Self-vehicle display method and device, medium and electronic equipment

Abstract

The invention discloses a self-vehicle display method and device, a medium and electronic equipment. The method comprises the steps that respective target collision points of a self-vehicle and a target obstacle are determined; determining a target observation point based on each target collision point; determining a target observation view angle based on the target observation point; and displaying a three-dimensional image including the vehicle and the target obstacle based on the target observation view angle. According to the scheme, the optimal observation view angle for observing the vehicle and the nearest obstacle can be determined in real time, so that the real-time condition between the vehicle and the target obstacle can be observed in a larger view angle range through the optimal observation view angle, and the three-dimensional image of the vehicle and the nearest obstacle can be displayed based on the optimal observation view angle; the distance information between the vehicle and the nearest obstacle can be displayed more visually and accurately, so that a driver can be reminded to adjust the vehicle in time to avoid the risk of vehicle collision caused by the blind area problem, and the driving safety of the vehicle is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of intelligent driving, and in particular, to a self-vehicle display method, apparatus, medium, and electronic device. Background Art

[0002] With the continuous development of automotive technology, cars are more widely used as a means of daily travel. However, during the parking process of a vehicle or when driving on a congested road, the risk coefficient of driving a vehicle increases, so it is necessary to monitor the driving process of the vehicle in real time.

[0003] In the related art, during the parking process of a vehicle, images of the surrounding environment of the vehicle can be collected by a plurality of fisheye cameras installed at the rear side of the vehicle, and the collected two-dimensional images are stitched and the stitched image is displayed to facilitate the driver to observe the real-time environment around the vehicle. However, due to the limited number of installed cameras, there will be a blind area problem, resulting in a risk of collision between the vehicle and an obstacle during the parking process.

[0004] Thus, how to solve the blind area problem during the parking process has become an urgent problem to be solved. Summary of the Invention

[0005] In order to solve the above technical problems, the present disclosure provides a self-vehicle display method, apparatus, medium, and electronic device to solve the blind area problem during the parking process, thereby improving vehicle safety.

[0006] On the one hand, a self-vehicle display method is provided, including:

[0007] Determining respective target collision points of the self-vehicle and a target obstacle;

[0008] Determining a target observation point based on each of the target collision points;

[0009] Determining a target observation angle based on the target observation point;

[0010] Displaying a three-dimensional image including the self-vehicle and the target obstacle based on the target observation angle.

[0011] On the other hand, a self-vehicle display apparatus is provided, including:

[0012] A first determination module, configured to determine respective target collision points of the self-vehicle and a target obstacle;

[0013] A second determination module, configured to determine a target observation point based on each of the target collision points;

[0014] A third determination module, configured to determine a target observation angle based on the target observation point;

[0015] An image display module, configured to display a three-dimensional image including the host vehicle and the target obstacle based on the target observation perspective.

[0016] In another aspect, a computer program product is provided, which, when executed by an instruction processor in the computer program product, executes the host vehicle display method provided in the first aspect embodiment of the present disclosure.

[0017] In yet another aspect, an electronic device is provided, which includes: a processor; a memory for storing executable instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the host vehicle display method described in the first aspect above.

[0018] The embodiments of the present disclosure provide a host vehicle display method, which can determine the respective target collision points of the host vehicle and the target obstacle, and determine a target observation point based on the target collision points. Since the target observation perspective is determined based on the target observation point, a three-dimensional image including the host vehicle and the target obstacle can be displayed based on the target observation perspective. The solution of the embodiments of the present disclosure can, in real time, determine the best observation perspective for observing the host vehicle and the nearest obstacle. Therefore, through this best observation perspective, the real-time situation between the host vehicle and the target obstacle can be observed within a larger perspective range. Thus, the three-dimensional images of the host vehicle and the nearest obstacle displayed based on this best observation perspective can more intuitively and accurately display the distance information between the host vehicle and the nearest obstacle, so as to remind the driver to adjust the vehicle in time to avoid the risk of vehicle collision caused by blind spot problems, thereby improving the driving safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system architecture diagram of a host vehicle display system provided by an exemplary embodiment of the present disclosure.

[0020] Figure 2 is a flowchart of a host vehicle display method provided by an exemplary embodiment of the present disclosure.

[0021] Figure 3 is a flowchart of a host vehicle display method provided by another exemplary embodiment of the present disclosure.

[0022] Figure 4 is a schematic diagram of a host vehicle and an obstacle provided by an exemplary embodiment of the present disclosure.

[0023] Figure 5 is a schematic diagram of the relationship of collision points provided by an exemplary embodiment of the present disclosure.

[0024] Figure 6 is a flowchart of a host vehicle display method provided by yet another exemplary embodiment of the present disclosure.

[0025] Figure 7 It is a schematic flowchart of a self-vehicle display method provided by another exemplary embodiment of the present disclosure.

[0026] Figure 8 It is a schematic diagram of the relationship of collision points provided by another exemplary embodiment of the present disclosure.

[0027] Figure 9 It is a schematic flowchart of a self-vehicle display method provided by an exemplary embodiment of the present disclosure.

[0028] Figure 10 It is a schematic diagram of an observation space provided by an exemplary embodiment of the present disclosure.

[0029] Figure 11 It is a schematic diagram of determining the field-of-view performance parameters of an observation perspective provided by an exemplary embodiment of the present disclosure.

[0030] Figure 12 It is a schematic diagram of determining candidate observation perspectives provided by an exemplary embodiment of the present disclosure.

[0031] Figure 13 It is a structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure.

[0032] Figure 14 It is a schematic flowchart of a self-vehicle display method provided by an exemplary embodiment of the present disclosure.

[0033] Figure 15 It is a schematic flowchart of a self-vehicle display method provided by an exemplary embodiment of the present disclosure.

[0034] Figure 16 It is a schematic structural diagram of a self-vehicle display device provided by an exemplary embodiment of the present disclosure.

[0035] Figure 17 It is a structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. Detailed implementation manners

[0036] To explain the present disclosure, exemplary embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. It should be understood that the present disclosure is not limited by the exemplary embodiments.

[0037] It should be noted that: unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0038] Application overview

[0039] In existing reverse driving technologies, the reverse image can be observed through a 360-degree surround view system. The reverse image is obtained by stitching two-dimensional images. However, due to the limited number of cameras (e.g., fisheye cameras) installed at the rear side of the vehicle, there are blind spot problems in the presented reverse image, which causes the driver to ignore the collision risk between the vehicle and obstacles.

[0040] Based on the above technical problems, the embodiments of the present disclosure provide a self-vehicle display method, which can determine the respective target collision points of the self-vehicle and the target obstacle, and determine the target observation points based on the target collision points. Since the target observation view angle is determined based on the target observation points, a three-dimensional image including the self-vehicle and the target obstacle can be displayed based on the target observation view angle. In the solution of the embodiments of the present disclosure, since the best observation view angle for observing the self-vehicle and the nearest obstacle can be determined in real time, the real-time situation between the self-vehicle and the target obstacle can be observed through the best observation view angle in a larger view angle range. Therefore, the three-dimensional images of the self-vehicle and the nearest obstacle displayed based on the best observation view angle can more intuitively and accurately display the distance information between the self-vehicle and the nearest obstacle, so as to remind the driver to adjust the vehicle in time to avoid the risk of vehicle collision caused by blind spot problems, thereby improving the driving safety of the vehicle.

[0041] Exemplary System

[0042] Figure 1 is the system architecture diagram of the self-vehicle display system provided by an exemplary embodiment of the present disclosure. Among them, the self-vehicle display system may include a data acquisition module 101, a view angle adjustment module 102, and a display interaction module 103. The modules are connected by solid lines, and the arrow indicates the data flow direction. The following is an exemplary description of these three modules.

[0043] As Figure 1 shown, the data acquisition module 101 can be used to collect image data around the self-vehicle in real time through multiple cameras at different view angles outside the self-vehicle to obtain multiple images, and collect environmental data around the self-vehicle in real time through other sensors (such as ultrasonic radars).

[0044] In some embodiments, based on the multiple images and environmental data, at least one obstacle around the self-vehicle and the relative spatial position relationship between the self-vehicle and each obstacle can be determined. Furthermore, based on the relative spatial position relationship between the self-vehicle and each obstacle, a target obstacle can be determined among at least one obstacle, and the respective target collision points of the self-vehicle and the target obstacle can be determined.

[0045] For example, the target obstacle is the obstacle closest to the self-vehicle among at least one obstacle.

[0046] Exemplarily, the perspective adjustment module 102 can be used to determine a target observation point based on the respective target collision points of the host vehicle and the target obstacle, and determine a target observation perspective based on the target observation point.

[0047] Exemplarily, the display interaction module 103 can be used to display a three-dimensional image including the host vehicle and the target obstacle based on the target observation perspective.

[0048] In some examples, the display interaction module 103 can be specifically used to display a three-dimensional image including the host vehicle and the target obstacle in a bubble manner based on the target observation perspective when the host vehicle meets the warning level.

[0049] In some other examples, the display interaction module 103 can also be used to highlight the three-dimensional image of the target obstacle, and can also display distance prompt information between the host vehicle and the target obstacle, etc.

[0050] In some embodiments, when the display interaction module 103 displays a three-dimensional image including the host vehicle and the target obstacle based on the target observation perspective, the three-dimensional image including the host vehicle and the target obstacle is displayed within the perspective range and visual focus corresponding to the target observation perspective.

[0051] In some embodiments, as Figure 1 shown, the above host vehicle display system may further include a three-dimensional modeling module 104 and a rendering module 105.

[0052] Exemplarily, the three-dimensional modeling module 104 can be used to combine multiple images and environmental data, and use the Simultaneous Localization and Mapping (SLAM) algorithm to real-time construct a three-dimensional environment model around the host vehicle; and extract key feature points from the multiple collected images based on a deep learning algorithm, generate a sparse three-dimensional point cloud using the key feature points through triangulation, and then reconstruct the three-dimensional point cloud data to obtain three-dimensional images of the host vehicle and the target obstacle.

[0053] Exemplarily, the rendering module 105 can be used to perform real-time rendering on the three-dimensional images of the host vehicle and the target obstacle when the three-dimensional modeling module 104 performs three-dimensional modeling based on multiple images and environmental data.

