Automatic emergency braking method, device, apparatus, and storage medium

Abstract

An automatic emergency braking method, apparatus, device, and storage medium are proposed, relating to the field of autonomous driving. The method includes: acquiring driving information of a vehicle during the current screening period; if the driving information determines that the vehicle's driving lane is an unstructured lane, predicting a candidate path set for the vehicle based on the vehicle's current driving state, the candidate path set including at least two candidate paths; and determining an emergency braking execution strategy for the vehicle based on obstacle overlap detection results of each candidate path in the candidate path set. This application can improve the accuracy of identifying obstacles overlapping with candidate paths, reduce the probability of falsely triggering emergency braking, and improve the safety of unstructured lanes.

Description

Technical Field

[0001] This application belongs to the field of autonomous driving technology, specifically relating to an automatic emergency braking method, device, equipment, and storage medium. Background Technology

[0002] Automatic Emergency Braking (AEB) is a safety technology that uses active braking or deceleration to avoid collisions between a vehicle and obstacles in its path. The accuracy of obstacle detection in the driving path is a key technology in AEB.

[0003] Currently, the obstacle selection mechanism in mainstream AEB technology is based on structured paths (such as paths with clearly defined lane lines). However, in unstructured driving scenarios (such as within residential areas and parking lots), the paths of obstacles are flexible and unpredictable. The mainstream selection mechanism cannot ensure the accuracy of the selected obstacles and is prone to misselecting obstacles, which may lead to false triggering of emergency braking and thus safety risks. Summary of the Invention

[0004] This application proposes an automatic emergency braking method, device, equipment, and storage medium that can promptly adjust the vehicle's trajectory when the vehicle's trajectory deviates laterally.

[0005] The first aspect of this application provides an automatic emergency braking method, comprising:

[0006] Obtain the vehicle's driving information for the current screening period, the driving information including at least one of the vehicle's driving mode, the vehicle's driving speed, and the vehicle's driving scenario;

[0007] If the driving lane of the vehicle is determined to be an unstructured lane based on the driving information, a candidate path set of the vehicle is predicted based on the current driving state of the vehicle, and the candidate path set includes at least two candidate paths.

[0008] Based on the obstacle overlap detection results of each candidate path in the candidate path set, the execution strategy for emergency braking of the vehicle is determined.

[0009] In some embodiments of this application, before predicting the candidate path set of the vehicle based on the current driving state of the vehicle, the method further includes:

[0010] If the vehicle's driving mode is detected as reversing, then the vehicle's driving lane is determined to be an unstructured lane; or,

[0011] If the vehicle's speed is detected to be lower than a preset speed, then the vehicle's lane is determined to be an unstructured lane; or,

[0012] If the system receives a message indicating that the vehicle's driving scenario has been switched to a preset driving scenario and the vehicle's driving speed is lower than the preset speed, then the vehicle's driving lane is determined to be an unstructured lane.

[0013] In some embodiments of this application, the current driving state of the vehicle includes the vehicle's current location information and current driving information, and the step of predicting the candidate path set of the vehicle based on the current driving state includes:

[0014] Determine whether the vehicle is traveling straight based on the current driving information;

[0015] If the vehicle is traveling straight, the candidate straight path, the candidate left turn path, and the candidate right turn path of the vehicle are predicted based on the current position information and the preset curvature change rate. The candidate straight path, the candidate left turn path, and the candidate right turn path constitute the candidate path set.

[0016] If the vehicle is turning, the straight-going candidate path and the turning candidate path in the turning direction are predicted based on the current position information and the preset curvature change rate. The straight-going candidate path and the turning candidate path constitute the candidate path set, and the turning direction is either a left turn or a right turn.

[0017] In some embodiments of this application, determining whether the vehicle is traveling straight based on the current driving information includes:

[0018] Read the current turning radius parameter of the vehicle;

[0019] If the turning radius parameter is greater than a preset threshold, then the vehicle is determined to be turning.

[0020] If the turning radius parameter is less than or equal to the preset threshold, then the vehicle is determined to be traveling straight.

