Vehicle collision risk determination method and device, equipment, storage medium and vehicle

Abstract

The invention relates to a vehicle collision risk determination method and device, equipment, a storage medium and a vehicle, and is characterized in that the method is applied to the vehicle, the method comprises the steps that a first predicted driving track of the vehicle at first time and a second predicted driving track of a vehicle adjacent to the vehicle at the first time are acquired, and the first time is after the current time; correcting the second predicted driving track based on the actual driving track of the adjacent vehicle in a preset time period, and correcting the first predicted driving track based on the corrected second predicted driving track to obtain a corrected first predicted driving track; the preset time period is before the first time; and determining the risk degree of collision between the vehicle and the adjacent vehicle based on the corrected second predicted driving track and the corrected first predicted driving track.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, in particular to the field of assisted driving technology, and specifically to a method, device, equipment, storage medium and vehicle for determining vehicle collision risk. Background Art

[0002] At present, the in-vehicle intelligent driving system is developing rapidly, and vehicles are getting closer and closer to an intelligent computing platform. In the in-vehicle intelligent driving system, as the demand for safe driving becomes stronger and stronger, the prediction of vehicle collision risk, as an important part of intelligent car safety technology, is gradually becoming a standard feature of modern cars. With the continuous advancement of autonomous driving technology, the development of vehicle automatic emergency braking trajectory prediction shows a positive trend.

[0003] When the current vehicle automatic emergency braking system faces dense traffic, it only considers the information of the drivable channel and cannot achieve high-accuracy target and trajectory prediction of the vehicle itself. It also requires multiple iterative learning of fixed routes to achieve high accuracy. Summary of the invention

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

[0005] According to a first aspect of the present application, a method for determining a vehicle collision risk is provided, comprising: obtaining a first predicted driving trajectory of the vehicle at a first time and a second predicted driving trajectory of an adjacent vehicle of the vehicle at a first time, the first time being after a current time; correcting the second predicted driving trajectory based on an actual driving trajectory of the adjacent vehicle 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; the preset time period is before the first time; and determining 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.

[0006] According to the above technical means, after obtaining the predicted driving trajectories of the own vehicle and the adjacent vehicle, the present application can correct the predicted driving estimate of the adjacent vehicle by using the actual driving trajectory of the adjacent vehicle within a preset time period to obtain a predicted driving trajectory with higher accuracy. Then, based on the corrected predicted driving trajectory of the adjacent vehicle, the predicted driving trajectory of the own vehicle is corrected to improve the accuracy of the predicted driving trajectory of the own vehicle. In this way, based on the corrected predicted driving trajectory of the adjacent vehicle and the corrected predicted driving trajectory of the vehicle, the risk of collision between the own vehicle and the adjacent vehicle can be accurately predicted.

[0007] In a possible implementation, the method further includes: determining at least one driving trajectory of the adjacent vehicle based on traffic information of the adjacent vehicle's position at the 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 vehicle at the first time.

[0008] According to the above technical means, the present application can determine at least one driving trajectory of an adjacent vehicle based on the traffic information of the adjacent vehicle, and obtain a driving trajectory of the adjacent vehicle with the highest possible driving probability through scoring, and use it as the second predicted driving trajectory of the adjacent vehicle. In this way, the driving trajectory of the adjacent vehicle can be predicted more accurately, and the vehicle's response speed to emergencies can be improved.

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

[0010] According to the above technical means, the present application can incorporate the vehicle into the trajectory prediction model by means of mathematical modeling, and obtain a more accurate predicted driving trajectory of adjacent vehicles according to the corresponding trajectory calculation formula. In this way, accurate adjacent vehicle driving trajectory data can be obtained.

[0011] In a possible implementation, the method further includes: determining a correction coefficient according to a current driving scenario of the vehicle; 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] According to the above technical means, the present application can correct the first predicted driving trajectory according to the correlation coefficient obtained from the model and the corrected second predicted driving trajectory, so as to obtain a more accurate driving trajectory of the vehicle, which helps to improve the accuracy of the vehicle's prediction of collision risk situations.

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

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

[0015] Among them, 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.

[0016] According to the above technical means, the present application can make a more accurate prediction of the first predicted trajectory through a trajectory correction formula.

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

[0018] According to the above technical means, the present application can obtain the driving information of the vehicle driver and the surrounding traffic information to determine the first predicted driving trajectory, and can provide more possible reference information for the vehicle, making the predicted information more reliable.

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

[0020] According to the above technical means, the present application can judge the risk of vehicle collision and activate the vehicle's automatic emergency braking system to reduce or avoid the losses caused by vehicle collision.

