Vehicle parameter determination method and device, vehicle and medium

Abstract

The invention relates to a vehicle parameter determination method and device, a vehicle and a medium. The method comprises the steps that multiple predicted tracks of obstacle vehicles of a vehicle are obtained; determining a plurality of anchor points of a plurality of parameter curves matched with the plurality of prediction trajectories, the plurality of parameter curves being related to an action planning target of the vehicle, and the action planning target being used for reflecting action parameters of the vehicle to be planned; determining a shared anchor point and a self-use anchor point according to the plurality of anchor points, the shared anchor point being an anchor point which can be commonly used by the plurality of parameter curves, and the self-use anchor point being an anchor point which can be independently used by each parameter curve; and determining the plurality of parameter curves of the own vehicle according to the shared anchor point and the own anchor point.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent driving technology, and in particular to a method, device, vehicle and medium for determining vehicle parameters. Background Art

[0002] With the development of autonomous driving technology, safe obstacle avoidance has become a key issue in the design of unmanned driving systems. In related technologies, the predicted trajectory of obstacles can be used to determine the decision-making planning results of the self-driving car, for example, to determine the parameters of the self-driving car. However, in related technologies, the decision-making planning results of the self-driving car may cause jumps and somatosensory problems, or the decision may be too conservative and not in line with human driving habits. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a method, device, vehicle and medium for determining vehicle parameters.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for determining vehicle parameters is provided, comprising: Obtain multiple predicted trajectories of obstacle vehicles of the ego vehicle; Determining a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories, the plurality of parameter curves being related to a motion planning target of the ego vehicle, the motion planning target being used to reflect motion parameters of the ego vehicle to be planned; Determine a shared anchor point and a private anchor point according to the plurality of anchor points, wherein the shared anchor point is an anchor point that can be used in common by the plurality of parameter curves, and the private anchor point is an anchor point that can be used individually by each parameter curve; The plurality of parameter curves of the vehicle are determined according to the shared anchor point and the own anchor point.

[0005] Optionally, the determining a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories comprises: For each of the predicted trajectories, in a first coordinate system, with the position of the vehicle as a starting position, repeatedly performing the steps of determining a first coordinate value of an anchor point of a parameter curve matching the predicted trajectory according to a preset interval, determining a second coordinate value of the anchor point according to a cost function, and determining the anchor point according to the first coordinate value and the second coordinate value, so as to determine the multiple anchor points of the multiple parameter curves; Among them, the first coordinate value is the coordinate value of the first direction of the first coordinate system, the second coordinate value is the coordinate value of the second direction of the first coordinate system, the cost function is determined at least based on the change between the second coordinate values ​​of adjacent anchor points, and when the anchor point is the first anchor point, the change is the change between the second coordinate value of the starting position and the second coordinate value of the first anchor point.

[0006] Optionally, the cost function is determined based on a first distance between the anchor point and the center line of the lane of the ego vehicle, the change, and a second distance between the anchor point and the position of the obstacle vehicle under the predicted trajectory.

[0007] Optionally, the action planning target includes path planning, the multiple parameter curves include multiple path curves, and when the multiple parameter curves include multiple path curves, the first coordinate system is a coordinate system composed of an X-axis and a Y-axis, the first direction represents the direction of the X-axis, the second direction represents the direction of the Y-axis, the X-axis is used to reflect the movement of the vehicle in the horizontal direction, and the Y-axis is used to reflect the movement of the vehicle in the vertical direction.

[0008] Optionally, the action planning target includes speed planning, the multiple parameter curves include multiple speed curves, and when the multiple parameter curves include the multiple speed curves, the first coordinate system is a coordinate system consisting of an S axis and a T axis, or a coordinate system consisting of a Y axis and a T axis, the first direction represents the direction of the T axis, the second direction represents the direction of the S axis or the Y axis, the T axis is used to reflect time, the S axis is used to reflect the mileage of the vehicle, and the Y axis is used to reflect the movement of the vehicle in the vertical direction.

[0009] Optionally, each anchor point of different parameter curves is aligned in a vertical direction of the vehicle, and the step of determining a shared anchor point and a self-use anchor point according to the plurality of anchor points includes: Determining a plurality of first weights according to a plurality of prediction probabilities of the plurality of prediction trajectories; According to the plurality of first weights and third distances between the plurality of first anchor points, the plurality of first anchor points are moved to determine target first anchor points whose positions overlap after the movement, the plurality of first anchor points including anchor points aligned in the vertical direction; The shared anchor point is determined according to the overlapping position of the target first anchor point, and the anchor points other than the target first anchor point among the multiple anchor points are determined as the self-use anchor points.

[0010] Optionally, determining the plurality of parameter curves of the vehicle according to the shared anchor point and the vehicle anchor point includes: Curve smoothing is performed according to the shared anchor point and the self-used anchor point to determine the plurality of parameter curves.

[0011] According to a second aspect of an embodiment of the present disclosure, there is provided a device for determining vehicle parameters, comprising: an acquisition module, configured to acquire a plurality of predicted trajectories of obstacle vehicles of the ego vehicle; A first determination module is configured to determine a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories, the plurality of parameter curves being related to an action planning target of the ego vehicle, the action planning target being used to reflect an action parameter of the ego vehicle to be planned; a second determining module configured to determine a shared anchor point and a private anchor point according to the plurality of anchor points, wherein the shared anchor point is an anchor point that can be used in common by the plurality of parameter curves, and the private anchor point is an anchor point that can be used individually by each parameter curve; The third determination module is configured to determine the plurality of parameter curves of the vehicle according to the shared anchor point and the vehicle's own anchor point.

