A vehicle curve lateral control method, system, electronic device and storage medium

Abstract

Embodiments of the present application disclose a vehicle curve lateral control method and system, an electronic device and a storage medium. The method comprises: determining whether a vehicle enters a curve according to real-time driving road conditions of the vehicle and steering information identified based on a radar; detecting a curve condition based on the radar after determining that the vehicle enters the curve; calculating a steering wheel angle schedule of the vehicle according to the curve condition and the real-time driving road conditions of the vehicle; matching the steering wheel angle corresponding to driving at a plurality of future time points of the vehicle by using a local nonlinear fitting method; and controlling the curve when driving at the plurality of future time points according to the steering wheel angle corresponding to driving at the plurality of future time points. The steering information at a future position of the vehicle is calculated in advance, which is not a correction feedback after an error occurs, and the future steering wheel angle corresponds to the future time point, so that the accuracy of future steering of the vehicle is ensured.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, specifically to a method, system, electronic device, and storage medium for lateral control of a vehicle during curves. Background Technology

[0002] Currently, major automakers worldwide are heavily investing in intelligent driving features, including warning functions, longitudinal control, and lateral control. This necessitates automotive engineers addressing the challenges posed by various road scenarios. For example, when intelligent driving vehicles traverse high-speed cross slopes, lateral deviations can occur.

[0003] In existing technologies, most lateral control of intelligent driving vehicles is based on preview theory + feedforward control + feedback control to keep the vehicle in the center of the lane. Currently, most lateral control methods consider image-based preview point algorithms or deep learning recognition algorithms. However, in curved road conditions with lateral slopes, image detection has significant limitations and slope calculation errors are large. At the same time, the control accuracy at the moment of steering wheel compensation is poor. Summary of the Invention

[0004] Therefore, embodiments of this application provide a vehicle curve lateral control method, system, electronic device, and storage medium. By pre-calculating the steering information of the vehicle at a future position, which is not a correction feedback after an error occurs, the accuracy of the vehicle's future steering is ensured.

[0005] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0006] According to a first aspect of the embodiments of this application, a vehicle cornering control method is provided, the method comprising:

[0007] Determine whether the vehicle has entered a curve based on real-time road conditions and radar-based steering information.

[0008] After determining that the vehicle has entered the curve, the road conditions of the curve are detected by radar.

[0009] Calculate the vehicle's steering wheel angle timing table based on the curved road conditions and the vehicle's real-time driving conditions;

[0010] The steering wheel angles at several future moments are matched using a local nonlinear fitting method.

[0011] Based on the steering wheel angle corresponding to the driving time at the aforementioned future several moments, cornering control is performed at those several moments.

[0012] Optionally, the road conditions of the curve include the outer boundary radius of the curve, the real-time distance between the vehicle and the outer boundary of the road, and the lateral slope at a set position in front of the vehicle; the outer boundary radius of the curve is determined based on nonlinear fitting using the outer boundary point cloud information of consecutive frames.

[0013] Optionally, the step of calculating the vehicle's steering wheel angle timing based on the curved road conditions and the vehicle's real-time driving conditions includes:

[0014] Calculate the turning center and turning radius of the vehicle when it reaches the set position ahead, based on the outer boundary radius of the curve and the real-time distance between the vehicle and the outer boundary of the road.

[0015] The steering wheel angle at the moment of reaching the set position is calculated based on the lateral slope at the set position in front of the vehicle, the vehicle's turning radius, and the real-time vehicle speed.

[0016] Calculate the actual distance traveled by the vehicle to reach the set location based on the lateral slope.

[0017] The actual time it takes for the vehicle to travel to the set position is calculated based on the real-time vehicle speed and the actual distance traveled, so as to calculate the turning timetable;

[0018] The steering wheel angle timetable of the vehicle is obtained by performing nonlinear fitting on the aforementioned angle timetable.

[0019] Optionally, the steering wheel angle at the moment of reaching the set position is calculated based on the lateral slope at the set position in front of the vehicle, the vehicle's turning radius, and the real-time vehicle speed, including:

[0020] Based on the lateral slope at a predetermined position in front of the vehicle, the vehicle's turning radius, and the real-time vehicle speed, the steering wheel angle at the moment of arrival at the predetermined position is calculated in real time using a pre-deployed and trained deep learning model in the domain controller.