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

[0022] According to the above technical means, the collision risk level is predicted by performing trajectory overlap prediction on the revised second predicted trajectory and the revised first predicted trajectory.

[0023] According to the second aspect provided by the present application, a device for determining vehicle collision risk is provided, comprising: an acquisition unit, a correction unit, and a determination unit. The acquisition unit is used to acquire a first predicted driving trajectory of the vehicle at a first time and a second predicted driving trajectory of an adjacent vehicle of the vehicle at a first time, the first time being after the current time; the correction unit is used to correct the second predicted driving trajectory based on the actual driving trajectory of the adjacent vehicle within a preset time period, and to correct 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; the determination unit is used to determine the risk level of collision between the vehicle and the adjacent vehicle based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory.

[0024] According to the third aspect provided by the present 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 above-mentioned first aspect and any possible implementation manner thereof.

[0025] According to the fourth aspect provided by the present application, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method in the above-mentioned first aspect and any possible implementation method thereof.

[0026] According to a fifth aspect provided by the present application, a vehicle is provided, comprising the electronic device provided by the third aspect above.

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

[0028] (1) After obtaining the predicted driving trajectories of the own vehicle and the adjacent vehicle, the present application can correct the predicted driving estimate of the adjacent vehicle by using the actual driving trajectory of the adjacent vehicle within a preset time period to obtain a predicted driving trajectory with higher accuracy. Then, based on the corrected predicted driving trajectory of the adjacent vehicle, the predicted driving trajectory of the own vehicle is corrected, thereby improving the accuracy of the predicted driving trajectory of the own vehicle. In this way, based on the corrected predicted driving trajectory of the adjacent vehicle and the corrected predicted driving trajectory of the vehicle, the risk of collision between the own vehicle and the adjacent vehicle can be accurately predicted.

[0029] (2) The present application can determine at least one driving trajectory of an adjacent vehicle based on the traffic information of the adjacent vehicle, and obtain a driving trajectory of the adjacent vehicle with the highest possible driving probability through scoring, and use it as the second predicted driving trajectory of the adjacent vehicle. This can more accurately predict the driving trajectory of the adjacent vehicle and improve the vehicle's response speed to emergencies.

[0030] (3) The present application can input the traffic information of the position of the adjacent vehicle into the trajectory prediction model to obtain at least one set of parameters, and determine the predicted driving trajectory of the adjacent vehicle based on the at least one set of parameters, which is simple and convenient.

[0031] (4) The present application can determine a correction coefficient according to the current scene of the vehicle, and correct the vehicle's driving trajectory based on the correction coefficient, thereby ensuring that the vehicle's corrected predicted driving trajectory better matches the scene.

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

[0033] (6) The present application can determine the predicted driving trajectory of the vehicle through the driving operation of the vehicle driver and the surrounding traffic information. Since the driving information of the vehicle can reflect the driving intention of the vehicle, the driving trajectory of the vehicle can be accurately predicted based on the driving information of the vehicle.

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

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

[0036] It should be noted that the technical effects brought about by any implementation method in the second to fifth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0037] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0039] Figure 1 is a flow chart of a method for determining a vehicle collision risk according to an exemplary embodiment;

[0040] Figure 2 is a flow chart showing trajectory prediction calculation of vehicle collision risk according to an exemplary embodiment;

[0041] Figure 3 is a flow chart of trajectory correction according to vehicle-related parameters according to an exemplary embodiment;

[0042] Figure 4 is a block diagram of a device for determining a vehicle collision risk according to an exemplary embodiment;

[0043] Figure 5 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0044] In order to enable ordinary persons in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0045] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0046] For ease of understanding, the method for determining the vehicle collision risk provided by the present application is specifically introduced below in conjunction with the accompanying drawings. The execution subject of the method provided by the present application can be a vehicle or a device in the vehicle (such as a vehicle-mounted terminal, a system (such as a vehicle-mounted system)), etc. The following description is based on the example of the execution subject being a vehicle terminal.

[0047] For ease of understanding, the method for determining the vehicle collision risk provided by the present application is specifically introduced below with reference to the accompanying drawings.

[0048] like Figure 1 , which is a flow chart of a method for determining a vehicle collision risk according to an exemplary embodiment, and the method may include the following steps.

[0049] S101, obtaining a first predicted driving trajectory of a vehicle at a first time and a second predicted driving trajectory of an adjacent vehicle of the vehicle at a first time.