[0054] Embodiments of the present disclosure provide a method for self-vehicle display, which can determine the respective target collision points of the self-vehicle and the target obstacle, and determine the target observation point based on each target collision point. Since the target observation angle is determined based on the target observation point, a three-dimensional image including the self-vehicle and the target obstacle can be displayed based on the target observation angle. In the solution of the embodiments of the present disclosure, since the best observation angle for observing the self-vehicle and the nearest obstacle can be determined in real time, the real-time situation between the self-vehicle and the target obstacle can be observed through the best observation angle in a larger viewing angle range. Therefore, the three-dimensional images of the self-vehicle and the nearest obstacle displayed based on the best observation angle can more intuitively and accurately display the distance information between the self-vehicle and the nearest obstacle, so as to remind the driver to adjust the vehicle in time to avoid the risk of vehicle collision caused by blind spot problems, thereby improving the driving safety of the vehicle.

[0055] Exemplary method

[0056] Figure 2 is a schematic flowchart of a self-vehicle display method provided by an exemplary embodiment of the present disclosure. This embodiment can be applied to an electronic device, such as Figure 2 as shown, including the following steps:

[0057] Step 201, determine the respective target collision points of the self-vehicle and the target obstacle.

[0058] In some embodiments, it is possible to detect whether there are obstacles within a preset range around the self-vehicle. When there is at least one obstacle around the self-vehicle, the target obstacle can be any one of the at least one obstacle, or a specific obstacle among the at least one obstacle. For example, the target obstacle is the obstacle closest to the self-vehicle among the at least one obstacle.

[0059] In some embodiments, the target collision point refers to the position point of contact between the self-vehicle and the target obstacle when a collision risk may occur between the self-vehicle and the target obstacle.

[0060] When determining the respective target collision points of the self-vehicle and the target obstacle, the self-vehicle and the target obstacle can be respectively converted into corresponding polygons first, and then the respective nearest points between the two polygons are calculated, so that these two nearest points can be respectively determined as the corresponding target collision points. For a detailed description of the respective target collision points of the self-vehicle and the target obstacle, reference can be made to the detailed description in the following embodiments, and the embodiments of the present disclosure will not elaborate here.

[0061] The self-vehicle display method provided by the embodiments of the present disclosure can be applied to scenarios that require high-precision environmental perception, such as intelligent vehicles, unmanned parking systems, or logistics vehicles.

[0062] Step 202, determine the target observation point based on each target collision point.

[0063] In some embodiments, the target observation point is a point between the respective target collision points of the host vehicle and the target obstacle, and is used to reflect the details of the collision risk between the host vehicle and the target obstacle.

[0064] In some examples, the target observation point can be determined based on the geometric position relationship between the respective target collision points of the host vehicle and the target obstacle.

[0065] Step 203: Determine the target observation perspective based on the target observation point.

[0066] In the embodiments of the present disclosure, the above-mentioned target observation perspective faces the target observation point and is used to observe the host vehicle and the target obstacle near the target observation point. Since the target observation perspective is determined based on the target observation point, different target observation points correspond to different target observation perspectives.

[0067] Step 204: Display a three-dimensional image including the host vehicle and the target obstacle based on the target observation perspective.

[0068] In some embodiments, multiple images at the current moment and the current environmental information can be obtained, and the target obstacle is included in the multiple images and the current environmental information; three-dimensional modeling and real-time rendering are performed based on the multiple images and the current environmental information to obtain a three-dimensional image of the host vehicle and the target obstacle, and then a three-dimensional image including the host vehicle and the target obstacle is displayed based on the target observation perspective.

[0069] Since the target observation perspective is determined in real time based on the relevant information of the host vehicle and the relative position relationship between the obstacles around the host vehicle and the host vehicle, during the driving process of the host vehicle, as the position relationship between the obstacles around the host vehicle and the host vehicle changes, different target obstacles can be determined, and then the respective target collision points of the host vehicle and the target obstacles can be determined, so that the target observation perspective obtained in real time is also different. Thus, the target observation perspective in the embodiments of the present disclosure is dynamically adjusted.

[0070] Embodiments of the present disclosure provide a method for self-vehicle display, which can determine the respective target collision points of the self-vehicle and the target obstacle, and determine the target observation point based on each target collision point. Since the target observation angle is determined based on the target observation point, a three-dimensional image including the self-vehicle and the target obstacle can be displayed based on the target observation angle. In the solution of the embodiments of the present disclosure, since the best observation angle for observing the self-vehicle and the nearest obstacle can be determined in real time, the real-time situation between the self-vehicle and the target obstacle can be observed through the best observation angle in a larger viewing angle range. Therefore, the three-dimensional images of the self-vehicle and the nearest obstacle displayed based on the best observation angle can more intuitively and accurately display the distance information between the self-vehicle and the nearest obstacle, so as to remind the driver to adjust the vehicle in time to avoid the risk of vehicle collision caused by blind spot problems, thereby improving the driving safety of the vehicle.

[0071] As Figure 3 shown, based on the above Figure 2 shown embodiment, step 201 may include the following steps:

[0072] Step 2011, determine the first collision risk point of the first obstacle at the current moment and the first self-vehicle collision risk point of the self-vehicle.

[0073] Wherein, the first obstacle is the obstacle with the smallest distance from the self-vehicle among the multiple obstacles at the current moment.

[0074] In some embodiments, image data around the self-vehicle can be collected in real time through multiple cameras with different perspectives to obtain multiple images at the current moment, and the current environmental information around the self-vehicle can be collected through sensors such as radar; based on the multiple images and the current environmental information, multiple obstacles within a certain range near the self-vehicle and the distance between each obstacle and the self-vehicle can be determined. Therefore, based on the distance between each obstacle and the self-vehicle, the obstacle with the closest distance to the self-vehicle can be selected from the multiple obstacles as the first obstacle.

[0075] In some embodiments, when calculating the collision risk point between the self-vehicle and any obstacle within a certain range near the self-vehicle, the self-vehicle and the obstacle can be converted into corresponding geometric shapes based on the types of the self-vehicle and the obstacle first, and then the respective target collision points of the self-vehicle and the obstacle can be determined based on the characteristics of the respective corresponding geometric shapes and the collision detection algorithm.

[0076] Exemplarily, as Figure 4 shown in (a), the self-vehicle is converted into a quadrilateral representation according to the vehicle type, and as Figure 4 shown in (b) and (c), the self-vehicle is converted into an octagon representation according to the vehicle type; different types of obstacles can be converted into different geometric shape representations. For example, as Figure 4As shown in (a) and (b) therein, when the obstacle is another vehicle or a pillar, the obstacle can be converted into a quadrilateral representation. For another example, as shown in (c) in Figure 4 wherein when the obstacle is a curb or a wall with height, the obstacle can be represented by a polyline.

[0077] In one example, taking the first obstacle as another vehicle as an example for exemplary illustration. The host vehicle is converted into a quadrilateral representation, and the other vehicle is converted into a quadrilateral representation. The shortest distance between the two quadrilaterals is calculated. When the shortest distance is less than the safety threshold, it indicates that there is a collision risk between the host vehicle and the other vehicle. The intersection points of the shortest distance with the quadrilateral corresponding to the host vehicle and the intersection points of the shortest distance with the quadrilateral corresponding to the other vehicle can be determined. Thus, the intersection point of the quadrilateral corresponding to the host vehicle can be used as the first host vehicle collision risk collision point of the host vehicle, and the intersection point of the quadrilateral corresponding to the other vehicle can be used as the first collision risk collision point of the other vehicle.

[0078] In another example, taking the first obstacle as a curb with height as an example for exemplary illustration. The host vehicle is converted into an octagon representation, and the curb is converted into a polyline representation. The shortest distance between the octagon and the polyline is calculated. When the shortest distance is less than the safety threshold, it indicates that there is a collision risk between the host vehicle and the curb. The intersection points of the shortest distance with the octagon corresponding to the host vehicle and the intersection points of the shortest distance with the polyline corresponding to the curb can be determined. Thus, the intersection point of the octagon corresponding to the host vehicle can be used as the first host vehicle collision risk collision point of the host vehicle, and the intersection point of the polyline corresponding to the curb can be used as the first collision risk collision point of the curb.

[0079] Step 2012, determine the second host vehicle collision risk point of the host vehicle at the first moment and the second collision risk point of the corresponding second obstacle.

[0080] Wherein, the first moment is the previous moment adjacent to the current moment; the second obstacle is the obstacle with the smallest distance from the host vehicle among the multiple obstacles at the first moment.

[0081] In some embodiments, the above-mentioned second obstacle and the first obstacle may be the same or different. For determining the second obstacle, reference may be made to the detailed description of determining the first obstacle in the above embodiments, and the embodiments of the present disclosure will not be elaborated herein.

[0082] In some embodiments, the second obstacle is the obstacle with the closest distance to the host vehicle at the first moment, and the second host vehicle collision risk point is the target collision point of the host vehicle determined based on the host vehicle and the second obstacle at the previous moment. For how to determine the second host vehicle collision risk point, reference may be made to the detailed description of determining the first host vehicle risk collision point in the above embodiments, and the embodiments of the present disclosure will not be elaborated herein again. In addition, since the second host vehicle collision risk point is the target collision point of the host vehicle determined based on the host vehicle and the second obstacle at the previous moment, the second collision risk point is known and can be directly obtained.

[0083] In some embodiments, to calculate the second collision risk point of the second obstacle, the second obstacle can be first converted into a corresponding geometric shape, and then multiple points on the side of the geometric shape close to the host vehicle are determined. The straight-line distance between each point and the second collision risk point of the host vehicle is calculated respectively, and the point with the shortest straight-line distance is used as the second collision risk point of the second obstacle.

[0084] Exemplarily, take the second obstacle as another vehicle. As Figure 5 shown, point a represents the first collision risk point of the host vehicle at the current moment, and point b represents the second collision risk point of the host vehicle at the first moment. The other vehicle is converted into a quadrilateral for representation, and candidate collision points on the side of the corresponding quadrilateral of the other vehicle close to the host vehicle are determined. The candidate collision points include point c, point d, and point e; the distances between each of point c, point d, and point e and the second collision risk point b of the host vehicle are calculated respectively. Since the distances between point c and point e and point b are both greater than the distance between point d and point b, point d can be used as the second collision risk point of the other vehicle. Of course, the above process is an exemplary illustration of selecting the second collision risk point from 3 points. In actual implementation, the point with the shortest distance from the second collision risk point of the host vehicle can be selected as the second collision risk point from more points.

