Methods, devices, equipment, storage media, and vehicles for determining vehicle collision risk.

Abstract

This application relates to a method, apparatus, device, storage medium, and vehicle for determining vehicle collision risk. The method, applied to a vehicle, includes: acquiring a first predicted driving trajectory of the vehicle at a first time and a second predicted driving trajectory of adjacent vehicles at the same first time, wherein the first time is after the current time; correcting the second predicted driving trajectory based on the actual driving trajectories of adjacent vehicles within a preset time period, and correcting the first predicted driving trajectory based on the corrected second predicted driving trajectory to obtain a corrected first predicted driving trajectory; wherein the preset time period is before the first time; and determining the degree of risk of a collision between the vehicle and adjacent vehicles based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and particularly to the field of driver assistance technology, specifically to a method, device, equipment, storage medium, and vehicle for determining vehicle collision risk. Background Technology

[0002] The development of in-vehicle intelligent driving systems is rapid, and vehicles are increasingly resembling intelligent computing platforms. Within these systems, as the demand for safe driving grows stronger, collision risk prediction, as a crucial component of intelligent vehicle safety technology, is gradually becoming a standard feature in modern automobiles. With the continuous advancement of autonomous driving technology, the development of vehicle automatic emergency braking trajectory prediction is showing a positive trend.

[0003] Current vehicle automatic emergency braking systems, when faced with dense traffic, only consider drivable lane information and cannot achieve high accuracy in predicting the target and the vehicle's trajectory. Furthermore, they require multiple iterative learning cycles on a fixed route to achieve high accuracy. Summary of the Invention

[0004] This application provides a method, apparatus, device, storage medium, and vehicle for determining vehicle collision risk, so as to improve the accuracy of predicting collision risk in emergency situations in complex road scenarios.

[0005] According to a first aspect of this application, a method for determining vehicle collision risk is provided, comprising: acquiring a first predicted driving trajectory of the vehicle at a first time and a second predicted driving trajectory of an adjacent vehicle at a first time, wherein the first time is after the current time; correcting the second predicted driving trajectory based on the actual driving trajectories of the adjacent vehicles within a preset time period, and correcting the first predicted driving trajectory based on the corrected second predicted driving trajectory to obtain a corrected first predicted driving trajectory; wherein the preset time period is before the first time; and determining the degree of risk of the vehicle colliding with an adjacent vehicle based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory.

[0006] Based on the aforementioned technical means, this application can, after obtaining the predicted driving trajectories of its own vehicle and adjacent vehicles, correct the predicted driving trajectories of adjacent vehicles by using the actual driving trajectories of adjacent vehicles within a preset time period, thereby obtaining a more accurate predicted driving trajectory. Then, based on the corrected predicted driving trajectories of adjacent vehicles, the predicted driving trajectory of the own vehicle is corrected, improving the accuracy of the predicted driving trajectory of the own vehicle. Thus, based on the corrected predicted driving trajectories of adjacent vehicles and the corrected predicted driving trajectory of the own vehicle, the risk level of a collision between the own vehicle and adjacent vehicles can be accurately predicted.

[0007] In one possible implementation, the method further includes: determining at least one driving trajectory of the adjacent vehicles based on traffic information of their locations at a first time; determining the driving trajectory with the highest score among the at least one driving trajectory based on a preset scoring mechanism, and using the driving trajectory with the highest score as the second predicted driving trajectory of the adjacent vehicles at a first time.

[0008] Based on the aforementioned technical means, this application can determine at least one driving trajectory of adjacent vehicles based on their traffic information, and obtain the driving trajectory of the adjacent vehicle with the highest probability of travel through scoring, and use this trajectory as the second predicted driving trajectory of the adjacent vehicle. This allows for more accurate prediction of the driving trajectories of adjacent vehicles, improving the vehicle's reaction speed to unexpected situations.