[0050] The first time is after the current time. The adjacent vehicle may be a vehicle whose distance from the vehicle (hereinafter referred to as the target vehicle in order to distinguish the adjacent vehicles) is less than a preset distance. The number of adjacent vehicles may be multiple. The preset distance may be set as required, for example, the preset distance may be a detection distance of a radar system configured for the vehicle. The driving trajectory may be used to reflect the route that the vehicle is about to travel, for example, the vehicle is about to travel in a straight line, turn left, turn right, make a U-turn, etc.

[0051] In the present 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 adjacent vehicles of the vehicle. The number of the second predicted driving trajectories can be one or more.

[0052] In a possible implementation, the vehicle-mounted terminal may determine a first predicted driving trajectory of the target vehicle at the first time based on operation information of the driver of the target vehicle or traffic information at the location of the target vehicle.

[0053] The driver's operation information may refer to the driver's operation when driving the vehicle. For example, the operation of stepping on the accelerator pedal, stepping on the brake pedal, turning the steering wheel, etc. Traffic information may refer to the road environment information of the vehicle and the information of surrounding vehicles. For example, the road environment information may include road data (such as lane lines, traffic lights, zebra crossings, distances to surrounding vehicles, etc.) collected by the vehicle's sensors (such as lidar, cameras), and the information of surrounding vehicles may include the coordinate position, speed, acceleration, heading angle, and other information of surrounding vehicles.

[0054] In one example, after obtaining the operation information of the driver at the first time, the vehicle terminal can predict the driving trajectory of the vehicle based on the operation information. For example, if the operation information of the driver is to turn the steering wheel, the vehicle terminal can determine the predicted driving trajectory of the vehicle based on the turning angle of the steering wheel. For example, the vehicle terminal can be pre-configured with the corresponding relationship between the turning angle of the steering wheel of the vehicle and the driving trajectory, so that based on the corresponding relationship, the vehicle terminal can determine the predicted driving trajectory corresponding to the turning angle of the steering wheel.

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

[0056] In another possible implementation, the vehicle terminal may determine the second predicted driving trajectory based on the traffic information of the adjacent vehicles. Figure 2 The description of the illustrated embodiment will not be repeated here.

[0057] S102: correcting the second predicted driving trajectory based on the actual driving trajectory of the adjacent vehicle within the 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.

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

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

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

[0061] Furthermore, after obtaining the revised second predicted driving trajectory, the vehicle-mounted terminal may correct the first predicted driving trajectory based on the revised second predicted driving trajectory to obtain a revised first predicted driving trajectory.

[0062] In one example, the vehicle terminal can correct the first predicted driving trajectory based on the correction coefficient and the modified second predicted driving trajectory to obtain the corrected first predicted driving trajectory. Figure 3 The description of the illustrated embodiment will not be repeated here.

[0063] S103: Determine the risk level of collision between the vehicle and an adjacent vehicle based on the corrected second predicted driving trajectory and the corrected first predicted driving trajectory.

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

[0065] It can be understood that the higher the degree of overlap between the revised first predicted driving trajectory and the revised second predicted driving trajectory, the higher the risk of the target vehicle colliding with the adjacent vehicle.

[0066] Furthermore, when it is determined that the risk of collision between the target vehicle and the adjacent vehicle is greater than a preset threshold, the vehicle-mounted terminal can activate the automatic emergency braking system, thereby ensuring safe driving of the vehicle.

[0067] The preset threshold value can be set as required, for example, it can be 50%. That is, the probability of collision between the target vehicle and the adjacent vehicle is relatively high.

[0068] It should be noted that in the above main execution operations, the prediction of the predicted driving trajectory of the target vehicle and the adjacent vehicles is based on the premise of complying with traffic regulations; and the subsequent prediction of the driving behavior of the target vehicle and the trajectory correction of the target vehicle and the adjacent vehicles are all based on the premise of complying with traffic regulations. The subsequent steps are similar.

[0069] based on Figure 1 In the present application, after obtaining the predicted driving trajectories of the vehicle and the adjacent vehicle, the vehicle-mounted terminal can correct the predicted driving estimate of the adjacent vehicle by using the actual driving trajectory of the adjacent vehicle within a preset time period to obtain a predicted driving trajectory with higher accuracy. Then, based on the corrected predicted driving trajectory of the adjacent vehicle, the predicted driving trajectory of the vehicle is corrected to improve the accuracy of the predicted driving trajectory of the vehicle. In this way, based on the corrected predicted driving trajectory of the adjacent vehicle and the corrected predicted driving trajectory of the vehicle, the risk of collision between the vehicle and the adjacent vehicle can be accurately predicted.