[0021] Optionally, the actual distance traveled by the vehicle to reach the set location based on the lateral slope is calculated, including:

[0022] The straight-line distance of the vehicle to the set location is determined by the lateral slope detected by radar.

[0023] The arc length of the actual driving distance is calculated based on the real-time road conditions and the straight-line distance of the vehicle to the set position, taking into account the lateral slope.

[0024] Optionally, the real-time road conditions for the vehicle include left-turn lanes, right-turn lanes, roads with a significant curvature, and turn signals from cooperating vehicles.

[0025] According to a second aspect of the embodiments of this application, a vehicle curve control system is provided, the system comprising:

[0026] The curve recognition module is used to determine whether a vehicle has entered a curve based on real-time road conditions and radar-based steering information.

[0027] The curve road condition detection module is used to detect the curve road conditions based on radar after determining that the vehicle has entered the curve.

[0028] The steering wheel angle timetable module is used to calculate the vehicle's steering wheel angle timetable based on the curved road conditions and the vehicle's real-time driving conditions.

[0029] The steering wheel angle module is used to match the steering wheel angle corresponding to the vehicle's driving at several future moments using a local nonlinear fitting method.

[0030] The cornering control module is used to perform cornering control at the specified future driving times based on the steering wheel angle corresponding to the driving time at those specified future driving times.

[0031] Optionally, the road conditions of the curve include the outer boundary radius of the curve, the real-time distance between the vehicle and the outer boundary of the road, and the lateral slope at a set position in front of the vehicle; the outer boundary radius of the curve is determined based on nonlinear fitting using the outer boundary point cloud information of consecutive frames;

[0032] The steering wheel angle timetable module is specifically used for:

[0033] Calculate the turning center and turning radius of the vehicle when it reaches the set position ahead, based on the outer boundary radius of the curve and the real-time distance between the vehicle and the outer boundary of the road.

[0034] The steering wheel angle at the moment of reaching the set position is calculated based on the lateral slope at the set position in front of the vehicle, the vehicle's turning radius, and the real-time vehicle speed.

[0035] Calculate the actual distance traveled by the vehicle to reach the set location based on the lateral slope.

[0036] The actual time it takes for the vehicle to travel to the set position is calculated based on the real-time vehicle speed and the actual distance traveled, so as to calculate the turning timetable;

[0037] The steering wheel angle timetable of the vehicle is obtained by performing nonlinear fitting on the aforementioned angle timetable.

[0038] According to a third aspect of the present application, an electronic device is provided, comprising: 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.

[0039] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided having computer-readable instructions stored thereon, which can be executed by a processor to implement the method described in the first aspect above.

[0040] In summary, this application provides a vehicle curve lateral control method, system, electronic device, and storage medium. It determines whether a vehicle has entered a curve based on real-time road conditions and radar-based steering information. After determining that the vehicle has entered a curve, it detects the road conditions based on radar. It calculates a steering wheel angle timetable based on the road conditions and the real-time road conditions. A local nonlinear fitting method is used to match the steering wheel angles corresponding to the vehicle's driving at several future times. Curve control is then performed based on the steering wheel angles corresponding to the future driving times. By pre-calculating the steering information of the vehicle at a future position, it ensures proactive control, avoiding error correction feedback, and achieving a correspondence between the future steering wheel angle and the future time, thus ensuring the accuracy of future vehicle steering. Considering the discreteness of radar detection data and the discontinuity of the steering wheel angle information with time, a nonlinear fitting method is used to ensure the continuity of steering information, guaranteeing the smoothness of the turning process and ride comfort. Attached Figure Description

[0041] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0042] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0043] Figure 1 This is a schematic flowchart of a vehicle curve control method provided in an embodiment of this application;

[0044] Figure 2 This application provides an overall flowchart of vehicle lateral control during curves.

