Vehicle collision detection method, system, and vehicle

Abstract

This application provides a vehicle collision detection method, system and vehicle. The vehicle collision detection method includes the steps of dividing an acquired set of obstacle information into a first obstacle map and a second obstacle map based on a preset height of an obstacle, then modeling a target vehicle with the first obstacle map and the second obstacle map, and fusing the modeling result with the first obstacle map and the second obstacle map to obtain a first collision detection model and a second collision detection model. Further, obtaining a distance relationship between an obstacle corresponding to the set of obstacle information at a preset position of the target vehicle by the first collision detection model and / or the second collision detection model, and determining whether the target vehicle will collide based on the distance relationship. This application can perform real-time and accurate collision risk analysis for different types of obstacle heights, improve the accuracy and efficiency of collision detection, and further ensure the driving safety of the vehicle and the personal safety of the people in the vehicle.

Description

Technical Field

[0001] This application relates to the technical field of intelligent driving, and particularly to a vehicle collision detection method, system and vehicle.

Background Art

[0002] With the rapid development of intelligent driving, automobiles equipped with autonomous driving functions are becoming increasingly popular. While the autonomous driving function brings convenience to people, at the same time, there are some problems with the user experience, such as damaging the rearview mirror, running onto the curb, or occurring a collision accident. Therefore, some users do not want to use the autonomous driving function, which is disadvantageous to the development of intelligent driving.

[0003] In the prior art, based on the data of the sensor, the distance between the target vehicle and the obstacle can be obtained, and further, collision detection analysis and early warning can be performed. However, due to the inaccuracy and delay in the data transmitted from the sensor, collision detection cannot be performed promptly. Moreover, the sensor cannot perform corresponding collision detection for different vehicles based on the height of the obstacle. Therefore, the target vehicle may collide with the obstacle during driving, and in serious cases, the passengers may be killed or injured.

Summary of the Invention

Problems to be Solved by the Invention

[0004] In this application, since the data transmitted from the sensor has inaccuracy and delay, collision detection cannot be performed promptly, and the sensor cannot perform corresponding collision detection for different vehicles based on the height of the obstacle. Therefore, in order to solve the technical problem of the prior art that the target vehicle may collide with the obstacle during driving, a method and a system capable of performing vehicle collision detection based on the height of the obstacle are provided.

Means for Solving the Problems

[0005] Specifically, this application provides a vehicle collision detection method including the following steps. S100, which obtains an obstacle information set within a preset range of a target vehicle and divides the obstacle information set into a first obstacle map and a second obstacle map based on a preset height of an obstacle.

[0006] S200, which models the target vehicle using the first obstacle map and the second obstacle map, and fuses the vehicle model with the corresponding obstacle map to obtain a first collision detection model and a second collision detection model respectively.

[0007] S300, which obtains the distance relationship between the target vehicle and an obstacle corresponding to an obstacle information set at a preset position of the target vehicle using the first collision detection model and / or the second collision detection model, and determines whether the target vehicle will collide based on the distance relationship.

[0008] In the above technical solution, the obstacle map is divided based on the height of the obstacle, and further, a corresponding collision detection model is established. The distance relationship between the target vehicle and the obstacle is determined using the collision detection model, and further, the collision risk is obtained. Real-time and accurate collision risk analysis can be performed for different types of obstacle heights, improving the accuracy and efficiency of collision detection, and further ensuring the driving safety of the vehicle and the personal safety of the people in the vehicle.

[0009] Furthermore, in step S100, dividing the obstacle information set into a first obstacle map and a second obstacle map based on a preset height of an obstacle includes the following. Calibrate the maximum lengths of the front wheels and rear wheels of the target vehicle respectively, and set the preset height of the obstacle based on the calibration result.

[0010] Determine whether the height of the obstacle corresponding to the obstacle information set is greater than the preset height of the obstacle. If it is greater, put the obstacle into the first obstacle map; otherwise, put the obstacle into the second obstacle map.

[0011] In the above technical solution, based on the lengths of the front wheels and rear wheels of the vehicle, the preset height of the obstacle is calibrated, and obstacles with different heights are placed in different maps, so that the collision detection analysis becomes more directional, and the accuracy and efficiency of collision detection are further improved.

[0012] Furthermore, step S200 includes the following. By modeling the target vehicle, a first vehicle model including the front overhang and rear overhang of the vehicle, and a second vehicle model obtained by removing the front overhang and rear overhang of the vehicle are obtained.

[0013] The first vehicle model and the second vehicle model are respectively fused with the first obstacle map and the second obstacle map to obtain a first collision detection model and a second collision detection model.

[0014] After obtaining the vehicle model and fusing the vehicle model with the corresponding obstacle map, a corresponding collision detection model for use in subsequent collision detection analysis is obtained.

[0015] In the above technical solution, by calibrating based on the maximum lengths of the front wheels and rear wheels of the target vehicle, the model corresponding to each vehicle and the collision detection model are corresponding and unique, so that the collision detection is more individualized and intelligent.

