Vehicle collision prediction method, device and equipment, vehicle, medium and program product

Abstract

The invention relates to the technical field of intelligent driving, and discloses a vehicle collision prediction method, device and equipment, a vehicle, a medium and a program product. The method comprises the following steps: at a first moment, acquiring a first driving speed and a first acceleration of a front vehicle and a second driving speed of a rear vehicle; based on the first driving speed, the second driving speed and the first acceleration, determining at least one of a first predicted collision interval of the front vehicle and the rear vehicle and a second acceleration of the rear vehicle in the first predicted collision interval under a preset collision condition; predicting a collision likelihood of the preceding vehicle and the following vehicle based on at least one of the first predicted collision interval and the second acceleration; wherein the preset collision condition comprises that the longitudinal speed of the front vehicle and the longitudinal speed of the rear vehicle are set to be the same during collision. The method can improve the determination accuracy of the collision possibility of the front vehicle and the rear vehicle.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and in particular to a vehicle collision prediction method, device, equipment, vehicle, medium, and program product. Background Technology

[0002] During vehicle operation, when two vehicles are traveling in the same direction, the following vehicle may collide with the one in front due to insufficient time to avoid a collision, threatening the driver's life. Therefore, it is possible to predict the likelihood of a collision between the two vehicles and issue warnings to the vehicle or driver. Thus, how to predict the potential for a collision between two vehicles is a technical problem that needs to be solved. Summary of the Invention

[0003] This application provides a vehicle collision prediction method, apparatus, device, vehicle, medium, and program product. This method can improve the accuracy of determining the probability of a collision between a vehicle in front and a vehicle behind.

[0004] In a first aspect, this application provides a vehicle collision prediction method, the method comprising: at a first moment, acquiring a first driving speed and a first acceleration of a preceding vehicle, and a second driving speed of a following vehicle; based on the first driving speed, the second driving speed, and the first acceleration, determining at least one of a first predicted collision interval between the preceding vehicle and the following vehicle and a second acceleration of the following vehicle within the first predicted collision interval under preset collision conditions; and predicting the probability of a collision between the preceding vehicle and the following vehicle based on at least one of the first predicted collision interval and the second acceleration; wherein the preset collision conditions include setting the longitudinal speed of the preceding vehicle and the following vehicle at the time of collision to be the same.

[0005] It is understood that the method provided in this application determines at least one of the first predicted collision interval and the second acceleration based on preset collision conditions, thereby predicting the collision probability between the vehicle in front and the vehicle behind, which can improve the accuracy of collision probability determination.

[0006] In one possible implementation of the first aspect, the preset collision condition further includes: setting the displacement difference between the front vehicle and the rear vehicle within the first predicted collision interval and the longitudinal distance at a preset time to be the same; wherein, the first predicted collision interval is the interval between the preset time and the first predicted collision time of the front vehicle and the rear vehicle, the preset time is a preset time when the rear vehicle begins to decelerate, and the preset time is the same as the first time or located after the first time.

[0007] It is understood that the preset time interval between the first moment and the preset moment is a preset reaction interval of the following vehicle, that is, the time interval from the start of the first moment to the moment when deceleration begins (i.e., the preset moment). Therefore, the method provided in this application takes into account the reaction interval of the following vehicle, making the determination of the collision probability more accurate.

[0008] In one possible implementation of the first aspect, predicting the collision probability of the preceding and following vehicles based on at least one of the predicted collision time and the second acceleration includes: predicting that the preceding and following vehicles have a collision probability if at least one of the first conditions is met; wherein the first conditions include: a first predicted collision interval is less than or equal to a first interval threshold; and the absolute value of the second acceleration is greater than a longitudinal acceleration threshold.

[0009] In one possible implementation of the first aspect, the vehicle collision prediction method further includes: determining a second predicted collision time for the preceding vehicle and the following vehicle based on a first driving speed and a second driving speed; determining predicted collision parameters based on the first driving speed, the second driving speed, and the second predicted collision time; wherein the predicted collision parameters include at least one of the following: the lateral predicted position difference between the preceding vehicle and the following vehicle at the second predicted collision time, the lateral predicted acceleration difference between the preceding vehicle and the following vehicle within the second predicted collision interval, and the second predicted collision interval being the interval between the first time and the second predicted collision time.

[0010] In one possible implementation of the first aspect, predicting the collision probability of a preceding vehicle and a following vehicle based on at least one of a first predicted collision interval and a second acceleration includes: predicting that the preceding vehicle and the following vehicle have a collision probability if at least one of a first condition and at least one of a second condition are satisfied; wherein the first condition includes: the first predicted collision interval is less than or equal to a first interval threshold; the absolute value of the second acceleration is greater than a longitudinal acceleration threshold; the second condition includes: the absolute value of the lateral predicted position difference is less than or equal to a first distance threshold; the absolute value of the lateral predicted acceleration difference is greater than a lateral acceleration threshold.

[0011] It is understood that the method provided in this application predicts the collision probability between the preceding and following vehicles based not only on at least one of the first predicted collision interval and the second acceleration, but also on at least one of the lateral predicted position difference and the lateral predicted acceleration difference. Therefore, this method can improve the comprehensiveness of the collision probability determination, thereby improving the accuracy of the collision probability determination.

[0012] In one possible implementation of the first aspect, the predicted collision parameters are determined based on the first driving speed, the second driving speed, and the second predicted collision time, including: determining the lateral predicted displacement difference between the front vehicle and the rear vehicle at the second predicted collision time based on the first driving speed, the second driving speed, the second predicted collision interval, and the lateral preset acceleration difference between the rear vehicle and the front vehicle within the second predicted collision interval; and determining the lateral predicted position difference based on the lateral predicted displacement difference and the initial lateral position difference between the front vehicle and the rear vehicle at the first moment.

[0013] It is understandable that the second left lateral velocity V can be based on the second driving speed V2. 2_left The first left lateral velocity V1 of the first driving speed V1 1_left The second predicted collision interval t1, and the left lateral preset acceleration difference a between the rear vehicle and the front vehicle within the second predicted collision interval. ps_left Determine the left lateral predicted displacement difference S between the front and rear vehicles at the second predicted collision time. pd_left’ .

[0014] Similarly, the second right lateral velocity V can be based on the second driving speed V2. 2_right The first right lateral velocity V1 of the first driving speed V1 1_right The second predicted collision interval t1, and the right lateral preset acceleration difference a between the rear vehicle and the front vehicle within the second predicted collision interval. ps_right Determine the right lateral predicted displacement difference S between the front and rear vehicles at the second predicted collision time. pd_right’ .

