Vehicle trajectory prediction methods, devices, vehicles and storage media

Abstract

This application relates to a vehicle trajectory prediction method, apparatus, vehicle, and storage medium, and pertains to the field of autonomous driving technology. The method determines target data for both the current vehicle and vehicles to be screened, filters target vehicles from the pool of vehicles to be screened based on the target data, and then predicts the trajectory of the target vehicles based on their target data. This narrows the screening scope to obtain target vehicles, reducing the number of vehicles involved in trajectory prediction. Furthermore, it reduces the time spent processing target vehicle data during trajectory prediction, thereby accelerating the speed and improving the efficiency of vehicle trajectory prediction.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and more particularly to the field of trajectory prediction technology, specifically to a vehicle trajectory prediction method, device, vehicle, and storage medium. Background Technology

[0002] In autonomous driving systems, the vehicle trajectory prediction module is a crucial component for achieving autonomous driving functionality. Accurately predicting the driving status and paths of surrounding vehicles is essential for planning a reasonable driving path and controlling autonomous driving.

[0003] In related technologies, it takes a long time to process vehicle data when running trajectory prediction algorithms, and the large amount of vehicle data leads to long inference time for trajectory prediction algorithms. Summary of the Invention

[0004] This application provides a vehicle trajectory prediction method, apparatus, vehicle, and storage medium to at least solve the problem of long vehicle data processing and algorithm inference times in related technologies. The technical solution of this application is as follows:

[0005] According to a first aspect of this application, a vehicle trajectory prediction method is provided, comprising: acquiring target data for both the vehicle and a vehicle to be screened; the target data including vehicle driving data and road data of the road where the vehicle is located; the vehicle to be screened being a vehicle within the perception range of the vehicle; selecting a target vehicle from the vehicles to be screened based on the target data of both the vehicle and the vehicle to be screened; the target vehicle being located within a preset target range of the vehicle; the preset target range being smaller than the perception range; and predicting the driving trajectory of the target vehicle based on the target data of the target vehicle.

[0006] Based on the above technical means, this application can obtain the target vehicle by narrowing the screening range of vehicles to be screened, thereby reducing the number of vehicles when predicting vehicle trajectories. When predicting vehicle trajectories, the processing time of the target data of the target vehicle can be reduced, thereby speeding up the vehicle trajectory prediction and improving the efficiency of vehicle trajectory prediction.

[0007] In one possible implementation, the process of selecting a target vehicle from the vehicles to be selected based on the target data of the vehicle and the vehicles to be selected includes: determining the current driving scenario of the vehicle; determining a target preset range based on the current driving scenario; the preset range is different for different driving scenarios; and selecting a target vehicle from the vehicles to be selected that are within the target preset range of the vehicle based on the target data of the vehicle and the vehicles to be selected.

[0008] Based on the aforementioned technical means, this application can filter vehicles by using different preset ranges corresponding to different scenarios, which not only improves the accuracy of filtering target vehicles, but also improves the accuracy of vehicle trajectory prediction.

[0009] In one possible implementation, the preset range includes a lateral range; determining the target preset range based on the current driving scenario includes: if the current driving scenario is a straight road scenario or a curved road scenario, determining the lateral range of the target preset range as a first lateral range; determining the target preset range based on the first lateral range; if the current driving scenario is an intersection scenario or a ramp scenario, determining the lateral range of the target preset range as a second lateral range; the second lateral range is larger than the first lateral range; determining the target preset range based on the second lateral range.

[0010] Based on the aforementioned technical means, the lateral range corresponding to straight road or curved road scenarios is narrow, while the lateral range corresponding to intersection or ramp scenarios is wide. Therefore, this application determines different lateral ranges by different driving scenarios, which not only improves the accuracy of determining the target preset range, but also improves the accuracy of screening target vehicles.

[0011] In one possible implementation, the driving data includes vehicle speed; the preset range also includes a longitudinal range; determining the target preset range based on the first lateral range includes: if the current vehicle speed is less than a preset speed threshold, determining a range within a first preset length in front of the vehicle and a second preset length behind the vehicle as the first longitudinal range; determining the target preset range based on the first lateral range and the first longitudinal range; if the current vehicle speed is greater than the preset speed threshold, determining a range within a third preset length in front of the vehicle and a fourth preset length behind the vehicle as the second longitudinal range; the third preset length is greater than the first preset length; the fourth preset length is greater than the second preset length; determining the target preset range based on the first lateral range and the second longitudinal range.

[0012] According to the aforementioned technical means, when the vehicle's speed is low, the longitudinal range corresponding to the vehicles to be screened that may collide with the vehicle is relatively small; when the vehicle's speed is high, the longitudinal range corresponding to the vehicles to be screened that may collide with the vehicle is relatively large. This application improves the accuracy of determining the target prediction range by assigning different longitudinal ranges based on the vehicle's speed—the longitudinal range corresponding to low vehicle speed is smaller than the longitudinal range corresponding to high vehicle speed—thereby improving the accuracy of selecting target vehicles.

[0013] In one possible implementation, the driving data further includes vehicle position; when the current driving speed of the vehicle is greater than a preset speed threshold, a target vehicle is selected from vehicles within the target preset range of the vehicle to be selected, based on the target data of the vehicle and the vehicles to be selected, including: determining the average driving speed of the vehicle within a preset time period before the current moment based on the driving data of the vehicle; determining the reference average speed of the initial vehicle within a preset time period before the current moment based on the driving data of the vehicles to be selected; the initial vehicle is a vehicle within the target preset range of the vehicle to be selected; determining the positional relationship between the vehicle and the initial vehicle based on the driving data of the vehicle and the driving data of the initial vehicle; determining candidate vehicles from the initial vehicles based on the average driving speed of the vehicle, the reference average speed of the initial vehicle, and the positional relationship; the candidate vehicles include: the initial vehicle located in front of the vehicle and whose reference average speed is less than the average driving speed of the vehicle, and the initial vehicle located behind the vehicle and whose reference average speed is greater than the average driving speed of the vehicle; and selecting the target vehicle from the candidate vehicles.