[0016] Furthermore, before executing step S300, It includes the step of establishing a first rectangular coordinate system with the geometric center of the target vehicle as the first origin, the width direction of the vehicle as the x-axis, and the length direction as the y-axis, and also establishing a second rectangular coordinate system with the midpoint of the rear axle of the target vehicle as the second origin, the width direction of the vehicle as the X-axis, and the length direction as the Y-axis.

[0017] In the above technical solution, establishing the first rectangular coordinate system and the second rectangular coordinate system can not only ensure that the position of the obstacle can be digitized, but also make it easier to determine the first preset circular area and the second preset circular area later.

[0018] Furthermore, step S300 includes the following. S301, in the first collision detection model, obtain the initial obstacle coordinates of the obstacle in the first orthogonal coordinate system.

[0019] S302, based on the initial obstacle coordinates and the coordinates of the first origin, calculate the distance of each obstacle to the first origin.

[0020] If the distance of at least one obstacle to the first origin is less than the first preset distance, execute step S303; otherwise, execute step S305.

[0021] In the above technical solution, first, obstacles with a collision risk are screened, and after narrowing down the scope, the collision risk is further detected.

[0022] S303, obtain the transformed obstacle coordinates of the obstacles within the first preset circular region in the second orthogonal coordinate system, and proceed to step S304.

[0023] In the above technical solution, the transformed obstacle coordinates in the second orthogonal coordinate system are used to mitigate the influence of the vehicle's yaw angle on the obstacle coordinates, thereby making the acquisition result of the distance between the obstacle and the target vehicle more accurate and further improving the accuracy of collision detection.

[0024] S304, based on the transformed obstacle coordinates and the coordinates of the second origin, calculate the distance of each obstacle within the first preset circular region to the second origin.

[0025] If the distance of at least one obstacle within the first preset circular region to the second origin is less than the second preset distance, it is determined that the target vehicle will collide; otherwise, execute step S305.

[0026] Note that the second preset distance is generally preferably half of the vehicle width, or the length of the rear overhang of the vehicle, or the distance to the front overhang of the rear axle of the vehicle. By doing this to further limit the obstacles that cause collisions, it is determined whether there is a collision risk for the target vehicle.

[0027] S305, it is determined that there is no collision risk for the target vehicle.

[0028] The first preset circular region has the first origin as the center and the first preset distance as the radius.

[0029] Furthermore, step S300 further includes the following. S311, in the second collision detection model, the initial obstacle coordinates in the first orthogonal coordinate system of the obstacle are obtained.

[0030] S312, based on the initial obstacle coordinates and the coordinates of the first origin, the distance from each obstacle to the first origin is calculated.

[0031] If the distance from at least one obstacle to the first origin is less than the third preset distance, step S313 is executed; otherwise, step S315 is executed.

[0032] S313, the converted obstacle coordinates in the second orthogonal coordinate system of the obstacle within the second preset circular region are obtained, and the process proceeds to step S314.

[0033] S314, based on the converted obstacle coordinates and the coordinates of the second origin, the distance from each obstacle within the second preset circular region to the second origin is calculated.

[0034] If the distance from at least one obstacle within the second preset circular region to the second origin is less than the second preset distance, it is determined that the target vehicle will collide; otherwise, step S315 is executed.

[0035] At S315, it is determined that there is no collision risk for the target vehicle.

[0036] The second preset circular area has the first origin as the center and the third preset distance as the radius.

[0037] Furthermore, step S303 or step S313 specifically includes the following. Obtain the yaw angle of the vehicle, the corresponding initial obstacle coordinates of the obstacle within the first preset circular area or the second preset circular area in the first orthogonal coordinate system, and the midpoint coordinates of the rear axle of the vehicle in the first orthogonal coordinate system.

[0038] Based on the initial obstacle coordinates and the midpoint coordinates of the rear axle of the vehicle, calculate a first included angle and the distance from the obstacle to the midpoint of the rear axle of the vehicle. The first included angle is the included angle between the line connecting the obstacle and the midpoint of the rear axle of the vehicle and the perpendicular line segment to the X-axis of the second orthogonal coordinate system of the obstacle.

[0039] Calculate a second included angle based on the first included angle and the yaw angle of the vehicle.

[0040] Calculate the transformed obstacle coordinates of the obstacle based on the distance from the obstacle to the midpoint of the rear axle of the vehicle and the second included angle.

[0041] In the above technical solution, the transformed obstacle coordinates avoid the influence of the vehicle's yaw angle on it, which is helpful for further improving the accuracy of subsequent collision detection.

[0042] Furthermore, when it is determined that the target vehicle will collide, step S304 or S314 further includes a step of issuing early collision warning information.

[0043] In the above technical solution, the early collision warning information is issued to prompt the driver to take emergency measures promptly, which can reduce the possibility of an accident, thereby improving driving safety and protecting the safety of the vehicle and its passengers.