[0021] In some embodiments of this application, when the vehicle is traveling forward, predicting the candidate paths for left turns and right turns based on the current position information and a preset rate of curvature change includes:

[0022] Using the current position information as the starting position information, the candidate path for the left turn is predicted based on the positive value of the preset rate of curvature change;

[0023] Using the current position information as the starting position information, the candidate path for the right turn is predicted based on the negative value of the preset rate of curvature change.

[0024] In some embodiments of this application, the length of any candidate path is the larger of an estimated length and a set length, wherein the estimated length is obtained by multiplying a preset time period by the current driving speed of the vehicle.

[0025] In some embodiments of this application, determining the execution strategy for emergency braking of the vehicle based on the obstacle overlap detection results of each candidate path in the candidate path set includes:

[0026] If the obstacle overlap detection results of each candidate path are all obstacle overlap, then emergency braking is triggered on the vehicle.

[0027] If the obstacle overlap detection result of at least one of the candidate paths is no obstacle overlap, then emergency braking will not be triggered.

[0028] An embodiment of the second aspect of this application provides an automatic emergency braking device, comprising:

[0029] The acquisition module is used to acquire the driving information of the vehicle in the current screening period. The driving information includes at least one of the vehicle's driving mode, the vehicle's driving speed, and the vehicle's driving scenario.

[0030] The prediction module is used to predict a candidate path set for the vehicle based on the current driving state of the vehicle if the driving lane of the vehicle is determined to be an unstructured lane based on the driving information. The candidate path set includes at least two candidate paths.

[0031] The determination module is used to determine the execution strategy for emergency braking of the vehicle based on the obstacle overlap detection results of each candidate path in the candidate path set.

[0032] An embodiment of the third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0033] An embodiment of the fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the method described in the first aspect above.

[0034] The technical solutions provided in this application embodiment have at least the following technical effects or advantages:

[0035] During vehicle operation, driving information for the current screening period can be acquired. This driving information includes at least one of the vehicle's driving mode, speed, and scenario. Since driving information reflects whether the vehicle is traveling in a structured lane, in this embodiment, if the driving lane is determined to be an unstructured lane based on the driving information, at least two candidate paths can be predicted. Furthermore, based on the obstacle overlap detection results of each candidate path in the candidate path set, an emergency braking strategy for the vehicle can be determined. In this way, when the vehicle is traveling in an unstructured lane, the autonomous driving system can estimate multiple possible driving paths as candidate paths and perform obstacle screening on all possible candidate paths. This improves the accuracy of identifying obstacles overlapping with candidate paths, reduces the probability of falsely triggering emergency braking, and enhances the safety of unstructured lanes.

[0036] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0037] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0038] In the attached diagram:

[0039] Figure 1 A schematic diagram of a typical automatic emergency braking principle is shown.

[0040] Figure 2A A flowchart of an automatic emergency braking method according to an embodiment of this application is shown;

[0041] Figure 2B A flowchart of a method for determining candidate paths provided in an embodiment of this application is shown;

[0042] Figure 3 A schematic diagram of an automatic emergency braking scenario provided by an embodiment of this application is shown;

[0043] Figure 4 This invention provides a schematic diagram of the structure of an automatic emergency braking device according to an embodiment of the present application.

[0044] Figure 5 This illustration shows a schematic diagram of the structure of an electronic device according to an embodiment of this application;

[0045] Figure 6 A schematic diagram of a storage medium provided in one embodiment of this application is shown. Detailed Implementation

[0046] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0047] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains.

[0048] This application relates to an autonomous driving technology scenario. In this scenario, to ensure the safety of the vehicle during its journey, the autonomous driving system is equipped with an AEB (Autonomous Emergency Braking) system. The AEB system detects whether there are obstacles on the vehicle's preset driving path and the distance to the obstacles to determine whether there is a potential collision risk. If a potential collision risk is determined, emergency braking is triggered to brake the vehicle urgently, thereby ensuring road safety.