[0012] According to a third aspect of an embodiment of the present disclosure, a vehicle is provided, comprising: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Obtain multiple predicted trajectories of obstacle vehicles of the ego vehicle; Determining a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories, the plurality of parameter curves being related to a motion planning target of the ego vehicle, the motion planning target being used to reflect motion parameters of the ego vehicle to be planned; Determine a shared anchor point and a private anchor point according to the plurality of anchor points, wherein the shared anchor point is an anchor point that can be used in common by the plurality of parameter curves, and the private anchor point is an anchor point that can be used individually by each parameter curve; The plurality of parameter curves of the vehicle are determined according to the shared anchor point and the own anchor point.

[0013] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the method for determining vehicle parameters provided in the first aspect of the present disclosure are implemented.

[0014] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: The shared anchor point and the self-use anchor point are determined by multiple anchor points of multiple parameter curves that match multiple predicted trajectories, and multiple parameter curves are generated according to the shared anchor point and the self-use anchor point. Since the multiple parameter curves match the multiple predicted trajectories, multiple prediction results of the future driving trajectories of the obstacle vehicle are taken into account, that is, multiple obstacle avoidance deduction scenarios are taken into account, so that the self-vehicle can avoid potential risks in advance and ensure the safety of the self-vehicle's driving.

[0015] In addition, since the vehicle cannot travel along two trajectories at the same time, by determining the shared anchor point, the shared curves composed of the shared anchor points of multiple parameter curves are the same. At this time, the execution result of the self-vehicle is unique, and deterministic actions are generated for uncertain future scenarios, making the driving of the self-vehicle more stable. In addition, the self-use anchor point can correspond to the self-use curve in the parameter curve. By including both the shared curve and the self-use curve, the trajectory of the self-vehicle can be switched when the predicted trajectory probability of the obstacle vehicle changes, avoiding the self-vehicle's decision being too conservative, and it is applicable to all dangerous scenarios interacting with dynamic obstacles (e.g., dynamic obstacle vehicles).

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0018] Figure 1 is a schematic diagram showing multiple predicted trajectories according to an exemplary embodiment.

[0019] Figure 2 The present invention is a flowchart of a method for determining vehicle parameters according to an exemplary embodiment.

[0020] Figure 3 is a schematic diagram of multiple anchor points of multiple path curves according to an exemplary embodiment.

[0021] Figure 4 is a schematic diagram showing multiple anchor points of multiple speed curves according to an exemplary embodiment.

[0022] Figure 5 The diagram is a schematic diagram showing shared anchor points and self-used anchor points of multiple path curves according to an exemplary embodiment.

[0023] Figure 6 is a schematic diagram of multiple path curves according to an exemplary embodiment.

[0024] Figure 7 is a schematic diagram showing a plurality of speed curves according to an exemplary embodiment.

[0025] Figure 8 is a schematic diagram showing path control and speed control of a vehicle under multiple predicted trajectories according to an exemplary embodiment.

[0026] Fig. 9 The invention is a flowchart of determining multiple anchor points according to an exemplary embodiment.

[0027] Fig.10 The invention is a flowchart of determining a shared anchor point and a private anchor point according to an exemplary embodiment.

[0028] Fig.11 is a schematic diagram showing moving a plurality of first anchor points according to an exemplary embodiment.

[0029] Fig.12 is a flow chart of trajectory planning according to an exemplary embodiment.

[0030] Fig.13 The invention is a block diagram of a device for determining vehicle parameters according to an exemplary embodiment.

[0031] Fig.14 is a block diagram of a vehicle according to an exemplary embodiment.

[0032] Fig.15 The invention is a block diagram of a device for determining vehicle parameters according to an exemplary embodiment. DETAILED DESCRIPTION

[0033] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0034] In the related art, the predicted trajectory of the obstacle can be used to determine the decision-making planning result of the ego vehicle, for example, to determine the parameters of the ego vehicle. Figure 1 , assuming that the road includes three lanes, namely L1, L2 and L3, the ego vehicle C1 cruises in lane L2 at a set speed V1, and the obstacle vehicle C2 travels in lane L3 at a speed V2 and travels in front of the right side of the ego vehicle C1. The obstacle vehicle C2 has two predicted trajectories, namely predicted trajectories S1 and S2, where the predicted trajectory S1 represents the obstacle vehicle C2 changing lanes to the left, and the predicted trajectory S2 represents the obstacle vehicle C2 staying straight in lane L3.

[0035] In the related technology, the result with the highest probability can be selected based on the predicted trajectory of the obstacle, for example, the trajectory with the highest probability among the predicted trajectories S1 and S2 is selected, and the vehicle makes decision planning based on this. However, the trajectory with the highest probability switches, causing the decision planning result of the vehicle to jump, causing somatosensory problems, such as path jitter and speed jitter. In addition, potential risks may be ignored, resulting in sudden braking or collision.

[0036] In addition, the relevant technology can also consider all predicted trajectories of obstacles at the same time. For example, considering predicted trajectories S1 and S2 at the same time, the safest trajectory of the ego vehicle is generated to ensure that there is no risk of collision in any situation. However, this solution ignores the mutual exclusivity of possible behaviors of obstacles and will not release them at the same time. The ego vehicle's decision is too conservative. Even if the obstacle keeps going straight, the ego vehicle will slow down to avoid it and dare not overtake. The decision is too conservative and does not conform to human driving habits.

[0037] Figure 2 is a flow chart of a method for determining vehicle parameters according to an exemplary embodiment, which method can be applied to a vehicle such as Figure 2 As shown, the method includes the following steps 210-240.

[0038] Step 210, obtaining multiple predicted trajectories of obstacle vehicles of the ego vehicle.

[0039] In a possible implementation, the vehicle can obtain multiple predicted trajectories of the obstacle vehicle through the unmanned driving system. For example, in the unmanned driving system, the perception module can obtain real-time data of the obstacle vehicle, and the real-time data may include but is not limited to at least one of the following: position, direction, length, width, height, speed and acceleration. The prediction module predicts the future movement trajectory of the obstacle vehicle based on the real-time data to obtain multiple predicted trajectories. The multiple predicted trajectories can carry corresponding prediction probabilities.