[0009] In one possible implementation, the method further includes: using a preset trajectory prediction model of the traffic information of adjacent vehicles at a first time to obtain at least one set of parameters corresponding to the traffic information; and determining at least one predicted driving trajectory of adjacent vehicles at a first time based on the at least one set of parameters and a preset trajectory calculation formula; with one set of parameters corresponding to one driving trajectory.

[0010] Based on the aforementioned technical means, this application can incorporate vehicles into a trajectory prediction model through mathematical modeling, and obtain more accurate predicted driving trajectories of adjacent vehicles according to the corresponding trajectory calculation formula. In this way, accurate driving trajectory data of adjacent vehicles can be obtained.

[0011] In one possible implementation, the method further includes: determining a correction coefficient based on the current driving scenario of the vehicle; and correcting the first predicted driving trajectory based on the corrected second predicted driving trajectory to obtain a corrected first predicted driving trajectory, including: correcting the first predicted driving trajectory based on the correction coefficient and the corrected second predicted driving trajectory to obtain a corrected first predicted driving trajectory.

[0012] Based on the aforementioned technical means, this application can correct the first predicted driving trajectory based on the correlation coefficient obtained from the model and the corrected second predicted driving trajectory. This results in a more accurate driving trajectory for the vehicle, helping to improve the accuracy of the vehicle's prediction of collision risks.

[0013] In one possible implementation, the method further includes: the correction coefficients include a first correction coefficient and a second correction coefficient; the corrected first predicted driving trajectory satisfies a preset formula, which is:

[0014] S = S1*A + S2*B;

[0015] Where S represents the trajectory parameters corresponding to the first predicted driving trajectory after correction, S1 represents the trajectory parameters corresponding to the first predicted driving trajectory, S2 represents the trajectory parameters corresponding to the second predicted driving trajectory after correction, A represents the first correction coefficient, and B represents the second correction coefficient.

[0016] Based on the above technical means, this application can make a more accurate prediction of the first predicted trajectory through the trajectory correction formula.

[0017] In one possible implementation, the method further includes: acquiring driving information of the vehicle at a first time, the driving information including: the driving behavior of the vehicle's driver and / or traffic information of the vehicle's location; and determining a first predicted driving trajectory of the vehicle at the first time based on the vehicle's driving information.

[0018] Based on the aforementioned technical means, this application can obtain the driver's driving information and the surrounding traffic information to determine the first predicted driving trajectory. This can provide the vehicle with more possible reference information, making the prediction information more reliable.

[0019] In one possible implementation, the method further includes activating the vehicle's automatic emergency braking system when the risk of a collision between the vehicle and an adjacent vehicle is determined to be greater than a preset threshold.

[0020] Based on the aforementioned technical means, this application can activate the vehicle's automatic emergency braking system by assessing the risk of vehicle collision, thereby reducing or avoiding losses caused by vehicle collisions.

[0021] In one possible implementation, the method further includes: determining the degree of overlap between the corrected second predicted driving trajectory and the corrected first predicted driving trajectory; determining the risk level of a collision between the vehicle and an adjacent vehicle based on the degree of overlap; wherein the degree of overlap is positively correlated with the risk level.

[0022] Based on the aforementioned technical means, the degree of collision risk is predicted by comparing the corrected second predicted trajectory with the corrected first predicted trajectory.

[0023] According to a second aspect of this application, a vehicle collision risk determination apparatus is provided, comprising: an acquisition unit, a correction unit, and a determination unit. The acquisition unit acquires a first predicted driving trajectory of the vehicle at a first time and a second predicted driving trajectory of adjacent vehicles at the same first time, wherein the first time is after the current time; the correction unit corrects the second predicted driving trajectory based on the actual driving trajectories of adjacent vehicles within a preset time period, and corrects the first predicted driving trajectory based on the corrected second predicted driving trajectory to obtain a corrected first predicted driving trajectory; the preset time period is before the first time; and the determination unit determines the degree of risk of a collision between the vehicle and adjacent vehicles based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory.