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

[0071] S201. Determine at least one predicted driving trajectory of an adjacent vehicle based on traffic information of a position of the adjacent vehicle at a first time.

[0072] In a possible implementation, the vehicle terminal may input the traffic information of the position of the adjacent vehicle at the first time into a preset trajectory prediction model to obtain at least one set of parameters for calculating the driving trajectory. 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 is obtained.

[0073] Among them, a set of parameters is used to calculate a predicted driving trajectory.

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

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

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

[0077] Based on the above formula, the vehicle-mounted terminal can perform fitting based on each set of parameters to obtain a function, and based on the function, a curve can be obtained, that is, a predicted driving trajectory of adjacent vehicles.

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

[0079] Among them, the prediction scoring mechanism may refer to scoring at least one predicted driving trajectory to obtain a score for each predicted driving trajectory, and the score may characterize the accuracy of the predicted driving trajectory. For example, the prediction scoring mechanism may include scoring the predicted driving trajectory based on the driving information of the vehicle. In one possible implementation, the vehicle-mounted terminal may predict the driving trajectory of an adjacent vehicle based on the driving information of the adjacent vehicle (such as speed, heading angle, acceleration, position), and compare the similarity between the driving trajectory and the 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-mounted 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 time.

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

[0081] based on Figure 2 The technical solution of the present application can determine at least one predicted driving trajectory of an adjacent vehicle based on the traffic information of the position of the adjacent vehicle at the first time. And judge the trajectory according to the formula. Then, based on the preset scoring mechanism, determine the driving trajectory with the highest score in at least one predicted driving trajectory, and use the driving trajectory with the highest score as the second predicted driving trajectory of the adjacent vehicle at the first time. In this way, the driving trajectory information of the adjacent vehicle can be accurately calculated.

[0082] In some embodiments, such as Figure 3 As shown, in the above S102, 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 a correction coefficient according to a current driving scenario of the vehicle.

[0084] Among them, the correction coefficient is used to correct the vehicle's driving trajectory.

[0085] In one example, the vehicle terminal can determine the corresponding correction coefficient according to the driving scene of the vehicle. Different driving scenes correspond to different correction coefficients. For example, the driving scene may include a city road driving scene, an off-road road driving scene, a village road scene, etc. For example, the vehicle terminal can be configured with a corresponding relationship between the driving scene and the correction coefficient, so that based on the corresponding relationship, the vehicle terminal can accurately determine the correction coefficient corresponding to the driving scene currently 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 a corrected first predicted driving trajectory.

[0087] In an example, taking the correction coefficient including the first correction coefficient and the second correction coefficient as an example, the corrected first predicted driving trajectory can satisfy the preset formula. The preset formula can be shown as Formula 2:

[0088] S=S1*A+S2*BFormula 2

[0089] Among them, 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.

[0090] Based on this embodiment, the vehicle-mounted terminal can determine the correction coefficient according to the current driving scene 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 scene.

[0091] based on Figure 3 According to the technical solution, the correction coefficient can be determined according to the current driving scene of the vehicle. The first predicted driving trajectory can also be corrected based on the correction coefficient and the corrected second predicted driving trajectory to obtain the corrected first predicted driving trajectory. In this way, based on the use of the correction system, the vehicle-mounted terminal can more accurately predict the driving trajectory of the vehicle.

[0092] Figure 4 FIG. 1 is a schematic diagram of a device for determining a vehicle collision risk according to an exemplary embodiment. Figure 4 The emergency brake system control device 40 includes: an acquisition unit 401, a correction unit 402, and a determination unit 403.

[0093] In a possible manner, the acquisition unit 401 acquires a first predicted driving trajectory of the vehicle at a first time and a second predicted driving trajectory of an adjacent vehicle of the vehicle at the first time, the first time being after the current time.

[0094] In one possible manner, the correction unit 402 corrects the second predicted driving trajectory based on the driving trajectory of the adjacent vehicle 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 a corrected first predicted driving trajectory.

[0095] In a possible manner, the determination unit 403 determines the modified second predicted driving trajectory and the modified first predicted driving trajectory to determine the risk level of collision between the vehicle and the adjacent vehicle.

[0096] In a possible manner, the determination unit 403 determines at least one driving trajectory of the adjacent vehicle based on traffic information of the position of the adjacent vehicle at the first time.

[0097] In a possible manner, the determination unit 403 determines the driving trajectory with the highest score among the at least one driving trajectory based on a preset scoring mechanism, and uses the driving trajectory with the highest score as the second predicted driving trajectory of the adjacent vehicle at the first time.

