Method and device for determining target which does not need to be concerned by self-vehicle, and medium

Abstract

The invention provides a method and device for determining that a vehicle does not need to pay attention to a target and a medium. The method comprises the steps that map data, road data and obstacle data are obtained through a vehicle-mounted sensor; fusing the map data, the road data and the obstacle data to determine a first obstacle in a lane where the vehicle is located; determining the cut-out duration of the first obstacle according to the obstacle data; according to the cut-out duration and the obstacle data, determining a predicted position where the first obstacle is located after the first obstacle travels towards the lane where the vehicle is located for the cut-out duration; according to the position relation between the predicted position and the lane where the vehicle is located, determining the cutting-out probability of the first obstacle cutting out of the lane where the vehicle is located; and according to the cut-out probability and a preset probability threshold, determining whether the first obstacle is a target obstacle that the vehicle does not need to pay attention to. The objective of the invention is to accurately and efficiently predict a target obstacle which does not need to be concerned by a vehicle in advance from obstacles in a lane where the vehicle of a current road is located.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle engineering, and particularly relates to a method, device and medium for determining a target that the host vehicle does not need to pay attention to. Background Art

[0002] In the field of autonomous driving, analyzing and processing obstacles in the lane where the host vehicle is located to determine the obstacles in the lane where the host vehicle is located that the host vehicle does not need to pay attention to and releasing them, that is, not paying attention to them, is one of the key factors for improving the driving experience. The existing method for analyzing and processing obstacles in the lane where the host vehicle is located to determine the obstacles in the lane where the host vehicle is located that the host vehicle does not need to pay attention to and releasing them mainly determines, according to the lane lines of the lane where the host vehicle is located, the obstacles in the lane where the host vehicle is located whose wheels cross the lane lines of the lane where the host vehicle is located during driving as the obstacles that the host vehicle does not need to pay attention to and releases them.

[0003] However, this method has a high false detection rate and is likely to release the obstacles in the lane where the host vehicle is located that the host vehicle needs to pay attention to. For example, if an obstacle in the lane where the host vehicle is located just crosses the lane lines of the lane where the host vehicle is located, but the obstacle crosses the lane lines of the lane where the host vehicle is located only due to a slight deviation in direction, and there is no possibility that the driving trajectory of the obstacle will leave the lane where the host vehicle is located, this obstacle will also be recognized by the host vehicle as an obstacle that needs to be released, resulting in false detection. At the same time, since an obstacle is determined to be an obstacle that the host vehicle needs to release only when it has crossed the lane lines of the lane where the host vehicle is located, it is impossible to predict in advance whether an obstacle is an obstacle that the host vehicle needs to release based on the driving data information of the obstacle before it crosses the lane lines of the lane where the host vehicle is located. Summary of the Invention

[0004] In view of this, the present invention provides a method, device and medium for determining a target that the host vehicle does not need to pay attention to, aiming to accurately and efficiently predict in advance the target obstacles that the host vehicle does not need to pay attention to from the obstacles in the lane where the host vehicle is located within a specified range of the current road.

[0005] To achieve the above object, the technical solution of the present invention is realized as follows:

[0006] A method for determining a target that the host vehicle does not need to pay attention to, the method comprising:

[0007] Obtaining map data, road data and obstacle data through in-vehicle sensors;

[0008] Fusing the map data, the road data and the obstacle data to determine, from the obstacles on the current road, the first obstacles in the lane where the host vehicle is located;

[0009] Determine the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position according to the obstacle type in the obstacle data and the position of the obstacle relative to the host vehicle;

[0010] According to the cut-out duration, the lateral driving speed of the obstacle in the obstacle data, and the position of the obstacle relative to the host vehicle, determine the predicted position of the first obstacle after driving for the cut-out duration during the process of the first obstacle cutting out of the lane where the host vehicle is located;

[0011] Determine the cut-out probability of the first obstacle cutting out of the lane where the host vehicle is located according to the position relationship between the predicted position and the lane where the host vehicle is located;

[0012] Determine whether the first obstacle is a target obstacle that the host vehicle no longer needs to pay attention to according to the cut-out probability and a preset probability threshold.

[0013] Further, the step of fusing the map data, the road data, and the obstacle data to determine the first obstacle in the lane where the host vehicle is located from the obstacles on the current road includes:

[0014] Perform road fusion on the map data and the road data to obtain lane parameter information of the current road;

[0015] Perform perception fusion on the lane parameter information and the obstacle data to determine the first obstacle in the lane where the host vehicle is located from the obstacles on the current road.

[0016] Further, the step of determining the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position according to the obstacle type in the obstacle data and the position of the obstacle relative to the host vehicle includes:

[0017] Determine an initial cut-out duration corresponding to the obstacle type of the first obstacle according to the obstacle type in the obstacle data of the first obstacle;

[0018] Modify the initial cut-out duration according to the longitudinal distance between the first obstacle and the host vehicle and the obstacle type of the first obstacle to obtain the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position.

[0019] Further, the step of modifying the initial cut-out duration according to the longitudinal distance between the first obstacle and the host vehicle and the obstacle type of the first obstacle to obtain the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position includes:

[0020] Determine a target correction strategy corresponding to the obstacle type according to the obstacle type of the first obstacle, where the target correction strategy represents the corresponding relationship between the longitudinal distance between the first obstacle and the host vehicle under this obstacle type and the first correction coefficient;

[0021] Determine a first correction coefficient according to the longitudinal distance between the first obstacle and the host vehicle and the target correction strategy;

[0022] Determine the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position by multiplying the first correction coefficient by the initial cut-out duration.

[0023] Further, the step of determining the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position by multiplying the first correction coefficient by the initial cut-out duration includes:

[0024] Determine the corrected cut-out duration by multiplying the first correction coefficient by the initial cut-out duration;

[0025] Determine a second correction coefficient corresponding to the target driving style according to the target driving style of the host vehicle selected by the user, and perform a secondary correction on the corrected cut-out duration based on the second correction coefficient to obtain the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position.