[0045] Figure 3 The steering wheel angle timetable calculation process provided in this application embodiment;

[0046] Figure 4 A block diagram of a vehicle curve control system provided in this application embodiment;

[0047] Figure 5 This illustration shows a structural schematic diagram of an electronic device provided in an embodiment of this application;

[0048] Figure 6 A schematic diagram of a computer-readable storage medium provided in an embodiment of this application is shown. Detailed Implementation

[0049] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Figure 1 This application illustrates a vehicle curve control method according to an embodiment of the present application, the method comprising:

[0051] Step 101: Determine whether the vehicle has entered the curve based on the real-time road conditions and the steering information identified by radar.

[0052] Step 102: After determining that the vehicle has entered the curve, the road conditions of the curve are detected by radar.

[0053] Step 103: Calculate the vehicle's steering wheel angle timing table based on the curved road conditions and the vehicle's real-time driving conditions;

[0054] Step 104: Use a local nonlinear fitting method to match the steering wheel angles corresponding to the vehicle's driving at several future moments;

[0055] Step 105: Based on the steering wheel angle corresponding to the driving time at the aforementioned future several moments, perform curve control at the driving time at those several moments.

[0056] In one possible implementation, the road conditions of the curve include the outer boundary radius of the curve, the real-time distance between the vehicle and the outer boundary of the road, and the lateral slope at a set position in front of the vehicle; the outer boundary radius of the curve is determined based on nonlinear fitting using the outer boundary point cloud information of consecutive frames.

[0057] In one possible implementation, step 103, calculating the vehicle's steering wheel angle timing schedule based on the curved road conditions and the vehicle's real-time driving conditions, includes:

[0058] The turning center and turning radius of the vehicle at the designated position ahead are calculated based on the outer boundary radius of the curve and the real-time distance between the vehicle and the outer boundary of the road. The steering wheel angle at the time of arrival at the designated position is calculated based on the lateral slope at the designated position ahead, the turning radius of the vehicle, and the real-time speed. The actual travel distance of the vehicle at the designated position based on the lateral slope is calculated. The actual time it takes for the vehicle to travel to the designated position is calculated based on the real-time speed and the actual travel distance to obtain the turning angle timetable. The steering wheel angle timetable of the vehicle is obtained by performing nonlinear fitting on the turning angle timetable.

[0059] In one possible implementation, the steering wheel angle at the moment of reaching the set position is calculated based on the lateral slope at the set position in front of the vehicle, the vehicle's turning radius, and the real-time vehicle speed, including:

[0060] Based on the lateral slope at a predetermined position in front of the vehicle, the vehicle's turning radius, and the real-time vehicle speed, the steering wheel angle at the moment of arrival at the predetermined position is calculated in real time using a pre-deployed and trained deep learning model in the domain controller.

[0061] In one possible implementation, calculating the actual distance traveled by the vehicle to reach the set location based on the lateral slope includes:

[0062] The radar detects the straight-line distance of the vehicle's lateral slope to the set location; the arc length of the actual travel distance is calculated based on the vehicle's real-time road conditions and the straight-line distance of the lateral slope to the set location.

[0063] In one possible implementation, the real-time road conditions for the vehicle include left-turn lanes, right-turn lanes, roads with a significant curvature, and turn signals from cooperating vehicles.

[0064] By using LiDAR to sense information in front of the vehicle and combining it with the vehicle's driving status, deep learning is used to solve for the appropriate steering information for the vehicle's position at a future time.

[0065] As can be seen, the vehicle cornering control method provided in this application ensures control advance by pre-calculating the steering information of the vehicle at a future position, avoiding correction feedback after an error occurs. It also ensures the correspondence between the future steering wheel angle and the future time, guaranteeing the accuracy of the vehicle's future steering. Considering the discreteness of radar detection data and the discontinuity between the obtained steering wheel angle information and time, a nonlinear fitting method is used to ensure the continuity of steering information, thus guaranteeing the smoothness of the turning process and ride comfort.

[0066] The vehicle curve control method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0067] Figure 2 The diagram illustrates the overall flowchart of vehicle lateral control during curves based on LiDAR perception. Using LiDAR to perceive curve information effectively compensates for the limitations of the limited detection range of cameras, ensuring comprehensive information while preventing interference from surrounding objects.