[0044] This application also provides a vehicle collision detection system based on the same concept, and the system includes the following. A first acquisition module, which is used to acquire a set of obstacle information within a preset range of the target vehicle.

[0045] A segmentation module, which is used to segment the set of obstacle information into a first obstacle map and a second obstacle map based on a preset height of the obstacle.

[0046] A second acquisition module, which is used to model the target vehicle based on the first obstacle map and the second obstacle map, and fuse the vehicle model with the corresponding obstacle map to obtain a first collision detection model and a second collision detection model.

[0047] A judgment module, which is used to obtain the distance relationship between the target vehicle and the obstacles corresponding to the set of obstacle information at a preset position of the target vehicle according to the first collision detection model and / or the second collision detection model, and judge whether the target vehicle will collide based on the distance relationship.

[0048] Furthermore, the system further includes the following. An establishment module, which is used to establish a first rectangular coordinate system and a second rectangular coordinate system. The first rectangular coordinate system takes the geometric center of the target vehicle as the first origin, the width direction of the vehicle as the x-axis, and the length direction as the y-axis. The second rectangular coordinate system takes the midpoint of the rear axle of the target vehicle as the second origin, the width direction of the vehicle as the X-axis, and the length direction as the Y-axis.

[0049] An early warning module, which is used to issue early collision warning information when it is determined that the target vehicle will collide.

[0050] Furthermore, the splitting module includes the following. A calibration unit, which is used to calibrate the maximum lengths of the front and rear wheels of the target vehicle, respectively.

[0051] A setting unit, which is used to set the height of a preset obstacle based on the calibration result of the calibration unit.

[0052] A splitting unit, which is used to determine whether the height of an obstacle corresponding to an obstacle information set is greater than the preset height of the obstacle. If it is greater, the obstacle is put into the first obstacle map; if it is smaller, the obstacle is put into the second obstacle map.

[0053] Furthermore, the second acquisition module includes the following. A modeling unit, which is used to obtain a first vehicle model and a second vehicle model.

[0054] A fusion unit, which is used to fuse the first vehicle model and the second vehicle model with the first obstacle map and the second obstacle map, respectively, to obtain a first collision detection model and a second collision detection model.

[0055] Furthermore, the determination module includes the following. An acquisition unit, which is used to acquire the initial obstacle coordinates in the first orthogonal coordinate system of an obstacle in the first collision detection model or the second collision detection model.

[0056] A first calculation unit, which is used to calculate the distance from each obstacle to the first origin based on the initial obstacle coordinates and the coordinates of the first origin, and to acquire the converted obstacle coordinates in the second orthogonal coordinate system of the obstacles within a second preset circular region when the distance is smaller than a first preset distance or a third preset distance.

[0057] A second calculation unit, which is configured to calculate the distance from each obstacle within the second preset circular area to the second origin based on the converted obstacle coordinates and the coordinates of the second origin, and is used to determine that the target vehicle will collide when the distance is less than a second preset distance.

[0058] The above system can perform accurate collision risk analysis for different types of obstacle heights, improve the accuracy of collision detection, and further ensure the driving safety of the vehicle and the personal safety of the people in the vehicle.

[0059] This application also provides a vehicle in which a vehicle collision detection system is arranged based on the same concept. The system detects whether the target vehicle will collide by using the vehicle collision detection method.

Advantages of the Invention

[0060] Compared with the prior art, the beneficial effects of this application are as follows. This application divides the obstacle information set into a first obstacle map and a second obstacle map based on the height of the obstacle, further establishes corresponding first and second collision detection models, determines the distance relationship between the target vehicle and the obstacle by the first collision detection model and / or the second collision detection model, and further obtains the risk of the target vehicle colliding with the obstacle. This application can perform accurate collision risk analysis for different types of obstacle heights, improve the accuracy of collision detection, and further ensure the driving safety of the vehicle and the personal safety of the people in the vehicle.

Brief Description of the Drawings

[0061]

Figure 1

Figure 2

Figure 3

Figure 4

Embodiment for Carrying Out the Invention

[0062] The present application provides a vehicle collision detection method, a system, and a vehicle, thereby solving the technical problem of the prior art that collision detection using a sensor to detect the distance between a vehicle and an obstacle is inaccurate and inefficient, and moreover, corresponding collision detection cannot be performed on different vehicles based on the height of the obstacle.

[0063] The present application provides a vehicle collision detection method, a system, and a vehicle, which are generally as follows. Obtain an obstacle information set within a preset range of the target vehicle, determine whether the height of the obstacle corresponding to the obstacle information set is greater than the preset height of the obstacle, if it is greater, put the obstacle into the first obstacle map, if not, put the obstacle into the second obstacle map, then model and fuse the target vehicle using the first obstacle map and the second obstacle map to obtain a first collision detection model and a second collision detection model respectively, and further obtain the distance relationship between the obstacle corresponding to the obstacle information set of the geometric center of the target vehicle by the first collision detection model and / or the second collision detection model. If the distance to the geometric center of at least one obstacle is less than a first preset distance, obtain the distance relationship between the obstacle and the midpoint of the rear axle of the target vehicle. If the distance to the midpoint of the rear axle of the target vehicle of at least one obstacle is less than a second preset distance, it is determined that the target vehicle will collide.