[0026] Further, the step of determining the predicted position where the first obstacle is located after traveling the cut-out duration during the process of cutting out of the lane where the host vehicle is located according to the cut-out duration, the lateral traveling speed of the obstacle in the obstacle data, and the position of the obstacle relative to the host vehicle includes:

[0027] Determine the initial predicted position where the first obstacle is located after traveling the cut-out duration during the process of cutting out of the lane where the host vehicle is located according to the cut-out duration and the lateral traveling speed of the obstacle in the obstacle data of the first obstacle, and according to the lateral distance between the first obstacle and the host vehicle;

[0028] Correct the initial predicted position according to the obstacle size in the obstacle data of the first obstacle to obtain the predicted position where the first obstacle is located after traveling the cut-out duration during the process of cutting out of the lane where the host vehicle is located.

[0029] Further, the step of determining the cut-out probability of the first obstacle cutting out of the lane where the host vehicle is located according to the positional relationship between the predicted position and the lane where the host vehicle is located includes:

[0030] Determine a target probability strategy corresponding to the obstacle type according to the obstacle type of the first obstacle, where the target probability strategy represents the corresponding relationship between the distance between the center line of the first obstacle under this obstacle type and the center line of the lane where the host vehicle is located and the cut-out probability;

[0031] Determine the distance between the center line of the first obstacle and the center line of the lane where the host vehicle is located according to the predicted position;

[0032] Determine the cut-out probability of the first obstacle cutting out of the lane where the host vehicle is located according to the distance and the target probability strategy.

[0033] Further, the determining whether the first obstacle is a target obstacle that the host vehicle no longer needs to pay attention to according to the cut-out probability and a preset probability threshold includes:

[0034] Determine a target probability threshold corresponding to the obstacle type of the first obstacle according to the obstacle type of the first obstacle;

[0035] When the cut-out probability exceeds the target probability threshold, determine that the first obstacle is a target obstacle that the host vehicle no longer needs to pay attention to.

[0036] A method for determining a target that the host vehicle does not need to pay attention to according to the present invention has the following advantages:

[0037] First, by fusing a variety of data information, it is possible to more accurately determine the obstacles in the lane where the host vehicle is located from the obstacles on the current road. And subsequently, when determining whether an obstacle is an obstacle that the host vehicle does not need to pay attention to, only the obstacles in this lane where the host vehicle is located are determined, and other obstacles on the current road are no longer paid attention to. Therefore, the determination efficiency can be effectively improved.

[0038] Secondly, through the relevant driving data of the obstacle, predict the predicted position where the obstacle is located after driving for the cut-out duration calculated for this obstacle. According to the positional relationship between the predicted position and the lane where the host vehicle is located, predict in advance whether the obstacle is a target obstacle that the host vehicle does not need to pay attention to. Thus, it is possible to predict in advance whether the obstacle is a target obstacle that the host vehicle does not need to pay attention to, so as to more proactively determine the obstacles that the host vehicle does not need to pay attention to, thereby improving the driving experience.

[0039] Another object of the present invention is to propose a device for determining a target that the host vehicle does not need to pay attention to. The aim is to accurately and efficiently predict in advance from the obstacles in the lane where the host vehicle is located within a specified range of the current road the target obstacles that the host vehicle does not need to pay attention to.

[0040] To achieve the above object, the technical solution of the present invention is realized as follows:

[0041] A device for determining a target that the host vehicle does not need to pay attention to, the device comprising:

[0042] A data acquisition module, configured to acquire map data, road data, and obstacle data;

[0043] A first obstacle determination module, configured to fuse the map data, the road data, and the obstacle data to determine a first obstacle in the lane where the host vehicle is located from the obstacles on the current road;

[0044] An exit duration determination module, configured to determine an exit duration for the first obstacle to exit the lane where the host vehicle is located from its current position according to the obstacle type in the obstacle data and the position of the obstacle relative to the host vehicle;

[0045] A position prediction module, configured to determine a predicted position where the first obstacle is located after traveling the exit duration during the process of the first obstacle exiting the lane where the host vehicle is located according to the exit duration, the lateral traveling speed of the obstacle in the obstacle data, and the position of the obstacle relative to the host vehicle;

[0046] An exit probability calculation module, configured to determine an exit probability for the first obstacle to exit the lane where the host vehicle is located according to the position relationship between the predicted position and the lane where the host vehicle is located;

[0047] A target obstacle determination module, configured to determine whether the first obstacle is a target obstacle that the host vehicle no longer needs to pay attention to according to the exit probability and a preset probability threshold.

[0048] The device for determining a target that the host vehicle does not need to pay attention to has the same advantages as the above-mentioned method for determining a target that the host vehicle does not need to pay attention to compared with the prior art, and will not be elaborated here.

[0049] Another object of the present invention is to provide a vehicle, aiming to accurately and efficiently predict in advance a target obstacle that the host vehicle does not need to pay attention to from the obstacles in the lane where the host vehicle is located within a specified range on the current road.

[0050] To achieve the above object, the technical solution of the present invention is implemented as follows:

[0051] A vehicle, comprising: the above-mentioned device for determining a target that the host vehicle does not need to pay attention to, configured to execute the steps in the above-mentioned method for determining a target that the host vehicle does not need to pay attention to.

[0052] The vehicle has the same advantages as the above-mentioned device for determining a target that the host vehicle does not need to pay attention to compared with the prior art, and will not be elaborated here.

[0053] Another object of the present invention is to provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method for determining that the host vehicle does not need to pay attention to a target are implemented. Description of the Drawings

[0054] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0055] Figure 1 is a flowchart of a method for determining that the host vehicle does not need to pay attention to a target shown in an embodiment of the present application;

[0056] Figure 2 is a schematic diagram of the position setting of in-vehicle sensors in a method for determining that the host vehicle does not need to pay attention to a target shown in an embodiment of the present application;

[0057] Figure 3 is a schematic diagram of road data in a method for determining that the host vehicle does not need to pay attention to a target shown in an embodiment of the present application;

[0058] Figure 4 is a schematic diagram of a target correction strategy in a method for determining that the host vehicle does not need to pay attention to a target shown in an embodiment of the present application;

[0059] Figure 5 is a schematic diagram of a target probability strategy in a method for determining that the host vehicle does not need to pay attention to a target shown in an embodiment of the present application;

[0060] Figure 6 is a schematic diagram of a device for determining that the host vehicle does not need to pay attention to a target shown in an embodiment of the present application. Detailed Embodiments