[0040] In some embodiments, the plurality of predicted trajectories may be predicted trajectories selected from all predicted trajectories, and all predicted trajectories may be all trajectories predicted by the autonomous driving system of the vehicle. For example, predicted trajectories with a predicted probability greater than a preset probability value may be selected from all predicted trajectories to obtain a plurality of predicted trajectories. The disclosed embodiment selects predicted trajectories with a predicted probability greater than a preset probability value, discards low-probability trajectories, and uses high-probability trajectories to generate the parameter curve of the vehicle, thereby preventing the vehicle from making overly conservative driving decisions based on the parameter curve.

[0041] Step 220, determining multiple anchor points of multiple parameter curves that match the multiple predicted trajectories, the multiple parameter curves are related to the action planning target of the ego vehicle, and the action planning target is used to reflect the action parameters of the ego vehicle to be planned.

[0042] The anchor point is used to generate a parameter curve. In some embodiments, the action planning target may include path planning and / or speed planning. Path planning matches multiple path curves, and speed planning matches multiple speed curves. The path planning target is used to reflect the path of the ego vehicle to be planned, and the speed planning target is used to reflect the speed of the ego vehicle to be planned.

[0043] Each path curve is used to reflect the change information of the path of the ego vehicle that can ensure safety under the corresponding predicted trajectory, and each speed curve is used to reflect the change information of the speed of the ego vehicle that can ensure safety under the corresponding predicted trajectory.

[0044] In a possible implementation, a preset planning algorithm may be used to determine multiple anchor points of multiple parameter curves that match multiple predicted trajectories. For example, when the multiple parameter curves include multiple path curves, a path planning algorithm may be used to determine multiple anchor points of the multiple path curves; when the multiple parameter curves include multiple speed curves, a speed planning algorithm may be used to determine multiple anchor points of the multiple speed curves. For specific details of the path planning algorithm and the speed planning algorithm, please refer to the relevant technology, which will not be repeated here.

[0045] In another possible implementation, multiple anchor points may be determined according to a preset interval and a cost function. For details on determining multiple anchor points, see the following Fig. 9 And the related description will not be repeated here.

[0046] For example, taking the case where multiple parameter curves include multiple path curves as an example, refer to Figure 3 , Figure 3 is a schematic diagram of multiple anchor points of multiple path curves according to an exemplary embodiment. Figure 3 The road conditions and vehicle driving conditions in the Figure 1 Same, for details, see Figure 1 The relevant description in will not be repeated here. Figure 3 As shown, assuming that for the predicted trajectory S1, the path decision 1 of the ego vehicle is that the obstacle vehicle changes lanes to the left, and the ego vehicle also changes lanes to the left. For the predicted trajectory S2, the path decision 2 of the ego vehicle is that the obstacle vehicle goes straight, and the ego vehicle goes straight. Therefore, the path decision 1 of the ego vehicle will generate the path curve H1, and the path decision 2 of the ego vehicle will generate the path curve H2. It can be understood that the path curve H1 matches the predicted trajectory S1, and the path curve H2 matches the predicted trajectory S2.

[0047] In order to generate path curves H1 and H2 that match the predicted trajectories S1 and S2 respectively, it is necessary to determine multiple anchor points of each of the two path curves, including multiple anchor points M of the path curve H1. S1 , and multiple anchor points M of the path curve H2 S2 , △S represents the distance between adjacent anchor points.

[0048] For example, taking the case where multiple parameter curves include multiple speed curves as an example, refer to Figure 4 , Figure 4 is a schematic diagram of multiple anchor points of multiple speed curves according to an exemplary embodiment, wherein: Figure 4The slope of the coordinate system represents the velocity. Figure 4 As shown in case a, for the predicted trajectory S1, the vehicle's speed decision 1 is to slow down and avoid, such as Figure 4 As shown in case b, for the predicted trajectory S2, the speed decision 2 of the vehicle is cruise control. Therefore, the speed decision 1 of the vehicle generates the speed curve W1, and the speed decision 2 of the vehicle generates the speed curve W2. It can be understood that the speed curve W1 matches the predicted trajectory S1, and the speed curve W2 matches the predicted trajectory S2.

[0049] In order to generate speed curves W1 and W2 that match the predicted trajectories S1 and S2, respectively, it is necessary to determine multiple anchor points of the two speed curves, including multiple anchor points M of the speed curve W1. W1 , multiple anchor points M of the speed curve W2 W2 .

[0050] Step 230: determining a shared anchor point and a private anchor point based on the plurality of anchor points. The shared anchor point is an anchor point that can be used by a plurality of parameter curves, and the private anchor point is an anchor point that can be used by each parameter curve individually.

[0051] In some embodiments, each anchor point of different parameter curves is aligned in the vertical direction of the vehicle, and determining shared anchor points and self-use anchor points based on multiple anchor points can include: determining a preset number of anchor points among the multiple anchor points that are aligned in the vertical direction and arranged in the front as shared anchor points, and determining anchor points other than the shared anchor points among the multiple anchor points as self-use anchor points.

[0052] The preset number can be determined based on at least one of the following considerations: ensuring that the trajectory segment used by the downstream control node belongs to the shared segment and that there is no collision with the obstacle vehicle. To ensure that the trajectory segment used by the downstream control node belongs to the shared segment, the number of shared anchor points cannot be too small. To ensure that there is no collision with the obstacle vehicle and to ensure safety, the number of shared anchor points cannot be too large. The downstream control node can be used to control the ego vehicle based on multiple parameter curves, for example, to control the throttle and steering wheel of the ego vehicle.