[0014] Based on the aforementioned technical means, this application can filter out vehicles in front of the vehicle but with a reference average speed lower than the vehicle's average speed and vehicles behind the vehicle but with a reference average speed higher than the vehicle's average speed by comparing the vehicle's average speed with the reference average speed of the vehicle to be screened and the distance between the vehicle and the vehicle to be screened. This improves the accuracy of screening target vehicles.

[0015] In one possible implementation, selecting the target vehicle from the candidate vehicles includes: selecting the M vehicles closest to the target vehicle from the candidate vehicles as the target vehicle; where M is a positive integer.

[0016] Based on the above technical means, this application can determine a preset number of target vehicles based on the vehicle position of the current vehicle and the vehicle positions of candidate vehicles. When there are many candidate vehicles, only the preset number of target vehicles closest to the current vehicle are selected, thereby reducing the number of target vehicles and improving the efficiency of vehicle trajectory prediction.

[0017] In one possible implementation, the driving data includes vehicle location; for any one of the target vehicles, the driving trajectory of the target vehicle is predicted based on the target data of the target vehicle, including: determining target road data and target driving data based on the vehicle location of the first target vehicle; the target road data is the road data of the road within a first range of the first target vehicle; the target driving data is the driving data of the vehicles surrounding the first target vehicle; and the driving trajectory of the first target vehicle is predicted based on the target road data and the target driving data.

[0018] Based on the above technical means, this application can obtain target road data within a first range and target driving data of vehicles around the first target vehicle by filtering the target data of the target vehicle, and then predict the trajectory of the target vehicle, thereby reducing the target data of the trajectory prediction algorithm and improving the efficiency of target vehicle trajectory prediction.

[0019] According to a second aspect of this application, a vehicle trajectory prediction device is provided, comprising: an acquisition module for acquiring target data of the vehicle itself and a vehicle to be screened; the target data includes vehicle driving data and road data of the road where the vehicle is located; the vehicle to be screened is a vehicle within the perception range of the vehicle itself; a processing module for selecting a target vehicle from the vehicles to be screened based on the target data of the vehicle itself and the vehicle to be screened; the target vehicle is located within a preset target range of the vehicle itself; the preset target range is smaller than the perception range; and predicting the driving trajectory of the target vehicle based on the target data of the target vehicle.

[0020] In one possible implementation, the processing module is specifically used to determine the current driving scenario of the vehicle; determine a target preset range based on the current driving scenario; the preset range is different for different driving scenarios; and select target vehicles from vehicles within the target preset range of the vehicle to be screened based on the target data of the vehicle and the vehicle to be screened.

[0021] In one possible implementation, the preset range includes a lateral range; the processing module is specifically configured to, when the current driving scenario is a straight road scenario or a curved road scenario, determine the lateral range of the target preset range as a first lateral range; determine the target preset range based on the first lateral range; when the current driving scenario is an intersection scenario or a ramp scenario, determine the lateral range of the target preset range as a second lateral range; the second lateral range is larger than the first lateral range; and determine the target preset range based on the second lateral range.

[0022] In one possible implementation, the driving data includes vehicle speed; the preset range also includes a longitudinal range; the processing module is specifically configured to, when the current vehicle speed is less than a preset speed threshold, determine a range within a first preset length in front of the vehicle and a second preset length behind the vehicle as a first longitudinal range; determine a target preset range based on a first lateral range and the first longitudinal range; when the current vehicle speed is greater than a preset speed threshold, determine a range within a third preset length in front of the vehicle and a fourth preset length behind the vehicle as a second longitudinal range; the third preset length is greater than the first preset length; the fourth preset length is greater than the second preset length; and determine the target preset range based on the first lateral range and the second longitudinal range.

[0023] In one possible implementation, the driving data further includes vehicle position; if the current driving speed of the vehicle is greater than a preset speed threshold, the processing module is specifically used to determine the average driving speed of the vehicle within a preset time period before the current moment based on the driving data of the vehicle; determine the reference average speed of the initial vehicle within a preset time period before the current moment based on the driving data of the vehicles to be screened; the initial vehicle is a vehicle within the target preset range of the vehicle to be screened; determine the positional relationship between the vehicle and the initial vehicle based on the driving data of the vehicle and the driving data of the initial vehicle; determine candidate vehicles from the initial vehicles based on the average driving speed of the vehicle, the reference average speed of the initial vehicle, and the positional relationship; the candidate vehicles include: the initial vehicle located in front of the vehicle and whose reference average speed is less than the average driving speed of the vehicle, and the initial vehicle located behind the vehicle and whose reference average speed is greater than the average driving speed of the vehicle; and select the target vehicle from the candidate vehicles.

[0024] In one possible implementation, the processing module is specifically used to select the M vehicles closest to the target vehicle from the candidate vehicles as the target vehicle; M is a positive integer.

[0025] In one possible implementation, the driving data includes vehicle location; for any one of the target vehicles, the processing module is specifically used to determine target road data and target driving data based on the vehicle location of the first target vehicle; the target road data is the road data of the road within a first range of the first target vehicle; the target driving data is the driving data of the vehicles surrounding the first target vehicle; and the driving trajectory of the first target vehicle is predicted based on the target road data and the target driving data.

[0026] According to a third aspect provided in this application, a vehicle is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect described above and any possible implementation thereof.