[0061] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0062] The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0063] Figure 1 is a flowchart of a method for determining that the host vehicle does not need to pay attention to a target shown in an embodiment of the present application. Referring to Figure 1 , the method for determining that the host vehicle does not need to pay attention to a target provided by the present application includes the following steps:

[0064] Step S11: Obtain map data, road data, and obstacle data through in-vehicle sensors;

[0065] Step S12: Fuse the map data, the road data, and the obstacle data to determine, from the obstacles on the current road, a first obstacle in the lane where the host vehicle is located;

[0066] Step S13: Determine the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position according to the obstacle type in the obstacle data and the position of the obstacle relative to the host vehicle;

[0067] Step S14: Determine the predicted position where the first obstacle is located after traveling the cut-out duration during the process of cutting out of the lane where the host vehicle is located according to the cut-out duration, the lateral traveling speed of the obstacle in the obstacle data, and the position of the obstacle relative to the host vehicle;

[0068] Step S15: Determine the cut-out probability of the first obstacle cutting out of the lane where the host vehicle is located according to the positional relationship between the predicted position and the lane where the host vehicle is located;

[0069] Step S16: Determine whether the first obstacle is a target obstacle that the host vehicle no longer needs to pay attention to according to the cut-out probability and a preset probability threshold.

[0070] In this embodiment, map data in the current road is obtained through an in-vehicle navigation map in the host vehicle. Road data within a preset range in front of the current road of the host vehicle is collected by in-vehicle sensors configured in the host vehicle, including lane lines, lane line types, etc. in the current road. Obstacles around the body of the host vehicle are collected by other in-vehicle sensors configured in the host vehicle to obtain obstacle data. Among them, each in-vehicle sensor preferably uses an all-weather sensor detection device to avoid unstable object target detection caused by rain, snow, fog, light, etc., so as to prevent inaccurate data collection.

[0071] In this embodiment, the obstacle data includes: obstacle type, position information of the obstacle relative to the host vehicle, lateral traveling speed of the obstacle, obstacle size, etc. And the in-vehicle sensors for collecting obstacles around the body of the host vehicle are not limited to being set at the middle position directly in front of the body of the host vehicle, nor are they limited to only setting 1. To improve the accuracy of object detection, radar sensors (such as lidar or millimeter-wave radar devices, etc.) and vision sensors can be arranged in front of and on the sides of the vehicle. At the same time, two corner radar devices can be installed at two left and right corner positions in front of the vehicle to reduce the misdetection and missed detection of object targets through device redundancy.

[0072] Figure 2 It is a schematic diagram of the position setting of in-vehicle sensors in a method for determining a target that the host vehicle does not need to pay attention to shown in an embodiment of the present application. Refer to Figure 2, 21 is an in-vehicle sensor for collecting road data within a preset range in front of the current road of the vehicle itself; 22 is an in-vehicle sensor for collecting obstacle data of obstacles around the vehicle body of the vehicle itself; 23 is a control unit for receiving information from different sensors and sending control information to the vehicle.

[0073] In step S11, after collecting map data, road data, and obstacle data by the vehicle itself, in step S12, the map data, road data, and obstacle data are fused to determine the obstacles in the current road that are in the lane where the vehicle itself is located, which are called the first obstacles. It should be understood that there may be one or more obstacles in the lane where the vehicle itself is located.

[0074] In this embodiment, after determining the first obstacles, according to the obstacle type in the obstacle data belonging to the first obstacles, the obstacle type to which the first obstacles belong can be determined. According to the obstacle type of the first obstacles, the cut-out duration for the first obstacles corresponding to the obstacle type to cut out from the lane where the vehicle itself is located from its current position is determined.

[0075] In this embodiment, the obstacle types include: household cars (such as sedans, SUVs, etc.), trucks, motorcycles, bicycles, pedestrians, etc. By configuring corresponding cut-out durations for different types of obstacles. According to the obstacle type of the first obstacles, the cut-out duration for the first obstacles to cut out from the lane where the vehicle itself is located from its current position can be determined.

[0076] According to the cut-out duration, the lateral driving speed of the obstacle in the obstacle data belonging to the first obstacles, and the position of the first obstacles relative to the vehicle itself, the position where the first obstacles are located after cutting out and driving for the cut-out duration into the lane where the vehicle itself is located can be calculated, that is, the predicted position.

[0077] According to the positional relationship between the predicted position of the first obstacles obtained by prediction and the lane where the vehicle itself is located, the cut-out probability of the first obstacles cutting out from the lane where the vehicle itself is located can be determined. The greater the cut-out probability, the greater the probability that the first obstacles will cut out from the lane where the vehicle itself is located in the current driving state; the smaller the cut-out probability, the smaller the probability that the first obstacles will cut out from the lane where the vehicle itself is located in the current driving state.

[0078] By comparing the cut-out probability of the first obstacles cutting out from the lane where the vehicle itself is located with a preset probability threshold. When the cut-out probability exceeds the preset probability threshold, it is very likely that the first obstacles will cut out from the lane where the vehicle itself is located. Therefore, it is determined that the first obstacles are target obstacles that the vehicle itself does not need to pay attention to, and the target obstacles are directly released, that is, no attention is paid to them.

[0079] A method for determining an object that the host vehicle does not need to pay attention to according to the present invention can more accurately determine the obstacles in the lane where the host vehicle is located from the obstacles on the current road by fusing various data information. And subsequently, when determining whether an obstacle is an obstacle that the host vehicle does not need to pay attention to, only the obstacles in the lane where the host vehicle is located are determined, and other obstacles on the current road are no longer considered. Therefore, the determination efficiency can be effectively improved.

[0080] And based on the relevant driving data of the obstacle, predict the predicted position of the obstacle after driving for the cut-out duration calculated for the obstacle. According to the positional relationship between the predicted position and the lane where the host vehicle is located, predict in advance whether the obstacle is an object obstacle that the host vehicle does not need to pay attention to. Thus, it can be predicted in advance whether the obstacle is an object obstacle that the host vehicle does not need to pay attention to, so that the obstacles that the host vehicle does not need to pay attention to can be determined more proactively, thereby improving the driving experience.

[0081] In this application, step S11 specifically includes:

[0082] Step S111: Perform road fusion on the map data and the road data to obtain the lane parameter information of the current road;

[0083] Step S112: Perform perception fusion on the lane parameter information and the obstacle data to determine the first obstacle in the lane where the host vehicle is located from the obstacles on the current road.

