Vehicle anti-collision control method and device and vehicle storage medium
By acquiring obstacle information from multiple detection devices, determining weight coefficients based on environmental information, and performing data fusion, the problem of inaccurate vehicle collision risk assessment in existing technologies is solved, and more efficient vehicle safety assessment and risk warnings are achieved.
Patent Information
- Application Number
- CN202510665990.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-22
- Publication Date
- 2025-09-19
AI Technical Summary
In the existing technology, due to the significant performance differences of different detection equipment in different environments, the accuracy of the data collected varies, and the accuracy of vehicle collision risk assessment cannot be guaranteed, resulting in lower vehicle driving safety.
By obtaining obstacle information collected by N types of detection devices, the weight coefficient of each obstacle information is determined according to the current environmental information, and data fusion processing is performed to determine the target obstacle information. Finally, it is predicted whether the vehicle has a collision risk and outputs risk warning information.
It improves the accuracy of vehicle collision risk assessment, enhances vehicle driving safety, and encourages users to quickly deal with potential dangers through graded risk warning information.
Smart Images
Figure CN120673622A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle anti-collision control method, device, and vehicle storage medium. Background Art
[0002] With the rapid development of intelligent automotive technology, driving safety systems have gradually become a core research area for active vehicle safety. Traditional vehicles rely on rearview mirrors for side and rear environmental awareness, but due to physical limitations, they have significant blind spots. This makes it difficult to accurately judge the distance and speed of vehicles passing by, especially during lane changes. To address this issue, millimeter-wave radar or cameras are used to detect the presence of vehicles in these blind spots.
[0003] In related technologies, in order to address the risk of side vehicle collision, a single detection device or a simple fusion of multiple detection devices can be set up to detect whether there is a risk of vehicle collision in the visual blind spot, thereby preventing the vehicle from colliding.
[0004] However, due to the significant performance differences between different detection devices in different environments, the accuracy of the data they collect may change, and ultimately the accuracy of vehicle collision risk assessment may not be guaranteed, resulting in lower vehicle driving safety.
[0005] It should be pointed out that the information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of implication that the information constitutes prior art already known to those skilled in the art. Summary of the Invention
[0006] In view of this, the present application provides a vehicle collision avoidance control method, device, and vehicle storage medium to help solve the problem in the prior art. However, due to the significant performance differences of different detection devices in different environments, the accuracy of the data collected may change, and ultimately the accuracy of the vehicle collision risk assessment may not be guaranteed, resulting in lower vehicle driving safety.
[0007] In a first aspect, an embodiment of the present application provides a vehicle collision avoidance control method, the method comprising: Obtain N types of obstacle information collected by N types of detection devices, where N>1; Determine, based on the current environment information, a weight coefficient corresponding to each type of obstacle information, wherein the weight coefficient is used to characterize the accuracy of the obstacle information collected by different detection devices under the current environment information; performing data fusion processing on the N types of obstacle information according to the weight coefficient corresponding to each type of obstacle information to determine target obstacle information; Based on the target obstacle information, it is predicted whether the vehicle has a collision risk and corresponding risk warning information is output.
[0008] In an embodiment of the present application, first, N types of obstacle information corresponding to N types of detection devices are obtained; then, based on the current environmental information, the weight coefficient corresponding to each type of obstacle information is determined; then, based on the weight coefficient corresponding to each type of obstacle information, the N types of obstacle information are subjected to data fusion processing to determine the target obstacle information; finally, based on the target obstacle information, whether the vehicle has a collision risk is predicted and the corresponding risk warning information is output. It can be understood that since the weight coefficient is used to characterize the accuracy of the obstacle information collected by different detection devices under the current environmental information, different weight coefficients are assigned to the obstacle information collected by different detection devices according to different environmental information. In other words, by raising the weight of the obstacle information collected by the accurate detection device under different environmental information, the accuracy of the vehicle collision risk assessment is improved, and the safety of vehicle driving is improved.
[0009] In a possible implementation, performing data fusion processing on the N types of obstacle information according to the weight coefficient corresponding to each type of obstacle information to determine the target obstacle information includes: According to the weight coefficient corresponding to each type of obstacle information, as well as the same coordinate system mapping and time stamp alignment, the N types of obstacle information are subjected to data fusion processing to determine the target obstacle information.