[0015] In one possible implementation of the first aspect, the predicted collision parameters are determined based on the first driving speed, the second driving speed, and the second predicted collision time, including: determining the lateral preset displacement difference between the front vehicle and the rear vehicle at the second predicted collision time based on the lateral preset position difference between the rear vehicle and the front vehicle at the second predicted collision time and the initial lateral position difference between the front vehicle and the rear vehicle at the first moment; and determining the lateral predicted acceleration difference based on the first driving speed, the second driving speed, the second predicted collision interval, and the lateral preset displacement difference.

[0016] It is understandable that the second left lateral velocity V can be based on the second driving speed V2. 2_left The first left lateral velocity V1 of the first driving speed V1 1_left Second predicted collision interval t1, left lateral preset displacement difference S ps_left’ Determine the left lateral predicted acceleration difference 'a' between the preceding and following vehicles within the second predicted collision interval. pd_left .

[0017] Similarly, the second right lateral velocity V can be based on the second driving speed V2. 2_right The first right lateral velocity V1 of the first driving speed V11_right Second predicted collision interval t1, right lateral preset displacement difference S ps_right’ Determine the right lateral predicted acceleration difference 'a' between the preceding and following vehicles within the second predicted collision interval. pd_right .

[0018] In one possible implementation of the first aspect, determining at least one of a first predicted collision interval between the preceding and following vehicles and a second acceleration of the following vehicle within the first predicted collision interval, based on a first driving speed, a second driving speed, and a first acceleration, under preset collision conditions, includes: if prediction conditions are met, determining at least one of a first predicted collision interval between the preceding and following vehicles and a second acceleration of the following vehicle within the first predicted collision interval, based on the first driving speed, the second driving speed, and the first acceleration; wherein the prediction conditions include: the second predicted collision interval between a first moment and a second predicted collision moment is less than or equal to a second interval threshold, the second predicted collision interval being determined based on the first driving speed and the second driving speed; at the first moment, the lateral overlap distance between the preceding and following vehicles is greater than an overlap distance threshold; at the first moment, the lateral speed of the second driving speed is less than or equal to a speed threshold; at the first moment, the angle between the orientation and longitudinal direction of the following vehicle is less than or equal to an angle threshold.

[0019] It is understandable that if the prediction conditions are not met, it can be directly determined that there is no possibility of a collision between the vehicles in front and behind. Therefore, this method can improve the efficiency of determining the probability of a collision.

[0020] Secondly, this application provides a vehicle collision prediction device, which includes: an acquisition module, configured to acquire a first driving speed and a first acceleration of a preceding vehicle and a second driving speed of a following vehicle at a first moment; a determination module, configured to determine, based on the first driving speed, the second driving speed, and the first acceleration, at least one of a first predicted collision interval between the preceding and following vehicles and a second acceleration of the following vehicle within the first predicted collision interval under preset collision conditions; and a prediction module, configured to predict the probability of a collision between the preceding and following vehicles based on at least one of the first predicted collision interval and the second acceleration; wherein the preset collision conditions include setting the longitudinal speeds of the preceding and following vehicles to be the same at the time of the collision.

[0021] Thirdly, this application provides an electronic device comprising: at least one processor; at least one memory; the at least one memory storing at least one program, which, when executed by at least one processor, causes the electronic device to perform the vehicle collision prediction method of the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a vehicle including the electronic equipment involved in the third aspect.

[0023] Fifthly, this application provides a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the vehicle collision prediction method of the first aspect and any possible implementation thereof.

[0024] In a sixth aspect, this application provides a computer program product comprising: computer instructions that, when executed on an electronic device, cause the electronic device to perform the vehicle collision prediction method of the first aspect and any possible implementation thereof.

[0025] The beneficial effects of the second to sixth aspects can be found in the first aspect and the beneficial effects of any possible implementation of the first aspect, and will not be repeated here. Attached Figure Description

[0026] Figure 1 According to some embodiments of this application, a schematic diagram of the positional relationship between a front vehicle and a rear vehicle is shown;

[0027] Figure 2 According to some embodiments of this application, a flowchart of a first vehicle collision prediction method is shown;

[0028] Figure 3 According to some embodiments of this application, a schematic diagram of the heading angle of a rear vehicle is shown;

[0029] Figure 4 According to some embodiments of this application, a flowchart of a second vehicle collision prediction method is shown;

[0030] Figure 5 According to some embodiments of this application, a flowchart illustrating a method for determining lateral predicted position differences is shown;

[0031] Figure 6 According to some embodiments of this application, a flowchart illustrating a method for determining lateral predicted acceleration differences is shown;

[0032] Figure 7 According to some embodiments of this application, a flowchart of a third vehicle collision prediction method is shown;

[0033] Figure 8 According to some embodiments of this application, a functional framework diagram of a vehicle is shown. Detailed Implementation

[0034] The illustrative embodiments of this application include, but are not limited to, vehicle collision prediction methods, apparatuses, devices, vehicles, media, and program products.

[0035] As mentioned above, during vehicle operation, when two vehicles are traveling in the same direction (hereinafter referred to as longitudinal), the following vehicle may collide with the preceding vehicle due to insufficient time to avoid a collision, threatening the driver's life. Therefore, it is possible to provide early warning to the vehicle or driver by predicting the possibility of a collision between the preceding and following vehicles.

[0036] Based on this, this application provides a vehicle collision prediction method. This method predicts the likelihood of a collision between the preceding and following vehicles by pre-setting collision conditions (including the condition of equal speed, i.e., setting the longitudinal speeds of the preceding and following vehicles to be the same at the time of collision).

[0037] Specifically, in this method, at a first moment, the first driving speed and first acceleration of the preceding vehicle, and the second driving speed of the following vehicle are acquired. Based on the first driving speed, the second driving speed, and the first acceleration, at least one of the following is determined under preset collision conditions (the preset collision conditions include setting the longitudinal speeds of the preceding and following vehicles to be the same at the time of collision): a first predicted collision interval between the preceding and following vehicles and a second acceleration of the following vehicle within the first predicted collision interval (i.e., the interval between the first moment and the first predicted collision moment). Then, based on at least one of the first predicted collision interval and the second acceleration, the probability of a collision between the preceding and following vehicles is predicted.

[0038] For example, Figure 1 According to some embodiments of this application, a schematic diagram of the positional relationship between a front vehicle and a rear vehicle is shown.

[0039] like Figure 1 As shown, at the first moment, both the preceding and following vehicles are traveling in lane Q, and their directions of travel are the extension of lane Q. For ease of description, the directions of travel of the preceding and following vehicles will be referred to as longitudinal (e.g., the y-axis direction in the figure), and the direction perpendicular to the longitudinal direction will be referred to as lateral (e.g., the x-axis direction in the figure), and lateral will further include left and right directions.