[0049] See Figure 1 , Figure 1 A schematic diagram of a typical automatic emergency braking principle is shown, such as... Figure 1 As shown, autonomous driving systems typically predict the vehicle's driving path over a future period of time. Then, they perform an overlap analysis between the predicted driving path and obstacles in the surrounding environment (such as other vehicles and pedestrians). If there are obstacles that overlap with the driving path, the obstacles can be identified as targets with potential collision risks. The information of the obstacles can be sent to the risk assessment module so that the risk assessment module can decide whether to trigger emergency braking based on the time to collision (TTC).

[0050] See you again Figure 1 It can be seen that, in the stage of predicting the driving path, the autonomous driving system typically only uses the trajectory in the direction of the vehicle's travel as a reference for prediction (reference). Figure 1 The "autonomous vehicle's estimated path" is shown in the diagram, and by default, vehicles traveling in adjacent lanes will not enter the autonomous vehicle's path (see reference). Figure 1(The image shows "filtered vehicles"). This implementation method, based on structured lanes with lane lines drawn and strongly constrained by them, ensures the accuracy of the filtered obstacles and reduces the likelihood of mistakenly selecting obstacles and triggering emergency braking. However, in unstructured driving scenarios such as residential areas and parking lots, where there are no strong lane line constraints, the obstacle's path is flexible and unpredictable. Figure 1 The obstacle screening mechanism illustrated in the diagram is difficult to ensure the accuracy of the selected obstacles, and is prone to misselecting obstacles, which may lead to accidental triggering of emergency braking and thus create safety risks.

[0051] Based on this, the technical solution provided in this application determines whether the vehicle's driving lane is an unstructured lane based on the vehicle's driving information. If the vehicle's driving lane is an unstructured lane, it predicts at least two candidate paths for the vehicle and determines the execution strategy for emergency braking of the vehicle based on the obstacle overlap detection results of each candidate path in the candidate path set. This improves the accuracy of the selected obstacles that overlap with the candidate paths, reduces the probability of falsely triggering emergency braking, and helps improve the safety of unstructured lanes.

[0052] The implementing entity of this technical solution can be any device that supports automatic emergency braking, including vehicles, ships, aircraft, or robots. Such devices can support the automatic emergency braking of the embodiments of this application through an autonomous driving system. This autonomous driving system may, for example, be equipped with related functional mechanisms such as a lane detection module, a path prediction module, and a risk assessment module. The autonomous driving system can also simulate candidate paths and vehicles through a simulation system.

[0053] The following description, in conjunction with the accompanying drawings, describes an automatic emergency braking method, apparatus, device, and storage medium according to embodiments of this application.

[0054] See Figure 2A , Figure 2A This application provides an embodiment of an automatic emergency braking method, which includes the following steps:

[0055] Step S101: Obtain the vehicle's driving information for the current screening period.

[0056] The driving information includes at least one of the vehicle's driving mode, the vehicle's driving speed, and the vehicle's driving scenario.

[0057] In some implementations, the vehicle's driving mode may include forward driving and backward driving (i.e., reversing); the vehicle's driving scenarios may include driving in a lane, parking, and driving out of a garage.

[0058] It should be noted that autonomous driving systems typically perform obstacle screening at a preset interval, for example, an autonomous driving system performs obstacle screening at a interval of 40 milliseconds (ms).

[0059] In real-world scenarios, vehicles typically do not reverse or travel at low speeds while driving on structured lanes. Instead, they usually exhibit low-speed driving or reversing when entering or exiting parking spaces, entering or exiting residential areas, or after receiving parking instructions. For example, vehicles typically travel at relatively high speeds (e.g., greater than or equal to 20 km / h (kph)) on structured roads (e.g., public roads), while their speeds are typically slower (e.g., less than or equal to 15 km / h (kph)) on unstructured roads (e.g., residential areas). Therefore, in this embodiment, the autonomous driving system can acquire the vehicle's driving information for each screening cycle to determine the road conditions based on this information.