[0024] According to a third aspect provided in this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect described above and any possible implementation thereof.

[0025] According to a fourth aspect provided in this application, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0026] According to the fifth aspect provided in this application, a vehicle is provided, including the electronic equipment provided in the third aspect above.

[0027] Therefore, the above-mentioned technical features of this application have the following beneficial effects:

[0028] (1) This application can, after obtaining the predicted driving trajectories of the vehicle and adjacent vehicles, correct the predicted driving trajectories of adjacent vehicles by using the actual driving trajectories of adjacent vehicles within a preset time period, thereby obtaining a more accurate predicted driving trajectory. Then, based on the corrected predicted driving trajectories of adjacent vehicles, the predicted driving trajectory of the vehicle is corrected, improving the accuracy of the vehicle's predicted driving trajectory. Thus, based on the corrected predicted driving trajectories of adjacent vehicles and the corrected predicted driving trajectory of the vehicle itself, the risk level of a collision between the vehicle and adjacent vehicles can be accurately predicted.

[0029] (2) This application can determine at least one driving trajectory of adjacent vehicles based on the traffic information of adjacent vehicles, and obtain the driving trajectory of the adjacent vehicle with the highest probability of driving by scoring, and use it as the second predicted driving trajectory of the adjacent vehicle. In this way, the driving trajectory of adjacent vehicles can be predicted more accurately, and the vehicle's response speed to emergencies can be improved.

[0030] (3) This application can obtain at least one set of parameters by inputting the traffic information of the location of adjacent vehicles into the trajectory prediction model, and determine the predicted driving trajectory of adjacent vehicles based on the at least one set of parameters, which is simple and convenient.

[0031] (4) This application can determine the correction coefficient based on the current scene of the vehicle, and correct the vehicle's driving trajectory based on the correction coefficient, thereby ensuring that the corrected predicted driving trajectory of the vehicle is more in line with the scene.

[0032] (5) This application can make more accurate predictions of the first predicted trajectory through the trajectory correction formula, which is simple and fast.

[0033] (6) This application can determine the predicted driving trajectory of a vehicle by the driver's driving operation and the surrounding traffic information. Since the vehicle's driving information can reflect the driver's intention, the vehicle's driving trajectory can be accurately predicted based on the vehicle's driving information.

[0034] (7) This application can activate the vehicle's automatic emergency braking system by assessing the risk of vehicle collision, so as to avoid vehicle collision as much as possible.

[0035] (8) This application can accurately determine the risk of a collision between vehicles based on the degree of overlap between the modified second predicted trajectory and the modified first predicted trajectory.

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

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

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

[0039] Figure 1 This is a flowchart illustrating a method for determining vehicle collision risk according to an exemplary embodiment;

[0040] Figure 2 This is a flowchart illustrating a trajectory prediction calculation method for vehicle collision risk according to an exemplary embodiment;

[0041] Figure 3 This is a flowchart illustrating trajectory correction based on vehicle-related parameters according to an exemplary embodiment;

[0042] Figure 4 This is a block diagram illustrating a vehicle collision risk determination device according to an exemplary embodiment;

[0043] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0045] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0046] For ease of understanding, the method for determining vehicle collision risk provided in this application will be described in detail below with reference to the accompanying drawings. The implementer of the method provided in this application can be a vehicle or equipment in a vehicle (such as an in-vehicle terminal, system (such as a vehicle infotainment system)). The following description takes the vehicle terminal as the implementer as an example.

[0047] For ease of understanding, the method for determining vehicle collision risk provided in this application will be described in detail below with reference to the accompanying drawings.

[0048] like Figure 1 The diagram shown is a flowchart illustrating a method for determining vehicle collision risk according to an exemplary embodiment, which may include the following steps.