[0085] Step 2013, based on the first collision risk point and the first collision risk point of the host vehicle and / or the second collision risk point and the second collision risk point of the host vehicle, determine the respective target collision points of the host vehicle and the target obstacle.

[0086] In the embodiments of the present disclosure, in order to ensure smooth transition when switching between different observation perspectives and avoid frequent switching, when determining the respective target collision points of the host vehicle and the obstacle, smooth transition is required, including the following two cases:

[0087] (1) If the target obstacle at the current moment is different from the target obstacle at the previous moment, smooth the switching between different target obstacles.

[0088] (2) If the target obstacle at the current moment is the same as the target obstacle at the previous moment, smooth the switching between different collision points of the same target obstacle.

[0089] In some embodiments, the matching relationship between the first obstacle and the second obstacle can be determined, and in combination with the smooth transition method and the matching relationship, based on the first collision risk point and the first collision risk point of the host vehicle and / or the second collision risk point and the second collision risk point of the host vehicle, determine the respective target collision points of the host vehicle and the target obstacle.

[0090] In some examples, the target collision point of the host vehicle may be the first host vehicle collision risk point or the second host vehicle collision risk point, and the target collision point of the target obstacle may be the first collision risk point or the second collision risk point.

[0091] The host vehicle display method provided by the embodiments of the present disclosure can determine the first collision risk point of the first obstacle and the first host vehicle collision risk point at the current moment, and determine the second host vehicle collision risk point of the host vehicle and the second collision risk point of the corresponding second obstacle at the first moment. Therefore, by combining the second host vehicle collision risk point and the corresponding second collision risk point at the previous moment of the host vehicle, as well as the first collision risk point and the first host vehicle collision risk point, it can ensure that the respective target collision points of the host vehicle and the target obstacle are more stable, thereby ensuring smooth switching between the observation perspectives at different moments.

[0092] As Figure 6 shown, based on the above Figure 3 shown embodiment, step 2013 may include the following steps:

[0093] Step 2013a, in response to the first obstacle being the same as the second obstacle, determine the first distance and the second distance.

[0094] Wherein, the first distance is the distance between the second host vehicle collision risk point and the second obstacle at the previous moment; the second distance is the distance between the first host vehicle collision risk point and the first obstacle at the current moment.

[0095] In some embodiments, to calculate the first distance between the second host vehicle collision risk point and the second obstacle, it is necessary to first convert the second obstacle into a corresponding geometric shape, and then calculate the minimum distance between the second host vehicle collision risk point and the geometric shape as the first distance. That is, it is necessary to find the nearest point on the geometric shape to the second host vehicle collision risk point, and this nearest point is the second collision risk point. Therefore, the first distance is the distance between the second host vehicle collision risk point and the second collision risk point, and thus the first distance can be calculated based on the coordinates of the second host vehicle collision risk point and the coordinates of the second collision risk point.

[0096] Exemplarily, when the first obstacle is the same as the second obstacle, as Figure 5As shown, the other vehicle 1 is the first obstacle with the closest distance to the host vehicle at the current moment, and the other vehicle 1 is also the second obstacle with the closest distance to the host vehicle at the previous moment; the first host vehicle collision risk point at the current moment of the host vehicle is point a, the first collision point of the other vehicle 1 at the current moment is point c, the second host vehicle collision risk point of the host vehicle at the previous moment is point b, and the second collision risk point of the other vehicle 1 corresponding to point b is point d. When calculating the first distance, it can be obtained by calculating the distance between point b and point d; when calculating the second distance, it can be obtained by calculating the distance between point c and point a.

[0097] In one example, the second obstacle is taken as the other vehicle. When the first obstacle and the second obstacle are the same, as Figure 5 shown, in the same coordinate system, the second host vehicle collision risk point of the host vehicle at the previous moment is point b(X1, Y1), and the corresponding second collision risk point of the other vehicle is d(X2, Y2). The distance between point b and point d is the first distance, and the first distance is denoted as D1. D1 can be calculated by the following formula (1):

[0098]

[0099] In some embodiments, the distance between the first host vehicle collision risk point and the first obstacle is the distance between the closest points of the two polygons corresponding to the host vehicle and the first obstacle respectively. Since the first host vehicle collision risk point and the first collision risk point of the first obstacle are the closest points corresponding to the host vehicle and the first obstacle respectively, in fact, calculating the distance between the first host vehicle collision risk point and the first collision risk point can obtain the second distance. After determining the coordinates of the first host vehicle collision risk point and the first collision risk point, based on the coordinates of the first host vehicle collision risk point and the coordinates of the first collision risk point, the second distance can be calculated according to the above formula (1).

[0100] Step 2013b, determine the first deviation value based on the first distance.

[0101] In some examples, the first deviation value is calculated according to the following formula (2), and the first deviation value is denoted as offset1:

[0102]

[0103] Substitute the first distance as dis_last into the above formula (2), and offset1 can be obtained.

[0104] Step 2013c, in response to the first distance, the second distance, and the first deviation value satisfying the first preset condition, determine the target collision point of the target obstacle based on the first collision risk point, and determine the target collision point of the host vehicle based on the first host vehicle collision risk point.

[0105] In some examples, the above-mentioned first distance, second distance, and first deviation value satisfying the first preset condition means that the second distance is less than or equal to the first distance and greater than the first deviation value.

[0106] In some embodiments, the first collision risk point is determined as the target collision point of the target obstacle, and the first self-vehicle collision risk point is determined as the target collision point of the self-vehicle.

[0107] Exemplarily, as Figure 5 shown, if the second distance between point c and point a is less than or equal to the first distance between point d and point b and greater than the second deviation value, then point a can be determined as the target collision point of the self-vehicle, and point c can be determined as the target collision point of the target obstacle.

[0108] Step 2013d, in response to the first distance, second distance, and first deviation value not satisfying the first preset condition, determining the target collision point of the target obstacle based on the second collision risk point, and determining the target collision point of the self-vehicle based on the second self-vehicle collision risk point.

[0109] In some examples, the above-mentioned first distance, second distance, and first deviation value not satisfying the first preset condition means that the second distance is greater than the first distance, or the second distance is less than or equal to the first deviation value.

[0110] In some embodiments, the second collision risk point is determined as the target collision point of the target obstacle, and the second self-vehicle collision risk point is determined as the target collision point of the self-vehicle.

[0111] Exemplarily, as Figure 5 shown, if the first distance between point c and point a is greater than the second distance between point g and point f, or if the first distance between point c and point a is less than or equal to the second deviation value, then point b is determined as the target collision point of the self-vehicle, and point d is determined as the target collision point of the target obstacle.

[0112] It should be noted that the first obstacle being the same as the second obstacle means that regardless of whether the first distance, second distance, and first deviation value satisfy the first preset condition, the target obstacle is the same obstacle, that is, the target obstacle is the first obstacle or the second obstacle.

[0113] The self-vehicle display method provided by the embodiments of the present disclosure, when the first obstacle closest to the self-vehicle at the current moment is the same as the second obstacle closest to the self-vehicle at the previous moment, by combining the historical collision point and the real-time collision point, can achieve smooth switching of different target collision points for the same obstacle, so it can ensure the smooth transition of the subsequent target observation perspective, thereby avoiding sudden changes in the observation perspective.

[0114] As Figure 7 shown, in the aboveFigure 3 Based on the illustrated embodiments, step 2013 may include the following steps:

[0115] Step 2013e, in response to the first obstacle being different from the second obstacle, determine a second distance and a third distance.

[0116] Wherein, the second distance is the distance between the vehicle itself and the first obstacle at the current moment, and the third distance is the distance between the vehicle itself and the second obstacle at the current moment.

[0117] When the first obstacle is different from the second obstacle, the calculation method of the second distance remains unchanged. For the relevant description of the second distance in the above embodiments, reference can be made, and the embodiments of the present disclosure will not elaborate herein.

[0118] In some embodiments, since the third distance is the distance between the vehicle itself and the second obstacle at the current moment, that is, the minimum distance between the polygons corresponding to the vehicle itself and the second obstacle. For calculating the minimum distance between two polygons, the nearest points of the two polygons can be determined first, and then the distance between the two nearest points is calculated as the minimum distance. For specific reference, the relevant description in the above embodiments can be referred to, and the embodiments of the present disclosure will not elaborate on this.

[0119] Exemplarily, when the first obstacle is different from the second obstacle, as Figure 8 shown, the other vehicle 1 is the first obstacle with the closest distance to the vehicle itself at the current moment, and the other vehicle 2 is the second obstacle with the closest distance to the vehicle itself at the previous moment; the first self-vehicle collision risk point of the vehicle itself at the current moment is point a, the first collision point of the other vehicle 1 at the current moment is point c, and the closest points of the vehicle itself and the other vehicle 2 are point f and point g respectively. When calculating the second distance, it can be obtained by calculating the distance between point c and point a; when calculating the third distance, it can be obtained by calculating the distance between point g and point f.

[0120] Step 2013f, determine a second deviation value based on the third distance.

[0121] In some examples, the third distance can be used as dis_last and substituted into the above formula (2) to obtain the second deviation value offset2.

[0122] Step 2013g, in response to the second distance, the third distance, and the second deviation value satisfying a second preset condition, determine the target collision point of the target obstacle based on the first collision risk point, and determine the target collision point of the vehicle itself based on the first self-vehicle collision risk point.

[0123] In some examples, the second distance, the third distance, and the second deviation value satisfying the second preset condition means that the second distance is less than or equal to the third distance and greater than the second deviation value.

[0124] In some embodiments, the first collision risk point is determined as the target collision point of the target obstacle, and the first self-vehicle collision risk point is determined as the target collision point of the self-vehicle. It should be noted that at this time, the target obstacle is still the first obstacle.

[0125] Exemplarily, as Figure 8 shown, if the first distance between point c and point a is less than or equal to the third distance between point g and point f and greater than the second deviation value, then point a can be determined as the target collision point of the self-vehicle, and point c can be determined as the target collision point of the target obstacle.