[0010] In this embodiment of the present application, N types of obstacle information are fused based on the weight coefficients corresponding to each obstacle information type, as well as the same coordinate system mapping and timestamp alignment, to determine target obstacle information. It can be understood that by performing spatiotemporal alignment of obstacle information collected by different detection devices based on the same coordinate system and timestamp, more accurate target obstacle information can be determined, thereby improving the accuracy of vehicle collision risk assessment and enhancing vehicle driving safety.
[0011] In one possible implementation, predicting whether the vehicle has a collision risk based on the target obstacle information and outputting corresponding risk warning information includes: predicting a risk level of the vehicle based on the target obstacle information; Output risk warning information corresponding to the risk level.
[0012] In this embodiment of the present application, the vehicle's risk level is first predicted based on the target obstacle information; then, risk warning information corresponding to the risk level is output. It can be understood that outputting corresponding warning information by level can encourage users to quickly handle the situation, thereby improving vehicle driving safety.
[0013] In a possible implementation, the risk levels are, in descending order of risk, a first risk level, a second risk level, and a third risk level; The outputting of risk warning information corresponding to the risk level includes: When the risk level is the first risk level, the display screen outputs a warning message; When the risk level is the second risk level, the warning light alarms; When the risk level is the third risk level, the warning light sounds and the vehicle seat vibrates.
[0014] In the embodiment of the present application, the risk levels are ranked from low to high as follows: first risk level, second risk level, and third risk level. When the risk level is the first risk level, the display screen outputs a warning message; when the risk level is the second risk level, the warning light sounds; and when the risk level is the third risk level, the warning light sounds and the vehicle seat vibrates. As the risk level increases, the output risk warning message becomes more perceptible, facilitating quicker user processing and improving vehicle safety.
[0015] In one possible implementation, predicting whether the vehicle has a collision risk based on the target obstacle information and outputting corresponding risk warning information includes: predicting a collision time based on the target obstacle information; According to the collision time, corresponding risk warning information is output.
[0016] In this embodiment of the present application, the collision time is predicted based on target obstacle information, and corresponding risk warning information is output based on the collision time. It can be understood that based on target obstacle information, the collision time can be predicted more accurately, thereby improving the accuracy of vehicle collision risk assessment and enhancing vehicle driving safety.
[0017] In a possible implementation, the N types of detection devices include radars, ultrasonic sensors, and cameras.
[0018] In a possible implementation, it is characterized in that the obstacle information includes: the relative distance to the side vehicle, the relative speed to the side vehicle and the visual speed, wherein the relative speed to the side vehicle is the speed determined based on the obstacle information collected by the radar, and the visual speed is the speed determined based on the obstacle information collected by the camera.
[0019] In a second aspect, an embodiment of the present application provides a vehicle collision avoidance control device, comprising: The obstacle information acquisition module is used to obtain N types of obstacle information collected by N types of detection devices, where N>1; A weight coefficient determination module is used to determine the weight coefficient corresponding to each type of obstacle information based on the current environment information. The weight coefficient is used to characterize the accuracy of the obstacle information collected by different detection devices under the current environment information; a target obstacle information determination module, configured to perform data fusion processing on the N types of obstacle information according to a weight coefficient corresponding to each type of obstacle information to determine target obstacle information; The risk prediction module is used to predict whether the vehicle has a collision risk based on the target obstacle information and output corresponding risk warning information.
[0020] In a third aspect, an embodiment of the present application provides a vehicle, including: processor; Memory; and a computer program, wherein the computer program is stored in the memory, the computer program comprising instructions which, when executed by the processor, cause the vehicle to execute the method according to any one of the first aspects.
[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described in the first aspect.
[0022] It is understandable that the vehicle collision avoidance control device provided in the second aspect, the vehicle provided in the third aspect, and the computer-readable storage medium provided in the fourth aspect are all used to execute part or all of the methods provided herein. Therefore, the beneficial effects achievable by these methods can be referenced to the beneficial effects of the corresponding methods and will not be further elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0024] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application.
