise, return to step 5.

[0161] 7. Determine if the candidate pose point is in a closed set. If yes, return to step 5; otherwise, continue to step 8.

[0162] 8. Calculate the motion cost g from the current pose point to the intermediate pose point, and the motion cost from the intermediate pose point to the candidate pose point.

[0163] 9. Determine whether the candidate pose point is in an open set. If yes, proceed to step 10; otherwise, proceed to step 12.

[0164] 10. Determine the magnitude of the motion cost g of the candidate pose point compared to the motion cost g of the candidate pose point stored in the open set. If the motion cost g of the candidate pose point is not less than the motion cost g of the candidate pose point stored in the open set, return directly to step 2. If the motion cost g of the candidate pose point is less than the motion cost g of the candidate pose point stored in the open set, proceed to step 11.

[0165] 11. Update the motion cost g of the candidate pose points in the open set, the parent node of the candidate pose points, and the total cost f of the candidate pose points, and return to step 2.

[0166] 12. Calculate the heuristic cost h and collision avoidance safety cost m of the candidate pose point. Based on the motion cost g, heuristic cost h, and collision avoidance safety cost m of the candidate pose point, calculate the total cost f of the candidate pose point. Denote its parent node as the intermediate pose point and add the candidate pose point to the open set. Return to step 2.

[0167] The above is a complete description of a safe path planning method. It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the indicated order, these steps are not necessarily executed in the order indicated. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the steps or stages of other steps.

[0168] Based on the same inventive concept, this application also provides a safety route planning apparatus for implementing the safety route planning method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more safety route planning apparatus embodiments provided below can be found in the limitations of the safety route planning method described above, and will not be repeated here.

[0169] In one embodiment, such as Figure 4 As shown, a safe path planning device is provided, including: a cost value acquisition module 401, a total cost value determination module 403, an intermediate pose point determination module 405, and a path generation module 407, wherein:

[0170] The cost value acquisition module 401 is used to acquire the collision avoidance safety cost value and driving cost value of each pose point in the open set; the open set includes pose points searched in the path planning.

[0171] The total value determination module 403 is used to obtain the total value of each pose point based on the collision avoidance safety value, the driving value, and weight parameters.

[0172] The intermediate pose point determination module 405 is used to determine the pose point with the minimum total algebraic value in the open set as the intermediate pose point.

[0173] The path generation module 407 is used to generate a safe path based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point, when there is a collision-free path from the intermediate pose point to the target pose point.

[0174] In one embodiment, the device further includes:

[0175] The determination module 409 (not shown in the figure) is used to determine the obstacle density and / or road state value of the path to be planned; wherein the obstacle density and road state value are both positively correlated with the weight parameters corresponding to the collision avoidance safety cost;

[0176] The parameter determination module 411 (not shown in the figure) is used to determine the cost weight parameter corresponding to the collision avoidance safety cost based on the obstacle density and / or the road state value.

[0177] In one embodiment, the determining module 409 is specifically used for:

[0178] Obtain the current weather data for the geographical location of the route to be planned;

[0179] Based on the current weather data and the pre-built correspondence between weather data and road status values, the current road status value of the path to be planned is determined.

[0180] In one embodiment, the cost acquisition module 401 includes a shape parameter acquisition unit, a multiple location point acquisition unit, and an obstacle avoidance safety cost calculation unit.

[0181] The shape parameter acquisition unit is used to acquire the shape parameters of the target vehicle;

[0182] Multiple position point acquisition unit is used to determine multiple position points of multiple preset parts of the target vehicle when the target vehicle is in the pose point, based on the shape parameters of the target vehicle;

[0183] The obstacle avoidance safety cost calculation unit is used to determine the collision avoidance safety cost of the first pose point based on the Voronoi potential energy values ​​of the multiple location points.

[0184] In one embodiment, the obstacle avoidance safety cost calculation unit is specifically used for:

[0185] Determine the obstacle density of the path to be planned;

[0186] Based on the obstacle density, determine the descent parameters and the maximum obstacle distance; wherein the descent parameters are negatively correlated with the obstacle density; and the maximum obstacle distance is negatively correlated with the obstacle density.

[0187] Based on the descent parameters and the maximum distance to the obstacle, the Voronoi potential energy values ​​of the multiple location points are calculated.

[0188] In one embodiment, the device further includes:

[0189] The neighboring pose point acquisition module 413 (not shown in the figure) is used to determine multiple neighboring pose points of the intermediate pose point when there is no collision-free path from the intermediate pose point to the target pose point.

