Method for adaptive adjustment of a vehicle suspension and suspension controller

Abstract

The application relates to an adaptive adjustment method of a vehicle suspension and a suspension controller, wherein the method comprises the following steps: acquiring at least one of a current position of a vehicle, image information of a front road and environment perception information; obtaining road condition information of the front road according to at least one of the current position, the image information of the front road and the environment perception information; generating a target adjustment action of a vehicle suspension system for adapting to a current road condition according to the current road condition information, and driving the vehicle suspension system to execute the target adjustment action until a road section corresponding to the current road condition information. According to the application, the target adjustment action of the vehicle suspension system for adapting to the current road condition can be generated according to the current road condition information, the vehicle suspension system can be accurately adjusted according to the actual road condition information without increasing the cost as much as possible, the driving experience of a user and the safety of the vehicle are improved, and the intelligent level of the vehicle is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle suspension control technology, and in particular to an adaptive adjustment method and suspension controller for vehicle suspension. Background Technology

[0002] Currently, some vehicles are equipped with adjustable suspension systems, allowing drivers to adjust the suspension travel and stiffness as needed. These systems offer various adjustment methods, such as adjusting the suspension height and damping to achieve safe and comfortable passage on different road surfaces.

[0003] In related technologies, such as patent CN114501382A "An Automatic Adjustment Method for Vehicle Suspension System Based on Vehicle-Road Cooperation", the main approach is to manually adjust the suspension system by the driver and to detect road conditions by installing sensors during road network construction.

[0004] However, in related technologies, manually adjusting the suspension system can distract the driver and pose safety hazards, and it lacks real-time performance. Installing sensors during road network construction to detect road conditions would result in high costs and difficult maintenance, and therefore urgently needs improvement. Summary of the Invention

[0005] This application provides an adaptive adjustment method and suspension controller for vehicle suspension to solve the problems in related technologies, such as driver distraction and poor immediacy when manually adjusting the suspension system, and high cost and maintenance difficulty when installing sensors during road network construction.

[0006] The first aspect of this application provides an adaptive adjustment method for vehicle suspension, comprising the following steps: acquiring at least one of the vehicle's current location, image information of the road ahead, and environmental perception information; obtaining road condition information of the road ahead based on at least one of the current location, image information of the road ahead, and environmental perception information; generating a target adjustment action for the vehicle suspension system to adapt to the current road condition based on the current road condition information, and driving the vehicle suspension system to execute the target adjustment action until passing through the road segment corresponding to the current road condition information.

[0007] Based on the above technical means, the embodiments of this application can use a suspension adjustment method based on vehicle-cloud collaborative vision processing. By uploading road condition information to the cloud through vehicle-side sensors, and obtaining the latest road condition information from the cloud through the vehicle-side positioning system, the suspension can be adjusted, thereby improving the driver's driving experience and safety without increasing costs.

[0008] Optionally, in one embodiment of this application, obtaining the road condition information of the road ahead based on at least one of the vehicle's current location, image information of the road ahead, and environmental perception information includes: uploading the current location to a server to receive the road condition information sent by the server based on the current location, wherein the road condition information of the server is obtained or updated based on uploads from other vehicles; or, extracting at least one road condition feature from the image information and / or the environmental perception information, and generating the road condition information based on the at least one road condition feature.

[0009] Based on the above technical means, the embodiments of this application can obtain the latest road condition information of the current location from the cloud through the vehicle's positioning system, or extract road condition features through the vehicle's visual sensors or environmental perception sensors, thereby generating road condition information, improving the user's driving experience and safety, and enhancing vehicle intelligence.

[0010] Optionally, in one embodiment of this application, after driving the vehicle suspension system to perform the target adjustment action, the method further includes: after passing through the road segment, obtaining a rating result of the driving experience and safety index of the target adjustment action, so as to optimize the target adjustment action based on the rating result.

[0011] Based on the above technical means, the embodiments of this application can obtain the scoring results of the target adjustment action corresponding to the location in the cloud. The scoring results are derived from the driving experience and safety index of the target adjustment action. Based on the scoring results, the adjustment action is optimized, which improves the vehicle's intelligence and timeliness.