[0053] For example, the above Figure 3 For example, in this scenario, there are two path curves H1 and H2. Assuming that the preset number is 4, the 4 anchor points that are aligned in the vertical direction and arranged in the front can be determined as shared anchor points. Figure 5 , M G is a shared anchor point, M H1 It is an anchor point that can be used by the path curve H1 alone. H2 It is an anchor point that can be used independently by the path curve H2.

[0054] In some embodiments, each anchor point of different parameter curves is aligned in the vertical direction of the vehicle, and determining shared anchor points and self-use anchor points based on multiple anchor points may include: determining multiple first weights based on multiple predicted probabilities of multiple predicted trajectories; moving multiple first anchor points based on multiple first weights and third distances between multiple first anchor points, and determining target first anchor points whose positions overlap after the movement, wherein the multiple first anchor points include anchor points aligned in the vertical direction; determining shared anchor points based on the overlapping positions of the target first anchor points, and determining anchor points other than the target first anchor points in the multiple anchor points as self-use anchor points. For specific details on determining shared anchor points and self-use anchor points, please refer to the following Fig.10 , I will not go into details here.

[0055] Step 240 , determining multiple parameter curves of the vehicle based on the shared anchor points and the vehicle's own anchor points.

[0056] The disclosed embodiment determines a shared anchor point and a self-use anchor point through multiple anchor points of multiple parameter curves that match multiple predicted trajectories, and generates multiple parameter curves based on the shared anchor points and the self-use anchor points. Since the multiple parameter curves match the multiple predicted trajectories, multiple prediction results of the future driving trajectories of the obstacle vehicle are taken into account, that is, multiple obstacle avoidance deduction scenarios are taken into account, so that the self-vehicle can avoid potential risks in advance and ensure the safety of the self-vehicle's driving.

[0057] In addition, since the vehicle cannot travel along two trajectories at the same time, by determining the shared anchor point, the shared curves composed of the shared anchor points of multiple parameter curves are the same. At this time, the execution result of the self-vehicle is unique, and deterministic actions are generated for uncertain future scenarios, making the driving of the self-vehicle more stable. In addition, the self-use anchor point can correspond to the self-use curve in the parameter curve. By including both the shared curve and the self-use curve, the trajectory of the self-vehicle can be switched when the predicted trajectory probability of the obstacle vehicle changes, avoiding the self-vehicle's decision being too conservative, and it is applicable to all dangerous scenarios interacting with dynamic obstacles (e.g., dynamic obstacle vehicles).

[0058] In some embodiments, multiple parameter curves of the vehicle are determined based on the shared anchor point and the own anchor point, including: performing curve smoothing processing based on the shared anchor point and the own anchor point to determine the multiple parameter curves.

[0059] The disclosed embodiment determines multiple parameter curves by performing curve smoothing according to the shared anchor points and the self-use anchor points, which is equivalent to performing curve smoothing on the self-use trajectory and the shared trajectory in the multiple parameter curves. Even if the predicted trajectory has jitters and the predicted probability value switches, the smoothness of the self-vehicle driving can be guaranteed. It also achieves the consideration of potential risks and avoidance of conservative decisions.

[0060] In a possible implementation, performing curve smoothing processing on the shared anchor points and the private anchor points to determine multiple parameter curves may include: performing curve smoothing processing on the shared anchor points and the private anchor points using a quadratic optimization algorithm to determine multiple parameter curves. A 5th-order polynomial curve may be obtained using the quadratic optimization algorithm.

[0061] For example, taking the multiple path curves including the aforementioned path curve H1 and path curve H2 as an example, see Figure 6 , Figure 6 The curve H1 (or curve segment) in the path is the curve formed by the anchor points of the path curve H1, and the curve H2 is the curve formed by the anchor points of the path curve H2. 12 is a curve formed by the shared anchor points of the path curve H1 and the path curve H2. It can be understood that the curve H 12 and curve H1 form path curve H1, curve H 12 And curve H2 constitute path curve H2.

[0062] For example, take multiple speed curves including speed curves N1 and N2 as an example, see Figure 7 , Figure 7 The curve N1 (or curve segment) in the figure is a curve formed by the anchor points of the speed curve N1, and the curve N2 is a curve formed by the anchor points of the speed curve N2. 12 is a curve formed by the shared anchor point of the speed curve N1 and the speed curve N2. It can be understood that the curve N 12 and curve N1 form path curve N1, curve N 12 And curve N2 constitute path curve N2.

[0063] In order to more clearly illustrate the control of the vehicle by the multiple parameter curves disclosed in the present invention, the action planning target includes path planning and speed planning, the multiple parameter curves matching the path planning include the aforementioned path curves H1 and H2, and the multiple parameter curves matching the speed planning include the aforementioned second speed curves N1 and N2. Figure 8 The path control and speed control of the vehicle under multiple predicted trajectories are explained.

[0064] like Figure 8 As shown in the figure, when the vehicle C1 overtakes the dynamic obstacle vehicle C2, the path it travels can be seen in Figure 8 Figure c in the figure shows the speed change. Figure 8Figure d in Figure c, where R1 in Figure c is the path of the ego vehicle during overtaking, R2 in Figure d is the speed of the ego vehicle during overtaking, and the coordinate system in Figure d is the VT coordinate system, which is used to characterize the change of speed V over time T. Due to the collision risk of the obstacle vehicle C2 changing lanes to the left, the path R1 of the ego vehicle C1 during overtaking is to deviate to the left from the center line of lane L2, and the speed is lower than the set cruising speed V1. This ensures safety and smoothness during the driving of the ego vehicle, which is closer to the driving style of experienced human drivers.

[0065] Fig. 9 FIG. 1 is a flowchart of determining multiple anchor points according to an exemplary embodiment. Fig. 9 , the process includes the following steps 910.