[0190] The candidate pose point determination module 415 (not shown in the figure) is used to select, from the plurality of neighboring pose points, a neighboring pose point that has a collision-free path with the intermediate point and is not in the closed set as a candidate pose point; the closed set includes each intermediate pose point.

[0191] The addition module 417 (not shown in the figure) is used to determine the collision avoidance safety value and driving value of the candidate pose point when the candidate pose point is not in the open set, and add the candidate pose point to the open set, and use the intermediate pose point as the parent node of the candidate pose point.

[0192] Each module in the aforementioned safe path planning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0193] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, and a communication interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a secure path planning method.

[0194] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0195] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0196] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0197] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0198] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0199] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A safe path planning method, characterized in that, The method includes: Obtain the collision avoidance safety value and driving value of each pose point in the open set; the open set includes pose points searched in the path planning. For each pose point, the total value of the pose point is obtained based on the collision avoidance safety value, the driving value, and the weighting parameters. The pose point with the minimum total algebraic value in the open set is determined as the intermediate pose point. If a collision-free path exists between the intermediate pose point and the target pose point, a safe path is generated based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point. If there is no collision-free path from the intermediate pose point to the target pose point, determine multiple neighboring pose points of the intermediate pose point. Among the plurality of neighboring pose points, one neighboring pose point that has a collision-free path with the intermediate pose point and is not in the closed set is selected as a candidate pose point; the closed set includes each intermediate pose point. If the candidate pose point is not in the open set, determine the collision avoidance safety value and driving value of the candidate pose point, add the candidate pose point to the open set, and use the intermediate pose point as the parent node of the candidate pose point.

2. The method according to claim 1, characterized in that, The method further includes: Determine the obstacle density and / or road state value of the path to be planned; wherein the obstacle density and road state value are both positively correlated with the weight parameters corresponding to the collision avoidance safety cost; Based on the obstacle density and / or the road condition value, determine the cost weight parameter corresponding to the collision avoidance safety cost.

3. The method according to claim 2, characterized in that, The road state values ​​for determining the path to be planned include: Obtain the current weather data for the geographical location of the route to be planned; Based on the current weather data and the pre-built correspondence between weather data and road status values, the current road status value of the path to be planned is determined.

4. The method according to claim 1, characterized in that, The method of obtaining the collision avoidance safety value of each pose point in the open set includes: Obtain the shape parameters of the target vehicle; Based on the shape parameters of the target vehicle, determine the multiple position points of multiple preset parts of the target vehicle when the target vehicle is in a pose point; The collision avoidance safety cost of the pose point is determined based on the Voronoi potential energy values ​​of the multiple location points.

5. The method according to claim 4, characterized in that, The process of obtaining the Voronoi potential values ​​at the multiple location points includes: Determine the obstacle density of the path to be planned; Based on the obstacle density, determine the descent parameters and the maximum obstacle distance; wherein the descent parameters are negatively correlated with the obstacle density; and the maximum obstacle distance is negatively correlated with the obstacle density. Based on the descent parameters and the maximum distance to the obstacle, the Voronoi potential energy values ​​of the multiple location points are calculated.

6. A safe path planning device, characterized in that, The device includes: The cost value acquisition module is used to acquire the collision avoidance safety cost value and driving cost value of each pose point in the open set; the open set includes pose points searched in the path planning. The total value determination module is used to obtain the total value of each pose point based on the collision avoidance safety value, the driving value, and weight parameters. The intermediate pose point determination module is used to determine the pose point with the minimum total algebraic value in the open set as the intermediate pose point. The path generation module is used to generate a safe path based on the path from the current pose point to the intermediate pose point and the collision-free path from the intermediate pose point to the target pose point, when there is a collision-free path from the intermediate pose point to the target pose point. The neighboring pose point acquisition module is used to determine multiple neighboring pose points of the intermediate pose point when there is no collision-free path from the intermediate pose point to the target pose point. The candidate pose point determination module is used to select, from the plurality of neighboring pose points, a neighboring pose point that has a collision-free path with the intermediate pose point and is not in a closed set as a candidate pose point; the closed set includes each intermediate pose point. The addition module is used to determine the collision avoidance safety value and driving value of the candidate pose point when the candidate pose point is not in the open set, and add the candidate pose point to the open set, and use the intermediate pose point as the parent node of the candidate pose point.

7. The apparatus according to claim 6, characterized in that, The device further includes: A determination module is used to determine the obstacle density and / or road state value of the path to be planned; wherein the obstacle density and road state value are both positively correlated with the weight parameters corresponding to the collision avoidance safety cost; The parameter determination module is used to determine the value weight parameter corresponding to the collision avoidance safety value based on the obstacle density and / or the road state value.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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