[0025] Figure 2 A flowchart of a vehicle anti-collision control method provided in an embodiment of the present application.
[0026] Figure 3 A schematic structural diagram of a vehicle anti-collision control device provided in an embodiment of the present application.
[0027] Figure 4 A schematic structural diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION
[0028] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.
[0029] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0030] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. The singular forms "a", "an", "the" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.
[0031] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.
[0032] With the rapid development of intelligent automotive technology, driving safety systems have gradually become a core research area for active vehicle safety. Traditional vehicles rely on rearview mirrors for side and rear environmental awareness, but due to physical limitations, they have significant blind spots. This makes it difficult to accurately judge the distance and speed of vehicles passing alongside, especially during lane changes. To facilitate understanding, the following will first illustrate specific application scenarios.
[0033] See also Figure 1 , is a schematic diagram of an application scenario provided by an embodiment of the present application. Figure 1As shown, this application scenario includes: road 101, ego vehicle 102, and other vehicles 103-105. As shown in the figure, the rearview area of ego vehicle 102 can be roughly divided into five areas: Area ① to Area ⑤. Areas ① and ⑤ are blind spots; Areas ② and ④ are visible areas of the left and right rearview mirrors, respectively; and Area ③ is the visible area of the rearview mirror. Therefore, the driver of ego vehicle 102 can observe the driving status of other vehicle 104 through the rearview mirror, but cannot observe the driving status of other vehicles 103 and 105.
[0034] To this end, detection equipment, such as millimeter-wave radar or cameras, can be used to detect the presence of vehicles in blind spots. Specifically, in related technologies, to address the risk of side collisions, a single detection device or a simple fusion of multiple detection devices can be used to detect whether there is a risk of vehicle collision in the visual blind spot, thereby preventing the vehicle from causing a collision.
[0035] However, due to significant performance differences between different detection devices in different environments, the accuracy of the data collected may vary, ultimately failing to accurately assess vehicle collision risk, leading to lower vehicle safety. For example, when camera accuracy decreases in rainy or foggy weather, over-reliance on camera data increases the risk of misjudging obstacles. In enclosed areas such as tunnels, interruptions in satellite navigation signals can lead to positioning errors.
[0036] It should be pointed out that Figure 1 The road 101 shown in the figure is only an exemplary description. According to actual needs, the road 101 can be a one-way lane or a two-way lane. The road 101 can include one lane or multiple lanes. This application does not impose any specific restrictions on this. Figure 1 The types of the self-vehicle 102 and other vehicles 103-105 described in the specification are merely exemplary descriptions. Depending on actual needs, the types of the self-vehicle and other vehicles may be sedans, vans, pickup trucks, and vans, etc. This application does not impose any specific restrictions on this. Figure 1 The number and positions of other vehicles described in the text are only an exemplary description. According to actual needs, the number of other vehicles can be zero, one, or more. The positions of other vehicles can be directly in front of, directly behind, behind the left, behind the right, in front of the left, and behind the left, etc. of the vehicle. This application does not impose any specific restrictions on this.
[0037] In response to the above problem, in an embodiment of the present application, first, N types of obstacle information corresponding to N types of detection devices are obtained; then, based on the current environmental information, the weight coefficient corresponding to each type of obstacle information is determined; then, based on the weight coefficient corresponding to each type of obstacle information, the N types of obstacle information are subjected to data fusion processing to determine the target obstacle information; finally, based on the target obstacle information, it is predicted whether the vehicle has a collision risk and the corresponding risk warning information is output. It can be understood that since the weight coefficient is used to characterize the accuracy of the obstacle information collected by different detection devices under the current environmental information, different weight coefficients are assigned to the obstacle information collected by different detection devices according to different environmental information. In other words, by raising the weight of the obstacle information collected by the accurate detection device under different environmental information, the accuracy of the vehicle collision risk assessment is improved, and the safety of the vehicle driving is improved. Specifically, a detailed description is given below in conjunction with the accompanying drawings and specific embodiments.