[0068] Step 1: Based on the vehicle's driving conditions, such as the left or right turn lane and the turn signals of the cooperating vehicle, and based on the lidar, determine whether to enter a curve. If entering a curve, proceed to Step 2.

[0069] Step 2: Perceive road condition information by radar. The perceived information includes, but is not limited to: the radius of the curve, i.e. the radius of the outer boundary of the curve, which can be determined by nonlinear fitting through continuous frame outer boundary point cloud information; the distance of the vehicle from the outer boundary of the road at this time; and the lateral slope at the specific location in front of the vehicle.

[0070] Step 3: Based on the vehicle's current driving state, calculate the vehicle's future time and its steering wheel angle, referred to as the steering wheel angle timetable;

[0071] By pre-calculating the vehicle's steering information at a future location, proactive control is ensured, avoiding the need for corrective feedback after errors occur. Furthermore, the future steering wheel angle corresponds to the future moment, guaranteeing the accuracy of future vehicle steering.

[0072] Step 4: Use a local nonlinear fitting method to obtain the steering wheel angles at different times during the vehicle's subsequent driving.

[0073] Considering the discreteness of radar detection data and the discontinuity of steering wheel angle information with time, a nonlinear fitting method was adopted to ensure the continuity of steering information, thus guaranteeing the smoothness of the turning process and ride comfort.

[0074] Considering the discreteness of the steering wheel angle timetable data, local nonlinear fitting is used to obtain the steering wheel angle value at uniform time.

[0075] Secondly, regarding the calculation process of the steering wheel angle timetable, such as... Figure 3 As shown. Figure 3 A flowchart for calculating the steering wheel angle timing is shown. By analyzing the actual distance traveled by the vehicle, the accurate correspondence between the steering wheel angle and the timing of the turn is determined.

[0076] Step 1: Given the radius of the curve detected by the radar in front of the vehicle and the distance between the vehicle and the outer edge of the curve, assuming that the width of the curve remains constant and the distance between the vehicle and the outer edge remains constant, the turning center and turning radius of the vehicle when it reaches the position in front can be calculated.

[0077] Step 2: Based on the known lateral slope information at this location and the vehicle's own state information, the steering wheel angle at the moment of arrival at this location is calculated in real time using a pre-trained deep learning model deployed in the domain controller.

[0078] Step 3: Considering that the radar detection distance is a straight-line distance and not the actual distance the vehicle travels to that position, the actual travel arc length is calculated based on the radar detection of the straight-line distance to the position of the lateral slope, according to the vehicle's turning state at this time.

[0079] Step 4: While ensuring the vehicle travels at a constant speed, calculate the actual time it takes for the vehicle to reach that position, so as to achieve an accurate correspondence between the steering wheel angle and the turning time.

[0080] By following the above process, the steering wheel angle at the specific position in front of the vehicle when driving on a curve can be determined in advance, ensuring that the vehicle makes an accurate and reasonable turn when it reaches that position.

[0081] LiDAR sensing effectively compensates for the limitations of limited camera detection range and prevents interference from surrounding objects. Using LiDAR to sense curves ensures comprehensive information coverage.

[0082] By pre-calculating the vehicle's steering information at a future location, proactive control is ensured, avoiding corrections after errors occur. This also ensures the correspondence between the future steering wheel angle and the future moment, guaranteeing accurate future steering. LiDAR senses information ahead of the vehicle, and combined with the vehicle's driving status, deep learning is used to calculate the appropriate steering information for the vehicle's position at a future moment.

[0083] Considering the discreteness of radar detection data and the discontinuity of steering wheel angle information with time, local nonlinear fitting is used to obtain the steering wheel angle value at a uniform time, which ensures the continuity of steering information, the smoothness of the turning process, and the ride comfort.