[0027] According to a fourth aspect provided in this application, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor of a vehicle, enables the vehicle to perform the methods described in the first aspect and any possible implementation thereof.

[0028] According to the fifth aspect provided in this application, a computer program product is provided, the computer program product including computer instructions that, when executed on a vehicle, cause the vehicle to perform the method described in the first aspect and any possible implementation thereof.

[0029] Therefore, the above-mentioned technical features of this application have the following beneficial effects:

[0030] (1) By narrowing the screening range, the target vehicles are obtained by screening the vehicles to be screened, reducing the number of vehicles when predicting vehicle trajectories. When predicting vehicle trajectories, the processing time of target data of target vehicles can be reduced, thereby speeding up the vehicle trajectory prediction and improving the efficiency of vehicle trajectory prediction.

[0031] (2) Since vehicles may be in straight or curved roads, intersections or ramps during driving, and vehicle behavior is different in different scenarios, the requirements for vehicle speed, distance and other factors are also different. Intersection or ramp scenarios focus more on vehicle driving decisions, such as parking or turning, while straight or curved scenarios focus more on vehicle speed. Therefore, when predicting vehicle trajectory, it is necessary to consider different preset ranges corresponding to different scenarios to screen the vehicles to be screened. Thus, this application screens vehicles by using different preset ranges corresponding to different scenarios, which not only improves the accuracy of screening target vehicles, but also improves the accuracy of vehicle trajectory prediction.

[0032] (3) When the vehicle is in motion, it is necessary to consider other vehicles traveling on both sides of the vehicle. Since the range to be considered in straight or curved scenarios is narrower than that in intersection or ramp scenarios, this application determines different lateral ranges through different driving scenarios. The lateral range corresponding to straight or curved scenarios is narrower than that corresponding to intersection or ramp scenarios, which not only improves the accuracy of determining the target preset range, but also improves the accuracy of screening target vehicles.

[0033] (4) When a vehicle is in motion, its speed determines the distance it travels in a short period of time. Therefore, speed is an indispensable factor when determining the longitudinal range of a vehicle. When the vehicle's speed is low, the longitudinal range corresponding to the vehicles to be screened that may collide with the vehicle is relatively small; when the vehicle's speed is high, the longitudinal range corresponding to the vehicles to be screened that may collide with the vehicle is relatively large. This application improves the accuracy of determining the target prediction range by using different longitudinal ranges corresponding to the vehicle's speed. The longitudinal range corresponding to a low vehicle speed is smaller than the longitudinal range corresponding to a high vehicle speed. This improves the accuracy of determining the target prediction range, thereby improving the accuracy of screening target vehicles.

[0034] (5) Due to the complexity of road conditions at higher speeds, vehicles traveling slower in front of the vehicle or faster behind the vehicle may collide with it at short distances. This application filters out vehicles in front of the vehicle but with a reference average speed lower than the vehicle's average speed, and vehicles behind the vehicle but with a reference average speed higher than the vehicle's average speed, by comparing the vehicle's average speed with the reference average speed of the vehicles to be screened and the distance between the vehicle and the vehicles to be screened. This improves the accuracy of screening target vehicles.

[0035] (6) By determining a preset number of target vehicles based on the vehicle position of the vehicle and the vehicle positions of the candidate vehicles, when there are many candidate vehicles, only the preset number of target vehicles closest to the vehicle are selected, thus reducing the number of target vehicles and improving the efficiency of vehicle trajectory prediction.

[0036] It should be noted that the technical effects of any of the implementation methods in aspects two through five can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0037] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0039] Figure 1 This is a flowchart illustrating a vehicle trajectory prediction method according to an exemplary embodiment;

[0040] Figure 2 This is a flowchart illustrating yet another vehicle trajectory prediction method according to an exemplary embodiment;

[0041] Figure 3 This is a schematic diagram illustrating yet another vehicle trajectory prediction method according to an exemplary embodiment;

[0042] Figure 4 This is a flowchart illustrating yet another vehicle trajectory prediction method according to an exemplary embodiment;

[0043] Figure 5 This is a block diagram illustrating a vehicle trajectory prediction device according to an exemplary embodiment;

[0044] Figure 6 This is a block diagram illustrating a vehicle according to an exemplary embodiment. Detailed Implementation

[0045] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0046] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0047] In autonomous driving systems, the vehicle trajectory prediction module is a crucial component for achieving autonomous driving functionality. Accurately predicting the driving status and paths of surrounding vehicles is essential for planning a reasonable driving path and controlling autonomous driving.

[0048] In related technologies, it takes a long time to process vehicle data when running trajectory prediction algorithms, and the large amount of vehicle data leads to long inference time for trajectory prediction algorithms.

[0049] To address the aforementioned problems, this application proposes a vehicle trajectory prediction method, apparatus, vehicle, and storage medium. By determining the target data for both the current vehicle and the vehicles to be screened, target vehicles are selected from the vehicles to be screened based on the target data. Then, the trajectory of the target vehicle is predicted based on its target data. Therefore, by narrowing the selection range to obtain the target vehicle, the number of vehicles involved in trajectory prediction is reduced, thus decreasing the time required to process the target vehicle's target data, thereby accelerating the vehicle trajectory prediction process and improving its efficiency.

[0050] For ease of understanding, the vehicle trajectory prediction method provided in this application will be described in detail below with reference to the accompanying drawings.

[0051] The vehicle trajectory prediction method provided in this application is executed by a vehicle trajectory prediction device, which can be configured in a vehicle to enable the vehicle to perform vehicle trajectory prediction. The vehicle trajectory prediction device can be an onboard intelligent computing platform within the vehicle, or it can be a functional module within the onboard intelligent computing platform used to perform vehicle trajectory prediction, etc. This application does not specifically limit this aspect.