[0049] S101. Obtain the first predicted driving trajectory of the vehicle at the first moment and the second predicted driving trajectory of the adjacent vehicles of the vehicle at the first moment.

[0050] The first time is after the current time. Adjacent vehicles can be those located less than a preset distance from the current vehicle (hereinafter referred to as the target vehicle for distinction). There can be multiple adjacent vehicles. The preset distance can be set as needed; for example, it can be the detection range of the radar system configured for the vehicle. The driving trajectory can be used to reflect the route the vehicle is about to take, such as driving straight, turning left, turning right, or making a U-turn.

[0051] In this application, the driving trajectory refers to the predicted driving trajectory of the vehicle. That is, the first predicted driving trajectory is the predicted driving trajectory of the vehicle, and the second predicted driving trajectory is the predicted driving trajectory of the vehicle's adjacent vehicles. The number of second predicted driving trajectories can be one or more.

[0052] In one possible implementation, the vehicle terminal can determine the first predicted driving trajectory of the target vehicle at the first moment based on the driver's operation information or the traffic information of the target vehicle's location.

[0053] Driver operation information refers to the actions taken by the driver while driving the vehicle. Examples include pressing the accelerator pedal, pressing the brake pedal, and turning the steering wheel. Traffic information refers to road environment information about the vehicle's location and information about surrounding vehicles. For example, road environment information can include road data collected by the vehicle's sensors (such as lidar and cameras) (e.g., lane markings, traffic lights, crosswalks, distances to surrounding vehicles), while information about surrounding vehicles can include their coordinates, speed, acceleration, and heading angle.

[0054] In one example, after acquiring the driver's immediate operation information, the in-vehicle terminal can predict the vehicle's trajectory based on this information. For instance, if the driver's operation is to turn the steering wheel, the in-vehicle terminal can determine the predicted trajectory based on the steering wheel's rotation angle. For example, the in-vehicle terminal can be pre-configured with a correspondence between the vehicle's steering wheel rotation angle and its trajectory; thus, based on this correspondence, the in-vehicle terminal can determine the predicted trajectory corresponding to the steering wheel rotation angle.

[0055] In another example, the vehicle-mounted terminal can be pre-configured with a driving trajectory prediction model, which can be used to predict the vehicle's driving trajectory. The input of this driving trajectory prediction model is the vehicle's traffic information, and the output is the vehicle's predicted driving trajectory. Thus, after obtaining the traffic information of the target vehicle's location, the vehicle-mounted terminal can input the traffic information into the driving trajectory prediction model to obtain the vehicle's first predicted driving trajectory.

[0056] In another possible implementation, the onboard terminal can determine the second predicted driving trajectory based on traffic information from neighboring vehicles. For details, please refer to [reference needed]. Figure 2 The description of the embodiments shown will not be repeated here.

[0057] S102. The second predicted driving trajectory is corrected based on the actual driving trajectories of adjacent vehicles within a preset time period, and the first predicted driving trajectory is corrected based on the corrected second predicted driving trajectory to obtain the corrected first predicted driving trajectory.

[0058] The preset time period is located before the first time. For example, if the current time is 13:00 and the first time is 13:05, then the preset time period can be the time between 13:00 and 13:05. For example, the preset time period can be 13:00-13:01. The driving trajectory of adjacent vehicles within the preset time period refers to the actual driving trajectory of adjacent vehicles within the preset time period.

[0059] In one possible implementation, the vehicle terminal corrects the second predicted driving trajectory based on the driving trajectories of adjacent vehicles within a preset time period.

[0060] In one example, the vehicle-mounted terminal can collect the actual driving trajectories of adjacent vehicles within a preset time period, and compare these actual trajectories with a second predicted driving trajectory to obtain difference data, such as speed difference and heading angle difference. Based on this difference data, the vehicle-mounted terminal can then correct the second predicted driving trajectory to obtain a corrected second predicted driving trajectory.