[0012] Optionally, in one embodiment of this application, optimizing the target adjustment action based on the scoring result includes: uploading the scoring result to the server, such that the server generates the optimal adjustment action as the target adjustment action based on the vehicle's scoring result and the scoring results of at least one other vehicle.

[0013] Based on the above technical means, the embodiments of this application can upload the scoring results to the cloud, and generate the best adjustment action of the suspension system adapted to the current road conditions by combining the scoring results of multiple vehicles in the cloud, thereby improving vehicle intelligence and achieving high calculation accuracy.

[0014] Optionally, in one embodiment of this application, generating a target adjustment action for the vehicle suspension system adapted to the current road conditions based on the current road condition information includes: obtaining at least one of the driver's actual preferences and the actual weather conditions of the current environment; and generating the target adjustment action by combining the driver's actual preferences, the actual weather conditions of the current environment, and the current road condition information.

[0015] Based on the above technical means, the embodiments of this application can obtain the driver's actual preferences through the cloud, obtain the actual weather of the current environment through the vehicle's body sensors, and generate target adjustment actions in combination with the current road condition information. No additional devices are required, the data is more complete, the calculation accuracy is higher, and it can adapt to the driving preferences of more drivers.

[0016] A second aspect of this application provides a suspension controller, comprising: a first acquisition module for acquiring at least one of the vehicle's current location, image information of the road ahead, and environmental perception information; a second acquisition module for obtaining road condition information of the road ahead based on at least one of the current location, image information of the road ahead, and environmental perception information; and a control module for generating a target adjustment action of the vehicle suspension system adapted to the current road condition based on the current road condition information, and driving the vehicle suspension system to execute the target adjustment action until passing through the road segment corresponding to the current road condition information.

[0017] Optionally, in one embodiment of this application, the first acquisition module includes: a communication module, configured to upload the current location to a server to receive the road condition information sent by the server based on the current location, wherein the road condition information of the server is obtained or updated based on uploads from other vehicles; or, a generation module, configured to extract at least one road condition feature from the image information and / or the environmental perception information, and generate the road condition information based on the at least one road condition feature.

[0018] Optionally, in one embodiment of this application, the suspension controller further includes: an evaluation module, used to obtain a rating result of the driving experience and safety index of the target adjustment action after passing through the road segment, so as to optimize the target adjustment action based on the rating result.

[0019] Optionally, in one embodiment of this application, the evaluation module includes: a first generation component, configured to upload the scoring results to the server, such that the server generates the optimal adjustment action as the target adjustment action based on the vehicle's scoring results combined with the scoring results of at least one other vehicle.

[0020] Optionally, in one embodiment of this application, the control module includes: an acquisition component for acquiring at least one of the driver's actual preferences and the actual weather conditions of the current environment; and a second generation component for generating the target adjustment action by combining the driver's actual preferences, the actual weather conditions of the current environment, and the current road condition information.

[0021] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the adaptive adjustment method for vehicle suspension as described in the above embodiments.

[0022] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the adaptive adjustment method for vehicle suspension as described in the above embodiments.

[0023] The beneficial effects of the embodiments of this application are as follows:

[0024] (1) There are no greater expenses on the vehicle side, saving time and economic costs, and it is widely applicable;

[0025] (2) By combining vehicle-side and cloud-based data, the data becomes more complete and the calculation accuracy is higher;

[0026] (3) The evaluation results of each suspension adjustment by different drivers can be saved in the cloud, thereby generating a personalized driving mode to suit the driving preferences of a wide range of drivers.

[0027] 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

[0028] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0029] Figure 1 This is a flowchart of an adaptive adjustment method for vehicle suspension according to an embodiment of this application;

[0030] Figure 2 This is a schematic diagram illustrating the working principle of the adaptive adjustment method for vehicle suspension according to an embodiment of this application;

[0031] Figure 3 This is a schematic diagram illustrating the principle of a specific adjustment method for the adaptive adjustment of vehicle suspension according to an embodiment of this application;

[0032] Figure 4 This is a schematic diagram of a suspension controller provided according to an embodiment of this application;

[0033] Figure 5 This is a structural schematic diagram of a vehicle according to an embodiment of this application.