[0126] Step 2013h, in response to the second distance, the third distance, and the second deviation value not satisfying the second preset condition, determine the respective target collision points based on the second obstacle and the self-vehicle.

[0127] Wherein, the second obstacle is the target obstacle.

[0128] In some examples, the second distance, the third distance, and the second deviation value not satisfying the second preset condition means that the second distance is greater than the third distance, or the second distance is less than or equal to the second deviation value.

[0129] In some embodiments, when the second distance, the third distance, and the second deviation value do not satisfy the second preset condition, the second obstacle is determined as the obstacle with the shortest distance from the self-vehicle at the current moment, that is, the second obstacle is the target obstacle at the current moment. Therefore, it is necessary to re-determine the respective target collision points of the self-vehicle and the second obstacle based on the relative position relationship between the second obstacle and the self-vehicle.

[0130] The self-vehicle display method provided by the embodiments of the present disclosure, when the first obstacle and the second obstacle are different, since it can determine whether the calculated second distance, third distance, and second deviation value satisfy the second preset condition, and when the second distance, third distance, and second deviation value satisfy the second preset condition, determine the target collision point of the target obstacle based on the first collision risk point, and determine the target collision point of the self-vehicle based on the first self-vehicle collision risk point, or, when the second distance, third distance, and second deviation value do not satisfy the second preset condition, re-determine the respective target collision points of the self-vehicle and the second obstacle based on the second obstacle and the self-vehicle, so it can achieve smooth switching between different target obstacles.

[0131] In some embodiments, the determining the respective target collision points based on the second obstacle and the self-vehicle in step 2013h above may specifically include the following steps:

[0132] (1) Determine the fourth distance and the fifth distance.

[0133] Wherein, the fourth distance is the distance between the second own-vehicle collision risk point and the second obstacle at the previous moment; the fifth distance is the distance between the own vehicle and the second obstacle at the current moment.

[0134] In some embodiments, the distance between the second own-vehicle collision risk point and the second obstacle at the previous moment is the shortest distance between the second own-vehicle collision risk point and the polygon corresponding to the second obstacle. Specifically, the point on the polygon corresponding to the second obstacle that is closest to the second own-vehicle collision risk point can be found, and then the distance between the second own-vehicle collision risk point and the closest point is calculated to obtain the fourth distance.

[0135] In some embodiments, the distance between the own vehicle and the second obstacle is the shortest distance between the polygons corresponding to the own vehicle and the second obstacle respectively. For the shortest distance between two polygons, reference can be made to the detailed description in the above embodiments, and the embodiments of the present disclosure will not be elaborated herein.

[0136] (2) Determine the third deviation value based on the fourth distance.

[0137] In some examples, the fourth distance can be used as dis_last and substituted into the above formula (2) to obtain the third deviation value offset3.

[0138] (3) In response to the fourth distance, the fifth distance, and the third deviation value satisfying the third preset condition, determine the third own-vehicle collision risk point of the own vehicle and the third collision risk point of the second obstacle at the current moment.

[0139] In some examples, the fourth distance, the fifth distance, and the third deviation value satisfying the third preset condition means that the fifth distance is less than or equal to the fourth distance and greater than the third deviation value.

[0140] In some examples, the third own-vehicle collision risk point of the own vehicle and the third collision risk point of the second obstacle are the collision risk points of the own vehicle and the second obstacle corresponding to the fifth distance, that is, the distance between the third own-vehicle collision risk point and the third collision risk point is the fifth distance. For the method of determining the third own-vehicle collision risk point and the third collision risk point, reference can be made to the detailed description in the above embodiments, and the embodiments of the present disclosure will not be elaborated herein.

[0141] (4) Determine the target collision point of the target obstacle based on the third collision risk point, and determine the target collision point of the own vehicle based on the third own-vehicle collision risk point.

[0142] In some embodiments, the third collision risk point is determined as the target collision point of the target obstacle, and the third own-vehicle collision risk point is determined as the target collision point of the own vehicle.

[0143] Exemplarily, such as Figure 8As shown, the other vehicle 2 is the second obstacle that was closest to the host vehicle at the previous moment. When taking the other vehicle 2 as the target obstacle, point g on the other vehicle 2 is the third collision risk point, point f on the host vehicle is the third host vehicle collision risk point, point b on the host vehicle is the second host vehicle collision risk point, and point h on the other vehicle 2 is the closest point corresponding to the second host vehicle collision risk point. If the fifth distance between point g and point f is less than or equal to the fourth distance between point b and point h and greater than the third deviation value, then point g is determined as the target collision point of the other vehicle 2, and point f is determined as the target collision point of the host vehicle.

[0144] In some embodiments, the host vehicle display method provided by the present disclosure embodiment may further include: in response to the fourth distance, the fifth distance, and the third deviation value not satisfying the third preset condition, determining a fourth collision risk point of the second obstacle corresponding to the second host vehicle collision risk point at the current moment; determining the target collision point of the target obstacle based on the fourth collision risk point, and determining the target collision point of the host vehicle based on the second host vehicle collision risk point.

[0145] In some examples, the situation where the fourth distance, the fifth distance, and the third deviation value do not satisfy the third preset condition means that the fifth distance is greater than the fourth distance, or the fifth distance is less than or equal to the third deviation value.

[0146] In some embodiments, since the distance between the second host vehicle collision risk point and the fourth collision risk point is the fourth distance, therefore, the fourth collision risk point can be determined based on the fourth distance and the second host vehicle collision risk point. Specifically, the closest point on the polygon corresponding to the second obstacle to the second host vehicle collision risk point can be found, and then this closest point is determined as the fourth collision risk point.

[0147] In some embodiments, the fourth collision risk point is determined as the target collision point of the target obstacle, and the second host vehicle collision risk point is determined as the target collision point of the host vehicle.

[0148] Exemplarily, as Figure 8 shown, the other vehicle 2 is the second obstacle that was closest to the host vehicle at the previous moment. When taking the other vehicle 2 as the target obstacle, point g on the other vehicle 2 is the third collision risk point, point f on the host vehicle is the third host vehicle collision risk point, point b on the host vehicle is the second host vehicle collision risk point, and point h on the other vehicle 2 is the fourth collision risk point corresponding to the second host vehicle collision risk point b. If the fifth distance between point g and point f is greater than the fourth distance between point b and point h, or the fifth distance between point g and point f is less than the third deviation value, then point b is determined as the target collision point of the host vehicle, and point h is determined as the target collision point of the other vehicle 2.

[0149] The self-vehicle display method provided by the embodiments of the present disclosure, when determining the second obstacle as the target obstacle, calculates the fourth distance between the second self-vehicle collision risk point and the second obstacle at the previous moment, the fifth distance between the self-vehicle and the second obstacle at the current moment, determines the third deviation value based on the fourth distance, and determines whether the fourth distance, the fifth distance, and the third deviation value meet the third preset condition. When the fourth distance, the fifth distance, and the third deviation value meet the third preset condition, it can determine the target collision point of the self-vehicle based on the third self-vehicle collision risk point of the self-vehicle and the target collision point of the target obstacle based on the third collision risk point of the second obstacle. Or, when the fourth distance, the fifth distance, and the third deviation value do not meet the third preset condition, it determines the target collision point of the target obstacle based on the fourth collision risk point and determines the target collision point of the self-vehicle based on the second self-vehicle collision risk point. Therefore, it can achieve smooth switching between different target collision points of the same target obstacle, thereby ensuring smooth transition of the subsequent target observation perspective and avoiding sudden changes in the observation perspective.

[0150] As Figure 9 shown, based on the above Figure 2 shown embodiment, step 203 may include the following steps:

[0151] Step 2031, create an observation space based on the target observation point and the yaw angle direction of the self-vehicle.

[0152] In some embodiments, the target observation point is determined based on the respective target collision points of the self-vehicle and the target obstacle. For specific details, reference may be made to the relevant descriptions in the following embodiments, and the embodiments of the present disclosure will not elaborate on this.

[0153] In some examples, the yaw angle direction of the self-vehicle can be obtained through an in-vehicle sensor or a positioning system. For example, the yaw angle direction of the self-vehicle is obtained through a gyroscope.

[0154] In some embodiments, a planar Cartesian coordinate system can be created with the target observation point as the origin and the yaw angle direction of the self-vehicle as the vertical axis (denoted as the Y axis) as the positive direction of the Y axis; a circle is formed with the target observation point as the center and a preset length as the radius, that is, the space surrounded by this circle is the observation space. In this way, the target observation perspective can be determined in this observation space.

[0155] Exemplarily, Figure 10 is a schematic diagram of the observation space provided by an exemplary embodiment of the present disclosure. As Figure 10 shown, point O represents the target observation point, a circle R is formed with point O as the center and a preset length as the radius, that is, the space surrounded by circle R is the observation space, and thus the target observation perspective can be determined on the circumference of circle R.

[0156] Step 2032: Determine multiple fixed observation viewpoints and candidate observation viewpoints in the observation space.

[0157] In some embodiments, the observation space may be divided into multiple fixed observation viewpoints, and the angle between each fixed observation viewpoint and its adjacent fixed observation viewpoint may be the same or different.

[0158] Exemplarily, taking 16 fixed observation viewpoints as an example. As Figure 10 shown, if the observation space is divided into 16 fixed observation viewpoints, the angle between each fixed observation viewpoint and its adjacent fixed observation viewpoint is 22.5 degrees; starting from the x-axis direction, a fixed observation viewpoint is determined on the circumference of circle O every 22.5 degrees, and a total of 16 fixed observation viewpoints (such as L1, L2, and L3) are obtained, among which the directions of 4 fixed observation viewpoints are the same as the coordinate axes. Since the center point of the observation space is the target observation point, each fixed observation viewpoint faces the target observation point.

[0159] In some embodiments, the above step 2032 may specifically include the following steps (a) to (d):

[0160] (a) Determine the first field-of-view performance parameter of each fixed observation viewpoint.

[0161] Among them, the first fixed observation viewpoint is the fixed observation viewpoint with the largest first field-of-view performance parameter among the multiple fixed observation viewpoints.

[0162] In some examples, the first field-of-view performance parameter may include visibility and viewing angle. Among them, visibility is used to represent the proportion of the number of unoccluded points that can be observed by the observation angle to the total number of points, and the viewing angle is used to represent the field-of-view range that can be observed by the observation angle.