[0061] Furthermore, after obtaining the corrected second predicted driving trajectory, the vehicle terminal can correct the first predicted driving trajectory based on the corrected second predicted driving trajectory to obtain the corrected first predicted driving trajectory.

[0062] In one example, the on-board terminal can correct the first predicted driving trajectory based on a correction coefficient and a modified second predicted driving trajectory, thus obtaining a corrected first predicted driving trajectory. For details, please refer to [reference needed]. Figure 3 The description of the embodiments shown will not be repeated here.

[0063] S103. Based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory, determine the risk level of a collision between the vehicle and an adjacent vehicle.

[0064] In one possible implementation, after obtaining the corrected first predicted driving trajectory and the corrected second predicted driving trajectory, the vehicle terminal can determine the risk level of a collision between the target vehicle and adjacent vehicles based on the degree of overlap between the corrected first predicted driving trajectory and the corrected second predicted driving trajectory.

[0065] Understandably, the greater the overlap between the corrected first predicted driving trajectory and the corrected second predicted driving trajectory, the higher the risk of the target vehicle colliding with adjacent vehicles.

[0066] Furthermore, if the risk of a collision between the target vehicle and an adjacent vehicle is determined to be greater than a preset threshold, the onboard terminal can activate the automatic emergency braking system. This ensures safe vehicle operation.

[0067] The preset threshold can be set as needed, for example, it can be 50%. That is, the probability of the target vehicle colliding with adjacent vehicles is relatively high.

[0068] It should be noted that the predictions of the target vehicle's and adjacent vehicles' trajectories in the main operations described above are all based on compliance with traffic regulations; and the subsequent predictions of the target vehicle's driving behavior and the corrections of the target vehicle's and adjacent vehicles' trajectories are also based on compliance with traffic regulations. The subsequent steps follow the same principle.

[0069] based on Figure 1 According to the technical solution in this application, after acquiring the predicted driving trajectories of its own vehicle and adjacent vehicles, the vehicle-mounted terminal can correct the predicted driving trajectory of the adjacent vehicles by using the actual driving trajectories of the adjacent vehicles within a preset time period, thus obtaining a more accurate predicted driving trajectory. Then, based on the corrected predicted driving trajectories of the adjacent vehicles, the predicted driving trajectory of the own vehicle is corrected, improving the accuracy of the predicted driving trajectory of the own vehicle. In this way, based on the corrected predicted driving trajectories of the adjacent vehicles and the corrected predicted driving trajectory of the own vehicle, the risk level of a collision between the own vehicle and adjacent vehicles can be accurately predicted.

[0070] In some embodiments, such as Figure 2 As shown, in S101 above, the second predicted driving trajectory of the adjacent vehicles of the vehicle at the first time can be obtained, which may specifically include S201-S202.

[0071] S201. Based on the traffic information of the location of adjacent vehicles at the first moment, determine at least one predicted driving trajectory of the adjacent vehicles.

[0072] In one possible implementation, the vehicle-mounted terminal can input the real-time traffic information of the locations of adjacent vehicles into a preset trajectory prediction model to obtain at least one set of parameters for calculating the driving trajectory. Based on this at least one set of parameters and the preset trajectory calculation formula, at least one predicted driving trajectory for the adjacent vehicles is obtained.

[0073] One set of parameters is used to calculate a predicted driving trajectory.

[0074] In one example, the preset trajectory calculation formula can be as shown in Formula 1:

[0075] y = C0 + C1x + 1 / 2C2x 2 +1 / 6C3x 3 Formula 1

[0076] Where y is a variable, x is a variable, and C0, C1, C2, and C3 are parameters.

[0077] Based on the above formula, the vehicle terminal can fit a function based on each set of parameters, and based on this function, a curve can be obtained, that is, a predicted driving trajectory of adjacent vehicles.

[0078] S202. Based on a preset scoring mechanism, determine the highest-scoring driving trajectory among at least one predicted driving trajectory, and use the highest-scoring driving trajectory as the second predicted driving trajectory of the adjacent vehicle at the first moment.