[0060] In some embodiments, if the vehicle's driving mode is detected to be reverse mode, the vehicle's driving lane is determined to be an unstructured lane. In other embodiments, if the vehicle's driving speed is detected to be lower than a preset speed, the vehicle's driving lane is determined to be an unstructured lane. In still other embodiments, if the vehicle's driving scenario is switched to a preset driving scenario and the vehicle's driving speed is lower than the preset speed, the vehicle's driving lane is determined to be an unstructured lane.

[0061] For example, the preset speed can be set based on empirical values, such as 15 kph. The preset driving scenario is, for example, a parking scenario.

[0062] Step S102: If the driving lane of the vehicle is determined to be an unstructured lane based on the driving information, predict the candidate path set of the vehicle based on the current driving state of the vehicle.

[0063] The candidate path set includes at least two candidate paths.

[0064] In some embodiments, one of the at least two candidate paths is the path that the autonomous driving system intends to drive, which can be used as the vehicle's main path. The other candidate paths besides the main path are the paths that the autonomous driving system will use to avoid obstacles if obstacles are predicted to exist on the main path, and can be used as alternative paths for the vehicle.

[0065] In some implementation scenarios, the at least two candidate paths may include two candidate paths; in other implementation scenarios, the at least two candidate paths may include three candidate paths (e.g., ...). Figure 3(As shown). Since the autonomous driving system executes this technology periodically, the length of any candidate path can be the greater of the estimated length and the set length. The estimated length is obtained by multiplying a preset time period by the current driving speed of the vehicle. The preset time period is, for example, 3 seconds, and the set length is, for example, 2 meters.

[0066] For example, if the vehicle's current speed is 10 kph, or 2.8 meters per second, then the estimated length of the candidate path could be 2.8 * 3 = 8.4 meters. Since 8.4 meters is greater than 2 meters, the length of the candidate path is set to 8.4 meters in this example.

[0067] For example, the current driving state of the vehicle includes the vehicle's current location information and current driving information. After determining that the vehicle's driving lane is an unstructured lane based on the driving information, the autonomous driving system can determine whether the vehicle is traveling straight based on the current driving information. If the vehicle is traveling straight, the autonomous driving system predicts the vehicle's straight-ahead candidate path, left-turn candidate path, and right-turn candidate path based on the current location information and a preset rate of curvature change. The vehicle's straight-ahead candidate path, left-turn candidate path, and right-turn candidate path can be as follows: Figure 3 As shown. The straight-ahead candidate path, the left-turn candidate path, and the right-turn candidate path constitute the candidate path set. If the vehicle is turning, the straight-ahead candidate path and the turning candidate path in the turning direction are predicted based on the current position information and a preset rate of curvature change. The straight-ahead candidate path and the turning candidate path constitute the candidate path set. It should be understood that the turning direction is either a left turn or a right turn.

[0068] Optionally, in a scenario where the candidate path set includes the straight-ahead candidate path, the left-turn candidate path, and the right-turn candidate path, the straight-ahead candidate path can be the main path, and the left-turn candidate path and the right-turn candidate path can be alternative paths. In a scenario where the candidate path set includes both the straight-ahead candidate path and the turning candidate path, the turning candidate path can be the main path, and the straight-ahead candidate path can be an alternative path.

[0069] The current driving information includes, for example, the vehicle's current turning radius parameter. The turning radius is the distance from any point on the circumference of the wheel track to the center of the turn during the vehicle's turn. The autonomous driving system can then read this turning radius parameter. If the turning radius parameter is greater than a preset threshold, the system determines that the vehicle is turning; if the turning radius parameter is less than or equal to the preset threshold, the system determines that the vehicle is traveling straight.

[0070] The turning radius parameter can be, for example, curvature (kappa). Kappa can be the reciprocal of the turning radius. The larger the curvature, the smaller the turning radius, which means a smaller turning angle. The smaller the curvature, the larger the turning radius, which means a larger turning angle. In the straight-ahead state, the curvature can be 0.

[0071] Furthermore, when the vehicle is traveling forward, if the vehicle is traveling straight, the autonomous driving system can use the current position information as the starting position information to predict the left-turn candidate path based on the positive value of the preset rate of curvature change (lambda), and use the current position information as the starting position information to predict the right-turn candidate path based on the negative value of the preset rate of curvature change.