[0084] In this embodiment, the road data in a preset range in front of the current road of the host vehicle is collected by on-vehicle sensors configured on the host vehicle, including lane lines, lane line types, etc. in the current road. Since this application only focuses on the lane where the host vehicle is located, as well as the left adjacent lane and the right adjacent lane of the lane where the host vehicle is located. Therefore, to save computing resources, only the road data of the lane where the host vehicle is located and the two adjacent lanes are collected when collecting road data. At the same time, since the host vehicle only needs to pay attention to the obstacles within a certain length range in front during the automatic driving process, and the host vehicle does not need to pay attention to the obstacles that are very far from the host vehicle. Therefore, to save computing resources, only the road data within the preset range in front is collected when collecting road data resources.

[0085] Exemplarily, Figure 3 is a schematic diagram of road data in a method for determining an object that the host vehicle does not need to pay attention to shown in an embodiment of this application. Refer to Figure 3, road data within a preset range ahead is collected through in-vehicle sensors. Preferably, the preset range is the road data within 120m ahead of the host vehicle. The road data includes the left lane line LL of the left adjacent lane of the host vehicle's lane, the right lane line of the left adjacent lane of the host vehicle's lane (which is also the left lane line LR of the host vehicle's lane), the right lane line RR of the right adjacent lane of the host vehicle's lane, and the left lane line of the right adjacent lane of the host vehicle's lane (which is also the right lane line RL of the host vehicle's lane). And the lane line types of these lane lines, such as dashed lines, double solid lines, single solid lines, etc.

[0086] In this embodiment, by performing road fusion on the map data of the host vehicle and the collected road data, lane parameter information within the preset range of the current road is obtained, including lane line position information, lane width information, lane curvature information, lane line type information, etc. within the preset range ahead of the host vehicle.

[0087] By performing perception fusion on the lane parameter information obtained through road fusion and the acquired obstacle data, the first obstacle in the host vehicle's lane can be determined. Subsequently, it is only necessary to determine whether each first obstacle is a target obstacle that the host vehicle does not need to pay attention to, and obstacles in other lanes do not need to be considered, thus effectively saving computing resources.

[0088] In this application, step S12 specifically includes:

[0089] Step S121: Determine the initial cut-out duration corresponding to the obstacle type of the first obstacle according to the obstacle type in the obstacle data of the first obstacle;

[0090] Step S122: Modify the initial cut-out duration according to the longitudinal distance between the first obstacle and the host vehicle and the obstacle type of the first obstacle to obtain the cut-out duration for the first obstacle to cut out of the host vehicle's lane from its current position.

[0091] In this embodiment, a corresponding initial cut-out duration is configured for obstacles of different obstacle types. The obstacle type and the initial cut-out duration are in one-to-one correspondence. According to the obstacle type of the first obstacle in the obstacle data, the initial cut-out duration corresponding to this obstacle type can be obtained.

[0092] Exemplarily, the obstacle types include household cars, trucks, motorcycles, bicycles, and pedestrians. An initial cut-out duration of 0.8S is configured for the household car obstacle type; an initial cut-out duration of 0.5S is configured for the truck obstacle type; an initial cut-out time of 0.3S is configured for obstacle types such as motorcycles, bicycles, and pedestrians. According to the obstacle type of the first obstacle, the initial cut-out duration of the first obstacle can be determined. This initial cut-out duration is configured for the first obstacle and is used to predict the predicted position where the first obstacle will travel after traveling such an initial cut-out duration during the process of cutting out from the lane where the host vehicle is located. This initial cut-out duration is not the duration required for the first obstacle to cut out from the lane where the host vehicle is currently located and enter the center line of the adjacent lane.

[0093] Since when the first obstacle has the intention to cut out from the lane where the host vehicle is located, if the distance between the first obstacle and the host vehicle is smaller, in order to avoid colliding with the host vehicle due to traveling too slowly during the cut-out, the first obstacle will cut out from the lane where the host vehicle is located in a more aggressive and rapid manner. Therefore, for the first obstacle, using a fixed initial cut-out duration to predict the predicted position where the first obstacle will travel after traveling such an initial cut-out duration during the process of cutting out from the lane where the host vehicle is located will result in an inaccurate final prediction result.

[0094] Therefore, after determining the initial cut-out duration corresponding to the obstacle type of the first obstacle according to the obstacle type in the obstacle data of the first obstacle. Then, according to the longitudinal distance between the first obstacle and the host vehicle and the obstacle type of the first obstacle, the initial cut-out duration configured for the first obstacle of this obstacle type is corrected to ensure a more accurate prediction result.

[0095] In this application, step S122 specifically includes:

[0096] Step S1221: According to the obstacle type of the first obstacle, determine the target correction strategy corresponding to the obstacle type, and the target correction strategy represents the corresponding relationship between the longitudinal distance between the first obstacle of this obstacle type and the host vehicle and the first correction coefficient;

[0097] Step S1222: According to the longitudinal distance between the first obstacle and the host vehicle and the target correction strategy, determine the first correction coefficient;

[0098] Step S1223: Determine the cut-out duration for the first obstacle to cut out from its current position from the lane where the host vehicle is located by multiplying the first correction coefficient by the initial cut-out duration.

[0099] In this embodiment, since the driving states of obstacles of different obstacle types are different. For example, a household car has a faster driving speed and a shorter braking time; while a truck has a slower driving speed and a longer braking time; for obstacle types such as motorcycles, bicycles, and pedestrians, although their driving speeds are relatively slow, their movements are more flexible. Therefore, correcting the initial cut-out duration in a correction manner corresponding to the obstacle type can ensure more accurate correction of the initial cut-out duration of the first obstacle for different obstacle types. Therefore, a corresponding target correction strategy is configured for each obstacle type, and the obstacle type corresponds one-to-one with the target correction strategy. The target correction strategy corresponding to the obstacle type represents the corresponding relationship between the longitudinal distance between the first obstacle of this obstacle type and the host vehicle and the first correction coefficient, that is, the corresponding relationship between this longitudinal distance and the first correction coefficient.