[0066] Step 910, for each predicted trajectory, in the first coordinate system, with the position of the vehicle as the starting position, repeatedly perform the steps of determining the first coordinate value of the anchor point of the parameter curve matching the predicted trajectory according to a preset interval, determining the second coordinate value of the anchor point according to the cost function, and determining the anchor point according to the first coordinate value and the second coordinate value, so as to determine multiple anchor points of multiple parameter curves; wherein the first coordinate value is a coordinate value in a first direction of the first coordinate system, the second coordinate value is a coordinate value in a second direction of the first coordinate system, the cost function is determined at least based on the change between the second coordinate values ​​of adjacent anchor points, and when the anchor point is the first anchor point, the change is the change between the second coordinate value of the starting position and the second coordinate value of the first anchor point.

[0067] The preset interval can be determined according to actual needs, for example, the preset interval can be 1m. For example, see Figure 3 , the preset interval can be △S=1m.

[0068] In some embodiments, the action planning goal includes path planning, the multiple parameter curves include multiple path curves, and when the multiple parameter curves include multiple path curves, the first coordinate system is a coordinate system composed of an X-axis and a Y-axis, the first direction represents the direction of the X-axis, the second direction represents the direction of the Y-axis, the X-axis is used to reflect the horizontal movement of the vehicle, and the Y-axis is used to reflect the vertical movement of the vehicle.

[0069] Since the first coordinate value of each anchor point is determined according to a preset interval, the change amounts of adjacent anchor points in the horizontal direction are the same, which are all preset intervals, that is, the change amounts between the first coordinate values ​​of adjacent anchor points are fixed, which are all preset intervals.

[0070] In some embodiments, the action planning target includes speed planning, the multiple parameter curves include multiple speed curves, and when the multiple parameter curves include multiple speed curves, the first coordinate system is a coordinate system composed of the S axis and the T axis, or a coordinate system composed of the Y axis and the T axis, the first direction represents the direction of the T axis, the second direction represents the direction of the S axis or the Y axis, the T axis is used to reflect time, the S axis is used to reflect the mileage of the vehicle, and the Y axis is used to reflect the movement of the vehicle in the vertical direction.

[0071] For example, when the first coordinate system corresponding to the speed curve is a coordinate system composed of the Y-axis and the T-axis, the anchor points of the path curve and the speed curve can be determined by the same spatial coordinate system, which can be a space-time coordinate system including the X-axis, the Y-axis and the T-axis.

[0072] In a possible implementation, the cost function may adopt a cost function in the related art. In some embodiments, the cost function may also be determined based on a first distance between the anchor point and the center line of the lane of the vehicle, a change amount, and a second distance between the anchor point and the position of the obstacle vehicle under the predicted trajectory.

[0073] Among them, the change can reflect the control of the steering wheel by the vehicle. The control of the steering wheel will affect the comfort of the passengers. The first distance and the second distance will affect the safety of the vehicle.

[0074] In some embodiments, the cost function may be obtained by performing a weighted summation process on the first distance, the change amount, the first-order derivative of the change amount, the second-order derivative of the change amount, and the second distance.

[0075] In the disclosed embodiment, the change amount determines the control angle of the steering wheel, the first-order derivative determines the speed of the steering wheel, and the second-order derivative determines the acceleration of the steering wheel turning speed. Among them, the second-order derivative can affect the comfort of passengers, for example, it can determine whether the passengers will feel thrown when riding.

[0076] In some embodiments, performing weighted summation according to the first distance, the change, the first-order derivative, the second-order derivative, and the second distance may include: performing weighted summation according to the first distance, the change, the first-order derivative, the second-order derivative, and the second distance according to the corresponding second weight. The second weight may be determined according to actual needs, for example, may be determined according to the importance of the corresponding parameter. In some embodiments, determining the second coordinate value of the anchor point according to the cost function may include: solving the cost function with the minimum function value of the cost function as the goal, and determining the second coordinate value of the anchor point. Since the change between the first coordinate values ​​of adjacent anchor points is fixed and is a preset interval, the change in the second coordinate value between adjacent anchor points will affect the second coordinate value of the anchor point. The embodiment of the present disclosure determines the cost function according to the change between the second coordinate values ​​of adjacent anchor points, and determines the second coordinate value of the anchor point according to the cost function, so as to determine the coordinate information of the next anchor point according to the coordinate information of the previous anchor point, thereby realizing local trajectory planning and improving planning efficiency, and the local trajectory planning is more in line with the obstacle avoidance planning of vehicles with dynamic obstacles.

[0077] Fig.10 FIG. 1 is a flowchart showing a method for determining a shared anchor point and a private anchor point according to an exemplary embodiment. Fig.10 , the process includes the following steps 1010-1030.

[0078] Step 1010 , determining a plurality of first weights according to a plurality of prediction probabilities of a plurality of prediction trajectories.

[0079] The predicted probability may be output by a prediction module in the autonomous driving system of the vehicle. For specific details of the autonomous driving system, please refer to the above step 210 and its related description, which will not be repeated here.

[0080] In some embodiments, determining multiple first weights according to multiple predicted probabilities of multiple predicted trajectories may include: normalizing the multiple predicted probabilities to determine the multiple first weights. For example, still taking the multiple predicted trajectories including two, namely S1 and S2 as an example, assuming that the predicted probability P1 of the predicted trajectory S1 is 0.5, and the predicted probability P2 of the predicted trajectory S2 is 0.3, then the first weight q1=0.5 / 0.5+0.3=0.6, and the first weight q2=0.3 / 0.5+0.3=0.4 can be obtained.

[0081] Step 1020, moving the multiple first anchor points according to the multiple first weights and the third distances between the multiple first anchor points, and determining target first anchor points whose positions overlap after the movement, wherein the multiple first anchor points include anchor points aligned in the vertical direction.