[0072] The following describes the implementation of the candidate path determination method in this application embodiment, taking the vehicle's current location information, including the vehicle's current coordinates and orientation, and the current driving information, including the curvature (kappa), as an example.

[0073] See Figure 2B , Figure 2B This application provides a flowchart of a method for determining candidate paths according to an embodiment of the present application. The method includes:

[0074] Step S21: If the vehicle is determined to travel forward based on its orientation, the current coordinates of the vehicle are used as the starting point of the candidate path to generate a straight path for forward travel.

[0075] Step S22: Determine whether the vehicle is traveling straight based on the curvature. If yes, proceed to step S23; otherwise, proceed to step S24.

[0076] For example, if the curvature is within the range of 0.02, the vehicle is considered to be traveling straight; otherwise, the vehicle is considered to be turning.

[0077] Step S23: Predict the left-turn candidate path based on the positive value of the preset curvature change rate, and predict the right-turn candidate path based on the negative value of the preset curvature change rate.

[0078] Step S24: Determine whether the vehicle is turning left based on the curvature. If yes, proceed to step S25; otherwise, proceed to step S26.

[0079] Step S25: Predict the left-turn candidate path based on the positive value of the preset curvature change rate.

[0080] Step S26: Predict the right-turn candidate path based on the negative value of the preset rate of curvature change.

[0081] As can be seen, by adopting this implementation method, when the vehicle is traveling in an unstructured lane, the autonomous driving system can estimate multiple possible driving paths of the vehicle as candidate paths, so as to include all possible avoidance paths as candidate paths, which helps to increase the range of paths for screening overlapping obstacles, thereby improving the accuracy of screening obstacles that overlap with candidate paths.

[0082] Step S103: Based on the obstacle overlap detection results of each candidate path in the candidate path set, determine the execution strategy for emergency braking of the vehicle.

[0083] In some embodiments, if the obstacle overlap detection results for all candidate paths show obstacle overlap, then emergency braking is triggered on the vehicle. In other embodiments, if the obstacle overlap detection results for at least one of the candidate paths show no obstacle overlap, then emergency braking is not triggered.

[0084] For example, combining Figure 3 In the illustrated scenario, the autonomous driving system can estimate obstacle overlap for each of the candidate paths—the straight-ahead candidate path, the left-turn candidate path, and the right-turn candidate path. If all three paths have overlapping obstacles, the autonomous driving system triggers emergency braking on the vehicle. If at least one of these paths has no overlapping obstacles, that path can be used as the vehicle's obstacle avoidance path, and the autonomous driving system does not need to trigger emergency braking.

[0085] In other embodiments, if the driving information determines that the vehicle's driving lane is a structured lane, the autonomous driving system can use conventional obstacle screening methods to screen overlapping obstacles on the vehicle's driving path and determine the emergency braking execution strategy based on the screening results. See details... Figure 1 The corresponding embodiments are described, and will not be repeated here.

[0086] In this embodiment, during vehicle operation, driving information for the current screening period can be acquired. This driving information includes at least one of the vehicle's driving mode, speed, and driving scenario. Since driving information reflects whether the vehicle is traveling in a structured lane, in this embodiment, if the driving lane is determined to be an unstructured lane based on the driving information, at least two candidate paths for the vehicle can be predicted. Furthermore, based on the obstacle overlap detection results of each candidate path in the candidate path set, an emergency braking strategy for the vehicle can be determined. Thus, when the vehicle is traveling in an unstructured lane, the autonomous driving system can estimate multiple possible driving paths as candidate paths and perform obstacle screening for all possible candidate paths. This improves the accuracy of identifying obstacles overlapping with candidate paths, reduces the probability of falsely triggering emergency braking, and enhances the safety of unstructured lanes.