[0100] According to the obstacle type of the first obstacle, the target correction strategy corresponding to this obstacle type can be determined. According to the position relative to the host vehicle in the obstacle data of the first obstacle, the longitudinal distance between the first obstacle and the host vehicle can be determined. By querying in this target correction strategy, the first correction coefficient corresponding to this longitudinal distance in this target correction strategy can be determined. By multiplying the initial cut-out duration of the first obstacle by the determined first correction coefficient, the cut-out duration for the first obstacle to cut out from its current position into the lane where the host vehicle is located is obtained.

[0101] Exemplarily, Figure 4 is a schematic diagram of the target correction strategy in a method for determining that the host vehicle does not need to pay attention to a target shown in an embodiment of the present application. Referring to Figure 4 , K represents the first correction coefficient, and dx represents the longitudinal distance between the first obstacle and the host vehicle. According to the obstacle type of the first obstacle, the target correction strategy corresponding to this obstacle type can be determined. Then, according to the longitudinal distance between the first obstacle and the host vehicle, the first correction coefficient of the first obstacle can be determined, and the initial cut-out duration determined according to the obstacle type of the first obstacle is multiplied by the first correction coefficient of the first obstacle to obtain the cut-out duration for the first obstacle to cut out from its current position into the lane where the host vehicle is located.

[0102] It should be understood that Figure 4 the specific values of K and dx in

[0103] In this application, another implementation of step S1223 includes: determining the product of the first correction coefficient and the initial cut-out duration as the corrected cut-out duration; determining a second correction coefficient corresponding to the target driving style according to the target driving style of the host vehicle selected by the user, and performing a secondary correction on the corrected cut-out duration based on the second correction coefficient to obtain the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position.

[0104] In this embodiment, in the previous implementation, when considering that the first obstacle intends to cut out of the lane where the host vehicle is located, the cut-out method of the first obstacle cutting out of the lane where the host vehicle is located is judged according to the distance of the longitudinal distance between the first obstacle and the host vehicle. When the longitudinal distance is relatively close, the cut-out method will be more aggressive, and the first obstacle will cut out of the lane where the host vehicle is located at a faster speed; when the longitudinal distance is relatively far, the cut-out method will be more conservative, and the first obstacle will cut out of the lane where the host vehicle is located at a slower speed. Therefore, the initial cut-out duration is corrected based on the longitudinal distance between the first obstacle and the host vehicle, so as to ensure that the configured cut-out duration for the first obstacle is more accurate.

[0105] In an actual scenario, different driving styles of the host vehicle will also affect the cut-out duration of the first obstacle. When the driving style of the host vehicle is relatively aggressive, the host vehicle will select the first obstacle as the target obstacle that the host vehicle does not need to pay attention to earlier. At this time, the configured cut-out time for the first obstacle will be larger to ensure that the host vehicle can select the first obstacle as the target obstacle that the host vehicle does not need to pay attention to earlier; when the driving style of the host vehicle is relatively conservative, the host vehicle will select the first obstacle as the target obstacle that the host vehicle does not need to pay attention to later. At this time, the configured cut-out time for the first obstacle will be smaller to ensure that the host vehicle can select the first obstacle as the target obstacle that the host vehicle does not need to pay attention to later.

[0106] Therefore, in this embodiment, in another implementation of step S1223, the driver can automatically select the driving style of the host vehicle, such as aggressive, normal, conservative, etc. A corresponding second correction coefficient is configured for each driving style.

[0107] Determine the product of the first correction coefficient of the first obstacle and the initial cut-out duration of the first obstacle as the corrected cut-out duration. Then, according to the driving style of the driver selected on the host vehicle, that is, the target driving style, determine the second correction coefficient corresponding to the target driving style. Determine the product of the corrected cut-out duration and the second correction coefficient as the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position.

[0108] Exemplarily, different driving styles are set in advance for the host vehicle, and the driving styles are divided into aggressive, normal, and conservative. The second correction coefficient configured for the aggressive driving style is 1.1; the second correction coefficient configured for the normal driving style is 1; the second correction coefficient configured for the conservative driving style is 0.9. According to the target driving style of the host vehicle selected by the driver, the second correction coefficient corresponding to the target driving style is determined. Multiply the corrected cut-out duration of the first obstacle by the second correction coefficient to obtain the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position.

[0109] In this application, step S14 specifically includes:

[0110] Step S141: According to the cut-out duration and the lateral driving speed in the obstacle data of the first obstacle, and according to the lateral distance between the first obstacle and the host vehicle, determine the initial predicted position after traveling the cut-out duration during the process of the first obstacle cutting out of the lane where the host vehicle is located;

[0111] Step S142: Correct the initial predicted position according to the obstacle size in the obstacle data of the first obstacle to obtain the predicted position after traveling the cut-out duration during the process of the first obstacle cutting out of the lane where the host vehicle is located.

[0112] In this embodiment, the lateral distance between the first obstacle and the host vehicle is determined through the position of the first obstacle relative to the host vehicle in the obstacle data of the first obstacle. Then, according to the cut-out duration of the first obstacle and the lateral driving speed in the obstacle data of the first obstacle, and according to the lateral distance between the first obstacle and the host vehicle, the initial predicted position after traveling the cut-out duration during the process of the first obstacle cutting out of the lane where the host vehicle is located is calculated. The formula for calculating the initial predicted position after traveling the cut-out duration during the process of the first obstacle cutting out of the lane where the host vehicle is located is as follows:

[0113] D Y =d y +T×v y

[0114] where D Y is the lateral distance traveled by the first obstacle after traveling the cut-out duration from its current position and cutting out of the lane where the host vehicle is located; d y is the lateral distance between the host vehicle and the first obstacle at the current moment, and this lateral distance is the lateral distance between the center line of the host vehicle and the center line of the first obstacle; T is the cut-out duration; v y is the lateral driving speed of the first obstacle. d y , v yFor the vector, when the first obstacle cuts into the adjacent lane on the right, d y is positive and v y is negative; when the first obstacle cuts into the adjacent lane on the left, d y is negative and v y is positive;

[0115] Since the initial predicted position does not consider the width and attitude information of the first obstacle, in order to obtain a more accurate predicted position, it is necessary to correct the initial predicted position of the first obstacle.