[0064] Hereinafter, based on specific examples and drawings, the vehicle collision detection method, system, and vehicle of the present application will be described in more detail.

[0065] (Example 1) Referring to FIG. 1, the present application provides a vehicle collision detection method including the following steps. S100, which acquires an obstacle information set within a preset range of the target vehicle and divides the obstacle information set into a first obstacle map and a second obstacle map based on a preset height of an obstacle.

[0066] Furthermore, in step S100, dividing the obstacle information set into a first obstacle map and a second obstacle map based on a preset height of an obstacle includes the following. Respectively, calibrate the maximum lengths of the front wheels and rear wheels of the target vehicle, and set the preset height of the obstacle based on the calibration result.

[0067] Determine whether the height of the obstacle corresponding to the obstacle information set is greater than the preset height of the obstacle. If it is greater, put the obstacle into the first obstacle map; otherwise, put the obstacle into the second obstacle map.

[0068] Since the height of the chassis of the vehicle is different from the height of the vehicle body, the preset height of the obstacle is calibrated based on the actual vehicle, and the value of the preset height of the obstacle is not specifically limited here.

[0069] It should be noted that it may be recognized that the first obstacle map is a map that the vehicle body should not intrude into, and the second obstacle map is a map that the vehicle body may intrude into. Here, "the vehicle body may intrude into" means that due to the existence of a limiter or a low curb, the front overhang or rear overhang of the vehicle may intrude.

[0070] After completing the division of the first obstacle map and the second obstacle map, step S200 can be executed.

[0071] S200, which models the target vehicle using the first obstacle map and the second obstacle map, and fuses the vehicle model with the corresponding obstacle map to obtain a first collision detection model and a second collision detection model respectively.

[0072] Furthermore, step S200 includes: obtaining a first vehicle model including the front overhang and rear overhang of the vehicle and a second vehicle model obtained by removing the front overhang and rear overhang of the vehicle by modeling the target vehicle.

[0073] Note that the vehicle model is not a three-dimensional model but a two-dimensional plane model regarding the contour around the vehicle. The first vehicle model is the contour around the whole vehicle including the front overhang and rear overhang of the vehicle, which is a rounded rectangle, and the front and rear wheels of the vehicle are respectively represented by four rectangles correspondingly. The second vehicle model is a contour diagram of a rectangle obtained by removing the front overhang and rear overhang of the vehicle, and the corners of the contour diagram are the positions where the wheels are located.

[0074] Fusing the first vehicle model and the second vehicle model with the first obstacle map and the second obstacle map respectively to obtain a first collision detection model and a second collision detection model.

[0075] The first collision detection model or the second collision detection model can represent the position of the obstacle in the obstacle information set with respect to the corresponding vehicle model, provided that the obstacle is displayed in the form of data points.

[0076] After completing the acquisition of the first collision detection model and the second collision detection model, step S300 can be executed.

[0077] S300: obtaining the distance relationship between the target vehicle and the obstacle corresponding to the obstacle information set at a preset position of the target vehicle by the first collision detection model and / or the second collision detection model, and determining whether the target vehicle will collide based on the distance relationship.

[0078] Before executing step S300, Establish a first orthogonal coordinate system with the geometric center of the target vehicle as the first origin, the width direction of the vehicle as the x-axis, and the length direction as the y-axis. Also, include the step of establishing a second orthogonal coordinate system with the midpoint of the rear axle of the target vehicle as the second origin, the width direction of the vehicle as the X-axis, and the length direction as the Y-axis.

[0079] Furthermore, referring to FIG. 2, step S300 includes the following. S301, in the first collision detection model, obtain the initial obstacle coordinates (x i , y i ) of the obstacle in the first orthogonal coordinate system.

[0080] S302, based on the initial obstacle coordinates (x i , y i ) and the coordinates (x0, y0) of the first origin, calculate the distance L 0i from each obstacle to the first origin, where L 0i = √((x i - x0) 2 + (y i - y0) 2 ).

[0081] If the distance from at least one obstacle to the first origin is less than a first preset distance, execute step S303; otherwise, execute step S305.

[0082] Note that the first preset distance is the distance from the geometric center of the target vehicle to any corner of the vehicle. Since the geometric center of the vehicle is selected, the distance to any corner of the vehicle from this geometric center is the same, and in practice, it can be arbitrarily selected. For example, measure the distance from the geometric center of the target vehicle to the right front corner of the vehicle and define this distance as the first preset distance.

[0083] When the vehicle collision detection method is used in the process of automatic parking, by determining the positional relationship between the obstacle and the target vehicle based on the distance from the geometric center of the vehicle to any corner of the vehicle, during the automatic parking process, for example, obstacles with a risk of collision during processes such as forward movement, backward movement, and turning can be screened out.