[0040] In this method, the probability of a collision between the preceding and following vehicles can be predicted by setting the longitudinal velocities of the two vehicles at the time of collision to be the same, thus determining at least one of a first predicted collision interval and a second acceleration. For example, a collision between the preceding and following vehicles is predicted if at least one of the following first conditions is met: the first predicted collision interval is less than or equal to a first interval threshold; and the absolute value of the second acceleration is greater than a longitudinal acceleration threshold. This method can improve the accuracy of determining the predicted collision probability of vehicles.

[0041] It is understood that the vehicle collision prediction method provided in this application is applicable to any electronic device that can predict the probability of a collision, such as electronic devices including but not limited to vehicles with in-vehicle systems (e.g., the vehicle in front or behind), or servers, etc.

[0042] The vehicle collision prediction method provided in this application will be described in detail below with reference to the accompanying drawings.

[0043] For example, Figure 2 According to some embodiments of this application, a schematic flowchart of a first vehicle collision prediction method is shown. Figure 2 The execution entities for each step of the process shown are electronic devices. For ease of description, the following will describe them separately. Figure 2 The execution entity of each step in the process shown will not be described again.

[0044] like Figure 2 As shown, the method includes, but is not limited to, the following S201-S203.

[0045] S201: At the first moment, obtain the first speed and first acceleration of the vehicle in front, and the second speed of the vehicle behind.

[0046] It is understood that the first moment in this application can be the current moment, that is, the first driving speed V1 and the first acceleration a1 of the preceding vehicle at the current moment, and the second driving speed V2 of the following vehicle at the current moment.

[0047] In this embodiment, the first driving speed V1, the first acceleration a1, and the second driving speed V2 are all vectors, that is, the first driving speed V1 can be decomposed into a first longitudinal speed V 1_y and the first left lateral velocity V 1_left Or the first right lateral velocity V 1_right The first acceleration a1 can be decomposed into the first longitudinal acceleration a 1_y The second driving speed V2 can be decomposed into a second longitudinal speed V. 2_y and the second left lateral velocity V 2_left Or the second right lateral velocity V 2_right .

[0048] It is understandable that, since both the front and rear vehicles in this application are traveling in the same direction, the first longitudinal velocity V 1_y Represents the direction of travel of the vehicle in front (e.g., Figure 1 The first longitudinal velocity V (shown in the y-axis direction) 1_y First longitudinal acceleration a 1_y The first longitudinal acceleration a of the vehicle in the direction of travel. 1_y Second longitudinal velocity V 2_yV represents the second longitudinal velocity of the following vehicle in the direction of travel. 2_y .

[0049] This application does not limit the method of obtaining the first driving speed V1, the first acceleration a1, and the second driving speed V2. For example, the first driving speed V1 can be obtained through a first vehicle speed sensor in the preceding vehicle, the first acceleration a1 can be obtained through a first acceleration sensor in the preceding vehicle, and the second driving speed V2 can be obtained through a second vehicle speed sensor in the following vehicle.

[0050] S202: Based on the first driving speed, the second driving speed, and the first acceleration, determine at least one of the first predicted collision distance between the preceding and following vehicles and the second acceleration of the following vehicle within the first predicted collision distance under preset collision conditions.

[0051] The first predicted collision interval is the interval between the preset time and the first predicted collision time. The preset time is the preset time when the following vehicle begins to decelerate.

[0052] In this embodiment of the application, the preset collision conditions include the conditions of the same velocity and the conditions of the same displacement.

[0053] For example, the condition of the same speed includes setting the longitudinal speed of the preceding and following vehicles to be the same at the time of the collision.

[0054] For example, the condition of identical displacement includes setting the displacement difference between the preceding and following vehicles within the first predicted collision interval and the longitudinal distance at a preset time to be the same. The preset time is either the same as the first time or located after the first time.

[0055] It is understandable that at least one of the first predicted collision interval and the second acceleration can be determined based on the conditions of the same speed and the same displacement, as well as the first driving speed, the second driving speed, and the first acceleration.

[0056] Taking the determination of the first predicted collision interval and the second acceleration as an example, the first predicted collision interval and the second acceleration can be determined based on the following formulas (1), (2) and (3). It can be understood that the second acceleration can refer to the longitudinal acceleration of the following vehicle.

[0057] V 1_y +a 1_y ×(t0+t delay )=V 2_y +a 2_y ×t0 formula (1)

[0058] d+V 1_y ×t0+1 / 2×a 1_y ×t0 2 =V 2_y×t0+1 / 2×a 2_y ×t0 2 Formula (2)

[0059] d=L1-(V 2_y ×t delay -(V) 1_y ×t delay +1 / 2×a 1_y ×t delay 2 )) Formula (3)

[0060] It can be understood that formula (1) represents the condition of the same velocity, and formula (2) represents the condition of the same displacement.

[0061] In formulas (1) and (2), t0 represents the first predicted collision interval between the preset time and the first predicted collision time; a 2_y This indicates the second acceleration of the following vehicle within the first predicted collision interval; t dealy This indicates the preset time interval between the first moment and the preset moment.

[0062] In this application, the preset time interval is a preset reaction interval of the following vehicle, that is, the time interval from the first moment to the moment when deceleration begins (i.e., the preset moment). This application does not limit the value of the preset time interval and it can be set based on experience. For example, the preset time interval can be 0 seconds, 2 seconds, 3 seconds, etc.

[0063] It is understandable that when the preset time interval is 0 seconds, the preset time is the same as the first time. When the preset time interval is greater than 0 seconds, the preset time is after the first time.

[0064] In addition, in formula (3), d represents the longitudinal distance between the front vehicle and the rear vehicle at the preset time; L1 represents the initial longitudinal distance (or initial longitudinal position difference) between the front vehicle and the rear vehicle at the first time.

[0065] Thus, if the front vehicle and the rear vehicle have an initial longitudinal distance L1 at the first moment, and the rear vehicle starts to decelerate only after a preset time interval (i.e. at a preset moment), then at the preset moment, the longitudinal distance between the front vehicle and the rear vehicle can be obtained based on the above formula (3).

[0066] Thus, by simultaneously solving equations (1) and (2), the first predicted collision interval t0 and the second acceleration a can be obtained. 2_y .

[0067] It is understood that the above description only describes determining at least one of the first predicted collision interval and the second acceleration after obtaining the first driving speed, the first acceleration, and the second driving speed. In other embodiments, after obtaining the first driving speed, the first acceleration, and the second driving speed, it may also be determined whether the prediction conditions are met, and if the prediction conditions are met, at least one of the first predicted collision interval and the second acceleration may be determined.

[0068] The specific details of the prediction conditions will be described in detail later, and will not be repeated here.