[0052] Figure 1 This is a flowchart illustrating a vehicle trajectory prediction method according to an exemplary embodiment, such as... Figure 1 As shown, the vehicle trajectory prediction method includes the following steps:

[0053] S101, Obtain the target data for this vehicle and the vehicles to be screened.

[0054] The target data includes vehicle driving data and road data of the road where the vehicle is located. Vehicles to be screened are those within the vehicle's perception range. Driving data includes vehicle speed and position, while road data includes lane number, road length, and lane information. The perception range is the furthest distance that the vehicle can detect using sensors such as radar and cameras.

[0055] As one possible implementation, the vehicle trajectory prediction device obtains the target data of the vehicle through the vehicle controller, such as the vehicle control system. The vehicle trajectory prediction device determines the vehicles to be screened and their target data within the perception range through the vehicle's radar, cameras, and other sensors.

[0056] S102, Based on the target data of this vehicle and the vehicles to be screened, select the target vehicle from the vehicles to be screened.

[0057] Among them, the target vehicle is located within the target preset range of this vehicle, and the target preset range is smaller than the perception range.

[0058] As one possible implementation, the vehicle trajectory prediction device can determine the target preset range based on the driving data of its own vehicle and the driving data of the vehicles to be screened, and then select the target vehicles within the target preset range from the vehicles to be screened.

[0059] For example, a vehicle trajectory prediction device can determine the distance between its own vehicle and the vehicle to be screened based on the vehicle's own position and the vehicle's position to be screened, and then determine the target vehicle whose position is within the target preset range.

[0060] As one possible implementation, the vehicle trajectory prediction device determines a preset target range for the vehicle based on road data of the road where the vehicle is located and road data of the road where the vehicle to be screened is located, and then selects target vehicles within the preset target range from the vehicles to be screened. For example, it determines target vehicles whose lane numbers are within the preset target range based on the lane number of the vehicle where the vehicle is located and the lane number of the vehicle to be screened.

[0061] As one possible implementation, the vehicle trajectory prediction device determines a preset target range for the vehicle based on its own driving data, road data of the road where the vehicle is located, driving data of the vehicle to be screened, and road data of the road where the vehicle to be screened is located. It then selects target vehicles corresponding to the preset target range from the vehicles to be screened. For example, it determines the distance between the vehicle and the vehicle to be screened based on the vehicle's position and the vehicle's position. Based on the lane number of the vehicle, the lane number of the vehicle to be screened, and the distance, it identifies target vehicles whose lane number and position are both within the preset target range.

[0062] S103, based on the target vehicle's target data, predict the target vehicle's driving trajectory.

[0063] As one possible implementation, the driving data includes vehicle location. For any first target vehicle among the target vehicles, the vehicle trajectory prediction device can determine the target road data and target driving data based on the vehicle location of the first target vehicle, and predict the driving trajectory of the first target vehicle based on the target road data and target driving data. The target road data is the road data of the road within a first range of the first target vehicle, and the target driving data is the driving data of the vehicles around the first target vehicle.

[0064] The first range refers to the range of road length and lane number corresponding to the first target vehicle, and the surrounding vehicles of the first target vehicle refer to the vehicles in the target preset range corresponding to the first target vehicle among the vehicles to be screened.

[0065] Specifically, the vehicle trajectory prediction device can first determine a first range and a target preset range corresponding to the first target vehicle based on the vehicle position of the first target vehicle, obtain target road data based on the first range, and obtain target driving data based on the target preset range corresponding to the first target vehicle; input the target road data and target driving data into the trajectory prediction algorithm to obtain the predicted driving trajectory of the first target vehicle. The trajectory prediction algorithm can be a deep learning trajectory prediction algorithm or a graph algorithm.

[0066] The target road data includes: road length, number of lanes, road width, and road connectivity. The target driving data includes: the position of the first target vehicle, the speed of the first target vehicle, the orientation of the first target vehicle, the positions of surrounding vehicles, the speeds of surrounding vehicles, and the orientations of surrounding vehicles.

[0067] Furthermore, when the first range is the range of lane numbers, the vehicle trajectory prediction device can determine the first range based on the vehicle position of the first target vehicle, including: determining the first range based on the lane number of the first target vehicle; when the current driving scenario of the first target vehicle is a straight road scenario or a curved road scenario, the first range is determined by adding or subtracting the lane number where the first target vehicle is located from the first lane number threshold; for example, if the first lane number threshold is 2, the first range is the four lanes adjacent to the left and right of the first target vehicle.

[0068] When the current driving scenario of the first target vehicle is an intersection scenario or a ramp scenario, the first range is determined by adding or subtracting the lane number where the first target vehicle is located from the second lane number threshold. For example, if the second lane number threshold is 4, the first range is the 8 lanes adjacent to the left and right of the first target vehicle.

[0069] Furthermore, when the first range is the length of the road, the vehicle trajectory prediction device can determine the first range based on the vehicle speed and position of the first target vehicle. This first range includes: when the vehicle speed of the first target vehicle is less than a preset speed threshold, the first range is a first distance in front of the first target vehicle and a second distance behind it. For example, the first range is the distance between 200 meters in front of the first target vehicle and 50 meters behind it.

[0070] When the speed of the first target vehicle is greater than the preset speed threshold, the first range is the third distance in front of the first target vehicle and the fourth distance behind it. For example, the first range is the distance between 300 meters in front of the first target vehicle and 50 meters behind it. When the speed of the first target vehicle is equal to the preset speed threshold, either the greater than or less than the above two cases can be referred to.