[0038] See also Figure 2 , is a flow chart of a vehicle anti-collision control method provided in an embodiment of the present application. This method can be applied to Figure 1 In the application scenario shown in Figure 2 As shown, it mainly includes the following steps.
[0039] Step S201: Obtain N types of obstacle information corresponding to N types of detection devices.
[0040] In the embodiment of the present application, first, N types of obstacle information collected by N types of detection devices are obtained, where N>1.
[0041] It can be understood that each of the N types of detection devices collects one type of obstacle information. In other words, the N types of obstacle information are obstacle information obtained by the N types of detection devices detecting the same obstacle.
[0042] In one possible implementation, the detection equipment includes radar, ultrasonic sensors, and cameras. Specifically, the radars may be three ultrasonic radars, each covering a 120° range; the cameras may be wide-angle cameras with a 190° field of view; and the ultrasonic sensors may be eight ultrasonic sensors covering the vehicle's near-field area.
[0043] The obstacle information collected is typically about other vehicles around the vehicle. In one possible implementation, the obstacle information includes the relative distance to the vehicle next to it, the relative speed to the vehicle next to it, and the visual speed. The relative speed to the vehicle next to it is determined based on the obstacle information collected by radar, while the visual speed is determined based on the obstacle information collected by the camera.
[0044] Specifically, radar can detect the distance to the vehicle next to it and its relative speed. Visual velocity can be tracked and calculated using feature points captured by the camera. For example, the vehicle's controller can track and calculate visual velocity based on feature points captured by the camera using the Doppler effect and a visual displacement algorithm.
[0045] Step S202: Determine the weight coefficient corresponding to each type of obstacle information based on the current environment information.
[0046] In this embodiment of the present application, a weight coefficient corresponding to each obstacle type is determined based on the current environmental information. The weight coefficient is used to characterize the accuracy of obstacle information collected by different detection devices under the current environmental information. The current environmental information includes external weather, road conditions, daytime and nighttime conditions, and vehicle speed.
[0047] Understandably, millimeter-wave radar has advantages in detecting high-speed moving targets, but its ability to identify stationary or slow-moving targets is limited. Cameras, while providing rich visual information in good lighting conditions, are significantly affected by weather and lighting variations. Therefore, the weighting coefficients for each obstacle type can be adjusted based on the specific current environmental information.
[0048] For example, if the detection equipment is a radar and a camera, when the current environment is sunny, the weight coefficient of the obstacle information collected by the radar is 0.6, and the weight coefficient of the obstacle information collected by the camera is 0.4; when the current environment is rainy, the weight coefficient of the obstacle information collected by the radar is 0.8, and the weight coefficient of the obstacle information collected by the camera is 0.2.
[0049] Step S203: performing data fusion processing on the N types of obstacle information according to the weight coefficient corresponding to each type of obstacle information to determine the target obstacle information.
[0050] In the embodiment of the present application, after the weight coefficient corresponding to each type of obstacle information is determined, the N types of obstacle information are subjected to data fusion processing according to the weight coefficient corresponding to each type of obstacle information to determine the target obstacle information.
[0051] It is understandable that data fusion processing can be performed on N types of obstacle information based on a multi-source data fusion algorithm, such as an improved Kalman filter algorithm.
[0052] Specifically, in one possible implementation, N types of obstacle information are fused based on the weight coefficients corresponding to each obstacle type, along with the same coordinate system mapping and timestamp alignment, to determine the target obstacle information. It can be understood that by temporally and spatially aligning obstacle information collected by different detection devices based on the same coordinate system and timestamps, more accurate target obstacle information can be determined, thereby improving the accuracy of vehicle collision risk assessment and enhancing vehicle driving safety.
[0053] Step S204: Based on the target obstacle information, predict whether the vehicle has a collision risk and output corresponding risk warning information.
[0054] In an embodiment of the present application, based on the target obstacle information, it is predicted whether the vehicle has a collision risk and corresponding risk warning information is output.
[0055] Specifically, in one possible implementation, the vehicle's risk level is first predicted based on the target obstacle information; then, risk warning information corresponding to the risk level is output. It can be understood that outputting corresponding warning information by level can encourage users to quickly handle the situation, thereby improving vehicle driving safety.