[0082] In some embodiments, moving the multiple first anchor points may be moving the multiple first anchor points toward each other.

[0083] In some embodiments, multiple first anchor points are moved based on multiple first weights and a third distance between the multiple first anchor points to determine a target first anchor point whose position overlaps after the movement, including: determining the moving distance of each of the multiple first anchor points based on the multiple first weights and the third distance; moving the multiple first anchor points based on the moving distance to determine a target first anchor point whose position overlaps after the movement.

[0084] For example, see Fig.11 , with multiple first anchor points being from Figure 3 Take any group of vertically aligned anchor points selected from as an example. According to the above example, Fig.11 The diamond anchor point (i.e., the upper anchor point) in is the anchor point in the path curve H1, and the circular anchor point (i.e., the lower anchor point) is the anchor point in the path curve H2. Assuming that two first weights are determined according to the two predicted trajectories, namely, the first weight α and the first weight (1-α), the moving distance of the diamond anchor point can be α×L3, where L3 is the third distance between the diamond anchor point and the circular anchor point, and the moving distance of the circular anchor point can be (1-α)×L3. Fig.11 As shown, after the two first anchor points are moved, the positions of the two first anchor points overlap, so the two first anchor points are target first anchor points.

[0085] Step 1030: determine a shared anchor point according to the overlapping position of the target first anchor point, and determine anchor points other than the target first anchor point among the multiple anchor points as self-use anchor points.

[0086] In some embodiments, a preset number of target first anchor points may be selected, and a shared anchor point may be determined based on the overlapped positions of the preset number of target first anchor points. For details about the preset number, please refer to the above description, which will not be repeated here. For example, taking the preset number as 4 as an example, the shared anchor point determined based on the 4 overlapped positions and the self-use anchor point determined may refer to Figure 6 And the related description will not be repeated here.

[0087] Fig.12 FIG. 1 is a flow chart of trajectory planning according to an exemplary embodiment. Fig.12 As shown, trajectory planning may include path planning and speed planning. Assuming that the multiple path curves corresponding to the path planning are path curves 1 and 2, the steps of dynamically planning path anchor points, determining shared path anchor points and self-use path anchor points, and smoothing the path curves are performed for both path curves 1 and 2. Assuming that the multiple speed curves corresponding to the speed planning are speed curves 1 and 2, the steps of dynamically planning speed anchor points, determining shared speed anchor points and self-use speed anchor points, and smoothing the speed curves are performed for both speed curves 1 and 2. Finally, the trajectory output is executed, and the trajectory output includes the path planning results and the speed planning results.

[0088] like Fig.12 As shown, the present disclosure can perform path planning first and then speed planning, but it is worth noting that the present disclosure does not impose any restrictions on the order of path planning and speed planning. In addition, the present disclosure can also perform path planning or speed planning separately.

[0089] about Fig.12 For specific details on executing dynamic planning path anchor points and executing dynamic planning speed anchor points, please refer to the aforementioned step 220 and its related description. For specific details on determining shared path anchor points and personal path anchor points, and determining shared speed anchor points and personal speed anchor points, please refer to the aforementioned step 230 and its related description. For specific details on smoothing the path curve and smoothing the speed curve, please refer to the aforementioned step 240 and its related description, which will not be repeated here.

[0090] Fig.13 is a block diagram of a device for determining vehicle parameters according to an exemplary embodiment. Fig.13 , the vehicle parameter determination device 1300 includes: An acquisition module 1310 is configured to acquire a plurality of predicted trajectories of obstacle vehicles of the ego vehicle; A first determination module 1320 is configured to determine a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories, the plurality of parameter curves being related to a motion planning target of the ego vehicle, the motion planning target being used to reflect a motion parameter of the ego vehicle to be planned; A second determining module 1330 is configured to determine a shared anchor point and a private anchor point according to the plurality of anchor points, wherein the shared anchor point is an anchor point that can be used in common by the plurality of parameter curves, and the private anchor point is an anchor point that can be used individually by each parameter curve; The third determination module 1340 is configured to determine the plurality of parameter curves of the vehicle according to the shared anchor point and the vehicle's own anchor point.

[0091] Optionally, the first determining module is further configured to: For each of the predicted trajectories, in a first coordinate system, with the position of the vehicle as a starting position, repeatedly performing the steps of determining a first coordinate value of an anchor point of a parameter curve matching the predicted trajectory according to a preset interval, determining a second coordinate value of the anchor point according to a cost function, and determining the anchor point according to the first coordinate value and the second coordinate value, so as to determine the multiple anchor points of the multiple parameter curves; Among them, the first coordinate value is the coordinate value of the first direction of the first coordinate system, the second coordinate value is the coordinate value of the second direction of the first coordinate system, the cost function is determined at least based on the change between the second coordinate values ​​of adjacent anchor points, and when the anchor point is the first anchor point, the change is the change between the second coordinate value of the starting position and the second coordinate value of the first anchor point.

[0092] Optionally, the cost function is determined based on a first distance between the anchor point and the center line of the lane of the ego vehicle, the change, and a second distance between the anchor point and the position of the obstacle vehicle under the predicted trajectory.

[0093] Optionally, the action planning target includes path planning, the multiple parameter curves include multiple path curves, and when the multiple parameter curves include multiple path curves, the first coordinate system is a coordinate system composed of an X-axis and a Y-axis, the first direction represents the direction of the X-axis, the second direction represents the direction of the Y-axis, the X-axis is used to reflect the movement of the vehicle in the horizontal direction, and the Y-axis is used to reflect the movement of the vehicle in the vertical direction.