[0084] In summary, this application provides a vehicle lateral control method for curves. It determines whether a vehicle has entered a curve based on real-time road conditions and radar-based steering information. After determining that the vehicle has entered a curve, it detects the road conditions based on radar. It calculates a steering wheel angle timetable based on the road conditions and the real-time road conditions. A local nonlinear fitting method is used to match the steering wheel angles corresponding to the vehicle's driving at several future times. Curve control is then performed based on the steering wheel angles corresponding to these future times. By pre-calculating the steering information for a future position of the vehicle, it ensures proactive control, avoiding error correction feedback, and achieving a correspondence between the future steering wheel angle and the future time, thus ensuring the accuracy of future vehicle steering. Considering the discreteness of radar detection data and the discontinuity between the obtained steering wheel angle information and time, a nonlinear fitting method is used to ensure the continuity of steering information, guaranteeing the smoothness of the turning process and ride comfort.

[0085] Based on the same technical concept, embodiments of this application also provide a vehicle curve control system, such as... Figure 4 As shown, the system includes:

[0086] The curve recognition module 401 is used to determine whether a vehicle has entered a curve based on real-time road conditions and radar-based steering information.

[0087] The curve road condition detection module 402 is used to detect the curve road conditions based on radar after determining that the vehicle has entered the curve.

[0088] Steering wheel angle timetable module 403 is used to calculate the vehicle's steering wheel angle timetable based on the curved road conditions and the vehicle's real-time driving conditions.

[0089] Steering wheel angle module 404 is used to match the steering wheel angle corresponding to the vehicle's driving at several future moments using a local nonlinear fitting method;

[0090] The curve control module 405 is used to perform curve control at the specified future travel times based on the steering wheel angle corresponding to the travel time at the specified future travel times.

[0091] In one possible implementation, the road conditions of the curve include the outer boundary radius of the curve, the real-time distance between the vehicle and the outer boundary of the road, and the lateral slope at a set position in front of the vehicle; the outer boundary radius of the curve is determined based on nonlinear fitting using the outer boundary point cloud information of consecutive frames;

[0092] The steering wheel angle timetable module 403 is specifically used for:

[0093] The turning center and turning radius of the vehicle at the designated position ahead are calculated based on the outer boundary radius of the curve and the real-time distance between the vehicle and the outer boundary of the road. The steering wheel angle at the time of arrival at the designated position is calculated based on the lateral slope at the designated position ahead, the turning radius of the vehicle, and the real-time speed. The actual travel distance of the vehicle at the designated position based on the lateral slope is calculated. The actual time it takes for the vehicle to travel to the designated position is calculated based on the real-time speed and the actual travel distance to obtain the turning angle timetable. The steering wheel angle timetable of the vehicle is obtained by performing nonlinear fitting on the turning angle timetable.

[0094] This application also provides an electronic device corresponding to the method provided in the foregoing embodiments. Please refer to... Figure 5 The diagram illustrates an electronic device provided by some embodiments of this application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202, and a communication interface 203, wherein the processor 200, the communication interface 203, and the memory 201 are connected via the bus 202; the memory 201 stores a computer program that can run on the processor 200, and when the processor 200 runs the computer program, it executes the method provided by any of the foregoing embodiments of this application.

[0095] The memory 201 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 203 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0096] Bus 202 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. The memory 201 is used to store programs. After receiving an execution instruction, the processor 200 executes the program. The method disclosed in any of the foregoing embodiments of this application can be applied to the processor 200, or implemented by the processor 200.

[0097] The processor 200 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 200 or by instructions in software form. The processor 200 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 201. The processor 200 reads the information in memory 201 and, in conjunction with its hardware, completes the steps of the above method.

[0098] The electronic devices and methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0099] This application also provides a computer-readable storage medium corresponding to the 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, which, when run by a processor, executes the methods provided in any of the foregoing embodiments.

[0100] 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.

[0101] The computer-readable storage medium provided in the above embodiments of this application and the 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.

[0102] It should be noted that:

[0103] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0104] 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 methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0105] Similarly, it should be understood that, in order to simplify this application and 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 into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are 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.