[0069] S203: Based on at least one of a first predicted collision interval and a second acceleration, predict the probability of a collision between the preceding and following vehicles.

[0070] For example, a collision is predicted between the preceding and following vehicles if at least one of the first conditions is met. The first conditions include: a first predicted collision interval less than or equal to a first interval threshold; and a second absolute value of acceleration greater than a longitudinal acceleration threshold.

[0071] This application does not limit the values ​​of the first interval threshold and the longitudinal acceleration threshold; they can be set based on experience.

[0072] It is understood that the method provided in this application, which determines at least one of a first predicted collision interval and a second acceleration based on preset collision conditions, and then predicts the probability of a collision between the preceding and following vehicles, can improve the accuracy of collision probability determination. Furthermore, the method provided in this application also considers the reaction interval of the following vehicle, resulting in even higher accuracy in determining the collision probability.

[0073] The following describes the prediction conditions involved in S202.

[0074] For example, the prediction conditions may include: a second predicted collision interval between a first moment and a second predicted collision moment is less than or equal to a second interval threshold; at the first moment, the lateral overlap distance between the preceding and following vehicles is greater than an overlap distance threshold; at the first moment, the lateral speed of the second driving speed V2 (e.g., V...) is... 2_left Or V 2_right The following conditions must be met: 1) The following conditions must be met: 2) The following conditions must be met: 3) The following conditions must be met: 4) The following conditions must be met: 5) The following conditions must be met: 6) The following conditions must be met: 7) The following conditions must be met: 8) The following conditions must be met: 9) The following conditions must be met: 1 ...

[0075] It is understandable that the sub-conditions included in the first condition above do not have a sequential relationship.

[0076] In some embodiments, such as Figure 1As shown, at the first moment, the absolute value of the longitudinal initial position difference between the front vehicle and the rear vehicle is L1. At this time, the second predicted collision interval between the first moment and the second predicted collision moment can be determined based on the following formula (4).

[0077] t1=L1 / (V 2_y -V 1_y ) Formula (4)

[0078] In the above formula (4), t1 represents the second predicted collision interval.

[0079] In some embodiments, such as Figure 1 As shown, the lateral overlap distance between the front and rear vehicles can be determined based on the following formula (5).

[0080] L overlap =1 / 2×width1+1 / 2×width2-L2 Formula (5)

[0081] In the above formula (5), L overlap L1 represents the lateral overlap distance between the front and rear vehicles; L2 represents the lateral width of the front vehicle; L3 represents the lateral width of the rear vehicle; L4 represents the lateral distance between the center axes of the front and rear vehicles, i.e., the initial lateral distance.

[0082] For example, Figure 3 According to some embodiments of this application, a schematic diagram of the heading angle of a rear vehicle is shown. For example... Figure 3 As shown, the angle between the orientation and longitudinal direction of the rear vehicle is the orientation angle θ.

[0083] This application does not limit the method of obtaining the heading angle of the following vehicle at the first moment. For example, the heading angle of the following vehicle at the first moment can be obtained based on an inertial measurement unit (e.g., including a gyroscope, accelerometer, etc.).

[0084] This application does not limit the values ​​of the second interval threshold, overlap distance threshold, velocity threshold, and angle threshold in the prediction conditions. For example, the above thresholds can be set based on experience or flexibly adjusted based on the actual application scenario.

[0085] As can be understood, the above method determines the predicted collision probability when the prediction conditions are met, and directly determines that the preceding and following vehicles have no possibility of colliding when the prediction conditions are not met. Therefore, this method can improve the efficiency of determining the collision probability.

[0086] For example, Figure 4 According to some embodiments of this application, a flowchart of a second vehicle collision prediction method is shown. Figure 4The execution entities for each step of the process shown are electronic devices. For ease of description, the following will describe them separately. Figure 4 The execution entity of each step in the process shown will not be described again.

[0087] like Figure 4 As shown, the method includes, but is not limited to, S401-S405 below.

[0088] S401: At the first moment, obtain the first speed and first acceleration of the vehicle in front, and the second speed of the vehicle behind.

[0089] S402: Based on a first driving speed, a second driving speed, and a first acceleration, determine at least one of a first predicted collision distance between the preceding and following vehicles and a second acceleration of the following vehicle within the first predicted collision distance under preset collision conditions.

[0090] It is understood that the execution principle of S401-S402 above is the same as the execution principle of the corresponding steps in S201-S202 above, and will not be repeated here.

[0091] S403: Based on the first and second driving speeds, determine the second predicted collision time between the preceding and following vehicles.

[0092] In this embodiment of the application, a second predicted collision interval between the preceding and following vehicles can be determined based on a first driving speed and a second driving speed, and then a second predicted collision time can be determined based on a first moment and a second predicted collision interval.

[0093] The method for determining the second predicted collision interval can be found in the formula (4) and related description above, and will not be repeated here.

[0094] S404: Determine the predicted collision parameters based on the first driving speed, the second driving speed, and the second predicted collision time.

[0095] The predicted collision parameters include at least one of the lateral predicted position difference and lateral predicted acceleration difference between the front vehicle and the rear vehicle at the second predicted collision time.

[0096] As is understandable, the process of determining the predicted collision parameters will be described later and will not be repeated here.

[0097] It is understood that the embodiments of this application do not limit the execution order of S402 and S404 described above. For example, at least one of the first predicted collision interval and the second acceleration can be determined first, and then the predicted collision parameters can be determined. As another example, the predicted collision parameters can be determined first, and then at least one of the first predicted collision interval and the second acceleration can be determined. Yet another example is that at least one of the first predicted collision interval and the second acceleration, as well as the predicted collision parameters, can be determined simultaneously.

[0098] S405: Based on at least one of a first predicted collision interval and a second acceleration, predict the probability of a collision between the preceding and following vehicles.

[0099] For example, the step includes: predicting the possibility of a collision between the preceding vehicle and the following vehicle if at least one of the first conditions and at least one of the second conditions are met.

[0100] The specific details of the first condition have been described above and will not be repeated here.

[0101] For example, the second condition includes: the absolute value of the lateral predicted position difference is less than or equal to a first distance threshold; and the absolute value of the lateral predicted acceleration difference is greater than a lateral acceleration threshold.

[0102] It is understood that, since the lateral prediction position difference in this application includes the left lateral prediction position difference S pd_left The difference between the predicted position and the right lateral position S pd_right Therefore, if the absolute value of the lateral predicted position difference is less than or equal to the first distance threshold, it indicates that the left lateral predicted position difference S pd_left The absolute value and the right lateral predicted position difference S pd_right The absolute values ​​of all of them are less than or equal to the first distance threshold.