[0084] S303, obtaining the converted obstacle coordinates of the obstacle within the first preset circular region in the second orthogonal coordinate system.

[0085] Here, the first preset circular region has the first origin as the center and the first preset distance as the radius.

[0086] Furthermore, step S303 specifically includes: obtaining the yaw angle θ of the vehicle, the corresponding initial obstacle coordinates (x n , y n ) of the obstacle within the first preset circular region in the first orthogonal coordinate system, and the midpoint coordinates (x, y) of the rear axle of the vehicle in the first orthogonal coordinate system.

[0087] Calculating a first included angle θ1 and the distance L from the obstacle to the midpoint of the rear axle of the vehicle based on the initial obstacle coordinates and the midpoint coordinates of the rear axle of the vehicle.

[0088] Here, θ1 = arctan(((x n - x) / (y n - y)), L = √((x n - x) 2 + (y n - y) 2 ).

[0089] The first included angle θ1 is the included angle between the line connecting the obstacle and the midpoint of the rear axle of the vehicle and the perpendicular line segment to the X-axis of the second orthogonal coordinate system of the obstacle.

[0090] Calculating a second included angle θ2 based on the first included angle and the yaw angle of the vehicle, where θ2 = θ1 - θ.

[0091] Based on the distance L from the obstacle to the midpoint of the rear axle of the vehicle and the second included angle θ2, the transformed obstacle coordinates (x m , y m ) of the obstacle are calculated, where x m = L × sin(θ2), y m = L × cos(θ2).

[0092] S304, based on the transformed obstacle coordinates and the coordinates of the second origin, calculates the distance from each obstacle within the first preset circular area to the second origin.

[0093] If the distance from at least one obstacle within the first preset circular area to the second origin is less than a second preset distance, it is determined that the target vehicle will collide; otherwise, step S305 is executed.

[0094] Note that the second preset distance is generally preferably half of the vehicle width, or the length of the vehicle's rear overhang, or the distance from the rear axle of the vehicle to the front overhang.

[0095] x m is less than half of the vehicle width, or y m is less than the length of the vehicle's rear overhang, or y m is less than the distance from the rear axle of the vehicle to the front overhang, in any of these cases, it is determined that the target vehicle will collide.

[0096] Furthermore, when it is determined that the target vehicle will collide, S304 further includes a step of issuing early collision warning information.

[0097] Here, the system may issue early collision warning information in real time through an in-vehicle display or voice notification. At the same time, the early warning information may be sent to the driver's mobile phone or an IoT platform for connected cars, and the driver may be alerted to take emergency measures, thereby reducing the occurrence of collision accidents.

[0098] In S305, it is determined that there is no collision risk for the target vehicle.

[0099] (Embodiment 2) Referring to FIG. 3, the present application also provides a modified example in which collision detection is performed on an obstacle that may be engaged by the second collision detection model. The step S300 further includes the following. In S311, in the second collision detection model, the initial obstacle coordinates in the first orthogonal coordinate system of the obstacle are obtained.

[0100] In S312, based on the initial obstacle coordinates and the coordinates of the first origin, the distance from each obstacle to the first origin is calculated.

[0101] When the distance from at least one obstacle to the first origin is smaller than a third preset distance, step S313 is executed; otherwise, step S315 is executed.

[0102] Note that the third preset distance is the distance from the geometric center of the target vehicle to either side of the front axle or the rear axle of the vehicle.

[0103] In S313, the converted obstacle coordinates in the second orthogonal coordinate system of the obstacles within the second preset circular region are obtained.

[0104] Here, the second preset circular region has the first origin as the center and the third preset distance as the radius.

[0105] Furthermore, the step S313 specifically includes the following. The yaw angle of the vehicle, the corresponding initial obstacle coordinates in the first orthogonal coordinate system of the obstacles within the second preset circular region, and the midpoint coordinates of the rear axle of the vehicle in the first orthogonal coordinate system are obtained.

[0106] Based on the initial obstacle coordinates and the midpoint coordinates of the rear axle of the vehicle, calculate a first included angle and the distance from the obstacle to the midpoint of the rear axle of the vehicle.

[0107] The first included angle is the included angle between the line connecting the obstacle and the midpoint of the rear axle of the vehicle and the perpendicular line segment from the obstacle to the X-axis of the second orthogonal coordinate system of the obstacle.

[0108] Based on the first included angle and the yaw angle of the vehicle, calculate a second included angle.

[0109] Based on the distance from the obstacle to the midpoint of the rear axle of the vehicle and the second included angle, calculate the transformed obstacle coordinates of the obstacle.

[0110] S314, calculate the distance from each obstacle within the second preset circular region to the second origin based on the transformed obstacle coordinates and the coordinates of the second origin.

[0111] If the distance from at least one obstacle within the second preset circular region to the second origin is less than the second preset distance, it is determined that the target vehicle will collide; otherwise, step S315 is executed.

[0112] Furthermore, when it is determined that the target vehicle will collide, S314 further includes a step of issuing early collision warning information.