[0094] Optionally, the action planning target includes speed planning, the multiple parameter curves include multiple speed curves, and when the multiple parameter curves include the multiple speed curves, the first coordinate system is a coordinate system consisting of an S axis and a T axis, or a coordinate system consisting of a Y axis and a T axis, the first direction represents the direction of the T axis, the second direction represents the direction of the S axis or the Y axis, the T axis is used to reflect time, the S axis is used to reflect the mileage of the vehicle, and the Y axis is used to reflect the movement of the vehicle in the vertical direction.

[0095] Optionally, each anchor point of different parameter curves is aligned in the vertical direction of the vehicle, and the second determination module 1330 is further configured to: Determining a plurality of first weights according to a plurality of prediction probabilities of the plurality of prediction trajectories; According to the plurality of first weights and third distances between the plurality of first anchor points, the plurality of first anchor points are moved to determine target first anchor points whose positions overlap after the movement, the plurality of first anchor points including anchor points aligned in the vertical direction; The shared anchor point is determined according to the overlapping position of the target first anchor point, and the anchor points other than the target first anchor point among the multiple anchor points are determined as the self-use anchor points.

[0096] Optionally, the third determining module 1340 is further configured to: Curve smoothing is performed according to the shared anchor point and the self-used anchor point to determine the plurality of parameter curves.

[0097] The device of the disclosed embodiment determines a shared anchor point and a self-use anchor point through multiple anchor points of multiple parameter curves that match multiple predicted trajectories, and generates multiple parameter curves based on the shared anchor points and the self-use anchor points. Since the multiple parameter curves match the multiple predicted trajectories, multiple prediction results of the future driving trajectories of the obstacle vehicle are taken into account, that is, multiple obstacle avoidance deduction scenarios are taken into account, so that the self-vehicle can avoid potential risks in advance and ensure the safety of the self-vehicle's driving.

[0098] In addition, since the vehicle cannot travel along two trajectories at the same time, by determining the shared anchor point, the shared curves composed of the shared anchor points of multiple parameter curves are the same. At this time, the execution result of the self-vehicle is unique, and deterministic actions are generated for uncertain future scenarios, making the driving of the self-vehicle more stable. In addition, the self-use anchor point can correspond to the self-use curve in the parameter curve. By including both the shared curve and the self-use curve, the trajectory of the self-vehicle can be switched when the predicted trajectory probability of the obstacle vehicle changes, avoiding the self-vehicle's decision being too conservative, and it is applicable to all dangerous scenarios interacting with dynamic obstacles (e.g., dynamic obstacle vehicles).

[0099] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0100] The present disclosure also provides a vehicle, comprising: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Obtain multiple predicted trajectories of obstacle vehicles of the ego vehicle; Determining a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories, the plurality of parameter curves being related to a motion planning target of the ego vehicle, the motion planning target being used to reflect motion parameters of the ego vehicle to be planned; Determine a shared anchor point and a private anchor point according to the plurality of anchor points, wherein the shared anchor point is an anchor point that can be used in common by the plurality of parameter curves, and the private anchor point is an anchor point that can be used individually by each parameter curve; The plurality of parameter curves of the vehicle are determined according to the shared anchor point and the own anchor point.

[0101] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon. When the program instructions are executed by a processor, the steps of the method for determining vehicle parameters provided by the present disclosure are implemented.

[0102] Fig.14 1 is a block diagram of a vehicle 1400 according to an exemplary embodiment. For example, the vehicle 1400 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. The vehicle 1400 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0103] Reference Fig.14 , the vehicle 1400 may include various subsystems, for example, an infotainment system 1410, a perception system 1420, a decision control system 1430, a drive system 1440, and a computing platform 1450. The vehicle 1400 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and each component of the vehicle 1400 may be interconnected by wire or wireless means.

[0104] In some embodiments, the infotainment system 1410 may include a communication system, an entertainment system, and a navigation system, etc.

[0105] The perception system 1420 may include several sensors for sensing information about the environment around the vehicle 1400. For example, the perception system 1420 may include a global positioning system (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0106] The decision control system 1430 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0107] The drive system 1440 may include components that provide powered motion for the vehicle 1400. In one embodiment, the drive system 1440 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, and an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0108] Some or all functions of the vehicle 1400 are controlled by a computing platform 1450. The computing platform 1450 may include at least one processor 1451 and a memory 1452, and the processor 1451 may execute instructions 1453 stored in the memory 1452.

[0109] The processor 1451 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0110] Memory 1452 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0111] In addition to instructions 1453 , memory 1452 may also store data, such as road maps, route information, and data such as the location, direction, and speed of the vehicle. The data stored in memory 1452 may be used by computing platform 1450 .

[0112] In the disclosed embodiment, the processor 1451 may execute instruction 1453 to complete all or part of the steps of the above-mentioned method for determining vehicle parameters.

[0113] Fig.15 1 is a block diagram of a vehicle parameter determination device 1500 according to an exemplary embodiment. For example, the device 1500 may be provided as a server. Fig.15 The apparatus 1500 includes a processing component 1522, which further includes one or more processors, and a memory resource represented by a memory 1532 for storing instructions executable by the processing component 1522, such as an application. The application stored in the memory 1532 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1522 is configured to execute instructions to perform the above-mentioned method for determining vehicle parameters.

[0114] The device 1500 may also include a power supply component 1526 configured to perform power management of the device 1500, a wired or wireless network interface 1550 configured to connect the device 1500 to a network, and an input / output interface 1558. The device 1500 may operate based on an operating system stored in the memory 1532, such as Windows Server 2003. TM , Mac OS X TM, Unix TM , Linux TM , FreeBSD TM or similar.