[0103] This method determines the left lateral prediction position difference S. pd_left The right lateral predicted position difference S was also determined. pd_right This approach can improve the comprehensiveness of predictions, thereby increasing the accuracy of determining the probability of collisions.

[0104] Similarly, if the absolute value of the lateral predicted acceleration difference is greater than the lateral acceleration threshold, it indicates that the left lateral predicted acceleration difference 'a' is greater than the threshold value. pd_left The absolute value and the difference in right lateral predicted acceleration a pd_right The absolute values ​​of all of them are greater than the lateral acceleration threshold.

[0105] This method determines the left lateral predicted acceleration difference a. pd_left Furthermore, the right lateral predicted acceleration difference a was determined. pd_right This approach can also improve the comprehensiveness of predictions, thereby increasing the accuracy of determining the probability of collisions.

[0106] This application does not limit the values ​​of the first distance threshold and the lateral acceleration threshold, and they can be set based on experience. The first distance threshold can be greater than or equal to half the sum of the lateral widths of the front vehicle and the rear vehicle.

[0107] It is understood that the method provided in this application predicts the collision probability between the preceding and following vehicles based not only on at least one of the first predicted collision interval and the second acceleration, but also on at least one of the lateral predicted position difference and the lateral predicted acceleration difference. Therefore, this method can improve the comprehensiveness of the collision probability determination, thereby improving the accuracy of the collision probability determination.

[0108] The method for determining the predicted collision parameters in S404 is described below.

[0109] First, the method for determining the lateral predicted position difference based on the first driving speed, the second driving speed, and the predicted collision time is described.

[0110] For example, Figure 5 According to some embodiments of this application, a flowchart illustrating a method for determining lateral predicted position differences is shown. Figure 5 The execution entities for each step of the process shown are electronic devices. For ease of description, the following will describe them separately. Figure 5 The execution entity of each step in the process shown will not be described again.

[0111] like Figure 5 As shown, the method includes the following steps S501-S502.

[0112] S501: Based on the first driving speed, the second driving speed, the second predicted collision interval, and the lateral preset acceleration difference between the rear vehicle and the front vehicle within the second predicted collision interval, determine the lateral predicted displacement difference between the front vehicle and the rear vehicle at the second predicted collision time.

[0113] For example, such as Figure 1 As shown, since the lateral direction further includes left and right, the lateral predicted displacement difference between the front and rear vehicles at the second predicted collision time includes the left lateral predicted displacement difference and the right lateral predicted displacement difference. It can be understood that the lateral predicted displacement difference between the front and rear vehicles at the second predicted collision time refers to the lateral predicted displacement difference between the front and rear vehicles between the first time and the second predicted collision time (i.e., within the second predicted collision interval).

[0114] For example, the left lateral predicted displacement difference can be determined based on the following formula (6).

[0115] S pd_left’ =(V 2_left -V 1_left )t1+1 / 2×a ps_left ×t1 2 Formula (6)

[0116] In the above formula (6), S pd_left’ This represents the difference in predicted left lateral displacement between the preceding and following vehicles at the second predicted collision time; aps_left This indicates the left lateral preset acceleration difference between the following vehicle and the preceding vehicle within the second predicted collision interval.

[0117] For example, the right lateral predicted displacement difference can be determined based on the following formula (7).

[0118] S pd_right’ =(V 2_right -V 1_right )t1+1 / 2×a ps_right ×t1 2 Formula (7)

[0119] In the above formula (7), S pd_right’ This represents the difference in right lateral predicted displacement between the preceding and following vehicles at the second predicted collision time; a ps_right This indicates the right lateral preset acceleration difference between the following vehicle and the preceding vehicle within the second predicted collision interval.

[0120] It is understood that the embodiments of this application do not preset the left lateral acceleration difference 'a'. ps_left The difference between the right lateral preset acceleration and a ps_right The values ​​of are limited; they can be the same or different.

[0121] S502: Determine the lateral predicted position difference based on the lateral predicted displacement difference and the initial lateral position difference between the front and rear vehicles at the first moment.

[0122] It is understood that the lateral predicted position difference in this application includes the left lateral predicted position difference and the right lateral predicted position difference. Therefore, in this step, the left lateral predicted position difference can be determined based on the left lateral predicted displacement difference and the initial lateral position difference, and the right lateral predicted position difference can be determined based on the right lateral predicted displacement difference and the initial lateral position difference.

[0123] Furthermore, if at the first moment, the center axis of the rear vehicle is to the left of the front vehicle, then the left lateral predicted position difference is determined based on the sum of the left lateral predicted displacement difference and the initial lateral position difference; and the right lateral predicted position difference is determined based on the difference between the right lateral predicted displacement difference and the initial lateral position difference.

[0124] Similarly, if at the first moment, the center axis of the following vehicle is to the right of the preceding vehicle, the left lateral predicted position difference is determined based on the difference between the left lateral predicted displacement difference and the initial lateral position difference; the right lateral predicted position difference is determined based on the sum of the right lateral predicted displacement difference and the initial lateral position difference.

[0125] For example, such as Figure 1 As shown, at the first moment, the center axis of the following vehicle is on the right side of the vehicle in front. Below is... Figure 1 The determination methods for the left lateral prediction position difference and the right lateral prediction position difference in the case shown are described.

[0126] For example, the left lateral prediction position difference can be determined based on the following formula (8).

[0127] S pd_left =S pd_left’ -L2 formula (8)

[0128] For example, the right lateral prediction position difference can be determined based on the following formula (9).

[0129] S pd_right =S pd_right’ +L2 formula (9)

[0130] The following describes the method for determining the lateral predicted acceleration difference based on the first driving speed, the second driving speed, and the second predicted collision time.

[0131] For example, Figure 6 According to some embodiments of this application, a flowchart illustrating a method for determining lateral predicted acceleration difference is shown. Figure 6 The execution entities for each step of the process shown are electronic devices. For ease of description, the following will describe them separately. Figure 6 The execution entity of each step in the process shown will not be described again.

[0132] like Figure 6 As shown, the method includes the following steps S601-S602.

[0133] S601: Based on the lateral preset position difference between the rear vehicle and the front vehicle at the second predicted collision time, and the initial lateral position difference between the front vehicle and the rear vehicle at the first moment, determine the lateral preset displacement difference between the front vehicle and the rear vehicle at the second predicted collision time.

[0134] It can be understood that the lateral pre-set displacement difference between the front vehicle and the rear vehicle at the second predicted collision time refers to the lateral pre-set displacement difference between the front vehicle and the rear vehicle between the first time and the second predicted collision time (i.e., within the second predicted collision interval).

[0135] The following is about Figure 1 The determination method of the left lateral preset displacement difference and the right lateral preset displacement difference in the case shown is described.