[0071] Furthermore, the vehicle trajectory prediction device can determine the target preset range corresponding to the first target vehicle based on the vehicle speed and vehicle position of the first target vehicle, and then obtain the surrounding vehicles of the first target vehicle. The process can be referred to as the process of selecting target vehicles from vehicles to be screened, and will not be repeated here.

[0072] The vehicle trajectory prediction method of this application determines the target data of both the current vehicle and the vehicles to be screened, filters the vehicles to be screened based on the target data, and then predicts the trajectory of the target vehicles based on their target data. Therefore, by narrowing the filtering range to obtain the target vehicles, the number of vehicles involved in trajectory prediction is reduced. This also reduces the time spent processing the target data of the target vehicles, thereby accelerating the speed and improving the efficiency of vehicle trajectory prediction.

[0073] It should be understood that due to the limited computing resources in a vehicle, a large amount of vehicle data can cause the trajectory prediction algorithm to consume a significant amount of computing resources, thus affecting the operation of other algorithms within the vehicle. The vehicle trajectory prediction method provided in this application determines the target data for both the vehicle itself and the vehicles to be screened, filters target vehicles from the vehicles to be screened based on the target data, and then predicts the trajectory of the target vehicles based on their target data. This reduces the number of vehicles involved in trajectory prediction, thereby lowering the proportion of vehicle computing resources consumed by the trajectory prediction algorithm.

[0074] In some embodiments, in order to accurately determine the target preset range... Figure 2 This is a flowchart illustrating yet another vehicle trajectory prediction method according to an exemplary embodiment, such as... Figure 2 As shown, S102 in the above embodiment includes the following steps:

[0075] S201, determine the current driving scenario of this vehicle.

[0076] As one possible implementation, the vehicle trajectory prediction device can first determine the vehicle's current position, then determine the road segment in which the vehicle is located based on its current position, and the scenario corresponding to the road segment in which the vehicle is located is the vehicle's current driving scenario. The driving scenario includes: straight road scenario, curved road scenario, intersection scenario, and ramp scenario, and the current driving scenario is any one of the above driving scenarios.

[0077] S202, determine the target preset range based on the current driving scenario.

[0078] The preset ranges differ depending on the driving scenario. The preset ranges are the same for straight road and curved road scenarios, and the same for intersection and ramp scenarios. The preset ranges include lateral and longitudinal ranges.

[0079] As one possible implementation, the vehicle trajectory prediction device can query the lateral and longitudinal ranges corresponding to the current driving scenario, which are the target preset ranges corresponding to the current driving scenario.

[0080] S203, based on the target data of the vehicle and the vehicle to be screened respectively, select the target vehicle from the vehicles that are within the preset target range of the vehicle to be screened.

[0081] As one possible implementation, when the current vehicle speed is less than a preset speed threshold, the vehicle trajectory prediction device can determine the distance between the current vehicle and the vehicles to be screened based on the current vehicle's position and the position of the vehicles to be screened. Based on this distance, it can identify initial vehicles within the current vehicle's target preset range and select M target vehicles closest to the current vehicle from these initial vehicles, based on their distance. Here, M is a positive integer, for example, 8; the preset speed threshold can be 50 km / h.

[0082] As another possible implementation, step S203 may also include the following steps:

[0083] Step 1: If the current vehicle speed is greater than the preset speed threshold, determine the average speed of the vehicle within the preset time period before the current moment based on the vehicle's driving data.

[0084] For example, if the preset duration includes the last three moments, the vehicle trajectory prediction device can obtain the vehicle's speed at each of the last three moments and calculate the average speed over those three moments. Taking moments in seconds as an example, if the last three moments are the last three seconds, the vehicle trajectory prediction device can obtain the vehicle's speed for each second within those last three seconds and calculate the average speed over those three seconds.

[0085] Step 2: Based on the driving data of the vehicles to be screened, determine the reference average speed of the initial vehicle within a preset time period before the current moment; the initial vehicle is a vehicle within the preset target range of the vehicles to be screened.

[0086] For example, if the preset duration includes the last three moments, the vehicle trajectory prediction device can obtain the initial vehicle's speed at each of the last three moments and calculate the reference average speed for the last three moments. Taking moments in seconds as an example, the last three moments are the last 3 seconds. The vehicle trajectory prediction device can obtain the initial vehicle's speed for each second within the last 3 seconds and calculate the reference average speed for the last 3 seconds.

[0087] Step 3: Determine the positional relationship between the current vehicle and the initial vehicle based on the current vehicle's driving data and the initial vehicle's driving data.

[0088] One possible implementation is a positional relationship including being in front of the current vehicle and being behind the current vehicle. The vehicle trajectory prediction device can compare the current vehicle's position with the position of the initial vehicle to determine whether the initial vehicle is in front of or behind the current vehicle.

[0089] Step 4: Based on the average speed of this vehicle, the reference average speed of the initial vehicles, and the positional relationship, determine the candidate vehicles from the initial vehicles; the candidate vehicles include: the initial vehicles located in front of this vehicle and whose reference average speed is less than the average speed of this vehicle, and the initial vehicles located behind this vehicle and whose reference average speed is greater than the average speed of this vehicle.

[0090] Step 5: Select the target vehicle from the candidate vehicles.

[0091] As one possible implementation, the vehicle trajectory prediction device can select the M vehicles closest to the target vehicle from the candidate vehicles as the target vehicle; M is a positive integer.

[0092] Furthermore, the vehicle trajectory prediction device can sort the candidate vehicles in order of distance from the vehicle to the nearest vehicle based on the vehicle position of the current vehicle and the vehicle positions of the candidate vehicles, and obtain a first sorting result; the first M vehicles in the first sorting result are taken as the target vehicle.