[0163] In some embodiments, the target obstacle and the target collision point of the vehicle itself may be connected, and a number of (such as 10) sampling points may be evenly set on the connected line segment. Each sampling point is connected to the fixed observation viewpoint, and the intersection situation with the vehicle itself, the target obstacle, or other obstacles is detected by connecting each sampling point to the fixed observation viewpoint. If there is an intersection situation between the line connecting a certain sampling point and the fixed observation viewpoint and the vehicle itself, the target obstacle, or other obstacles, it means that the sampling point is occluded. Or, if there is no intersection situation between the line connecting a certain sampling point and the fixed observation viewpoint and the vehicle itself, the target obstacle, or other obstacles, it means that the sampling point is not occluded.

[0164] It should be noted that the above-mentioned several sampling points include the target collision points of the target obstacle and the host vehicle respectively, and these two target collision points serve as the endpoints of the line segment connecting the two target collision points; for these two endpoints among the several sampling points, when the number of intersection points between the lines connecting these two endpoints to the fixed observation perspective and the host vehicle, the target obstacle or other obstacles is greater than 1, it means that these two endpoints are blocked, or when the number of intersection points between the lines connecting these two endpoints to the fixed observation perspective and the host vehicle, the target obstacle or other obstacles is 1, it means that these two endpoints are not blocked.

[0165] In some examples, the visibility is denoted as t, and t = m1 / m2; where m1 represents the number of unblocked sampling points, and m2 represents the number of all sampling points.

[0166] Exemplarily, as Figure 11 shown, the target collision points of the target obstacle and the host vehicle are respectively denoted as A and B, and the observation space includes fixed observation angles C, D, E, F, and G, and there are obstacles around the host vehicle: other vehicle 1 and other vehicle 2. Connect A and B, and evenly set 10 sampling points on the line segment AB; for the fixed observation perspective D, since the lines connecting the 10 sampling points on the line segment AB to the fixed observation perspective D respectively do not intersect with the host vehicle, the target obstacle or other obstacles, there are no blocked sampling points on the line segment AB, so the visibility t of the fixed observation perspective D = m1 / m2; since m1 and m2 are the same, t = 1; for the fixed observation perspectives C and D, they are similar and not blocked by other vehicles.

[0167] For the fixed observation perspective E, since the lines connecting the 10 sampling points on the line segment AB to the fixed observation perspective D respectively intersect with the host vehicle, all sampling points on the line segment AB are blocked, so the visibility t of the fixed observation perspective E = m1 / m2, since m1 = 0, t = 0; for the fixed observation perspectives F and G, they are similar to the fixed observation perspective E and are blocked by other vehicles.

[0168] In some examples, as Figure 11 shown, in the plane Cartesian coordinate system, for the fixed observation perspective D, connect the fixed observation perspective D to the target collision points A and B respectively, to obtain a triangle with the fixed observation perspective D as the vertex and the line segment AB as the base, and determine the vertex angle ∠ADB of the triangle, that is, the included angle between AD and BD, as the visible angle of the fixed observation perspective D, so that the vertex angle ∠ADB can be calculated by the dot product of vectors. For the fixed observation perspective C, the visible angle can also be calculated by referring to the above method, and the embodiments of the present disclosure will not elaborate on this.

[0169] (b) Determine the first fixed observation perspective based on the first field of view performance parameters of each fixed observation perspective.

[0170] In some embodiments, the first field of view performance parameters of each fixed observation perspective are compared, and the fixed observation perspective with the largest first field of view performance parameter is selected from multiple fixed observation perspectives as the first fixed observation perspective.

[0171] Exemplarily, as Figure 11 shown, it is assumed that multiple fixed observation perspectives include C, D, …, and G. Since the fixed observation perspectives E, F, and G are significantly blocked by other vehicles or the host vehicle, the visible angles of the fixed observation perspectives E, F, and G are each 0; the visible angle of the fixed observation perspective C is ∠ACB, and the visible angle of the fixed observation perspective D is ∠ADB. If ∠ACB is greater than ∠ADB, then the fixed observation perspective C is selected as the first fixed observation perspective, or, if ∠ACB is less than ∠ADB, then the fixed observation perspective D is selected as the first fixed observation perspective.

[0172] (c) Based on the first fixed observation perspective, determine multiple alternative observation perspectives.

[0173] Among them, the multiple alternative observation perspectives are multiple adjacent observation perspectives obtained by rotating in a preset direction relative to the first fixed observation perspective.

[0174] In some examples, in the observation space, with the target observation point as the center, starting from the first fixed observation perspective, rotate clockwise and / or counterclockwise by a fixed angle, and select a preset number of fixed observation perspectives adjacent to the first fixed observation perspective from other fixed observation perspectives except the first fixed observation perspective as the multiple alternative observation perspectives. Among them, the multiple alternative observation perspectives may include the first fixed observation perspective, or the multiple alternative observation perspectives do not include the first fixed observation perspective.

[0175] (d) Determine the historical observation perspective at the second moment; where the historical observation perspective is used to indicate the observation perspective for observing the previous target observation point at the second moment; the second moment is the previous moment adjacent to the current moment.

[0176] In some embodiments, since the historical observation perspective is the observation perspective for observing the previous target observation point at the previous moment adjacent to the current moment, the historical observation perspective is known, and thus it can be directly obtained from the memory based on the timestamp information of the historical observation perspective.

[0177] (e) Determine the candidate observation perspective based on the historical observation perspective and the target control amount.

[0178] Among them, the target control amount is the increment between the historical observation perspective and the target alternative observation perspective, and the target alternative observation perspective is the alternative observation perspective with the largest first field of view performance parameter among the multiple alternative observation perspectives.

[0179] In some embodiments, the first field of view performance parameter of each alternative observation view among multiple alternative observation views may be calculated first, and then the first field of view performance parameters of each alternative observation view are compared, and the alternative observation view with the largest first field of view performance parameter is selected from the multiple alternative observation views as the target alternative observation view.

[0180] In some embodiments, the position increment and the heading angle increment between the historical observation view and the target alternative observation view are calculated, and the position increment and the heading angle increment are used as the target control quantity; adding the target control quantity to the historical observation view can obtain the candidate observation view.

[0181] Exemplarily, Figure 12 (a) in shows the connection line of the target collision points of the host vehicle and the target obstacle at the previous moment and the previous target observation view (i.e., the historical observation view), Figure 12 (b) in shows the connection line of the target collision points of the host vehicle and the target obstacle at the current moment, the first fixed observation view, other fixed observation views, alternative observation view 1 and alternative observation view 2. The multiple alternative observation views include the first fixed observation view, alternative observation view 1 and alternative observation view 2. By calculating the visible angles of the first fixed observation view, alternative observation view 1 and alternative observation view 2 respectively, the alternative observation view 2 with the largest visible angle is used as the target observation view; based on the target observation view and the historical observation view, the target control quantity is determined, and based on the target control quantity and the historical observation view, the candidate observation view at the current moment as shown in Figure 12 can be obtained.

[0182] In the above embodiments, by calculating the first field of view performance parameter of each fixed observation view, the fixed observation view with the largest first field of view performance parameter is selected as the first fixed observation view, and multiple alternative observation views are determined based on the fixed observation view. Since the candidate observation view can be determined based on the historical observation view and the target control quantity between the historical observation view and the target alternative observation view, the observation view is smoothly processed between different moments, thereby reducing the view jitter when the observation view is switched.

[0183] Step 2033, determine the target observation view based on multiple fixed observation views and / or candidate observation views.

[0184] In some embodiments, when there is no valid target observation view at the previous moment, there is no candidate observation view at this time, so the target observation view is determined among multiple fixed observation views; or, when there is a valid target observation view at the previous moment, there is a candidate observation view at this time, so the target observation view is determined based on multiple fixed observation views and the candidate observation view.

[0185] When determining the target observation perspective based on multiple fixed observation perspectives and candidate observation perspectives, the candidate observation perspective is preferentially selected as the target observation perspective. For specific details, reference can be made to the detailed description in the following embodiments, which will not be elaborated herein in the embodiments of the present disclosure.

[0186] The ego-vehicle display method provided by the embodiments of the present disclosure can construct an observation space based on the target observation point and the yaw angle direction of the ego-vehicle, determine multiple fixed observation perspectives and candidate observation perspectives in the observation space, and then determine the target observation perspective based on the multiple fixed observation perspectives and / or candidate observation perspectives. This solution constructs an observation space through the target observation point and the yaw angle direction of the ego-vehicle, which can ensure that the vehicle-mounted system always focuses on the key area strongly related to the driving direction of the ego-vehicle, avoid the observation blind area caused by vehicle turning, and can select the optimal observation perspective as the target observation perspective based on the multiple fixed observation perspectives and candidate observation perspectives, so that the ego-vehicle and the target obstacle can be observed through the optimal observation perspective.

[0187] In some embodiments, when there is no valid target observation perspective at the previous moment, step 2033 may specifically include the following steps: determining a second fixed observation perspective based on the first field-of-view performance parameter of each fixed observation perspective; wherein, the second fixed observation perspective is the observation perspective with the largest first field-of-view performance parameter among the multiple observation perspectives; determining the target observation perspective based on the second fixed observation perspective.

[0188] In some examples, the first field-of-view performance parameters of each fixed observation perspective are compared, the largest first field-of-view performance parameter is selected from the multiple first field-of-view performance parameters, and the observation perspective corresponding to the largest first field-of-view performance parameter among the multiple observation perspectives is determined as the second fixed observation perspective, that is, the second fixed observation perspective is the target observation perspective.

[0189] Since the ego-vehicle display method provided by the embodiments of the present disclosure can calculate the first field-of-view performance parameter of each fixed observation perspective and select the second fixed observation perspective corresponding to the largest first field-of-view performance parameter among the multiple fixed observation perspectives, the second fixed observation perspective can be determined as the target observation perspective, thereby avoiding the problem of perspective blind area caused by selecting an observation perspective with too small viewing angle and / or visibility, and being able to observe the ego-vehicle and the target obstacle within the largest field of view.