[0136] For example, the left lateral preset displacement difference can be determined based on the following formula (10).

[0137] S ps_left’ =S ps_left +L2 formula (10)

[0138] In the above formula (10), S ps_left’ S represents the preset left lateral displacement difference; ps_left This indicates the left horizontal preset position difference.

[0139] For example, the right lateral preset displacement difference can be determined based on the following formula (11).

[0140] S ps_right’ =S ps_right -L2 formula (11)

[0141] In the above formula (11), S ps_right’ S represents the preset right lateral displacement difference; ps_right This indicates the preset position difference on the right horizontal axis.

[0142] It is understood that the embodiments of this application do not preset the left lateral position difference S. ps_left Difference S between the preset position and the right horizontal direction ps_right The values ​​of are limited; they can be the same or different.

[0143] S602: Determine the lateral predicted acceleration difference based on the first driving speed, the second driving speed, the second predicted collision interval between the first moment and the second predicted collision moment, and the lateral preset displacement difference.

[0144] For example, the left lateral predicted acceleration difference can be determined based on the following formula (12).

[0145] a pd_left =(S ps_left’ -(V) 2_left -V 1_left )t1)×2 / t1 2 Formula (12)

[0146] In the above formula (12), a pd_left This indicates the difference in predicted acceleration on the left lateral side.

[0147] For example, the right lateral predicted displacement difference can be determined based on the following formula (13).

[0148] a pd_right =(S ps_right’ -(V) 2_right -V 1_right )t1)×2 / t1 2 Formula (13)

[0149] In the above formula (13), a pd_right This indicates the difference in predicted acceleration to the right lateral direction.

[0150] For example, Figure 7 According to some embodiments of this application, a flowchart of a third vehicle collision prediction method is shown. Figure 7 The execution entities for each step of the process shown are electronic devices. For ease of description, the following will describe them separately. Figure 7 The execution entity of each step in the process shown will not be described again.

[0151] like Figure 7 As shown, the method includes the following steps S701-S707.

[0152] S701: At the first moment, obtain the first speed and first acceleration of the vehicle in front, and the second speed of the vehicle behind.

[0153] It is understandable that the execution principle of this step is the same as that of S201 mentioned above, so it will not be repeated here.

[0154] S702: Determine whether the prediction conditions are met.

[0155] It is understandable that if the prediction conditions are met, then S703 is executed to determine, based on the first driving speed, the second driving speed, and the first acceleration, at least one of the first predicted collision interval between the preceding and following vehicles and the second acceleration of the following vehicle within the first predicted collision interval under the preset collision conditions.

[0156] If not, i.e. the prediction conditions are not met, then execute S707 to predict that the vehicle in front and the vehicle behind have no possibility of collision.

[0157] The specific details of the prediction conditions can be found in the previous description and will not be repeated here.

[0158] S703: Based on a first driving speed, a second driving speed, and a first acceleration, determine at least one of a first predicted collision distance between the preceding and following vehicles and a second acceleration of the following vehicle within the first predicted collision distance under preset collision conditions.

[0159] It is understandable that the execution principle of this step is the same as that of S202 mentioned above, so it will not be repeated here.

[0160] S704: Determine the predicted collision parameters based on the first driving speed, the second driving speed, and the second predicted collision time.

[0161] It is understandable that the execution principle of this step is the same as that of S404 mentioned above, so it will not be repeated here.

[0162] S705: Determine whether at least one of the first conditions and at least one of the second conditions are satisfied.

[0163] It is understandable that if at least one of the first conditions and at least one of the second conditions are met, then S706 is executed to predict the possibility of a collision between the vehicle in front and the vehicle behind.

[0164] If not, that is, if at least one of the first conditions is not met, or if at least one of the second conditions is not met, then execute S707 to predict that the vehicle in front and the vehicle behind have no possibility of collision.

[0165] The specific details of the first and second conditions can be found in the previous description and will not be repeated here.

[0166] S706: Predicts the possibility of a collision between the vehicle in front and the vehicle behind.

[0167] S707: Predicts that there is no possibility of a collision between the vehicle in front and the vehicle behind.

[0168] In some embodiments, after predicting a potential collision between the vehicle in front and the vehicle behind, a warning command can be sent to at least one of the vehicles in front and the vehicle behind to indicate the possibility of a collision. This application does not limit the form of the warning command. For example, the warning command can be a voice command, a text command, etc. If the warning command is a text command, the text content corresponding to the text command can be displayed on the display screen of at least one of the vehicles in front and the vehicle behind.

[0169] It is understood that the method provided in this application embodiment can provide collision warnings to the vehicle in front and / or the vehicle behind before a collision occurs, for example, at the first moment, which can ensure the driving safety of the vehicle in front and the vehicle behind.

[0170] In some embodiments, this application provides a vehicle collision prediction device, the device comprising: an acquisition module, configured to acquire, at a first moment, a first driving speed and a first acceleration of a preceding vehicle, and a second driving speed of a following vehicle; a determination module, configured to determine, based on the first driving speed, the second driving speed, and the first acceleration, at least one of a first predicted collision interval between the preceding and following vehicles and a second acceleration of the following vehicle within the first predicted collision interval, under preset collision conditions; and a prediction module, configured to predict the probability of a collision between the preceding and following vehicles based on at least one of the first predicted collision interval and the second acceleration; wherein the preset collision conditions include setting the longitudinal speeds of the preceding and following vehicles at the time of collision to be the same.

[0171] In some embodiments, this application provides a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the vehicle collision prediction method described in the above embodiments.

[0172] In some embodiments, this application provides a computer program product, including: computer instructions, which, when executed on an electronic device, cause the electronic device to perform the vehicle collision prediction method described in the above embodiments.

[0173] In some embodiments, this application provides an electronic device including: at least one processor; at least one memory; the at least one memory storing at least one program, which, when executed by the at least one processor, causes the electronic device to perform the vehicle collision prediction method described in the above embodiments.

[0174] In some embodiments, this application provides a vehicle that includes the electronic devices described in the above embodiments.

[0175] For example, Figure 8 According to some embodiments of this application, a functional framework diagram of a vehicle is shown. The vehicle can be the front vehicle or the rear vehicle described in the above embodiments. For example... Figure 8 As shown, the functional framework of vehicle 00 may include various subsystems, such as Figure 8 The sensor system 10, control system 20, and one or more peripheral devices 30 shown are described. Figure 8 (Taking one as an example) , power supply 40, computer system 50. Optionally, vehicle 00 may also include other functional systems, such as an engine system that provides power to vehicle 00, etc., which are not limited in this application.