[0113] S315, determine that there is no collision risk for the target vehicle.

[0114] (Embodiment 3) Referring to FIG. 4, the present application also provides a vehicle collision detection system, and the system includes the following. A first acquisition module, which is used to acquire a set of obstacle information within a preset range of a target vehicle.

[0115] In a possible embodiment, the obstacle information set may be obtained by a target sensor, and the target sensor may be a sensor corresponding to a lidar and a stereo camera. The lidar may be provided symmetrically left and right or front and back on both sides of the bottom of the vehicle, so as to obtain obstacle information that is far away, low in position, and requires high detection accuracy. The stereo camera may be provided on the upper part of the vehicle, so as to obtain obstacle information of nearby and high-position obstacles. By using the lidar and the stereo camera in combination, the obtained obstacle information set becomes more comprehensive and accurate, so that when collision detection is performed later, it can be detected more accurately, and it is possible to prevent a collision accident from occurring due to detection omission of obstacles.

[0116] A splitting module, which is used to split the obstacle information set into a first obstacle map and a second obstacle map based on a preset height of an obstacle.

[0117] The splitting module includes the following. A calibration unit, which is used to calibrate the maximum lengths of the front wheels and the rear wheels of the target vehicle respectively.

[0118] A setting unit, which is used to set a preset height of an obstacle based on the calibration result of the calibration unit.

[0119] Note that since the height of the chassis of the vehicle is different from the height of the vehicle, the preset height of the obstacle is calibrated based on the actual vehicle, which is not limited here.

[0120] A splitting unit, which is used to determine whether the height of the obstacle corresponding to the obstacle information set is greater than the preset height of the obstacle. If it is greater, the obstacle is put into the first obstacle map, and if it is smaller, the obstacle is put into the second obstacle map.

[0121] A second acquisition module, which is used to model a target vehicle based on the first obstacle map and the second obstacle map, and fuse the vehicle model with the corresponding obstacle map to obtain a first collision detection model and a second collision detection model.

[0122] The second acquisition module includes the following. A modeling unit, which is used to obtain a first vehicle model and a second vehicle model.

[0123] A fusion unit, which is used to fuse the first vehicle model and the second vehicle model with the first obstacle map and the second obstacle map respectively to obtain a first collision detection model and a second collision detection model.

[0124] A judgment module, which is used to obtain the distance relationship between the target vehicle and the obstacles corresponding to the set of obstacle information at a preset position of the target vehicle according to the first collision detection model and / or the second collision detection model, and judge whether the target vehicle will collide based on the distance relationship.

[0125] The system further includes the following. An establishment module, which is used to establish a first rectangular coordinate system and a second rectangular coordinate system. The first rectangular coordinate system takes the geometric center of the target vehicle as the first origin, the width direction of the vehicle as the x-axis, and the length direction as the y-axis. The second rectangular coordinate system takes the midpoint of the rear axle of the target vehicle as the second origin, the width direction of the vehicle as the X-axis, and the length direction as the Y-axis.

[0126] The judgment module includes the following. An acquisition unit, which is used to acquire the initial obstacle coordinates of the obstacles in the first rectangular coordinate system in the first collision detection model or the second collision detection model.

[0127] A first calculation unit, which is used to calculate the distance from each obstacle to the first origin based on the initial obstacle coordinates and the coordinates of the first origin, and obtain the transformed obstacle coordinates in the second orthogonal coordinate system of the obstacles within a second preset circular region when the distance is smaller than a first preset distance or a third preset distance.

[0128] A second calculation unit, which is used to calculate the distance from each obstacle within the second preset circular region to the second origin based on the transformed obstacle coordinates and the coordinates of the second origin, and determine that the target vehicle will collide when the distance is smaller than a second preset distance.

[0129] The system further includes the following. An early warning module, which is used to issue early collision warning information when it is determined that the target vehicle will collide.

[0130] (Embodiment 4) The present application also provides a vehicle in which a vehicle collision detection system is arranged, and the system detects whether the target vehicle will collide by using the vehicle collision detection method.

[0131] In summary, the present application provides a vehicle collision detection method, system and vehicle, which divides an obstacle map based on the height of obstacles, further establishes a corresponding collision detection model, determines the distance relationship between the target vehicle and the obstacles by the collision detection model, and further obtains a collision risk. Real-time and accurate collision risk analysis can be performed for different types of obstacle heights, improving the accuracy and efficiency of collision detection, and further ensuring the driving safety of the vehicle and the personal safety of the people in the vehicle.

[0132] Here, exemplary embodiments are described with reference to the drawings, but it should be understood that the above embodiments are merely exemplary and are not intended to limit the scope of the present application thereto. Those skilled in the art may make various changes and modifications thereto without departing from the scope and spirit of the present application. All of these changes and modifications shall be included in the scope of the present application claimed in the claims.