[0056] Furthermore, in one possible implementation, risk levels can be divided into first, second, and third risk levels, from low to high. When the risk level is the first risk level, the display screen outputs a warning message; when the risk level is the second risk level, the warning light sounds; and when the risk level is the third risk level, the warning light sounds and the vehicle seat vibrates. As the risk level increases, the output risk warning message becomes more perceptible, facilitating quicker user processing and improving vehicle safety.
[0057] In an embodiment of the present application, the time when the vehicle may collide, i.e., the collision time, can be determined based on the relative distance from the vehicle on the side and the relative speed between the vehicles; then, corresponding risk warning information is output based on the collision time.
[0058] Specifically, in one possible implementation, the collision time is predicted based on the target obstacle information; and corresponding risk warning information is output based on the collision time.
[0059] For example, the first risk level is a collision time greater than 5 seconds, the second risk level is a collision time less than or equal to 5 seconds and greater than 3 seconds, and the third risk level is a collision time less than or equal to 3 seconds. When the target obstacle information predicts a collision time of 2 seconds, since 2 seconds is less than or equal to 3 seconds, the risk warning information corresponding to the third risk level is output, i.e., a warning light alarm and vehicle seat vibration. When the target obstacle information predicts a collision time of 4 seconds, since 4 seconds is greater than 3 seconds and less than or equal to 5 seconds, the risk warning information corresponding to the second risk level is output, i.e., a warning light alarm. When the target obstacle information predicts a collision time of 7 seconds, since 7 seconds is greater than 5 seconds, the risk warning information corresponding to the first risk level is output, i.e., a warning message is output on the display screen.
[0060] In the embodiment of the present application, based on the target obstacle information, the collision time can be predicted more accurately, thereby improving the accuracy of vehicle collision risk assessment and improving the safety of vehicle driving.
[0061] In an embodiment of the present application, first, N types of obstacle information corresponding to N types of detection devices are obtained; then, based on the current environmental information, the weight coefficient corresponding to each type of obstacle information is determined; then, based on the weight coefficient corresponding to each type of obstacle information, the N types of obstacle information are subjected to data fusion processing to determine the target obstacle information; finally, based on the target obstacle information, whether the vehicle has a collision risk is predicted and the corresponding risk warning information is output. It can be understood that since the weight coefficient is used to characterize the accuracy of the obstacle information collected by different detection devices under the current environmental information, different weight coefficients are assigned to the obstacle information collected by different detection devices according to different environmental information. In other words, by raising the weight of the obstacle information collected by the accurate detection device under different environmental information, the accuracy of the vehicle collision risk assessment is improved, and the safety of vehicle driving is improved.
[0062] Corresponding to the above embodiment, the present application also provides a structural diagram of a vehicle anti-collision control device. Figure 3, which is a structural diagram of a vehicle anti-collision control device provided in an embodiment of the present application. As shown in the figure, a vehicle anti-collision control device 300 is shown. Among them, the vehicle anti-collision control device 300 includes: an obstacle information acquisition module 301, a weight coefficient determination module 302, a target obstacle information determination module 303 and a risk prediction module 304. Among them, the obstacle information acquisition module is used to obtain the corresponding N types of obstacle information collected by N types of detection equipment; the weight coefficient determination module is used to determine the weight coefficient corresponding to each type of obstacle information according to the current environmental information; the target obstacle information determination module is used to perform data fusion processing on the N types of obstacle information according to the weight coefficient corresponding to each type of obstacle information, and determine the target obstacle information; the risk prediction module is used to predict whether the vehicle has a collision risk based on the target obstacle information and output corresponding risk warning information.
[0063] For specific details, please refer to the above method embodiments. For the sake of brevity, this application does not impose any specific limitations on this.
[0064] Corresponding to the above embodiment, the present application also provides a structural diagram of a vehicle. Figure 4 , is a schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle 400 may include: a processor 401, a memory 402, and a communication unit 403. These components communicate via one or more buses. Those skilled in the art will appreciate that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiments of the present invention. It may be a bus structure or a star structure, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.