[0176] The sensor system 10 may include several detection devices that can sense the measured information and convert the sensed information into electrical signals or other required forms of information output according to a certain rule. For example... Figure 8 As shown, these detection devices may include a global positioning system (GPS), a vehicle speed sensor (12), an inertial measurement unit (IMU), etc., which are not limited in this application.

[0177] The Global Positioning System (GPS) 11 is a system that uses GPS positioning satellites to perform real-time positioning and navigation globally. In this application, GPS 11 can be used to achieve real-time positioning of vehicle 00, providing the geographical location information of vehicle 00. Vehicle speed sensor 12 is used to detect the vehicle speed of vehicle 00. In this embodiment, vehicle speed sensor 12 can be used to detect a first driving speed and a second driving speed.

[0178] The inertial measurement unit 13 may include a combination of an accelerometer and a gyroscope, and is a device for measuring the angular rate and acceleration of the vehicle 00. For example, during the movement of the vehicle 00, the inertial measurement unit 13 can measure the position and angular changes of the vehicle body based on the inertial acceleration of the vehicle 00, such as measuring the acceleration and angular rate of the vehicle 00. In this embodiment, the inertial measurement unit 13 can be used to detect the heading angle of a following vehicle.

[0179] The control system 20 may include a steering unit 21, a braking unit 22, etc.

[0180] Steering unit 21 can represent a system for adjusting the direction of travel of vehicle 00, which may include, but is not limited to, a steering wheel or other structural devices for adjusting or controlling the direction of travel of vehicle 00. Braking unit 22 can represent a system for slowing down the speed of vehicle 00, and may also be called a vehicle braking system. It may include, but is not limited to, a brake controller, a reducer, or other structural devices for slowing down vehicle 00. In practical applications, braking unit 22 can use friction to slow down the tires of vehicle 00, thereby slowing down the speed of vehicle 00.

[0181] Peripheral device 30 may include several components, such as Figure 8 The diagram shows a communication system 31, a touchscreen 32, a user interface 33, etc. The communication system 31 is used to enable network communication between the vehicle 00 and other devices besides the vehicle 00. In practical applications, the communication system 31 can employ wireless communication technology or wired communication technology to achieve network communication between the vehicle 00 and other devices. This wired communication technology can refer to communication between the vehicle 00 and other devices via network cables or optical fibers. This wireless communication technology includes, but is not limited to, Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wide Band Code Division Multiple Access (WCDMA), Time Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Wireless Local Area Networks (WLAN) (such as Wireless Fidelity (Wi-Fi) networks), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), and Infrared (IR) technology, etc.

[0182] The touchscreen 32 can be used to detect operation commands on the touchscreen 32. For example, the user can perform touch operations on the content data displayed on the touchscreen 32 according to actual needs to achieve the corresponding function, such as playing music, video, or other multimedia files. The user interface 33 can specifically be a touch panel, used to detect operation commands on the touch panel. The user interface 33 can also be a physical button or a mouse. The user interface 33 can also be a display screen, used to output data and display images or data. Optionally, the user interface 33 can also be at least one device belonging to the category of peripheral devices, such as a touchscreen, microphone, and speaker.

[0183] Several functions of vehicle 00 are controlled and implemented by computer system 50. Computer system 50 may include multiple processors such as processor 51, continuous damping control (CDC) 52, mobile data center (MDC) 53, telematics box (T-BOX) 54, as well as memory 55 (also referred to as storage device) and gateway 56. In practical applications, memory 55 may be located inside computer system 50 or outside computer system 50, for example, as a cache in vehicle 00, etc., and this application does not limit this.

[0184] Among them, processor 51 can be a graphics processing unit (GPU), etc. Processor 51, CDC 52, MDC 53, and T-BOX 54 can be used to run relevant programs or corresponding instructions stored in memory 55 to realize the corresponding functions of vehicle 00, such as network switching function based on service.

[0185] The memory 55 may include volatile memory, such as RAM; it may also include non-volatile memory, such as ROM, flash memory, HDD, or SSD; or it may include a combination of the above types of memory. The memory 55 can be used to store a set of program code or instructions corresponding to program code, so that the processor 51 can call the program code or instructions stored in the memory 55 to implement the corresponding functions of the vehicle 00. This function includes, but is not limited to, […]. Figure 8 The functional framework diagram shown includes some or all of the functions. In this application, the memory 55 can store a set of program code for vehicle control. The processor 51, CDC 52, MDC 53, and T-BOX 54 can call this program code to control the vehicle 00 to perform the process of predicting the probability of collision as described in this application.

[0186] Optionally, in addition to storing program code or instructions, memory 55 may also store information such as road maps, driving routes, and sensor data. Computer system 50 can be combined with other components in the vehicle functional framework diagram, such as sensors in the sensor system and GPS, to realize the relevant functions of vehicle 00. For example, computer system 50 can control the driving direction or speed of vehicle 00 based on data input from sensor system 10; this application does not limit this.

[0187] It should be noted that the above Figure 8 This is merely a schematic diagram of one possible functional framework for vehicle 00. In practical applications, a vehicle may include more or fewer systems or components, and this application does not impose any limitations.

[0188] It is understood that, as used herein, the term “module” may refer to or include, or be part of, an application-specific integrated circuit (ASIC), electronic circuitry, a processor (shared, dedicated, or grouped) and / or memory that executes one or more software or firmware programs, combinational logic circuitry, and / or other suitable hardware components that provide the described functionality.

[0189] It is understood that in the various embodiments of this application, the processor may be a microprocessor, a digital signal processor, a microcontroller, etc., and / or any combination thereof. According to another aspect, the processor may be a single-core processor, a multi-core processor, etc., and / or any combination thereof.

[0190] The embodiments disclosed in this application can be implemented in hardware, software, firmware, or a combination of these implementation methods. Embodiments of this application can be implemented as computer programs or program code executable on a programmable system, the programmable system including at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0191] Program code can be applied to input instructions to execute the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, the processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor.

[0192] The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used when needed. In fact, the mechanisms described in this application are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0193] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored thereon on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or via other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine-readable (e.g., computer-readable) form, including but not limited to floppy disks, optical disks, CD-ROMs, magneto-optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic cards or optical cards, flash memory, or tangible machine-readable storage for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in the form of electrical, optical, acoustic, or other forms of propagated signals. Therefore, machine-readable media includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a machine-readable (e.g., computer-readable) form.

[0194] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Furthermore, the inclusion of structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0195] It should be noted that all units / modules mentioned in the device embodiments of this application are logical units / modules. Physically, a logical unit / module can be a physical unit / module, a part of a physical unit / module, or a combination of multiple physical units / modules. The physical implementation of these logical units / modules themselves is not the most important factor; the combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed in this application. Furthermore, to highlight the innovative aspects of this application, the above-described device embodiments of this application have not introduced units / modules that are not closely related to solving the technical problems proposed in this application. This does not mean that the above-described device embodiments do not contain other units / modules.