[0133] Those skilled in the art will recognize that each exemplary unit and algorithm step described in the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are ultimately implemented in hardware or software is determined by the specific application and design constraints related to the technical solution. Those skilled in the art may implement the described functions in different ways for each specific application, but such implementation should not be recognized as exceeding the scope of the present application.

[0134] It should be understood that the devices and methods disclosed in some embodiments provided by the present application may be implemented in other ways. For example, the device embodiments described above are merely exemplary. For example, the division of the above units is only a logical function division, and may actually be divided in other ways when implemented. For example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted or not executed.

[0135] Examples of each component of the present application may be implemented in hardware, or may be implemented by software modules executed by one or more processors, or may be implemented by a combination thereof. Those skilled in the art should understand that in practice, a microprocessor or a digital signal processor (DSP) may be used to implement some or all of the functions of some modules based on the examples of the present application. The present application may be implemented as a device program (for example, a computer program, a computer program product) used to execute part or all of the methods described herein. Such a program for implementing the present application may be stored in a computer-readable medium or may comprise one or more signals. Such signals may be obtained by downloading from a website on the Internet, or may be provided in a carrier signal, or may be provided in any other form.

[0136] Note that in this specification, relational terms such as first, second, etc. are merely for distinguishing one entity or operation from another entity or operation, and do not necessarily stipulate or imply that there is actually such a relationship or order between these entities or operations. Also, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or further includes elements specific to such a process, method, article or device. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of another same element in the process, method, article or device comprising the said element.

[0137] Although the description of the present application has been made based on the above specific embodiments, it is obvious that those skilled in the art may make various substitutions, modifications and changes based on the above content. Therefore, all such substitutions, improvements and changes shall be included within the spirit and scope of the claims.

Claims

1. S100, which includes the steps of obtaining an obstacle information set within a preset range of a target vehicle, and dividing the obstacle information set into a first obstacle map and a second obstacle map based on a preset height of an obstacle; S200, which includes the steps of modeling the target vehicle with the first obstacle map and the second obstacle map, and fusing the vehicle model with the corresponding obstacle map to obtain a first collision detection model and a second collision detection model respectively; S300, which includes the steps of obtaining the distance relationship between an obstacle corresponding to the obstacle information set at a preset position of the target vehicle by the first collision detection model and / or the second collision detection model, and determining whether the target vehicle will collide based on the distance relationship. A vehicle collision detection method characterized by the above.

2. In step S100, dividing the obstacle information set into a first obstacle map and a second obstacle map based on a preset height of an obstacle includes: Calibrating the maximum lengths of the front wheels and rear wheels of the target vehicle respectively, and setting the preset height of the obstacle based on the calibration result; Determining whether the height of the obstacle corresponding to the obstacle information set is greater than the preset height of the obstacle, and if it is greater, putting the obstacle into the first obstacle map, otherwise putting the obstacle into the second obstacle map. The vehicle collision detection method according to claim 1, characterized by the above.

3. Step S200 includes: Obtaining a first vehicle model including the front overhang and rear overhang of the vehicle, and a second vehicle model obtained by removing the front overhang and rear overhang of the vehicle by modeling the target vehicle; Fusing the first vehicle model and the second vehicle model with the first obstacle map and the second obstacle map respectively to obtain a first collision detection model and a second collision detection model. The vehicle collision detection method according to claim 2, characterized by the above.

4. Before executing step S300, it includes the step of establishing a first rectangular coordinate system with the geometric center of the target vehicle as the first origin, the width direction of the vehicle as the x-axis, and the length direction as the y-axis. The vehicle collision detection method according to claim 3, characterized by the above.

5. Before executing step S300, further: ​ The method for detecting a vehicle collision according to claim 4, comprising the step of establishing a second orthogonal coordinate system with the midpoint of the rear axle of the target vehicle as the second origin, the width direction of the vehicle as the X-axis, and the length direction as the Y-axis.

6. The step S300 is S301, which is a step of obtaining the initial obstacle coordinates in the first orthogonal coordinate system of the obstacle in the first collision detection model; S302, which is a step of calculating the distance from each obstacle to the first origin based on the initial obstacle coordinates and the coordinates of the first origin; If the distance from at least one obstacle to the first origin is less than a first preset distance, step S303 is executed; otherwise, step S305 is executed; S303, which is a step of obtaining the transformed obstacle coordinates in the second orthogonal coordinate system of the obstacles within a first preset circular region and proceeding to step S304; S304, which is a step of calculating the distance from each obstacle within the first preset circular region to the second origin based on the transformed obstacle coordinates and the coordinates of the second origin; If the distance from at least one obstacle within the first preset circular region to the second origin is less than a second preset distance, it is determined that the target vehicle will collide; otherwise, step S305 is executed; S305, which is a step of determining that there is no collision risk for the target vehicle, and The first preset circular region is centered at the first origin and has the first preset distance as the radius. The method for detecting a vehicle collision according to claim 5 is characterized by this.