[0190] In some embodiments, when there is a valid target observation perspective at the previous moment, step 2033 may specifically include:

[0191] Step 2033a, determining the first field-of-view performance parameter of the multiple fixed observation perspectives and the second field-of-view performance parameter of the candidate observation perspective.

[0192] In some examples, the above-mentioned second field of view performance parameter and the first field of view performance parameter may be the same or different. For example, both the second field of view performance parameter and the first field of view performance parameter include viewing angle and visibility; for another example, the second field of view performance parameter includes viewing angle and visibility, and the first field of view performance parameter includes viewing angle or visibility.

[0193] For the second field of view performance parameter, reference may be made to the detailed description of the first field of view performance parameter in the above embodiments, and the embodiments of the present disclosure will not repeat it here.

[0194] Step 2033b, in response to the second field of view performance parameter being greater than or equal to a preset threshold, determine a target observation angle based on the candidate observation angles.

[0195] In some examples, the above-mentioned preset threshold may include a preset visibility threshold and a preset viewing angle threshold. When the visibility of the candidate observation angle is greater than or equal to the preset visibility threshold and the viewing angle of the candidate observation angle is greater than or equal to the preset viewing angle threshold, the candidate observation angle is determined as the target observation angle.

[0196] Exemplarily, when the visibility of the candidate observation angle as shown in Figure 12 is greater than or equal to the preset visibility threshold and the viewing angle of the candidate observation angle is greater than or equal to the preset viewing angle threshold, the candidate observation angle is determined as the target observation angle.

[0197] Step 2033c, in response to the second field of view performance parameter being less than the preset threshold, determine a target observation angle based on a plurality of fixed observation angles.

[0198] In some examples, when the visibility of the candidate observation angle is less than the preset visibility threshold and the viewing angle of the candidate observation angle is less than the preset viewing angle threshold, a target observation angle is determined based on a plurality of fixed observation angles.

[0199] In some embodiments, the step of determining a target observation angle based on a plurality of fixed observation angles in step 2033c above may specifically include the following steps:

[0200] (1) Determine a second fixed observation angle based on the first field of view performance parameter of each fixed observation angle.

[0201] Wherein, the second fixed observation angle is the observation angle with the largest first field of view performance parameter among the plurality of fixed observation angles.

[0202] For the second fixed observation angle and the first field of view performance parameter, reference may be made to the detailed description of the above embodiments, and the embodiments of the present disclosure will not repeat it here.

[0203] (2) Determine a plurality of candidate observation angles based on the second fixed observation angle.

[0204] Among them, multiple alternative observation perspectives are multiple adjacent observation perspectives obtained by rotating relative to the first fixed observation perspective in a preset direction.

[0205] In some examples, in the observation space, with the target observation point as the center, starting from the second fixed observation perspective, rotate clockwise and / or counterclockwise by a fixed angle, and select a preset number of fixed observation perspectives adjacent to the second fixed observation perspective from other fixed observation perspectives except the second fixed observation perspective as multiple alternative observation perspectives. Among them, the multiple alternative observation perspectives may include the second fixed observation perspective, or the multiple alternative observation perspectives do not include the second fixed observation perspective.

[0206] (3) Determine the third field of view performance parameter of each alternative observation perspective.

[0207] In some examples, the third field of view performance parameter is different from the first field of view performance parameter. For example, the third field of view performance parameter is the visible angle, and the first field of view performance parameter is the visibility; for another example, the third field of view performance parameter is the visibility, and the first field of view performance parameter is the visible angle. For the third field of view performance parameter, reference may be made to the detailed description in the above embodiments, and the embodiments of the present disclosure will not elaborate on this.

[0208] (4) Based on multiple third field of view performance parameters, determine the target observation perspective among multiple alternative observation perspectives.

[0209] Among them, the target observation perspective is the alternative observation perspective with the largest third field of view performance parameter among multiple alternative observation perspectives.

[0210] In some examples, when the third field of view performance parameter is the visible angle, the visible angles of each alternative observation perspective can be compared, and the alternative observation perspective with the largest third field of view performance parameter is selected from multiple alternative observation perspectives, and this alternative observation perspective is determined as the target observation perspective.

[0211] The ego-vehicle display method provided by the embodiments of the present disclosure calculates the first field of view performance parameter of each fixed observation perspective, selects the fixed observation perspective with the largest first field of view performance parameter as the second fixed observation perspective, determines multiple alternative observation perspectives based on the second fixed observation perspective, and then selects the alternative observation perspective with the largest third field of view performance parameter as the target observation perspective from multiple alternative observation perspectives based on the third field of view performance parameters of multiple alternative observation perspectives. When the candidate observation perspectives do not meet the conditions, since the alternative observation perspective with the largest visible angle and visibility can be selected as the target observation perspective, the ego-vehicle and obstacles can be observed from the optimal observation perspective subsequently, thereby being able to minimize the blind area risk between the ego-vehicle and obstacles to the greatest extent.

[0212] As Figure 13 shown, based on the above Figure 2 shown embodiment, step 204 may include the following steps:

[0213] Step 2041, when the host vehicle meets the collision warning condition, display the three-dimensional images of the host vehicle and the target obstacle corresponding to the target observation perspective.

[0214] In some examples, the above collision warning condition may include: the distance between the host vehicle and the target collision object is less than the safety distance threshold, and the speed of the host vehicle is less than or equal to the preset speed when the host vehicle is in the D gear or R gear. For example, the above safety distance threshold may be 0.9 meters, and the preset speed may be 10 km / h.

[0215] In some other embodiments, when the host vehicle meets the non-collision warning condition, hide the three-dimensional images of the host vehicle and the target obstacle corresponding to the target observation perspective.

[0216] For the host vehicle display method provided by the embodiments of the present disclosure, when the host vehicle meets the collision warning condition, since the three-dimensional images of the host vehicle and the target obstacle corresponding to the target observation perspective can be displayed, on the one hand, when there is a collision risk for the host vehicle, the relative position between the host vehicle and the target obstacle can be intuitively observed by the displayed three-dimensional images of the host vehicle and the target obstacle corresponding to the target observation perspective, so as to timely adjust the host vehicle to avoid the collision risk with the target obstacle. On the other hand, it avoids distracting the driver's attention due to the display of visual images when the host vehicle does not meet the collision warning condition, thus improving the driving safety of the host vehicle.

[0217] As Figure 14 shown, based on the above Figure 2 shown embodiment, before step 201, the host vehicle display method provided by the embodiments of the present disclosure may further include the following steps:

[0218] Step 205, obtain multiple images around the host vehicle at the current moment and the current environmental information.

[0219] In some embodiments, multiple images around the vehicle are collected in real time by multiple cameras located at different positions outside the host vehicle to obtain multiple images, and the multiple images are collected by the multiple cameras at the same moment. Since the multiple cameras are located at different positions outside the host vehicle, the perspectives of the multiple images are different.

[0220] Exemplarily, the above multiple cameras may include a front camera, a surround view camera or a side camera, etc.

[0221] In some examples, each of the above multiple images may include roads, buildings, special road signs, pedestrians or vehicles around the host vehicle, etc.

[0222] In some embodiments, the current environmental information around the host vehicle is obtained from the point cloud data collected in real time by ultrasonic sensors, lidar, millimeter-wave radars, etc. of the host vehicle. The current environmental information may include information such as the distances, speeds, orientations, and postures between the host vehicle and the obstacles around the host vehicle.

[0223] Step 206: Based on the multiple images and the current environmental information, determine the obstacle information around the host vehicle.

[0224] In some embodiments, the above-mentioned obstacle information may include the number of obstacles, types, and the distances between the orientations and the host vehicle.

[0225] In some examples, a deep learning model can be used to perform object detection on the multiple images to identify multiple objects such as roads, buildings, special road signs, pedestrians, and vehicles. Pedestrians and vehicles are selected from these multiple objects as obstacles, so that the number and types of obstacles can be determined; combined with the current environmental information, the distances, speeds, and directions of each obstacle relative to the host vehicle can be determined.

[0226] Step 207: In response to the obstacle information including at least one obstacle around the host vehicle and at least one distance between the at least one obstacle and the host vehicle, based on the at least one distance, determine the target obstacle among the at least one obstacle.

[0227] Wherein, the target obstacle is the obstacle with the smallest distance from the host vehicle among the at least one obstacle.

[0228] In some examples, all the distances among the at least one distance can be compared to determine the smallest distance, and then the obstacle corresponding to the smallest distance is determined as the target obstacle.

[0229] During the driving process of the host vehicle, as the driving direction and position of the host vehicle change, the at least one obstacle around the host vehicle and the distances between each obstacle and the host vehicle also change, so that the determined target obstacle with the closest distance to the host vehicle changes accordingly, that is, the target obstacle is updated in real time, thus realizing the real-time tracking of the host vehicle and the closest obstacle.

[0230] The host vehicle display method provided by the embodiments of the present disclosure can determine the obstacle information around the host vehicle based on the multiple images and the current environmental information obtained in real time, and in response to the obstacle information including at least one obstacle around the host vehicle and the distance between each obstacle and the host vehicle, select the target obstacle with the closest distance to the host vehicle from the at least one obstacle. Therefore, the target obstacle with the closest distance to the host vehicle can be determined in real time, so as to avoid the collision risk of the host vehicle by real-time tracking of the host vehicle and the closest obstacle.

[0231] Such as Figure 15As shown above, based on the Figure 2 above-described embodiment, step 202 may include the following steps:

[0232] Step 2021, determine the geometric position between the target collision point of the host vehicle and the target collision point of the target obstacle.

[0233] In some examples, a line segment can be connected between the target collision point of the host vehicle and the target collision point of the target obstacle, and then a position on this line segment is selected as the geometric position. For example, the midpoint position of the line segment or the 1 / 3 position of the line segment.

[0234] During the driving process of the host vehicle, the spatio-temporal relationship between the host vehicle and the target obstacle is updated in real time. Therefore, the target collision point of the host vehicle and the target collision point of the target obstacle will change, that is, the geometric position between the determined target collision point of the host vehicle and the target collision point of the target obstacle will also change accordingly.

[0235] Step 2022, determine the target observation point based on the geometric position.

[0236] In some examples, the geometric position can be directly determined as the target observation point, or, in combination with the speeds of the host vehicle and / or the target obstacle, the geometric position is adjusted, and the adjusted position is determined as the target observation point.