[0196] It should be noted that in the examples and description of this application, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0197] Although this application has been illustrated and described with reference to certain embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made thereto without departing from the scope of this application.

Claims

1. A vehicle collision prediction method, characterized in that, The method includes: At the first moment, obtain the first speed and first acceleration of the vehicle in front, and the second speed of the vehicle behind; Based on the first driving speed, the second driving speed, and the first acceleration, determine at least one of the first predicted collision distance between the preceding vehicle and the following vehicle and the second acceleration of the following vehicle within the first predicted collision distance under preset collision conditions; Based on at least one of the first predicted collision interval and the second acceleration, the probability of a collision between the preceding vehicle and the following vehicle is predicted; The preset collision conditions include setting the longitudinal speed of the front vehicle and the rear vehicle to be the same at the time of the collision.

2. The vehicle collision prediction method according to claim 1, characterized in that, The preset collision conditions also include: The displacement difference between the preceding vehicle and the following vehicle within the first predicted collision interval and the longitudinal distance at a preset time are set to be the same. Wherein, the first predicted collision interval is the interval between a preset time and the first predicted collision time of the preceding vehicle and the following vehicle, the preset time is a preset time when the following vehicle begins to decelerate, the preset time is the same as the first time, or is located after the first time.

3. The vehicle collision prediction method according to claim 1, characterized in that, The method of predicting the probability of a collision between the preceding vehicle and the following vehicle based on at least one of the first predicted collision interval and the second acceleration includes: If at least one of the first conditions is met, it is predicted that the preceding vehicle and the following vehicle have a possibility of collision; The first condition includes: The first predicted collision interval is less than or equal to the first interval threshold; The absolute value of the second acceleration is greater than the longitudinal acceleration threshold.

4. The vehicle collision prediction method according to claim 1, characterized in that, The method further includes: Based on the first driving speed and the second driving speed, determine the second predicted collision time between the preceding vehicle and the following vehicle; Based on the first driving speed, the second driving speed, and the second predicted collision time, the predicted collision parameters are determined; The predicted collision parameters include at least one of the following: the difference in lateral predicted position between the preceding vehicle and the following vehicle at the second predicted collision time, the difference in lateral predicted acceleration between the preceding vehicle and the following vehicle within the second predicted collision interval, wherein the second predicted collision interval is the interval between the first time and the second predicted collision time.

5. The vehicle collision prediction method according to claim 4, characterized in that, The method of predicting the probability of a collision between the preceding vehicle and the following vehicle based on at least one of the first predicted collision interval and the second acceleration includes: If at least one of the first conditions and at least one of the second conditions are met, it is predicted that the preceding vehicle and the following vehicle are likely to collide. The first condition includes: The first predicted collision interval is less than or equal to the first interval threshold; The absolute value of the second acceleration is greater than the longitudinal acceleration threshold; The second condition includes: The absolute value of the lateral predicted position difference is less than or equal to the first distance threshold; The absolute value of the lateral predicted acceleration difference is greater than the lateral acceleration threshold.

6. The vehicle collision prediction method according to claim 4 or 5, characterized in that, The step of determining the predicted collision parameters based on the first driving speed, the second driving speed, and the second predicted collision time includes: Based on the first driving speed, the second driving speed, the second predicted collision interval, and the lateral preset acceleration difference between the rear vehicle and the front vehicle within the second predicted collision interval, the lateral predicted displacement difference between the front vehicle and the rear vehicle at the second predicted collision time is determined. The lateral predicted position difference is determined based on the lateral predicted displacement difference and the initial lateral position difference between the front vehicle and the rear vehicle at the first moment.

7. The vehicle collision prediction method according to claim 4 or 5, characterized in that, The step of determining the predicted collision parameters based on the first driving speed, the second driving speed, and the second predicted collision time includes: Based on the lateral preset position difference between the rear vehicle and the front vehicle at the second predicted collision time, and the initial lateral position difference between the front vehicle and the rear vehicle at the first time, the lateral preset displacement difference between the front vehicle and the rear vehicle at the second predicted collision time is determined. The lateral predicted acceleration difference is determined based on the first driving speed, the second driving speed, the second predicted collision interval, and the lateral preset displacement difference.

8. The vehicle collision prediction method according to claim 1, characterized in that, The step of determining, based on the first driving speed, the second driving speed, and the first acceleration, at least one of a first predicted collision distance between the preceding vehicle and the following vehicle and a second acceleration of the following vehicle within the first predicted collision distance under preset collision conditions includes: Under the condition of meeting the prediction conditions, based on the first driving speed, the second driving speed and the first acceleration, at least one of the first predicted collision interval between the front vehicle and the rear vehicle and the second acceleration of the rear vehicle within the first predicted collision interval is determined under the preset collision conditions. The prediction conditions include: The second predicted collision interval between the first time point and the second predicted collision time point is less than or equal to the second interval threshold, and the second predicted collision interval is determined based on the first driving speed and the second driving speed. At the first moment, the lateral overlap distance between the preceding vehicle and the following vehicle is greater than the overlap distance threshold. At the first moment, the second driving speed is less than or equal to the speed threshold in the lateral direction; At the first moment, the angle between the orientation and longitudinal direction of the rear vehicle is less than or equal to an angle threshold.

9. A vehicle collision prediction device, characterized in that, The device includes: The acquisition module is used to acquire the first driving speed and first acceleration of the vehicle in front, and the second driving speed of the vehicle behind at the first moment; The determination module is configured to determine, based on the first driving speed, the second driving speed, and the first acceleration, at least one of a first predicted collision interval between the preceding vehicle and the following vehicle and a second acceleration of the following vehicle within the first predicted collision interval under preset collision conditions; A prediction module is configured to predict the probability of a collision between the preceding vehicle and the following vehicle based on at least one of the first predicted collision interval and the second acceleration. The preset collision conditions include setting the longitudinal speed of the front vehicle and the rear vehicle to be the same at the time of the collision.

10. An electronic device, characterized in that, include: At least one processor; at least one memory; The at least one memory stores at least one program, which, when executed by the at least one processor, causes the electronic device to perform the vehicle collision prediction method according to any one of claims 1 to 8.

11. A vehicle, characterized in that, Includes the electronic device as described in claim 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on an electronic device, cause the electronic device to perform the vehicle collision prediction method according to any one of claims 1 to 8.

13. A computer program product, characterized in that, include: Computer instructions, when executed on an electronic device, cause the electronic device to perform the vehicle collision prediction method according to any one of claims 1 to 8.

Images