[0093] In the case where the current vehicle speed is equal to the preset speed threshold, either of the two scenarios mentioned above can be considered. For example, if the current vehicle speed is equal to the preset speed threshold and the target preset range is the same as the target preset range corresponding to a speed lower than the preset speed threshold, the vehicle trajectory prediction device can determine the distance between the current vehicle and the vehicle to be screened based on the current vehicle's position and the position of the vehicle to be screened. Based on the distance between the current vehicle and the vehicle to be screened, it can determine the initial vehicles located within the current vehicle's target preset range and select M target vehicles closest to the current vehicle from the initial vehicles based on their distance.

[0094] The vehicle trajectory prediction method of this application determines the current driving scenario of the vehicle, determines a preset target range for the vehicle based on the current driving scenario, and then filters out target vehicles from the preset target range. Therefore, by filtering vehicles using different preset ranges corresponding to different scenarios, not only is the accuracy of target vehicle filtering improved, but the accuracy of vehicle trajectory prediction is also enhanced.

[0095] In some embodiments, in order to accurately determine the target preset range based on the lateral range, Figure 3 This is a schematic diagram illustrating yet another vehicle trajectory prediction method according to an exemplary embodiment, such as... Figure 3 As shown, S202 in the above embodiment includes the following steps:

[0096] S301, when the current driving scenario is a straight road scenario or a curved road scenario, the lateral range of the target preset range is determined as the first lateral range.

[0097] As one possible implementation, the vehicle trajectory prediction device can determine the first lateral range based on the sum / difference between the lane number where the vehicle is located and a first preset threshold. For example, if the first preset threshold is 1, the first lateral range is the two adjacent lanes on both sides of the lane where the vehicle is located.

[0098] S302, determine the target preset range based on the first lateral range.

[0099] S303, when the current driving scenario is an intersection scenario or a ramp scenario, the lateral range of the target preset range is determined as the second lateral range.

[0100] As one possible implementation, the vehicle trajectory prediction device can determine a second lateral range based on the sum / difference between the lane number where the vehicle is located and a second preset threshold. For example, if the first preset threshold is 2, the second lateral range would be the four adjacent lanes on both sides of the lane where the vehicle is located, with two lanes on each side. The second lateral range is larger than the first lateral range.

[0101] S304, Determine the target preset range based on the second lateral range.

[0102] The vehicle trajectory prediction method in this application distinguishes between the lateral range corresponding to straight or curved road scenarios and intersection or ramp scenarios. The lateral range corresponding to straight or curved road scenarios is narrow, while the lateral range corresponding to intersection or ramp scenarios is wide. Therefore, by determining different lateral ranges for different scenarios, the accuracy of selecting target vehicles is improved.

[0103] In some embodiments, in order to accurately determine the target preset range based on the lateral range and the longitudinal range, Figure 4 This is a schematic diagram illustrating yet another vehicle trajectory prediction method according to an exemplary embodiment, such as... Figure 4 As shown, S302 in the above embodiment includes the following steps:

[0104] S401, when the current vehicle speed is less than a preset speed threshold, the range within the first preset length in front of the vehicle and the second preset length behind the vehicle is determined as the first longitudinal range.

[0105] As one possible implementation, the vehicle trajectory prediction device can determine the current vehicle speed and the value of a preset speed threshold. When the vehicle speed is less than the preset speed threshold, the device determines the vehicle's position and then determines a first longitudinal range based on the vehicle's position and a first preset length and a second preset length. The preset speed threshold can be 50 km / h, the first preset length can be 100 meters, the second preset length can be 40 meters, and the first longitudinal range is 100 meters in front of the vehicle and 40 meters behind it.

[0106] S402, determine the target preset range based on the first lateral range and the first longitudinal range.

[0107] As one possible implementation, when the current driving scenario is a straight road scenario or a curved road scenario, and the current vehicle speed is less than a preset speed threshold, the lateral range of the target preset range is the first lateral range, and the longitudinal range is the first longitudinal range.

[0108] S403, when the current vehicle speed is greater than a preset speed threshold, the range within the third preset length in front of the vehicle and the fourth preset length behind the vehicle is determined as the second longitudinal range.

[0109] As one possible implementation, when the vehicle speed exceeds a preset speed threshold, the vehicle trajectory prediction device can determine a second longitudinal range based on the vehicle's position and a third and fourth preset length. The third preset length can be 200 meters, the fourth preset length can be 50 meters, and the second longitudinal range is 200 meters in front of the vehicle and 50 meters behind it. The third preset length is greater than the first preset length, and the fourth preset length is greater than the second preset length.

[0110] When the vehicle speed is equal to the preset speed threshold, the corresponding longitudinal range can be referenced from the first longitudinal range or the second longitudinal range.

[0111] S404, determine the target preset range based on the first lateral range and the second longitudinal range.

[0112] As one possible implementation, when the current driving scenario is a straight road scenario or a curved road scenario, and the current vehicle speed is greater than a preset speed threshold, the lateral range of the target preset range is the first lateral range, and the longitudinal range is the second longitudinal range.

[0113] In some other embodiments of this application, when the current driving scenario is an intersection scenario or a ramp scenario, the vehicle trajectory prediction device can determine the third longitudinal range based on the vehicle's position and the fifth preset length and the sixth preset length. The fifth preset length can be 60 meters, the sixth preset length can be 60 meters, and the third longitudinal range is 60 meters in front of the vehicle and 60 meters behind the vehicle.

[0114] Furthermore, the vehicle trajectory prediction device can determine the distance between the vehicle and the vehicle to be screened based on the position of the vehicle and the position of the vehicle to be screened within the second lateral range and the third longitudinal range. Based on the distance between the vehicle and the vehicle to be screened, it can determine the initial vehicles located within the preset target range of the vehicle and select M target vehicles closest to the vehicle from the initial vehicles based on the distance.