[0115] In addition, the word "exemplary" is used herein to indicate serving as an example, instance, or diagram. Any aspect or design described as "exemplary" in this article is not necessarily understood to be advantageous compared to other aspects or designs. On the contrary, the use of the word exemplary is intended to present concepts in a specific way. As used herein, the term "or" is intended to represent an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X applies A or B" is intended to represent any one of the natural inclusive arrangements. That is, if X applies A; X applies B; or X applies both A and B, "X applies A or B" is satisfied under any of the aforementioned examples. In addition, unless otherwise specified or clearly pointed to a singular form from the context, the articles "one" and "an" as used in this application and the appended claims are generally understood to mean "one or more".

[0116] Likewise, although the present disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art after reading and understanding the specification and drawings. The present disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components (e.g., elements, resources, etc.) described above, unless otherwise indicated, the terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific functions of the described components, even if the structure is not equivalent to the disclosed structure. In addition, although specific features of the present disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations as may be desired and beneficial to any given or specific application. In addition, with respect to "including", "having", "having", "having", or variations thereof used in a specific embodiment or claim, such terms are intended to be inclusive in a manner similar to the term "comprising".

[0117] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

[0118] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for determining vehicle parameters, characterized in that: include: Obtain multiple predicted trajectories of obstacle vehicles of the ego vehicle; Determining a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories, the plurality of parameter curves being related to a motion planning target of the ego vehicle, the motion planning target being used to reflect motion parameters of the ego vehicle to be planned; Determine a shared anchor point and a private anchor point according to the plurality of anchor points, wherein the shared anchor point is an anchor point that can be used in common by the plurality of parameter curves, and the private anchor point is an anchor point that can be used individually by each parameter curve; The plurality of parameter curves of the vehicle are determined according to the shared anchor point and the own anchor point.

2. The method according to claim 1, wherein determining a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories comprises: For each of the predicted trajectories, in a first coordinate system, with the position of the vehicle as a starting position, repeatedly performing the steps of determining a first coordinate value of an anchor point of a parameter curve matching the predicted trajectory according to a preset interval, determining a second coordinate value of the anchor point according to a cost function, and determining the anchor point according to the first coordinate value and the second coordinate value, so as to determine the multiple anchor points of the multiple parameter curves; Among them, the first coordinate value is the coordinate value of the first direction of the first coordinate system, the second coordinate value is the coordinate value of the second direction of the first coordinate system, the cost function is determined at least based on the change between the second coordinate values ​​of adjacent anchor points, and when the anchor point is the first anchor point, the change is the change between the second coordinate value of the starting position and the second coordinate value of the first anchor point.

3. According to the method of claim 2, the cost function is determined based on a first distance between the anchor point and the center line of the lane of the ego vehicle, the change, and a second distance between the anchor point and the position of the obstacle vehicle under the predicted trajectory.

4. According to the method of claim 2, the action planning target includes path planning, the multiple parameter curves include multiple path curves, and when the multiple parameter curves include multiple path curves, the first coordinate system is a coordinate system composed of an X-axis and a Y-axis, the first direction represents the direction of the X-axis, the second direction represents the direction of the Y-axis, the X-axis is used to reflect the movement of the vehicle in the horizontal direction, and the Y-axis is used to reflect the movement of the vehicle in the vertical direction.

5. According to the method of claim 2 or 4, the action planning target includes speed planning, the multiple parameter curves include multiple speed curves, and when the multiple parameter curves include the multiple speed curves, the first coordinate system is a coordinate system composed of an S axis and a T axis, or a coordinate system composed of a Y axis and a T axis, the first direction represents the direction of the T axis, the second direction represents the direction of the S axis or the Y axis, the T axis is used to reflect time, the S axis is used to reflect the mileage of the vehicle, and the Y axis is used to reflect the movement of the vehicle in the vertical direction.

6. The method according to claim 1, wherein each anchor point of different parameter curves is aligned in the vertical direction of the vehicle, and the step of determining a shared anchor point and a self-use anchor point based on the plurality of anchor points comprises: Determining a plurality of first weights according to a plurality of prediction probabilities of the plurality of prediction trajectories; According to the plurality of first weights and third distances between the plurality of first anchor points, the plurality of first anchor points are moved to determine target first anchor points whose positions overlap after the movement, the plurality of first anchor points including anchor points aligned in the vertical direction; The shared anchor point is determined according to the overlapping position of the target first anchor point, and the anchor points other than the target first anchor point among the multiple anchor points are determined as the self-use anchor points.

7. The method according to claim 1, wherein determining the plurality of parameter curves of the vehicle according to the shared anchor point and the self-use anchor point comprises: Curve smoothing is performed according to the shared anchor point and the self-used anchor point to determine the plurality of parameter curves.

8. A device for determining vehicle parameters, characterized in that: include: an acquisition module, configured to acquire a plurality of predicted trajectories of obstacle vehicles of the ego vehicle; A first determination module is configured to determine a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories, the plurality of parameter curves being related to an action planning target of the ego vehicle, the action planning target being used to reflect an action parameter of the ego vehicle to be planned; a second determining module configured to determine a shared anchor point and a private anchor point according to the plurality of anchor points, wherein the shared anchor point is an anchor point that can be used in common by the plurality of parameter curves, and the private anchor point is an anchor point that can be used individually by each parameter curve; The third determination module is configured to determine the plurality of parameter curves of the vehicle according to the shared anchor point and the vehicle's own anchor point.

9. A vehicle, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Obtain multiple predicted trajectories of obstacle vehicles of the ego vehicle; Determining a plurality of anchor points of a plurality of parameter curves matching the plurality of predicted trajectories, the plurality of parameter curves being related to a motion planning target of the ego vehicle, the motion planning target being used to reflect motion parameters of the ego vehicle to be planned; Determine a shared anchor point and a private anchor point according to the plurality of anchor points, wherein the shared anchor point is an anchor point that can be used in common by the plurality of parameter curves, and the private anchor point is an anchor point that can be used individually by each parameter curve; The plurality of parameter curves of the vehicle are determined according to the shared anchor point and the own anchor point.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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