[0087] This application also provides an automatic emergency braking device for performing the automatic emergency braking method provided in any of the above embodiments. For example... Figure 4 As shown, the device includes:

[0088] The acquisition module 401 is used to acquire the vehicle's driving information during the current screening period, the driving information including at least one of the vehicle's driving mode, the vehicle's driving speed, and the vehicle's driving scenario; the prediction module 402 is used to predict a candidate path set for the vehicle based on the current driving state of the vehicle if the driving information determines that the vehicle's driving lane is an unstructured lane, the candidate path set including at least two candidate paths; the determination module 403 is used to determine the execution strategy for emergency braking of the vehicle based on the obstacle overlap detection results of each candidate path in the candidate path set.

[0089] Optionally, the determining module 403 is further configured to determine that the vehicle's driving lane is an unstructured lane if the vehicle's driving mode is detected to be a reversing mode; the determining module 403 is further configured to determine that the vehicle's driving lane is an unstructured lane if the vehicle's driving speed is detected to be lower than a preset speed; the determining module 403 is further configured to determine that the vehicle's driving lane is an unstructured lane if it receives a switch to a preset driving scenario for the vehicle and the vehicle's driving speed is lower than the preset speed.

[0090] Optionally, the current driving state of the vehicle includes the vehicle's current location information and current driving information. The prediction module 402 is further configured to determine whether the vehicle is traveling straight based on the current driving information; and if the vehicle is traveling straight, to predict the vehicle's straight candidate path, left turn candidate path, and right turn candidate path based on the current location information and a preset curvature change rate, wherein the straight candidate path, the left turn candidate path, and the right turn candidate path constitute the candidate path set; the prediction module 402 is further configured to, if the vehicle is turning, to predict the vehicle's straight candidate path and turning candidate path in the turning direction based on the current location information and a preset curvature change rate, wherein the straight candidate path and the turning candidate path constitute the candidate path set, wherein the turning direction is a left turn or a right turn.

[0091] Optionally, the prediction module 402 is further configured to read the current turning radius parameter of the vehicle; if the turning radius parameter is greater than a preset threshold, the vehicle is determined to be turning; if the turning radius parameter is less than or equal to the preset threshold, the vehicle is determined to be traveling straight.

[0092] Optionally, when the vehicle is traveling forward, the prediction module 402 is further configured to predict the left-turn candidate path based on the positive value of the preset rate of curvature change, using the current position information as the starting position information; and to predict the right-turn candidate path based on the negative value of the preset rate of curvature change, using the current position information as the starting position information.

[0093] Optionally, the length of any candidate path is the larger of an estimated length and a set length, wherein the estimated length is obtained by multiplying a preset time period by the current driving speed of the vehicle.

[0094] Optionally, the determining module 403 is further configured to trigger emergency braking on the vehicle if the obstacle overlap detection results of all the candidate paths are obstacle overlaps; the determining module 403 is further configured to not trigger emergency braking if the obstacle overlap detection result of at least one of the candidate paths is obstacle overlap-free.

[0095] The automatic emergency braking device and the automatic emergency braking method provided in this application are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0096] This application also provides an electronic device for performing the above-described automatic emergency braking method. Please refer to... Figure 5 It illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 5As shown, the electronic device 5 includes: a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, the communication interface 503, and the memory 501 are connected via the bus 502. The memory 501 stores a computer program that can run on the processor 500. When the processor 500 runs the computer program, it executes the automatic emergency braking method provided in any of the foregoing embodiments of this application.

[0097] The memory 501 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 503 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0098] Bus 502 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Memory 501 is used to store programs. After receiving an execution instruction, the processor 500 executes the program. The automatic emergency braking method disclosed in any of the foregoing embodiments of this application can be applied to the processor 500, or implemented by the processor 500.

[0099] The processor 500 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 500 or by instructions in software form. The processor 500 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 501. The processor 500 reads the information in memory 501 and, in conjunction with its hardware, completes the steps of the above method.

[0100] The electronic device provided in this application embodiment and the automatic emergency braking method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0101] This application also provides a computer-readable storage medium corresponding to the automatic emergency braking method provided in the foregoing embodiments. Please refer to... Figure 6 The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the automatic emergency braking method provided in any of the foregoing embodiments.