[0115] The vehicle trajectory prediction method of this application improves the accuracy of determining the target prediction range by using different longitudinal ranges corresponding to the vehicle's driving speed. The longitudinal range corresponding to the vehicle's low driving speed is smaller than the longitudinal range corresponding to the vehicle's high driving speed. This improves the accuracy of selecting target vehicles.

[0116] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the vehicle trajectory prediction device or vehicle includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] This application embodiment can, according to the above method, exemplarily divide a vehicle trajectory prediction device or vehicle into functional modules. For example, the vehicle trajectory prediction device or vehicle may include various functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0118] Figure 5 This is a block diagram illustrating a vehicle trajectory prediction device according to an exemplary embodiment. (Refer to...) Figure 5 The vehicle trajectory prediction device includes an acquisition module 510 and a processing module 520.

[0119] The acquisition module 510 is used to acquire target data for both the vehicle and the vehicle to be screened. The target data includes the vehicle's driving data and the road data of the road where the vehicle is located. The vehicle to be screened is a vehicle within the vehicle's perception range.

[0120] The processing module 520 is used to filter out target vehicles from the vehicles to be filtered based on the target data of the vehicle itself and the vehicles to be filtered; the target vehicle is located within the target preset range of the vehicle itself; the target preset range is smaller than the perception range; and predict the driving trajectory of the target vehicle based on the target data of the target vehicle.

[0121] In one possible implementation, the processing module 520 is specifically used to determine the current driving scenario of the vehicle; determine the target preset range based on the current driving scenario; the preset range is different for different driving scenarios; and select the target vehicle from the vehicles within the target preset range of the vehicle to be screened based on the target data of the vehicle and the vehicle to be screened.

[0122] In one possible implementation, the preset range includes a lateral range; the processing module 520 is specifically configured to, when the current driving scenario is a straight road scenario or a curved road scenario, determine the lateral range of the target preset range as a first lateral range; determine the target preset range based on the first lateral range; when the current driving scenario is an intersection scenario or a ramp scenario, determine the lateral range of the target preset range as a second lateral range; the second lateral range is larger than the first lateral range; and determine the target preset range based on the second lateral range.

[0123] In one possible implementation, the driving data includes vehicle speed; the preset range also includes a longitudinal range; the processing module 520 is specifically configured to, when the current vehicle speed is less than a preset speed threshold, determine a range within a first preset length in front of the vehicle and a second preset length behind the vehicle as a first longitudinal range; determine a target preset range based on a first lateral range and the first longitudinal range; when the current vehicle speed is greater than a preset speed threshold, determine a range within a third preset length in front of the vehicle and a fourth preset length behind the vehicle as a second longitudinal range; the third preset length is greater than the first preset length; the fourth preset length is greater than the second preset length; and determine the target preset range based on the first lateral range and the second longitudinal range.

[0124] In one possible implementation, the driving data also includes vehicle position; if the current driving speed of the vehicle is greater than a preset speed threshold, the processing module 520 is specifically used to determine the average driving speed of the vehicle within a preset time period before the current moment based on the driving data of the vehicle; determine the reference average speed of the initial vehicle within a preset time period before the current moment based on the driving data of the vehicles to be screened; the initial vehicle is a vehicle within the target preset range of the vehicle to be screened; determine the positional relationship between the vehicle and the initial vehicle based on the driving data of the vehicle and the driving data of the initial vehicle; determine candidate vehicles from the initial vehicles based on the average driving speed of the vehicle, the reference average speed of the initial vehicle, and the positional relationship; the candidate vehicles include: the initial vehicle located in front of the vehicle and whose reference average speed is less than the average driving speed of the vehicle, and the initial vehicle located behind the vehicle and whose reference average speed is greater than the average driving speed of the vehicle; and select the target vehicle from the candidate vehicles.

[0125] In one possible implementation, the processing module 520 is specifically used to select the M vehicles closest to the target vehicle from the candidate vehicles as the target vehicle; M is a positive integer.

[0126] In one possible implementation, the driving data includes vehicle location; for any one of the target vehicles, the processing module 520 is specifically used to determine target road data and target driving data based on the vehicle location of the first target vehicle; the target road data is the road data of the road within a first range of the first target vehicle; the target driving data is the driving data of the vehicles surrounding the first target vehicle; and the driving trajectory of the first target vehicle is predicted based on the target road data and the target driving data.

[0127] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0128] Figure 6 This is a block diagram illustrating a vehicle according to an exemplary embodiment. Figure 6 As shown, vehicle 600 includes, but is not limited to, processor 601 and memory 602.

[0129] The memory 602 described above is used to store the executable instructions of the processor 601. It is understood that the processor 601 is configured to execute instructions to implement the vehicle trajectory prediction method in the above embodiments.

[0130] It should be noted that those skilled in the art will understand that Figure 6 The vehicle structure shown does not constitute a limitation on the vehicle; a vehicle may include, but is not limited to, other types of vehicles. Figure 6 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0131] The processor 601 is the control center of the vehicle, connecting various parts of the vehicle through various interfaces and lines. It performs various vehicle functions and processes data by running or executing software programs and / or modules stored in the memory 602, and by calling data stored in the memory 602, thereby providing overall vehicle monitoring. The processor 601 may include one or more processing units. Optionally, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 601.

[0132] The memory 602 can be used to store software programs and various data. The memory 602 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0133] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 602 including instructions, which can be executed by a processor 601 of a vehicle 600 to implement the methods in the above embodiments.

[0134] In actual implementation, Figure 5 The functions of the acquisition module 510 and the processing module 520 can both be provided by Figure 6 The processor 601 calls the computer program stored in the memory 602 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0135] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0136] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by a vehicle processor 601 to perform the methods described above.