[0034] Wherein: 10-suspension controller; 100-first acquisition module, 200-second acquisition module, 300-control module; 501-memory, 502-processor and 503-communication interface. Detailed Implementation

[0035] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0036] The adaptive adjustment method and suspension controller for vehicle suspension according to embodiments of this application are described below with reference to the accompanying drawings. Addressing the problems mentioned in the background art, such as driver distraction, poor real-time performance, high cost and maintenance difficulty of installing sensors during road network construction, this application provides an adaptive adjustment method for vehicle suspension. In this method, embodiments of this application can collect road condition information through vehicle-side sensors such as visual sensors and LiDAR and upload it to the cloud, or obtain the latest road condition information from the cloud through a positioning system, thereby determining the optimal suspension adjustment scheme and generating a personalized adjustment mode to adapt to the driving preferences of more drivers. Embodiments of this application can adjust the vehicle suspension system based on actual road condition information and driver preferences with minimal cost increase, thereby improving the user's driving experience and safety, and enhancing the vehicle's intelligence level. This solves the problems in the related art, such as driver distraction, poor real-time performance, high cost and maintenance difficulty of installing sensors during road network construction, and other related issues.

[0037] Specifically, Figure 1 This is a schematic flowchart illustrating an adaptive adjustment method for vehicle suspension provided in an embodiment of this application.

[0038] like Figure 1 As shown, the adaptive adjustment method for the vehicle suspension includes the following steps:

[0039] In step S101, at least one of the following is acquired: the vehicle's current location, image information of the road ahead, and environmental perception information.

[0040] It is understandable that during vehicle operation, adjusting the height and damping of the suspension system in real time according to different road conditions can enable the vehicle to pass safely and comfortably on various road surfaces, thereby improving the driver's driving experience and safety. Therefore, this application embodiment needs to first determine the road conditions ahead, and there are many ways to obtain this information, as illustrated below.

[0041] For example, embodiments of this application can obtain road condition features by calling images captured by visual sensors such as vehicle cameras for real-time calculation, or obtain environmental perception information of the vehicle's environment by using environmental perception sensors such as lidar, and obtain the vehicle's current location by using a positioning system, providing data for subsequent calculation of road condition information of the road, thereby improving the driver's driving experience and safety without increasing costs.

[0042] In step S102, road condition information of the road ahead is obtained based on at least one of the current location, image information of the road ahead, and environmental perception information.

[0043] Understandably, road condition information can include both temporary and long-term information. For example, road conditions that are easily changed, such as water accumulation, snow accumulation, and debris on the road, can be considered temporary information; while road conditions that are not easily changed, such as speed bumps, road depressions, and paving conditions, can be considered long-term information.

[0044] In practical implementation, this embodiment can obtain road condition information in real time by calling on images captured by vehicle-side visual sensors such as cameras, or by obtaining environmental perception information of the vehicle's surroundings through environmental perception sensors such as LiDAR, making the data more complete and incorporating the environmental perception information into the calculation process, thereby improving calculation accuracy. Furthermore, if the vehicle is not equipped with sensors such as cameras and LiDAR, this embodiment can also obtain the vehicle's current location through the vehicle-side positioning system and obtain the latest road condition information obtained by other vehicles that recently passed through the same road segment from the cloud. The specific process will be described in detail below.

[0045] The embodiments of this application can obtain road information in a timely manner while saving time and economic costs. They are widely applicable, thereby improving the driver's driving experience and safety.

[0046] Optionally, in one embodiment of this application, obtaining road condition information of the road ahead based on at least one of the vehicle's current location, image information of the road ahead, and environmental perception information includes: uploading the current location to a server to receive road condition information sent by the server based on the current location, wherein the server's road condition information is obtained or updated based on uploads from other vehicles; or, extracting at least one road condition feature from image information and / or environmental perception information, and generating road condition information based on at least one road condition feature.

[0047] It is understood that the embodiments of this application can be applied to servers such as vehicle network data platforms to provide high-quality processing capabilities for vehicle data information, making resource allocation more reasonable. For example, the target vehicle of the server can be one or more vehicles, effectively improving resource utilization, making it more reasonable and reliable, and receiving the latest road condition information of the vehicle's location, thereby preparing for the subsequent generation of corresponding suspension adjustment actions.

[0048] In practical implementation, this embodiment of the application can use data collected by vehicle-mounted sensors such as in-vehicle cameras and LiDAR to determine the current road conditions, upload the road condition information and location information to the cloud, and update the road condition information stored in the cloud. The cloud then updates the road condition information at the corresponding location on the map based on the road condition information and location information uploaded by the vehicle.