[0116] According to the obstacle size in the obstacle data of the first obstacle, correct the initial predicted position of the first obstacle to obtain the predicted position after driving for the cut-out duration during the process of the first obstacle cutting out from the lane where the host vehicle is located. The specific correction formula is as follows:

[0117] When the first obstacle cuts out to the right, the predicted position is:

[0118] |f_D Y | = |d y + D Y - Obj_width / 2|

[0119] When the first obstacle cuts out to the left, the predicted position is:

[0120] |f_D Y | = |d y + D Y + Obj_width / 2|

[0121] where Obj_width is the width of the first obstacle.

[0122] In this application, step S15 specifically includes:

[0123] Step S151: Determine the target probability strategy corresponding to the obstacle type according to the obstacle type of the first obstacle. The target probability strategy represents the corresponding relationship between the distance between the center line of the first obstacle and the center line of the lane where the host vehicle is located and the cut-out probability under this obstacle type;

[0124] Step S152: Determine the distance between the center line of the first obstacle and the center line of the lane where the host vehicle is located according to the predicted position;

[0125] Step S153: Determine the cut-out probability of the first obstacle cutting out of the lane where the host vehicle is located according to the distance and the target probability strategy.

[0126] In this embodiment, since the driving states of obstacles of different obstacle types are different. For example, a household car has a faster driving speed and a shorter braking time; while a truck has a slower driving speed and a longer braking time; for obstacle types such as motorcycles, bicycles, and pedestrians, although their driving speeds are relatively slow, their movements are more flexible. Therefore, for the first obstacle of different obstacle types at the same predicted position, the corresponding cut-out probability is different.

[0127] For example, if the predicted position of the household car obtained by prediction just exceeds the lane line of the lane where the host vehicle is located, at this time, due to the large volume of the household car, it often happens that it just presses on the adjacent lane line during driving. Therefore, for the first obstacle of the household car obstacle type that just exceeds the lane line of the lane where the host vehicle is located, its corresponding cut-out probability will be relatively low, that is, the probability of this first obstacle cutting out of the lane where the host vehicle is located is relatively low. For the first obstacle of the obstacle type such as a bicycle or a pedestrian, since its movement is more flexible during driving, for such a first obstacle, when it exceeds the lane line of the lane where the host vehicle is located, its corresponding cut-out probability will be relatively high, that is, the probability of this first obstacle cutting out of the lane where the host vehicle is located is relatively high.

[0128] Therefore, in order to make the determined cut-out probability of the first obstacle more accurate, a corresponding probability determination method is configured for each obstacle type to ensure that the probability determination of the first obstacle of different obstacle types is more accurate.

[0129] Therefore, a corresponding target probability strategy is configured for each obstacle type, and the obstacle type corresponds to the target probability strategy one by one. The target probability strategy corresponding to the obstacle type represents the corresponding relationship between the distance between the center line of the first obstacle of this obstacle type and the center line of the lane where the host vehicle is located and the cut-out probability, that is, the corresponding relationship between this distance and the cut-out probability.

[0130] According to the obstacle type of the first obstacle, the target probability strategy corresponding to this obstacle type can be determined.

[0131] According to the predicted position of the first obstacle, the distance between the center line of the first obstacle and the center line of the lane where the host vehicle is located can be determined. By querying in this target probability strategy, the cut-out probability corresponding to this distance in this target probability strategy can be determined, and this cut-out probability is the cut-out probability of this first obstacle.

[0132] Exemplarily, Figure 5 is a schematic diagram of the target probability strategy in a method for determining a target that the host vehicle does not need to pay attention to according to an embodiment of the present application. Refer to Figure 5, taking the example of cutting out the lane where the host vehicle is located to the left by the first obstacle for illustration. In the x-axis, m represents the distance between the center line of the first obstacle and the left lane line of the lane where the host vehicle is located; in the y-axis, Pro represents the cutting-out probability. According to the obstacle type of the first obstacle, the target probability strategy corresponding to this obstacle type can be determined. Then, the cutting-out probability Pro value of the first obstacle can be determined according to the value of the lateral distance m between the first obstacle and the host vehicle.

[0133] In this application, step S16 specifically includes:

[0134] Step S161: Determine the target probability threshold corresponding to the obstacle type of the first obstacle according to the obstacle type of the first obstacle;

[0135] Step S162: When the cutting-out probability exceeds the target probability threshold, determine that the first obstacle is the target obstacle that the host vehicle no longer needs to pay attention to.

[0136] In this embodiment, after calculating the cutting-out probability of the first obstacle, due to different obstacles having different mobilities. For example, a truck is huge in size. When it has the intention of cutting out from the lane where the host vehicle is located, even if there is only a small situation of deflecting towards the adjacent lane, the probability of the truck cutting out from the lane where the host vehicle is located will be very high. Therefore, even if the cutting-out probability of the truck is small, it will be determined that the truck is about to cut out from the lane where the host vehicle is located. While a passenger car has stronger mobility compared to a truck. When determining that the cutting-out probability of a passenger car needs to reach a relatively large value, it is only then determined that the passenger car is about to cut out from the lane where the host vehicle is located. Therefore, in order to more accurately determine the target obstacles that the host vehicle does not need to pay attention to from the first obstacles, a corresponding target probability threshold is configured for each obstacle type. According to the obstacle type of the first obstacle, the target probability threshold corresponding to this obstacle type is determined. When the cutting-out probability of this first obstacle exceeds the determined target probability threshold, this first obstacle is determined as the target obstacle that the host vehicle does not need to pay attention to.

[0137] In this embodiment, it is preferably to configure a target probability threshold with a value of 0.5 for passenger cars; configure a target probability threshold with a value of 0.7 for trucks; configure a target probability threshold with a value of 0.8 for pedestrians, bicycles, and motorcycles. It should be understood that the different target probability threshold values configured for different obstacle types above are only a preferred implementation manner and do not limit the target probability threshold in this application.

[0138] A method for determining a target that the host vehicle does not need to pay attention to according to the present invention can more accurately determine the obstacles in the host vehicle's lane from the obstacles on the current road by fusing various data information. And subsequently, when determining whether an obstacle is an obstacle that the host vehicle needs to pay attention to, only the obstacles in the host vehicle's lane are determined, and other obstacles on the current road are no longer considered. Therefore, the determination efficiency can be effectively improved. And based on the relevant driving data of the obstacle, the predicted position of the obstacle after driving for the cut-out duration calculated for the obstacle is predicted. According to the positional relationship between the predicted position and the host vehicle's lane, it is predicted in advance whether the obstacle is a target obstacle that the host vehicle does not need to pay attention to. Thus, it can be estimated in advance whether the obstacle is a target obstacle that the host vehicle does not need to pay attention to, so that the obstacles that the host vehicle does not need to pay attention to can be determined more ahead, thereby improving the driving experience.