[0106] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[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 various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation apparatus according to embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0109] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0110] 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. A vehicle curve control method characterized by comprising: The method includes: Determine whether the vehicle has entered a curve based on real-time road conditions and radar-based steering information. After determining that the vehicle has entered the curve, the road conditions of the curve are detected by radar. The road conditions include the outer boundary radius of the curve, the real-time distance between the vehicle and the outer boundary of the road, and the lateral slope at a set position in front of the vehicle. The outer boundary radius of the curve is determined by nonlinear fitting based on the outer boundary point cloud information of consecutive frames. Calculate the vehicle's steering wheel angle timing table based on the curved road conditions and the vehicle's real-time driving conditions; The steering wheel angles at several future moments are matched using a local nonlinear fitting method. Based on the steering wheel angle corresponding to the driving time at the aforementioned future several moments, cornering control is performed at the driving time at those several moments. The step of calculating the vehicle's steering wheel angle timetable based on the curved road conditions and the vehicle's real-time driving conditions includes: calculating the turning center and turning radius of the vehicle at a predetermined position based on the outer boundary radius of the curve and the vehicle's real-time distance from the outer boundary of the road; calculating the steering wheel angle at the predetermined position based on the lateral slope, the vehicle's turning radius, and the real-time vehicle speed; calculating the actual distance traveled by the vehicle to reach the predetermined position based on the lateral slope; calculating the actual time taken by the vehicle to reach the predetermined position based on the real-time vehicle speed and the actual distance traveled, thereby calculating the steering wheel angle timetable; and performing nonlinear fitting on the steering wheel angle timetable to obtain the vehicle's steering wheel angle timetable.

2. The method of claim 1, wherein, The steering wheel angle at the moment of reaching the set position is calculated based on the lateral slope at the set position in front of the vehicle, the vehicle's turning radius, and the real-time vehicle speed, including: Based on the lateral slope at a predetermined position in front of the vehicle, the vehicle's turning radius, and the real-time vehicle speed, the steering wheel angle at the moment of arrival at the predetermined position is calculated in real time using a pre-trained deep learning model deployed in the domain controller.

3. The method of claim 1, wherein, Calculate the actual distance traveled by the vehicle to reach the set location based on the lateral slope, including: The straight-line distance of the vehicle to the set location is determined by the lateral slope detected by radar. The arc length of the actual driving distance is calculated based on the real-time road conditions and the straight-line distance of the vehicle to the set position, taking into account the lateral slope.

4. The method as described in claim 1, characterized in that, The real-time road conditions for vehicles include left-turn lanes, right-turn lanes, roads with obvious curvature, and turn signals from cooperating vehicles.

5. A vehicle curve control system, characterized in that, The system includes: The curve recognition module is used to determine whether a vehicle has entered a curve based on real-time road conditions and radar-based steering information. The curve road condition detection module is used to detect the curve road conditions based on radar after determining that the vehicle has entered the curve. The curve road conditions include the outer boundary radius of the curve, the real-time distance between the vehicle and the outer boundary of the road, and the lateral slope at a set position in front of the vehicle. The outer boundary radius of the curve is determined based on nonlinear fitting using the outer boundary point cloud information of consecutive frames. The steering wheel angle timetable module is used to calculate the vehicle's steering wheel angle timetable based on the curved road conditions and the vehicle's real-time driving conditions. Specifically, the steering wheel angle timetable module is used to: calculate the turning center and turning radius of the vehicle at a predetermined position based on the outer boundary radius of the curve and the vehicle's real-time distance from the outer boundary of the road; calculate the steering wheel angle at the time of arrival at the predetermined position based on the lateral slope at the predetermined position, the vehicle's turning radius, and the real-time vehicle speed; calculate the actual distance traveled by the vehicle to reach the predetermined position based on the lateral slope; calculate the actual time taken for the vehicle to travel to the predetermined position based on the real-time vehicle speed and the actual distance traveled, thereby calculating the angle timetable; and perform nonlinear fitting on the angle timetable to obtain the vehicle's steering wheel angle timetable. The steering wheel angle module is used to match the steering wheel angle corresponding to the vehicle's driving at several future moments using a local nonlinear fitting method. The cornering control module is used to perform cornering control at the specified future driving times based on the steering wheel angle corresponding to the driving time at those specified future driving times.

6. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method as claimed in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to implement the method as described in any one of claims 1-4.

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