[0049] In other embodiments, the present application embodiments can extract at least one road condition feature, such as road damage, road water accumulation, road paving conditions, and speed bumps, from image information or environmental perception information obtained by the vehicle, thereby generating road condition information based on the road condition features, thus preparing for subsequent generation of suspension adjustment actions.

[0050] This application embodiment can generate road condition information based on the image information and environmental perception information of the current road obtained by the vehicle-side sensors. It can also obtain road condition information based on the current location through the server, which increases the intelligence of the vehicle, improves the driver's driving experience, and enhances the overall safety protection level of the vehicle.

[0051] In step S103, a target adjustment action for the vehicle suspension system is generated based on the current road condition information to adapt to the current road condition, and the vehicle suspension system is driven to perform the target adjustment action until the road segment corresponding to the current road condition information is passed.

[0052] Understandably, considering the safety and passability of the vehicle, different suspension adjustment methods need to be considered when encountering different road conditions. For example, when encountering potholes, abnormal bumps, or speed bumps, the vehicle can make a judgment on whether it can pass through the Forbidden City based on its own chassis height and suspension travel. If it can pass through, it can be raised to a suitable height. If it cannot pass through, the driver can be reminded to take timely avoidance or braking operations.

[0053] For example, when encountering roads with poor smoothness, this embodiment can reduce the damping of the electromagnetic suspension or air suspension, increasing suspension elasticity and reducing rigidity, thereby reducing the amplitude of bumps and increasing driving comfort. For areas with larger potholes, the height of the air spring assembly can be adjusted to prevent the chassis from bottoming out. When encountering smooth roads without standing water or road obstacles, this embodiment can increase the damping of the electromagnetic suspension or air suspension and lower the height of the air spring assembly to reduce wind resistance and improve response speed. When encountering standing water or road obstacles, this embodiment can determine whether it is safe to pass by combining the chassis height and the travel of the air spring assembly. If it is safe, the height can be adjusted to a safe value; if it is not safe, the driver is alerted and can be prompted to detour or stop.

[0054] The embodiments of this application can generate target adjustment actions for the vehicle suspension system based on current road condition information to adapt to the current road conditions, until the vehicle passes through the road segment corresponding to the current road condition information. This improves the vehicle's intelligence, enhances the vehicle's driving stability, comfort, and safety, and improves the user's driving experience.

[0055] Optionally, in one embodiment of this application, generating a target adjustment action for the vehicle suspension system adapted to the current road conditions based on current road condition information includes: obtaining at least one of the driver's actual preferences and the actual weather conditions of the current environment; and generating the target adjustment action by combining the driver's actual preferences, the actual weather conditions of the current environment, and the current road condition information.

[0056] Understandably, in order to make driving more comfortable and safer for drivers, when generating appropriate target adjustment actions based on current road conditions, the actual preferences of drivers and the actual weather conditions of the environment should be fully considered.

[0057] Specifically, the embodiments of this application can store the driver's suspension adjustment preferences in the driving state on the vehicle network data platform, obtain the driver's actual preferences before generating the target adjustment action of the vehicle suspension system adapted to the current road conditions, and combine the actual weather of the current environment obtained from the cloud, so as to generate the target adjustment action based on the current road condition information, making the driving process more comfortable and safe, improving the intelligence of the vehicle, and enhancing the user's driving experience.

[0058] Optionally, in one embodiment of this application, after the vehicle suspension system has completed the target adjustment action, the adaptive adjustment method of the vehicle suspension further includes: after passing through the road segment, obtaining the driving experience and safety index score results of the target adjustment action, so as to optimize the target adjustment action based on the score results.

[0059] Understandably, in order to optimize the target adjustment action and make it more adaptable to different road conditions, the driving experience and safety index of the corresponding target adjustment action should be scored to obtain user feedback and further improve the suspension adjustment parameters.

[0060] In actual implementation, this application embodiment can request the driver to rate the driving experience and safety index of the road section after completing the suspension adjustment and passing through the relevant road section, and store the rating in the user's exclusive database. Subsequently, when passing through similar road conditions, the suspension adjustment scheme can be adjusted accordingly based on the driver's rating, making the driving process more comfortable and safe, improving the vehicle's intelligence, and enhancing the user's driving experience.