[0237] The host vehicle display method provided by the embodiments of the present disclosure can determine the target observation point based on the geometric position between the determined target collision point of the host vehicle and the target collision point of the target obstacle. Therefore, through the target observation point, the possible potential collision points between the host vehicle and the target obstacle can be known in advance, so as to facilitate subsequent adjustment of the observation angle based on the target observation point to focus on the perception blind area of the host vehicle.

[0238] Exemplary Device

[0239] Figure 16 It is a schematic structural diagram of a host vehicle display device provided by an exemplary embodiment of the present disclosure. This host vehicle display device can be set in electronic devices such as terminal devices and servers, or on objects such as vehicles, and execute the host vehicle display method of any of the above embodiments of the present disclosure.

[0240] As Figure 16 shown, the device 300 may include:

[0241] The first determination module 301, which can be used to determine the respective target collision points of the host vehicle and the target obstacle;

[0242] The first determination module 302, which can be used to determine the target observation point based on each of the target collision points;

[0243] The third determination module 303 can be used to determine a target observation angle based on the target observation point;

[0244] The image display module 304 can be used to display a three-dimensional image including the host vehicle and the target obstacle based on the target observation angle.

[0245] In a possible implementation, the first determination module can specifically be used to determine a first collision risk point of a first obstacle at the current moment and a first host vehicle collision risk point of the host vehicle; wherein, the first obstacle is the obstacle with the smallest distance from the host vehicle among multiple obstacles at the current moment; determine a second host vehicle collision risk point of the host vehicle at a first moment and a second collision risk point of a corresponding second obstacle; wherein, the first moment is the previous moment adjacent to the current moment; the second obstacle is the obstacle with the smallest distance from the host vehicle among multiple obstacles at the first moment; based on the first collision risk point, the first host vehicle collision risk point and / or the second collision risk point and the second host vehicle collision risk point, determine the respective target collision points of the host vehicle and the target obstacle.

[0246] In a possible implementation, the first determination module can specifically be used to, in response to the first obstacle being the same as the second obstacle, determine a first distance and a second distance; wherein, the first distance is the distance between the second host vehicle collision risk point and the second obstacle at the previous moment; the second distance is the distance between the first host vehicle collision risk point and the first obstacle at the current moment;

[0247] Determine a first deviation value based on the first distance;

[0248] In response to the first distance, the second distance and the first deviation value satisfying a first preset condition, determine the target collision point of the target obstacle based on the first collision risk point and determine the target collision point of the host vehicle based on the first host vehicle collision risk point; or,

[0249] In response to the first distance, the second distance and the first deviation value not satisfying the first preset condition, determine the target collision point of the target obstacle based on the second collision risk point and determine the target collision point of the host vehicle based on the second host vehicle collision risk point.

[0250] In a possible implementation, the first determination module may specifically be configured to, in response to the first obstacle being different from the second obstacle, determine a second distance and a third distance; wherein, the second distance is the distance between the host vehicle and the first obstacle at the current moment, and the third distance is the distance between the host vehicle and the second obstacle at the current moment;

[0251] Determine a second deviation value based on the third distance;

[0252] In response to the second distance, the third distance, and the second deviation value satisfying a second preset condition, determine the target collision point of the target obstacle based on the first collision danger point, and determine the target collision point of the host vehicle based on the first host vehicle collision danger point; or,

[0253] In response to the second distance, the third distance, and the second deviation value not satisfying the second preset condition, determine the respective target collision points of the second obstacle and the host vehicle; wherein, the second obstacle is the target obstacle.

[0254] In a possible implementation, the first determination module may specifically be configured to determine a fourth distance and a fifth distance; wherein, the fourth distance is the distance between the second host vehicle collision danger point and the second obstacle at the previous moment; the fifth distance is the distance between the host vehicle and the second obstacle at the current moment;

[0255] Determine a third deviation value based on the fourth distance;

[0256] In response to the fourth distance, the fifth distance, and the third deviation value satisfying a third preset condition, determine the third host vehicle collision danger point of the host vehicle and the third collision danger point of the second obstacle at the current moment;

[0257] Determine the target collision point of the target obstacle based on the third collision danger point, and determine the target collision point of the host vehicle based on the third host vehicle collision danger point.

[0258] In a possible implementation, the first determination module may specifically be configured to, in response to the fourth distance, the fifth distance, and the third deviation value not satisfying the third preset condition, determine the fourth collision danger point of the second obstacle corresponding to the second host vehicle collision danger point at the current moment;

[0259] Determine the target collision point of the target obstacle based on the fourth collision danger point, and determine the target collision point of the host vehicle based on the second host vehicle collision danger point.

[0260] In a possible implementation, the third determination module may be specifically configured to create an observation space based on the target observation point and the yaw angle direction of the host vehicle; determine multiple fixed observation perspectives and candidate observation perspectives in the observation space; and determine the target observation perspective based on the multiple fixed observation perspectives and / or the candidate observation perspectives.

[0261] In a possible implementation, the third determination module may be specifically configured to determine a first field-of-view performance parameter for each fixed observation perspective; determine a first fixed observation perspective based on the first field-of-view performance parameter of each fixed observation perspective, where the first fixed observation perspective is the fixed observation perspective with the largest first field-of-view performance parameter among the multiple fixed observation perspectives; determine multiple candidate observation perspectives based on the first fixed observation perspective, where the multiple candidate observation perspectives are adjacent observation perspectives obtained by rotating in a preset direction relative to the first fixed observation perspective; determine a historical observation perspective at a second moment, where the historical observation perspective is used to indicate the observation perspective for observing the previous target observation point at the second moment, and the second moment is the previous moment adjacent to the current moment; and determine the candidate observation perspective based on the historical observation perspective and the target control quantity, where the target control quantity is the increment between the historical observation perspective and the target candidate observation perspective, and the target candidate observation perspective is the candidate observation perspective with the largest first field-of-view performance parameter among the multiple candidate observation perspectives.

[0262] In a possible implementation, the third determination module may be specifically configured to determine a first field-of-view performance parameter for the multiple fixed observation perspectives and a second field-of-view performance parameter for the candidate observation perspective.

[0263] In response to the second field-of-view performance parameter being greater than or equal to a preset threshold, determine the target observation perspective based on the candidate observation perspective; or,

[0264] In response to the second field-of-view performance parameter being less than the preset threshold, determine the target observation perspective based on the multiple fixed observation perspectives.

[0265] In a possible implementation, the third determination module may be specifically configured to determine a second fixed observation perspective based on the first field of view performance parameters of each fixed observation perspective; wherein, the second fixed observation perspective is the observation perspective with the largest first field of view performance parameter among the multiple fixed observation perspectives; determine multiple alternative observation perspectives based on the second fixed observation perspective; wherein, the multiple alternative observation perspectives are multiple adjacent observation perspectives obtained by rotating in a preset direction relative to the first fixed observation perspective; determine the third field of view performance parameter of each alternative observation perspective; and determine the target observation perspective among the multiple alternative observation perspectives based on the multiple third field of view performance parameters, where the target observation perspective is the alternative observation perspective with the largest third field of view performance parameter among the multiple alternative observation perspectives.

[0266] In a possible implementation, the third determination module may be specifically configured to determine a second fixed observation perspective based on the first field of view performance parameters of each fixed observation perspective; wherein, the second fixed observation perspective has the largest first field of view performance parameter among the multiple fixed observation perspectives; and determine the target observation perspective based on the second fixed observation perspective.

[0267] In a possible implementation, the image display module may be configured to display a three-dimensional image of the host vehicle and the target obstacle corresponding to the target observation perspective when the host vehicle meets the collision warning condition.

[0268] In a possible implementation, the above device may further include:

[0269] The first acquisition module may be configured to acquire multiple images around the host vehicle and the current environment information at the current moment;

[0270] The fourth determination module may be configured to determine the obstacle information around the host vehicle based on the multiple images and the current environment information;

[0271] The fifth determination module may be configured to, in response to the obstacle information including at least one obstacle around the host vehicle and at least one distance between the at least one obstacle and the host vehicle, determine the target obstacle among the at least one obstacle based on the at least one distance; wherein, the target obstacle is the obstacle with the smallest distance from the host vehicle among the at least one obstacle.

[0272] In a possible implementation, the second determination module may be specifically configured to determine the geometric position between the target collision point of the host vehicle and the target collision point of the target obstacle; and determine the target observation point based on the geometric position.

[0273] For the beneficial technical effects corresponding to the exemplary embodiments of the present device, reference may be made to the corresponding beneficial technical effects in the above-mentioned exemplary method section, which will not be elaborated here.

[0274] Exemplary electronic device

[0275] Figure 17 The figure shows a structural diagram of an electronic device provided by an embodiment of the present disclosure, including at least one processor 111 and a memory 112.

[0276] The processor 111 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 11 to perform desired functions.

[0277] The memory 112 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 111 may run one or more computer program instructions to implement the self-vehicle display method of various embodiments of the present disclosure above and / or other desired functions.

[0278] In one example, the electronic device 11 may further include: an input device 113 and an output device 114, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0279] The input device 113 may include various sensors, including but not limited to: a distance sensor for detecting the distance between the target object and the vehicle; an image sensor for collecting information about the vehicle's surrounding environment. In some examples, the input device may also include a pressure sensor for detecting seat pressure, determining whether there is a passenger, and the position of the passenger; a temperature sensor for monitoring the temperature in the cabin; a humidity sensor for monitoring the humidity in the cabin to assist in adjusting the interior environment; an air quality sensor for monitoring the air quality in the vehicle, such as carbon dioxide, volatile organic compounds (VOCs), etc.; a light sensor for detecting the light intensity inside and outside the vehicle; an acceleration sensor for detecting changes in the acceleration of the vehicle; a distance sensor for detecting the distance between the vehicle and other objects; a touch screen sensor for interaction with the vehicle's infotainment system; a biometric sensor, such as fingerprint recognition, facial recognition, etc.; a heart rate monitor for monitoring the driver's heart rate; a sound sensor for voice recognition and interaction to achieve voice control functions; a seat sensor for monitoring the use of the seat, such as whether the seat is occupied and the body shape of the passenger; a wireless communication sensor, such as Bluetooth, Wi-Fi, etc., for connecting with smart devices to achieve data transmission and remote control. In addition to the examples given above, the input device may also include more or fewer sensors, which will not be described in detail here.