7. The step S300 is S311, which is a step of obtaining the initial obstacle coordinates in the first orthogonal coordinate system of the obstacle in the second collision detection model; S312, which is a step of calculating the distance from each obstacle to the first origin based on the initial obstacle coordinates and the coordinates of the first origin; If the distance from at least one obstacle to the first origin is less than a third preset distance, step S313 is executed; otherwise, step S315 is executed; S313, which is a step of obtaining the transformed obstacle coordinates in the second orthogonal coordinate system of the obstacles within a second preset circular region and proceeding to step S314; S314, which calculates the distances from each obstacle within the second preset circular area to the second origin based on the coordinates of the conversion obstacle and the coordinates of the second origin, if the distance from at least one obstacle within the second preset circular area to the second origin is less than a second preset distance, it is determined that the target vehicle will collide; otherwise, step S315 is executed, S315, which includes the step of determining that there is no collision risk for the target vehicle, The second preset circular area has the first origin as the center and the third preset distance as the radius. The vehicle collision detection method according to claim 5 is characterized by this.

8. Specifically, step S303 or step S313 includes the steps of obtaining the yaw angle of the vehicle, the corresponding initial obstacle coordinates of the obstacles within the first preset circular area or the second preset circular area in the first orthogonal coordinate system, and the midpoint coordinates of the rear axle of the vehicle in the first orthogonal coordinate system, calculating a first included angle and the distance from the obstacle to the midpoint of the rear axle of the vehicle based on the initial obstacle coordinates and the midpoint coordinates of the rear axle of the vehicle, where the first included angle is the included angle between the line connecting the obstacle and the midpoint of the rear axle of the vehicle and the perpendicular line segment to the X-axis of the second orthogonal coordinate system of the obstacle, calculating a second included angle based on the first included angle and the yaw angle of the vehicle, and calculating the converted obstacle coordinates of the obstacle based on the distance from the obstacle to the midpoint of the rear axle of the vehicle and the second included angle. The vehicle collision detection method according to claim 6 or 7 is characterized by this.

9. When it is determined that the target vehicle will collide, step S304 or S314 further includes the step of issuing early collision warning information. The vehicle collision detection method according to claim 8 is characterized by this.

10. A first acquisition module used to acquire a set of obstacle information within a preset range of the target vehicle, A splitting module used to split the set of obstacle information into a first obstacle map and a second obstacle map based on a preset height of the obstacle, A second acquisition module used to model the target vehicle with the first obstacle map and the second obstacle map, and fuse the vehicle model with the corresponding obstacle map to obtain a first collision detection model and a second collision detection model; A determination module used to obtain the distance relationship between the target vehicle and an obstacle corresponding to a set of obstacle information at a preset position of the target vehicle according to the first collision detection model and / or the second collision detection model, and determine whether the target vehicle will collide based on the distance relationship. A vehicle collision detection system, characterized in that it comprises:

11. The system is An establishment module used to establish a first rectangular coordinate system and a second rectangular coordinate system, wherein the first rectangular coordinate system takes the geometric center of the target vehicle as the first origin, the width direction of the vehicle as the x-axis, and the length direction as the y-axis, and the second rectangular coordinate system takes the midpoint of the rear axle of the target vehicle as the second origin, the width direction of the vehicle as the X-axis, and the length direction as the Y-axis. The system according to claim 10, further comprising an early warning module used to issue early warning information for collision when it is determined that the target vehicle will collide.

12. The splitting module is A calibration unit used to calibrate the maximum lengths of the front wheels and rear wheels of the target vehicle respectively; A setting unit used to set the preset height of the obstacle based on the calibration result of the calibration unit; A splitting unit used to determine whether the height of the obstacle corresponding to the set of obstacle information is greater than the preset height of the obstacle, and if it is greater, put the obstacle into the first obstacle map, and if it is smaller, put the obstacle into the second obstacle map. The system according to claim 11, characterized in that it comprises:

13. The second acquisition module is A modeling unit used to obtain a first vehicle model and a second vehicle model; The system according to claim 12, characterized in that it comprises a fusion unit used to fuse the first vehicle model and the second vehicle model with the first obstacle map and the second obstacle map respectively to obtain a first collision detection model and a second collision detection model.

14. The determination module is In the first collision detection model or the second collision detection model, an acquisition unit used to acquire the initial obstacle coordinates in the first orthogonal coordinate system of the obstacle; Based on the initial obstacle coordinates and the coordinates of the first origin, calculate the distance from each obstacle to the first origin, and when the distance is smaller than a first preset distance or a third preset distance, a first calculation unit used to acquire the converted obstacle coordinates in the second orthogonal coordinate system of the obstacle within a second preset circular region; Based on the converted obstacle coordinates and the coordinates of the second origin, calculate the distance from each obstacle within the second preset circular region to the second origin, and a second calculation unit used to determine that the target vehicle will collide when the distance is smaller than a second preset distance. The system according to claim 13, characterized by comprising:

15. A vehicle, characterized in that a vehicle collision detection system for detecting whether a target vehicle will collide using the vehicle collision detection method according to any one of claims 1 to 9 is arranged.

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