[0079] The prediction scoring mechanism can refer to scoring at least one pair of predicted driving trajectories to obtain a score for each predicted driving trajectory, which can characterize the accuracy of the predicted driving trajectory. For example, the prediction scoring mechanism may include scoring the predicted driving trajectory based on vehicle driving information. In one possible implementation, the vehicle terminal can predict the driving trajectory of adjacent vehicles based on the driving information of adjacent vehicles (such as speed, heading angle, acceleration, and position), and compare the similarity between each driving trajectory and the aforementioned at least one predicted driving trajectory. The higher the similarity, the higher the score of the predicted driving trajectory; the lower the similarity, the lower the score of the predicted driving trajectory. In this way, the vehicle terminal can determine the score of each predicted driving trajectory and use the predicted driving trajectory with the highest score as the second predicted driving trajectory of the adjacent vehicle at the first moment.

[0080] In one example, the vehicle-mounted terminal can input the driving information of adjacent vehicles into the trajectory prediction model to obtain the driving trajectories of the adjacent vehicles. For details, please refer to the relevant description in S101 above, which will not be repeated here.

[0081] based on Figure 2 According to the technical solution described in this application, at least one predicted driving trajectory for adjacent vehicles can be determined based on traffic information indicating their locations at a given time. The trajectory is then evaluated using a formula. Subsequently, based on a preset scoring mechanism, the driving trajectory with the highest score among the at least one predicted driving trajectory is determined and used as the second predicted driving trajectory for the adjacent vehicle at the given time. In this way, the driving trajectory information of adjacent vehicles can be accurately calculated.

[0082] In some embodiments, such as Figure 3 As shown, in S102 above, the first predicted driving trajectory is corrected based on the corrected second predicted driving trajectory to obtain the corrected first predicted driving trajectory, which may specifically include: S301-S302.

[0083] S301. Determine the correction coefficient based on the current driving scenario of the vehicle.

[0084] The correction factor is used to correct the vehicle's trajectory.

[0085] In one example, the in-vehicle terminal can determine the corresponding correction coefficient based on the driving scenario in which the vehicle is located. Different driving scenarios correspond to different correction coefficients. For example, driving scenarios may include urban road driving scenarios, off-road road driving scenarios, and rural road driving scenarios. For instance, the in-vehicle terminal can be configured with a correspondence between driving scenarios and correction coefficients. Based on this correspondence, the in-vehicle terminal can accurately determine the correction coefficient corresponding to the current driving scenario in which the vehicle is located.

[0086] S302. Based on the correction coefficient and the corrected second predicted driving trajectory, the first predicted driving trajectory is corrected to obtain the corrected first predicted driving trajectory.

[0087] In one example, taking a correction coefficient that includes a first correction coefficient and a second correction coefficient, the corrected first predicted driving trajectory can satisfy a preset formula. The preset formula can be shown in Formula 2:

[0088] Formula 2: S = S1*A + S2*B

[0089] Where S represents the trajectory parameters corresponding to the first predicted driving trajectory after correction, S1 represents the trajectory parameters corresponding to the first predicted driving trajectory, S2 represents the trajectory parameters corresponding to the second predicted driving trajectory after correction, A represents the first correction coefficient, and B represents the second correction coefficient.

[0090] Based on this embodiment, the vehicle terminal can determine the correction coefficient according to the current driving scenario of the vehicle, and correct the predicted driving trajectory of the target vehicle based on the correction coefficient, so as to accurately obtain the predicted driving trajectory that matches the current driving scenario.

[0091] based on Figure 3 The technical solution can determine the correction coefficient based on the current driving scenario of the vehicle. Alternatively, it can correct the first predicted driving trajectory based on the correction coefficient and the corrected second predicted driving trajectory, resulting in a corrected first predicted driving trajectory. Thus, by using the correction system, the on-board terminal can predict the vehicle's driving trajectory more accurately.