[0102] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0103] The computer-readable storage medium provided in the above embodiments of this application and the automatic emergency braking method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0104] It should be noted that:

[0105] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known structures and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0106] Similarly, it should be understood that, for the sake of brevity and to aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting a schematic diagram in which the claimed application requires more features than expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0107] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0108] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An automatic emergency braking method, characterized in that, include: Obtain the vehicle's driving information for the current screening period, the driving information including at least one of the vehicle's driving mode, the vehicle's driving speed, and the vehicle's driving scenario; If the driving lane of the vehicle is determined to be an unstructured lane based on the driving information, a candidate path set of the vehicle is predicted based on the current driving state of the vehicle, and the candidate path set includes at least two candidate paths. Based on the obstacle overlap detection results of each candidate path in the candidate path set, the execution strategy for emergency braking of the vehicle is determined.

2. The method of claim 1, wherein, Before predicting the candidate path set of the vehicle based on the vehicle's current driving state, the method further includes: If the vehicle's driving mode is detected as reversing, then the vehicle's driving lane is determined to be an unstructured lane; or, If the vehicle's speed is detected to be lower than a preset speed, then the vehicle's lane is determined to be an unstructured lane; or, If the system receives a message indicating that the vehicle's driving scenario has been switched to a preset driving scenario and the vehicle's driving speed is lower than the preset speed, then the vehicle's driving lane is determined to be an unstructured lane.

3. The method of claim 1, wherein, The current driving status of the vehicle includes the vehicle's current location information and current driving information. Predicting the candidate path set of the vehicle based on its current driving status includes: Determine whether the vehicle is traveling straight based on the current driving information; If the vehicle is traveling straight, the candidate straight path, the candidate left turn path, and the candidate right turn path of the vehicle are predicted based on the current position information and the preset curvature change rate. The candidate straight path, the candidate left turn path, and the candidate right turn path constitute the candidate path set. If the vehicle is turning, the straight-going candidate path and the turning candidate path in the turning direction are predicted based on the current position information and the preset curvature change rate. The straight-going candidate path and the turning candidate path constitute the candidate path set, and the turning direction is either a left turn or a right turn.

4. The method of claim 3, wherein, The step of determining whether the vehicle is traveling straight based on the current driving information includes: Read the current turning radius parameter of the vehicle; If the turning radius parameter is greater than a preset threshold, then the vehicle is determined to be turning. If the turning radius parameter is less than or equal to the preset threshold, then the vehicle is determined to be traveling straight.

5. The method of claim 3, wherein, When the vehicle is traveling forward, predicting the candidate paths for left turns and right turns based on the current position information and a preset rate of curvature change includes: Using the current position information as the starting position information, the candidate path for the left turn is predicted based on the positive value of the preset rate of curvature change; Using the current position information as the starting position information, the candidate path for the right turn is predicted based on the negative value of the preset rate of curvature change.

6. The method according to claim 3, characterized in that, The length of any candidate path is the greater of the estimated length and the set length, wherein the estimated length is obtained by multiplying the preset time period by the current driving speed of the vehicle.

7. The method of claim 1, wherein, The step of determining the execution strategy for emergency braking of the vehicle based on the obstacle overlap detection results of each candidate path in the candidate path set includes: If the obstacle overlap detection results of each candidate path are all obstacle overlap, then emergency braking is triggered on the vehicle. If the obstacle overlap detection result of at least one of the candidate paths is no obstacle overlap, then emergency braking will not be triggered.

8. An automatic emergency braking device, characterized in that The device includes: The acquisition module is used to acquire the driving information of the vehicle in the current screening period. The driving information includes at least one of the vehicle's driving mode, the vehicle's driving speed, and the vehicle's driving scenario. The prediction module is used to predict a candidate path set for the vehicle based on the current driving state of the vehicle if the driving lane of the vehicle is determined to be an unstructured lane based on the driving information. The candidate path set includes at least two candidate paths. The determination module is used to determine the execution strategy for emergency braking of the vehicle based on the obstacle overlap detection results of each candidate path in the candidate path set.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by a processor to implement the method as described in any one of claims 1-7.

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