[0139] An embodiment of the present invention further provides a device 600 for determining a target that the host vehicle does not need to pay attention to. Figure 6 It is a schematic diagram of a device for determining a target that the host vehicle does not need to pay attention to shown in an embodiment of the present application. Refer to Figure 6 The present application provides a device 600 for determining a target that the host vehicle does not need to pay attention to, including:

[0140] A data acquisition module 601, configured to acquire map data, road data, and obstacle data;

[0141] A first obstacle determination module 602, configured to fuse the map data, the road data, and the obstacle data to determine a first obstacle in the host vehicle's lane from the obstacles on the current road;

[0142] A cut-out duration determination module 603, configured to determine the cut-out duration for the first obstacle to cut out of the host vehicle's lane from its current position according to the obstacle type in the obstacle data and the position of the obstacle relative to the host vehicle;

[0143] A position prediction module 604, configured to determine the predicted position of the first obstacle after driving for the cut-out duration during the process of the first obstacle cutting out of the host vehicle's lane according to the cut-out duration, the lateral driving speed of the obstacle in the obstacle data, and the position of the obstacle relative to the host vehicle;

[0144] A cut-out probability calculation module 605, configured to determine the cut-out probability of the first obstacle cutting out of the host vehicle's lane according to the positional relationship between the predicted position and the host vehicle's lane;

[0145] A target obstacle determination module 606, configured to determine whether the first obstacle is a target obstacle that the host vehicle no longer needs to pay attention to according to the cut-out probability and a preset probability threshold.

[0146] In the present application, the first obstacle determination module 602 includes:

[0147] A road fusion module, configured to perform road fusion on the map data and the road data to obtain lane parameter information of the current road;

[0148] A perception fusion module, configured to perform perception fusion on the lane parameter information and the obstacle data to determine a first obstacle in the lane where the self-vehicle is located from the obstacles on the current road.

[0149] In the present application, the cut-out duration determination module 603 includes:

[0150] An initial cut-out duration determination module, configured to determine an initial cut-out duration corresponding to the obstacle type of the first obstacle according to the obstacle type in the obstacle data of the first obstacle;

[0151] A cut-out duration determination module, configured to correct the initial cut-out duration according to the longitudinal distance between the first obstacle and the self-vehicle and the obstacle type of the first obstacle to obtain the cut-out duration for the first obstacle to cut out from its current position into the lane where the self-vehicle is located.

[0152] In the present application, the cut-out duration determination module includes:

[0153] A correction strategy determination module, configured to determine a target correction strategy corresponding to the obstacle type according to the obstacle type of the first obstacle, where the target correction strategy represents the corresponding relationship between the longitudinal distance between the first obstacle of this obstacle type and the self-vehicle and the first correction coefficient;

[0154] A first correction coefficient determination module, configured to determine a first correction coefficient according to the longitudinal distance between the first obstacle and the self-vehicle and the target correction strategy;

[0155] A cut-out duration determination sub-module, configured to determine the product of the first correction coefficient and the initial cut-out duration as the cut-out duration for the first obstacle to cut out from its current position into the lane where the self-vehicle is located.

[0156] In the present application, the cut-out duration determination sub-module includes:

[0157] A corrected cut-out duration calculation module, configured to determine the product of the first correction coefficient and the initial cut-out duration as the corrected cut-out duration;

[0158] The cut-out duration calculation module is used to determine a second correction coefficient corresponding to the target driving style of the host vehicle selected by the user, and perform a secondary correction on the corrected cut-out duration based on the second correction coefficient to obtain the cut-out duration for the first obstacle to cut out of the lane where the host vehicle is located from its current position.

[0159] In the present application, the position prediction module 604 includes:

[0160] The initial position prediction module is used to determine the initial predicted position after traveling the cut-out duration during the process of the first obstacle cutting out of the lane where the host vehicle is located according to the cut-out duration and the lateral driving speed in the obstacle data of the first obstacle, and according to the lateral distance of the first obstacle relative to the host vehicle.

[0161] The position prediction sub-module is used to correct the initial predicted position according to the obstacle size in the obstacle data of the first obstacle to obtain the predicted position after traveling the cut-out duration during the process of the first obstacle cutting out of the lane where the host vehicle is located.

[0162] In the present application, the cut-out probability calculation module 605 includes:

[0163] The target probability strategy determination module is used to determine a target probability strategy corresponding to the obstacle type according to the obstacle type of the first obstacle, and the target probability strategy represents the corresponding relationship between the distance between the center line of the first obstacle of this obstacle type and the center line of the lane where the host vehicle is located and the cut-out probability.

[0164] The distance determination module is used to determine the distance between the center line of the first obstacle and the center line of the lane where the host vehicle is located according to the predicted position.

[0165] The cut-out probability calculation sub-module is used to determine the cut-out probability of the first obstacle cutting out of the lane where the host vehicle is located according to the distance and the target probability strategy.

[0166] In the present application, the target obstacle determination module 606 includes:

[0167] The target probability threshold determination module is used to determine a target probability threshold corresponding to the obstacle type of the first obstacle according to the obstacle type of the first obstacle.

[0168] The target obstacle determination sub-module is used to determine that the first obstacle is the target obstacle that the host vehicle no longer needs to pay attention to when the cut-out probability exceeds the target probability threshold.

[0169] An embodiment of the present invention further provides a vehicle, which may specifically include: the above device for determining that the host vehicle does not need to pay attention to a target, and is used to execute the steps in the method for determining that the host vehicle does not need to pay attention to a target.

[0170] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method for determining that the host vehicle does not need to pay attention to a target are implemented.