[0061] Optionally, in one embodiment of this application, optimizing the target adjustment action based on the scoring results includes: uploading the scoring results to a server, such that the server generates the best adjustment action as the target adjustment action based on the vehicle's scoring results combined with the scoring results of at least one other vehicle.

[0062] It is understandable that a single user's rating of a suspension adjustment scheme may lack universality. Therefore, it is possible to combine the ratings of multiple users on suspension adjustment schemes for the same type of road conditions to obtain the optimal adjustment action.

[0063] For example, in this application embodiment, a complete set of optimal suspension adjustment schemes for various road conditions can be calculated in the cloud by using a large number of car owners' ratings of their passing schemes for different road conditions. If the current user has no experience passing the current road condition or has not evaluated the passing experience, the optimal suspension adjustment scheme in the cloud can be applied first, thereby making the driving process more comfortable and safe, improving the vehicle's intelligence, and enhancing the user's driving experience.

[0064] Combination Figure 2 and Figure 3 As shown, the working principle of the adaptive adjustment method for vehicle suspension according to an embodiment of this application will be explained in detail with reference to one example.

[0065] Understandably, with the popularization of intelligent driving assistance, vehicles are equipped with more sensors such as cameras and lidar, which can obtain road condition information of the road section where the vehicle is located, thereby achieving real-time road information without increasing additional costs.

[0066] Among them, such as Figure 2 As shown in the left-hand type of vehicle, the lidar equipped on the vehicle can identify the size of road surface undulations or obstacles, while the camera can acquire more image features to identify the attributes of obstacles and the materials of road paving.

[0067] like Figure 2As shown in the middle of the vehicle-mounted image, images captured by the vehicle's camera are processed to identify temporary and long-term road information. Temporary information includes easily changeable road conditions such as water accumulation, snow accumulation, and debris on the road surface; while long-term information includes less changeable road conditions such as speed bumps, road depressions, and paving conditions.

[0068] like Figure 2 As shown in the middle of the vehicle, some vehicles are not equipped with sensors such as cameras and lidar, so they cannot obtain road condition information through their own sensors. If additional sensing equipment is installed, it will increase costs and power consumption. At the same time, there are also difficulties in powering the sensors. Therefore, the vehicle's positioning system can obtain road condition information from the cloud when other vehicles recently passed through the same road section.

[0069] The cloud-stored map displays road conditions for various road sections, including but not limited to temporary and long-term information such as road damage, flooding, road construction, paving conditions, speed bumps, and abnormal protrusions. When a vehicle without the corresponding sensors cannot obtain real-time road conditions and passes through a road with temporary information, it can make predictions based on actual weather conditions if there is flooding or snow accumulation. Information such as previously reported obstacles on the road will be confirmed with the user through voice prompts and the central control screen to determine whether the temporary information has expired and to further determine the suspension adjustment plan.

[0070] It is understandable that the suspension adjustment scheme should prioritize safety and passability. For example, when encountering road conditions such as potholes, abnormal bumps, or speed bumps, the embodiments of this application can first make a judgment on whether it is possible to pass by combining its own chassis height and suspension travel. If it is possible to pass, it can be raised to a suitable height. If it is not possible to pass, the driver can be reminded to avoid or stop in time.

[0071] like Figure 3 As shown, the specific adjustment method of the suspension system in this embodiment is as follows:

[0072] Method S1: such as Figure 3 As shown in the fourth vehicle, when encountering roads with poor smoothness, the embodiments of this application can reduce the damping of the electromagnetic suspension / air suspension, increase the suspension elasticity and reduce the rigidity, thereby reducing the amplitude of bumps and increasing driving comfort; in some areas with larger potholes, the height of the air spring assembly can be adjusted to prevent the chassis from bottoming out.

[0073] Method S2: When encountering a surface with good flatness and no water accumulation or road obstacles, the embodiments of this application can increase the damping of the electromagnetic suspension and air suspension and reduce the height of the air spring assembly to reduce wind resistance and improve response speed.