[0137] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the vehicle's processor, they implement the various processes of the above method embodiments and achieve the same technical effects as the above methods. To avoid repetition, they will not be described again here.

[0138] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0140] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0141] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0142] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0143] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle trajectory prediction method, characterized in that, include: Obtain the target data for both the current vehicle and the vehicles to be screened; The target data includes vehicle driving data and road data of the road where the vehicle is located; The vehicles to be screened are those within the vehicle's perception range; Determine the current driving scenario of the vehicle; Based on the current driving scenario, determine the target preset range; The preset range varies depending on the driving scenario; the target preset range is smaller than the perception range. Based on the target data of this vehicle and the vehicle to be screened, target vehicles are selected from those vehicles that are within the target preset range of this vehicle. Based on the target data of the target vehicle, predict the driving trajectory of the target vehicle; The preset range includes a lateral range; determining the target preset range based on the current driving scenario includes: When the current driving scenario is a straight road scenario or a curved road scenario, the lateral range of the target preset range is determined as the first lateral range; The target preset range is determined based on the first lateral range; When the current driving scenario is an intersection scenario or a ramp scenario, the lateral range of the target preset range is determined as the second lateral range; the second lateral range is larger than the first lateral range; The target preset range is determined based on the second lateral range; The driving data includes vehicle speed; the preset range also includes a longitudinal range; determining the target preset range based on the first lateral range includes: When the current vehicle speed is less than a preset speed threshold, the range within the first preset length in front of the vehicle and the second preset length behind the vehicle is determined as the first longitudinal range. The target preset range is determined based on the first lateral range and the first longitudinal range; If the current vehicle speed is greater than the preset speed threshold, a range within a third preset length in front of the vehicle and a fourth preset length behind the vehicle is determined as the second longitudinal range; the third preset length is greater than the first preset length; the fourth preset length is greater than the second preset length. The target preset range is determined based on the first lateral range and the second longitudinal range.

2. The method according to claim 1, characterized in that, The driving data also includes vehicle location; when the current vehicle speed is greater than the preset speed threshold, the step of selecting the target vehicle from vehicles within the preset target range of the vehicle to be selected, based on the target data of both the vehicle and the vehicle to be selected, includes: Based on the vehicle's driving data, determine the vehicle's average driving speed over a preset period of time prior to the current moment; Based on the driving data of the vehicles to be screened, determine the reference average speed of the initial vehicle within a preset time period before the current moment; the initial vehicle is a vehicle within the preset target range of the vehicles to be screened. Based on the driving data of this vehicle and the driving data of the initial vehicle, determine the positional relationship between this vehicle and the initial vehicle; Based on the average speed of the vehicle, the reference average speed of the initial vehicles, and the positional relationship, candidate vehicles are determined from the initial vehicles; the candidate vehicles include: initial vehicles located in front of the vehicle and whose reference average speed is less than the average speed of the vehicle, and initial vehicles located behind the vehicle and whose reference average speed is greater than the average speed of the vehicle. The target vehicle is selected from the candidate vehicles.

3. The method according to claim 2, characterized in that, The step of selecting the target vehicle from the candidate vehicles includes: The M vehicles closest to the target vehicle are selected from the candidate vehicles and designated as the target vehicle; M is a positive integer.

4. The method according to any one of claims 1-3, characterized in that, The driving data includes vehicle location; for any one of the target vehicles, the step of predicting the driving trajectory of the target vehicle based on the target data of the target vehicle includes: Based on the vehicle position of the first target vehicle, target road data and target driving data are determined; the target road data is the road data of the roads within a first range of the first target vehicle; the target driving data is the driving data of vehicles surrounding the first target vehicle. Based on the target road data and the target driving data, predict the driving trajectory of the first target vehicle.

5. A vehicle trajectory prediction device, characterized in that, include: The acquisition module is used to acquire target data for both the current vehicle and the vehicles to be screened. The target data includes vehicle driving data and road data of the road where the vehicle is located; The vehicles to be screened are those within the vehicle's perception range; The processing module is used to determine the current driving scenario of the vehicle; and to determine a target preset range based on the current driving scenario. The preset range varies depending on the driving scenario; the target preset range is smaller than the perception range; based on the target data of the vehicle and the vehicle to be screened, the target vehicle is selected from the vehicles within the target preset range of the vehicle to be screened; Based on the target vehicle's target data, the driving trajectory of the target vehicle is predicted; the preset range includes a lateral range; the processing module is configured to, when the current driving scenario is a straight road scenario or a curved road scenario, determine the lateral range of the target preset range as a first lateral range; determine the target preset range based on the first lateral range; when the current driving scenario is an intersection scenario or a ramp scenario, determine the lateral range of the target preset range as a second lateral range; the second lateral range is larger than the first lateral range; determine the target preset range based on the second lateral range; The driving data includes vehicle speed; the preset range also includes a longitudinal range; the processing module is used to determine, when the current vehicle speed is less than a preset speed threshold, a range within a first preset length in front of the vehicle and a second preset length behind the vehicle as a first longitudinal range; and to determine the target preset range based on the first lateral range and the first longitudinal range. When the current vehicle speed is greater than the preset speed threshold, a range within a third preset length in front of the vehicle and a fourth preset length behind the vehicle is determined as the second longitudinal range; the third preset length is greater than the first preset length; the fourth preset length is greater than the second preset length; and the target preset range is determined based on the first lateral range and the second longitudinal range.

6. A vehicle, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, When the computer-executable instructions stored in the computer-readable storage medium are executed by the vehicle's processor, the vehicle is able to perform the method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, When the computer program product is run in a vehicle, it causes the vehicle to perform the method as described in any one of claims 1 to 4.

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