[0171] The foregoing are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining a target that a vehicle does not need to pay attention to, characterized in that: The method comprises: Obtain map data, road data, and obstacle data through on-board sensors; The map data, the road data and the obstacle data are integrated to determine a first obstacle in a lane where the vehicle is located from obstacles on the current road; Determine, according to the obstacle type in the obstacle data and the position of the obstacle relative to the ego vehicle, the time it takes for the first obstacle to cut out of the lane where the ego vehicle is located from its current position; Determine, according to the cut-out duration, the lateral speed of the obstacle in the obstacle data, and the position of the obstacle relative to the ego vehicle, a predicted position of the first obstacle after traveling the cut-out duration in the process of cutting out the lane where the ego vehicle is located; Determining a probability of the first obstacle cutting out of the lane where the ego vehicle is located according to a positional relationship between the predicted position and the lane where the ego vehicle is located; According to the cut-out probability and a preset probability threshold, it is determined whether the first obstacle is a target obstacle that the ego vehicle no longer needs to pay attention to.

2. The method according to claim 1, characterized in that The step of fusing the map data, the road data and the obstacle data to determine a first obstacle located in a lane where the vehicle is located from obstacles on the current road includes: Fusing the map data with the road data to obtain lane parameter information of the current road; The lane parameter information and the obstacle data are sensed and fused to determine the first obstacle in the lane where the vehicle is located from the obstacles on the current road.

3. The method according to claim 1, characterized in that The determining, based on the obstacle type in the obstacle data and the position of the obstacle relative to the vehicle, the time it takes for the first obstacle to cut out of the lane where the vehicle is located from its current position, includes: Determining, according to the obstacle type in the obstacle data of the first obstacle, an initial cut-out duration corresponding to the obstacle type of the first obstacle; The initial cut-out duration is corrected according to the longitudinal distance between the first obstacle and the ego vehicle and the obstacle type of the first obstacle to obtain the cut-out duration of the first obstacle cutting out of the lane where the ego vehicle is located from its current position.

4. The method according to claim 3, characterized in that The step of correcting the initial cut-out duration according to the longitudinal distance between the first obstacle and the vehicle and the obstacle type of the first obstacle to obtain the cut-out duration of the first obstacle cutting out the lane where the vehicle is located from its current position includes: Determining, according to the obstacle type of the first obstacle, a target correction strategy corresponding to the obstacle type, wherein the target correction strategy represents a corresponding relationship between a longitudinal distance between the first obstacle and the vehicle under the obstacle type and a first correction coefficient; determining a first correction coefficient according to the longitudinal distance between the first obstacle and the ego vehicle and the target correction strategy; The product of the first correction coefficient and the initial cut-out time is determined as the cut-out time of the first obstacle cutting out of the lane where the vehicle is located from its current position.

5. The method according to claim 4, characterized in that The step of multiplying the first correction coefficient by the initial cut-out time to determine the cut-out time of the first obstacle from the current position of the vehicle to the lane where the vehicle is located includes: Determine the product of the first correction coefficient and the initial cut-out duration as the corrected cut-out duration; According to the target driving style of the vehicle selected by the user, a second correction coefficient corresponding to the target driving style is determined, and the corrected cut-out duration is secondarily corrected based on the second correction coefficient to obtain the cut-out duration of the first obstacle cutting out of the lane where the vehicle is located from its current position.

6. The method according to claim 1, characterized in that The determining, according to the cut-out duration, the lateral speed of the obstacle in the obstacle data, and the position of the obstacle relative to the vehicle, the predicted position of the first obstacle after the first obstacle has traveled the cut-out duration in the process of cutting out the lane where the vehicle is located, comprises: Determine, according to the cut-out duration and the lateral speed of the obstacle in the obstacle data of the first obstacle, and according to the lateral distance of the first obstacle relative to the ego vehicle, an initial predicted position of the first obstacle after traveling the cut-out duration in the process of cutting out the lane where the ego vehicle is located; The initial predicted position is corrected according to the obstacle size in the obstacle data of the first obstacle to obtain a predicted position of the first obstacle after the vehicle cuts out of the lane where the vehicle is located for the cut-out time.

7. The method according to claim 1, characterized in that The determining, based on the positional relationship between the predicted position and the lane where the vehicle is located, a probability of the first obstacle cutting out of the lane where the vehicle is located, includes: Determining, according to the obstacle type of the first obstacle, a target probability strategy corresponding to the obstacle type, wherein the target probability strategy represents a corresponding relationship between a distance between a center line of the first obstacle and a center line of a lane where the vehicle is located and a cut-out probability under the obstacle type; Determining, based on the predicted position, a distance between a center line of the first obstacle and a center line of a lane where the vehicle is located; The probability of the first obstacle cutting out of the lane where the ego vehicle is located is determined according to the distance and the target probability strategy.

8. The method according to claim 1, characterized in that The determining, based on the cut-out probability and a preset probability threshold, whether the first obstacle is a target obstacle that the vehicle no longer needs to pay attention to includes: determining, according to the obstacle type of the first obstacle, a target probability threshold corresponding to the obstacle type of the first obstacle; When the cut-out probability exceeds the target probability threshold, the first obstacle is determined to be a target obstacle that the vehicle no longer needs to pay attention to.

9. A device for determining that a vehicle does not need to pay attention to a target, characterized in that: The device comprises: A data acquisition module, used to acquire map data, road data and obstacle data; A first obstacle determination module, used for fusing the map data, the road data and the obstacle data to determine a first obstacle in the lane where the vehicle is located from obstacles on the current road; a cut-out time determination module, configured to determine a cut-out time for the first obstacle to cut out of the lane where the ego vehicle is located from its current position according to the obstacle type in the obstacle data and the position of the obstacle relative to the ego vehicle; a position prediction module, for determining, according to the cut-out duration, the lateral speed of the obstacle in the obstacle data, and the position of the obstacle relative to the ego vehicle, the predicted position of the first obstacle after the first obstacle has traveled the cut-out duration in the process of cutting out the lane where the ego vehicle is located; a cut-out probability calculation module, configured to determine a cut-out probability of the first obstacle cutting out of the lane where the ego vehicle is located according to a positional relationship between the predicted position and the lane where the ego vehicle is located; The target obstacle determination module is used to determine whether the first obstacle is a target obstacle that the vehicle no longer needs to pay attention to based on the cut-out probability and a preset probability threshold.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the method for determining a target that the vehicle does not need to pay attention to as claimed in any one of claims 1 to 8 are implemented.

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