[0092] Figure 4 This is a schematic diagram illustrating a vehicle collision risk determination device according to an exemplary embodiment. (Refer to...) Figure 4 The emergency braking system control device 40 includes: an acquisition unit 401, a correction unit 402, and a determination unit 403.

[0093] In one possible approach, the acquisition unit 401 acquires the first predicted driving trajectory of the vehicle at a first time and the second predicted driving trajectory of the adjacent vehicles of the vehicle at a first time, wherein the first time is after the current time.

[0094] In one possible approach, the correction unit 402 corrects the second predicted driving trajectory based on the driving trajectories of adjacent vehicles within a preset time period after the first time, and corrects the first predicted driving trajectory based on the corrected second predicted driving trajectory to obtain the corrected first predicted driving trajectory.

[0095] In one possible approach, the determining unit 403 determines the corrected second predicted driving trajectory and the corrected first predicted driving trajectory, and determines the risk level of a collision between the vehicle and an adjacent vehicle.

[0096] In one possible approach, the determining unit 403 determines at least one driving trajectory of the adjacent vehicles based on traffic information about the location of the adjacent vehicles at the first moment.

[0097] In one possible approach, the determining unit 403 determines the highest-scoring driving trajectory among at least one driving trajectory based on a preset scoring mechanism, and uses the highest-scoring driving trajectory as the second predicted driving trajectory of the adjacent vehicle at the first moment.

[0098] In one possible approach, the acquisition unit 401 acquires the trajectory calculation formula corresponding to the traffic information and presets a trajectory prediction model based on the traffic information of the locations of adjacent vehicles at the first time.

[0099] In one possible approach, the determining unit 403 determines at least one driving trajectory of adjacent vehicles in the first moment based on the trajectory calculation formula.

[0100] In one possible approach, unit 403 is determined to determine the first correction coefficient and the second correction coefficient.

[0101] In one possible approach, the correction unit 402 corrects the first predicted driving trajectory based on the corrected second predicted driving trajectory to obtain the corrected first predicted driving trajectory.

[0102] In one possible approach, the correction unit 402 corrects the first predicted driving trajectory based on the first correction coefficient, the second correction coefficient, and the corrected second predicted driving trajectory to obtain the corrected first predicted driving trajectory.

[0103] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0104] Figure 5 This is a schematic diagram illustrating an electronic device according to an exemplary embodiment. Figure 5 As shown, the electronic device 50 includes, but is not limited to, a processor 501 and a memory 502.

[0105] The memory 502 described above is used to store the executable instructions of the processor 501. It is understood that the processor 501 is configured to execute instructions to implement the vehicle collision risk determination method in the above embodiments.

[0106] It should be noted that those skilled in the art will understand that Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 5 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0107] Processor 501 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 502, and by calling data stored in memory 502, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 501 may include one or more processing units. Optionally, processor 501 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 501.

[0108] The memory 502 can be used to store software programs and various data. The memory 502 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0109] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 502 including instructions, which can be executed by a processor 501 of an electronic device 50 to implement the methods in the above embodiments.

[0110] In actual implementation, Figure 4 The functions of the acquisition unit 401, correction unit 402, and determination unit 403 can all be provided by... Figure 5The processor 501 calls the computer program stored in the memory 502 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0111] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0112] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by a processor 501 of an electronic device to perform the methods described above.

[0113] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of an electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0116] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0117] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0119] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of determining a risk of a vehicle collision, characterized by, Applied to vehicles, the method includes: Obtain the first predicted driving trajectory of the vehicle at a first time and the second predicted driving trajectory of the adjacent vehicles of the vehicle at the first time, wherein the first time is after the current time; The second predicted driving trajectory is corrected based on the actual driving trajectories of adjacent vehicles within a preset time period. Then, based on the correction coefficient and the corrected second predicted driving trajectory, the first predicted driving trajectory is corrected to obtain the corrected first predicted driving trajectory. The preset time period is prior to the first time period. The correction coefficient is determined based on the current driving scenario of the vehicle. Different driving scenarios correspond to different correction coefficients. Based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory, the risk level of the collision between the vehicle and the adjacent vehicle is determined.