[0065] The communication unit 403 is configured to establish a communication channel so that the electronic device can communicate with other devices, receive user data sent by other devices, or send user data to other devices.
[0066] The processor 401 is the control center of the vehicle. It uses various interfaces and lines to connect various parts of the entire vehicle. It runs or executes software programs, instructions, and / or modules stored in the memory 402, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 401 can only include a central processing unit (CPU). In an embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.
[0067] The memory 402 is used to store execution instructions of the processor 401. The memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
[0068] When the execution instructions in the memory 402 are executed by the processor 401, the vehicle 400 is able to perform Figure 2 Some or all of the steps in the illustrated embodiments.
[0069] In a specific implementation, the present application further provides a computer storage medium, wherein the computer storage medium may store a program that, when executed, may include some or all of the steps of each embodiment of the simulation scenario generation method provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).
[0070] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.
[0071] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0072] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0073] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.
[0074] In this specification, reference can be made to the same or similar parts between the various embodiments. In particular, for the device embodiment and the terminal embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.
Claims
1. A vehicle anti-collision control method, characterized in that: The method comprises: Obtain N types of obstacle information collected by N types of detection devices, where N>1; Determine, based on the current environment information, a weight coefficient corresponding to each type of obstacle information, wherein the weight coefficient is used to characterize the accuracy of the obstacle information collected by different detection devices under the current environment information; performing data fusion processing on the N types of obstacle information according to the weight coefficient corresponding to each type of obstacle information to determine target obstacle information; Based on the target obstacle information, it is predicted whether the vehicle has a collision risk and corresponding risk warning information is output.
2. The method according to claim 1, characterized in that The step of performing data fusion processing on the N types of obstacle information according to the weight coefficient corresponding to each type of obstacle information to determine target obstacle information includes: According to the weight coefficient corresponding to each type of obstacle information, as well as the same coordinate system mapping and time stamp alignment, the N types of obstacle information are subjected to data fusion processing to determine the target obstacle information.
3. The method according to claim 1, characterized in that The predicting, based on the target obstacle information, whether the vehicle has a collision risk and outputting corresponding risk warning information includes: predicting a risk level of the vehicle based on the target obstacle information; Output risk warning information corresponding to the risk level.
4. The method according to claim 3, characterized in that The risk levels are ranked from low to high as follows: first risk level, second risk level and third risk level; The outputting of risk warning information corresponding to the risk level includes: When the risk level is the first risk level, the display screen outputs a warning message; When the risk level is the second risk level, the warning light alarms; When the risk level is the third risk level, the warning light sounds and the vehicle seat vibrates.
5. The method according to claim 1, wherein The predicting, based on the target obstacle information, whether the vehicle has a collision risk and outputting corresponding risk warning information includes: predicting a collision time based on the target obstacle information; According to the collision time, corresponding risk warning information is output.
6. The method according to any one of claims 1 to 6, characterized in that The N types of detection equipment include radars, ultrasonic sensors and cameras.
7. The method according to any one of claims 1 to 6, characterized in that The obstacle information includes: the relative distance to the side vehicle, the relative speed to the side vehicle, and the visual speed, wherein the relative speed to the side vehicle is the speed determined based on the obstacle information collected by the radar, and the visual speed is the speed determined based on the obstacle information collected by the camera.
8. A vehicle anti-collision control device, characterized in that: include: The obstacle information acquisition module is used to obtain N types of obstacle information collected by N types of detection devices, where N>1; A weight coefficient determination module is used to determine the weight coefficient corresponding to each type of obstacle information based on the current environment information. The weight coefficient is used to characterize the accuracy of the obstacle information collected by different detection devices under the current environment information; a target obstacle information determination module, configured to perform data fusion processing on the N types of obstacle information according to a weight coefficient corresponding to each type of obstacle information to determine target obstacle information; The risk prediction module is used to predict whether the vehicle has a collision risk based on the target obstacle information and output corresponding risk warning information.
9. A vehicle, characterized in that: include: processor; Memory; and a computer program, wherein the computer program is stored in the memory, the computer program comprising instructions that, when executed by the processor, cause the vehicle to perform the method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.