[0280] The output device 114 can output various information or signals to other hardware or devices, which may include displays, vehicle audio, seats, windows, steering wheels, etc., as well as communication networks and remote output devices connected thereto, etc. The displays may include a plurality of different display screens such as a driver's display screen, a co-driver's display screen, and a rear display screen, and the vehicle audio may include a plurality of speakers arranged at different positions in the vehicle cabin, and different display screens or speakers may work independently.

[0281] Of course, to simplify, Figure 17 Only some of the components related to the present disclosure in the electronic device 11 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 11 may also include any other appropriate components.

[0282] Exemplary Computer Program Product and Computer Readable Storage Medium

[0283] In addition to the above-mentioned methods and devices, embodiments of the present disclosure may also provide a computer program product, including computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the self-vehicle display method of various embodiments of the present disclosure described in the above-mentioned "Exemplary Method" section.

[0284] A computer program product may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present disclosure. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The programming code may be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0285] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are run by a processor, the processor is caused to execute the steps in the ego vehicle display method of various embodiments of the present disclosure described in the above "Exemplary Method" section.

[0286] The computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium, for example but not limited to, includes systems, devices or components of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0287] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, the advantages, benefits, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that they are essential for each embodiment of the present disclosure. In addition, the above-disclosed specific details are only for the purposes of illustration and easy understanding, rather than limitations, and the above details do not limit the present disclosure to necessarily adopt the above specific details for implementation.

[0288] Those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present disclosure and their equivalent technologies, the present disclosure also intends to include these modifications and variations therein.

Claims

1. A self-vehicle display method, comprising: Determine the respective target collision points of the ego vehicle and the target obstacle; Determining a target observation point based on each of the target collision points; Determining a target observation angle based on the target observation point; A three-dimensional image including the vehicle and the target obstacle is displayed based on the target observation angle.

2. The method according to claim 1, wherein: Determining respective target collision points of the vehicle and the target obstacle includes: Determine a first collision risk point of a first obstacle at a current moment and a first collision risk point of the vehicle; wherein the first obstacle is an obstacle with the shortest distance from the vehicle among a plurality of obstacles at a current moment; Determine a second collision risk point of the ego vehicle at a first moment and a second collision risk point of a corresponding second obstacle; wherein the first moment is a previous moment adjacent to the current moment; and the second obstacle is an obstacle with the smallest distance from the ego vehicle among a plurality of obstacles at the first moment; The target collision points of the own vehicle and the target obstacle are determined based on the first collision risk point and the first own vehicle collision risk point and / or the second collision risk point and the second own vehicle collision risk point.

3. The method according to claim 2, wherein: The determining the respective target collision points of the ego vehicle and the target obstacle based on the first collision risk point and the first ego vehicle collision risk point and / or the second collision risk point and the second ego vehicle collision risk point includes: In response to the first obstacle being the same as the second obstacle, determining a first distance and a second distance; wherein the first distance is the distance between the second ego-vehicle collision danger point and the second obstacle at the previous moment; and the second distance is the distance between the first ego-vehicle collision danger point and the first obstacle at the current moment; determining a first deviation value based on the first distance; In response to the first distance, the second distance, and the first deviation value satisfying a first preset condition, determining the target collision point of the target obstacle based on the first collision risk point, and determining the target collision point of the ego vehicle based on the first ego vehicle collision risk point; or, In response to the first distance, the second distance and the first deviation value not satisfying a first preset condition, the target collision point of the target obstacle is determined based on the second collision risk point, and the target collision point of the ego vehicle is determined based on the second ego vehicle collision risk point.

4. The method according to claim 2, wherein: The determining the respective target collision points of the ego vehicle and the target obstacle based on the first collision risk point and the first ego vehicle collision risk point and / or the second collision risk point and the second ego vehicle collision risk point includes: In response to the first obstacle being different from the second obstacle, determining a second distance and a third distance; wherein the second distance is the distance between the vehicle and the first obstacle at the current moment, and the third distance is the distance between the vehicle and the second obstacle at the current moment; determining a second deviation value based on the third distance; In response to the second distance, the third distance, and the second deviation value satisfying a second preset condition, determining the target collision point of the target obstacle based on the first collision risk point, and determining the target collision point of the ego vehicle based on the first ego vehicle collision risk point; or, In response to the second distance, the third distance and the second deviation value not satisfying a second preset condition, the respective target collision points are determined based on the second obstacle and the ego vehicle; wherein the second obstacle is the target obstacle.

5. The method according to claim 4, wherein: The determining the respective target collision points based on the second obstacle and the ego vehicle includes: Determine a fourth distance and a fifth distance; wherein the fourth distance is the distance between the second ego vehicle collision risk point and the second obstacle at the previous moment; and the fifth distance is the distance between the ego vehicle and the second obstacle at the current moment; determining a third deviation value based on the fourth distance; In response to the fourth distance, the fifth distance, and the third deviation value satisfying a third preset condition, determining a third collision hazard point of the ego vehicle and a third collision hazard point of the second obstacle at the current moment; The target collision point of the target obstacle is determined based on the third collision risk point, and the target collision point of the own vehicle is determined based on the third own vehicle collision risk point.

6. The method according to claim 5, wherein: The method further comprises: In response to the fourth distance, the fifth distance and the third deviation value not satisfying a third preset condition, determining a fourth collision hazard point of the second obstacle corresponding to the collision hazard point of the second ego-vehicle at the current moment; The target collision point of the target obstacle is determined based on the fourth collision risk point, and the target collision point of the own vehicle is determined based on the second own vehicle collision risk point.

7. The method according to claim 1, wherein: The determining the target observation angle based on the target observation point includes: Creating an observation space based on the target observation point and the yaw angle direction of the ego vehicle; Determining a plurality of fixed observation angles and selected observation angles in the observation space; The target observation angle is determined based on a plurality of fixed observation angles and / or selected observation angles.

8. The method according to claim 7, wherein: The determining of a plurality of fixed observation angles and a plurality of observation angles to be selected in the observation space comprises: Determining first field of view performance parameters for each fixed observation angle; Determine a first fixed observation viewing angle based on the first viewing angle performance parameter of each fixed observation viewing angle; wherein the first fixed observation viewing angle is a fixed observation viewing angle having the largest first viewing angle performance parameter among the multiple fixed observation viewing angles; Based on the second fixed observation angle, a plurality of candidate observation angles are determined; wherein the plurality of candidate observation angles are a plurality of adjacent observation angles obtained by rotating in a preset direction relative to the first fixed observation angle; Determine a historical observation angle at a second moment; wherein the historical observation angle is used to indicate the observation angle of the previous target observation point at the second moment; the second moment is the previous moment adjacent to the current moment; The selected observation angle is determined based on the historical observation angle and the target control amount; wherein the target control amount is the increment between the historical observation angle and the target alternative observation angle, and the target alternative observation angle is the alternative observation angle with the largest first field of view performance parameter among multiple alternative observation angles.

9. The method according to claim 7, wherein: The determining the target observation angle based on multiple fixed observation angles and / or selected observation angles includes: Determining a first field of view performance parameter of a plurality of the fixed observation angles and a second field of view performance parameter of the selected observation angles; In response to the second field of view performance parameter being greater than or equal to a preset threshold, determining the target observation angle based on the candidate observation angle; or In response to the second field of view performance parameter being less than a preset threshold, the target observation viewing angle is determined based on a plurality of fixed observation viewing angles.

10. The method according to claim 9, wherein: The determining the target observation angle based on a plurality of fixed observation angles includes: Determine a second fixed observation viewing angle based on the first viewing angle performance parameter of each fixed observation viewing angle; wherein the second fixed observation viewing angle is an observation viewing angle having the largest first viewing angle performance parameter among the plurality of fixed observation viewing angles; Based on the second fixed observation angle, a plurality of candidate observation angles are determined; wherein the plurality of candidate observation angles are a plurality of adjacent observation angles obtained by rotating in a preset direction relative to the first fixed observation angle; Determining third field of view performance parameters for each alternative observation viewing angle; Based on the multiple third field of view performance parameters, the target observation angle is determined from the multiple candidate observation angles, and the target observation angle is the candidate observation angle with the largest third field of view performance parameter from the multiple candidate observation angles.

11. The method according to claim 7, wherein: The determining the target observation angle based on multiple fixed observation angles and / or selected observation angles includes: Based on the first field of view performance parameter of each fixed observation angle, determining a second fixed observation angle; wherein the second fixed observation angle is an observation angle having the largest first field of view performance parameter among the plurality of fixed observation angles; The target observation angle is determined based on a second fixed observation angle.

12. The method according to claim 1, wherein: The displaying of a three-dimensional image including the vehicle and the target obstacle based on the target observation angle includes: When the ego vehicle meets the collision warning condition, a three-dimensional image of the ego vehicle and the target obstacle corresponding to the target observation angle is displayed.

13. The method according to claim 1, wherein: Before determining the respective target collision points of the vehicle and the target obstacle, the method further includes: Acquire multiple images and current environment information around the vehicle at the current moment; Determining obstacle information around the vehicle based on the multiple images and the current environment information; In response to the obstacle information including at least one obstacle around the ego vehicle and at least one distance between the at least one obstacle and the ego vehicle, the target obstacle among the at least one obstacle is determined based on the at least one distance; wherein the target obstacle is the obstacle among the at least one obstacle having the shortest distance from the ego vehicle.

14. The method according to claim 1, wherein: The determining of the target observation point based on each of the target collision points comprises: Determining a geometric position between the target collision point of the ego vehicle and the target collision point of the target obstacle; Based on the geometric position, the target observation point is determined.

15. A vehicle display device, comprising: A first determination module is used to determine respective target collision points of the ego vehicle and the target obstacle; A first determination module, configured to determine a target observation point based on each of the target collision points; A third determination module is used to determine a target observation angle based on the target observation point; An image display module is used to display a three-dimensional image including the ego vehicle and the target obstacle based on the target observation angle.

16. A computer-readable storage medium storing a computer program, wherein the computer program is used to execute the vehicle display method according to any one of claims 1 to 14.

17. An electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the vehicle display method described in any one of claims 1-14 above.

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