[0098] In a possible manner, the acquisition unit 401 acquires a trajectory calculation formula corresponding to the traffic information, and presets a trajectory prediction model according to the traffic information of the positions of the adjacent vehicles at the first time.

[0099] In a possible manner, the determination unit 403 determines at least one driving trajectory of the adjacent vehicle at the first time based on a trajectory calculation formula.

[0100] In a possible manner, the determination unit 403 determines a first correction coefficient and a second correction coefficient.

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

[0102] In a possible manner, 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 device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0104] Figure 5 FIG. 1 is a schematic diagram of 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 is used to store executable instructions of the processor 501. It can be understood that the processor 501 is configured to execute instructions to implement the method for determining the vehicle collision risk in the above embodiment.

[0106] It should be noted that those skilled in the art can understand that Figure 5 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include Figure 5 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0107] The processor 501 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 502, and calling data stored in the memory 502, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 501 may include one or more processing units. Optionally, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 501.

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

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

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

[0111] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0112] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, and the one or more instructions can be executed by the processor 501 of the electronic device to complete the method in the above embodiment.

[0113] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0114] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned 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 the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0117] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0118] If the integrated unit is implemented in the form of 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 embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or CD and other media that can store program code.

[0119] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining vehicle collision risk, characterized in that: Applied to a vehicle, the method comprises: Acquire a first predicted driving trajectory of the vehicle at a first time and a second predicted driving trajectory of an adjacent vehicle of the vehicle at the first time, the first time being after a current time; The second predicted driving trajectory is corrected based on the actual driving trajectory of the adjacent vehicle within a preset time period, and the first predicted driving trajectory is corrected 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; Based on the revised second predicted driving trajectory and the revised first predicted driving trajectory, a risk level of collision between the vehicle and the adjacent vehicle is determined.

2. The method according to claim 1, characterized in that Obtaining a second predicted driving trajectory of an adjacent vehicle of the vehicle at the first time, comprising: Determining at least one driving trajectory of the adjacent vehicle based on traffic information of the position of the adjacent vehicle at the first time; Based on a preset scoring mechanism, a driving trajectory with a 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 according to claim 2, characterized in that The determining at least one driving track of the adjacent vehicle based on the traffic information of the position of the adjacent vehicle at the first time includes: Preset a trajectory prediction model for traffic information of the adjacent vehicles at the first time to obtain at least one set of parameters corresponding to the traffic information; Based on the at least one set of parameters and a preset trajectory calculation formula, at least one predicted driving trajectory of the adjacent vehicle at the first time is determined; a set of parameters corresponds to one driving trajectory.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: According to the current driving scenario of the vehicle; Determining a correction coefficient according to a current driving scenario of the vehicle; The step of correcting the first predicted driving trajectory based on the corrected second predicted driving trajectory to obtain the corrected first predicted driving trajectory includes: Based on the correction coefficient and the corrected second predicted driving trajectory, the first predicted driving trajectory is corrected to obtain a corrected first predicted driving trajectory.

5. The method according to claim 4, characterized in that The correction coefficient includes 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; Among them, 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.

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

7. The method according to any one of claims 1 to 3, characterized in that The method further comprises: When it is determined that the risk of collision between the vehicle and the adjacent vehicle is greater than a preset threshold, the automatic emergency braking system of the vehicle is activated.

8. The method according to any one of claims 1 to 3, characterized in that The determining, based on the modified second predicted driving trajectory and the modified first predicted driving trajectory, the risk level of collision between the vehicle and the adjacent vehicle comprises: Determining a degree of overlap between the modified second predicted driving trajectory and the modified first predicted driving trajectory; The risk level of collision between the vehicle and the adjacent vehicle is determined based on the overlap level, wherein the overlap level is positively correlated with the risk level.

9. A device for determining vehicle collision risk, characterized in that: Applied to a vehicle, the device comprises: An acquisition unit, configured to acquire a first predicted driving trajectory of the vehicle at a first time and a second predicted driving trajectory of an adjacent vehicle of the vehicle at the first time, the first time being after a current time; a correction unit, configured to correct the second predicted driving trajectory based on the actual driving trajectory of the adjacent vehicle within a preset time period, and to correct 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; A determination unit is used to determine the risk level of collision between the vehicle and the adjacent vehicle based on the modified second predicted driving trajectory and the modified first predicted driving trajectory.

10. An electronic device, characterized in that: include: a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: When the computer-executable instructions stored in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can perform the method as claimed in any one of claims 1 to 8.

12. A vehicle, characterized in that: The electronic device comprising the electronic device described in claim 10.

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