2. The method of claim 1, wherein, Obtaining the second predicted driving trajectory of the vehicle's neighboring vehicles at the first time point includes: Based on the traffic information of the adjacent vehicles at the first time, at least one driving trajectory of the adjacent vehicles is determined. Based on a preset scoring mechanism, the driving trajectory with the highest score among the at least one driving trajectory is determined, and the driving trajectory with the highest score is used as the second predicted driving trajectory of the adjacent vehicle at the first time.

3. The method of claim 2, wherein, Determining at least one travel trajectory of the adjacent vehicles based on traffic information indicating their locations at the first time includes: The traffic information of the location of adjacent vehicles at the first time is input into a preset trajectory prediction model to obtain at least one set of parameters corresponding to the traffic information; Based on the at least one set of parameters and the preset trajectory calculation formula, at least one predicted driving trajectory of the adjacent vehicle at the first time is determined; one set of parameters corresponds to one driving trajectory.

4. The method of claim 1, wherein, The correction coefficients include a first correction coefficient and a second correction coefficient; the corrected first predicted driving trajectory satisfies a preset formula, which is: S = S1*A + S2*B; Where S represents the trajectory parameters corresponding to the corrected first predicted driving trajectory, S1 represents the trajectory parameters corresponding to the first predicted driving trajectory, S2 represents the trajectory parameters corresponding to the corrected second predicted driving trajectory, A represents the first correction coefficient, and B represents the second correction coefficient.

5. The method according to any one of claims 1-3, characterized in that, The step of obtaining the first predicted driving trajectory of the vehicle at the first moment includes: Obtain the driving information of the vehicle at the first time, the driving information including: the driving behavior of the driver of the vehicle and / or traffic information of the location of the vehicle; Based on the vehicle's driving information, a first predicted driving trajectory of the vehicle at the first time point is determined.

6. The method according to any one of claims 1-3, characterized in that, The method further includes: If the risk of a collision between the vehicle and the adjacent vehicle is determined to be greater than a preset threshold, the vehicle's automatic emergency braking system is activated.

7. The method according to any one of claims 1-3, characterized in that, The determination of the risk level of a collision between the vehicle and the adjacent vehicle based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory includes: Determine the degree of overlap between the corrected second predicted driving trajectory and the corrected first predicted driving trajectory; Based on the degree of overlap, the risk level of a collision between the vehicle and the adjacent vehicle is determined; wherein, the degree of overlap is positively correlated with the risk level.

8. A device for determining vehicle collision risk, characterized in that, Applied to vehicles, the device includes: The acquisition unit is used to acquire the first predicted driving trajectory of the vehicle at a first time and the second predicted driving trajectory of the adjacent vehicles of the vehicle at the first time, wherein the first time is after the current time; The correction unit is used to correct the second predicted driving trajectory based on the actual driving trajectories of the adjacent vehicles within a preset time period, and to correct the first predicted driving trajectory based on the correction coefficient and the corrected second predicted driving trajectory, to obtain the corrected first predicted driving trajectory; the preset time period is located before the first time; the correction coefficient is determined based on the driving scenario in which the vehicle is currently located; different driving scenarios correspond to different correction coefficients; The determining unit is used to determine the risk level of a collision between the vehicle and the adjacent vehicle based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory.

9. An electronic device, characterized in that, include: Memory used to store processor-executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the computer-executable instructions stored in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is capable of performing the method as described in any one of claims 1 to 7.

11. A vehicle, characterized in that, Includes the electronic device as described in claim 9.

Images