[0074] Method S3: Encountering standing water (such as...) Figure 3 The first vehicle in the middle) or there are obstacles on the road (such as Figure 3 When passing a second or third vehicle, this embodiment of the application can determine whether it is safe to pass by combining the chassis height and the travel of the air spring assembly. If it is safe to pass, the height is adjusted to a safe value. If it is not safe to pass, the driver is alerted and a detour or braking action is performed.

[0075] According to the adaptive suspension adjustment method proposed in this application, the method can collect road condition information through vehicle-side sensors such as visual sensors and LiDAR and upload it to the cloud, or obtain the latest road condition information from the cloud through a positioning system, thereby determining the optimal suspension adjustment scheme and generating a personalized adjustment mode to suit the driving preferences of more drivers. This method can adjust the vehicle suspension system based on actual road condition information and driver preferences with minimal cost increase, thereby improving the user's driving experience and safety, and enhancing the vehicle's intelligence level. This solves the problems in related technologies, such as driver distraction and poor immediacy when manually adjusting the suspension system, and the high cost and maintenance difficulty of installing sensors during road network construction.

[0076] Next, the suspension controller proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0077] Figure 4 This is a block diagram of the suspension controller according to an embodiment of this application.

[0078] like Figure 4 As shown, the suspension controller 10 includes: a first acquisition module 100, a second acquisition module 200, and a control module 300.

[0079] Specifically, the first acquisition module 100 is used to acquire at least one of the vehicle's current location, image information of the road ahead, and environmental perception information.

[0080] The second acquisition module 200 is used to obtain road condition information of the road ahead based on at least one of the current location, image information of the road ahead, and environmental perception information.

[0081] The control module 300 is used to generate a target adjustment action of the vehicle suspension system that is adapted to the current road conditions based on the current road condition information, and drive the vehicle suspension system to perform the target adjustment action until it passes through the road segment corresponding to the current road condition information.

[0082] Optionally, in one embodiment of this application, the first acquisition module 100 includes a communication module or a generation module.

[0083] The communication module is used to upload the current location to the server in order to receive traffic information sent by the server based on the current location. The traffic information of the server is obtained or updated based on the uploads of other vehicles.

[0084] Alternatively, a generation module is configured to extract at least one road condition feature from image information and / or environmental perception information, and generate road condition information based on at least one road condition feature.

[0085] Optionally, in one embodiment of this application, the suspension controller 10 further includes an evaluation module.

[0086] The evaluation module is used to obtain the driving experience and safety index scores of the target adjustment actions after passing through the road segment, so as to optimize the target adjustment actions based on the scores.

[0087] Optionally, in one embodiment of this application, the evaluation module includes: a first generation component.

[0088] The first generation component is used to upload the scoring results to the server, so that the server generates the best adjustment action as the target adjustment action based on the vehicle's scoring results and the scoring results of at least one other vehicle.

[0089] Optionally, in one embodiment of this application, the control module 300 includes: an acquisition component and a second generation component.

[0090] The acquisition component is used to acquire at least one of the driver's actual preferences and the actual weather conditions of the current environment.

[0091] The second generation component is used to generate a target adjustment action by combining at least one of the driver's actual preferences, the actual weather conditions of the current environment, and current road condition information.

[0092] It should be noted that the explanation of the aforementioned adaptive adjustment method embodiment for vehicle suspension also applies to the suspension controller of this embodiment, and will not be repeated here.

[0093] According to the suspension controller proposed in this application, the embodiment can collect road condition information through vehicle-side sensors such as vision sensors and LiDAR and upload it to the cloud, or obtain the latest road condition information from the cloud through a positioning system, thereby determining the optimal suspension adjustment scheme and generating a personalized adjustment mode to adapt to the driving preferences of more drivers. This embodiment can adjust the vehicle suspension system based on actual road condition information and driver preferences with minimal cost increase, thereby improving the user's driving experience and safety, and enhancing the vehicle's intelligence level. This solves the problems in related technologies, such as driver distraction and poor immediacy when manually adjusting the suspension system, and the high cost and maintenance difficulty of installing sensors during road network construction.

[0094] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0095] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0096] When the processor 502 executes the program, it implements the adaptive adjustment method for vehicle suspension provided in the above embodiments.

[0097] Furthermore, the vehicle also includes:

[0098] Communication interface 503 is used for communication between memory 501 and processor 502.

[0099] The memory 501 is used to store computer programs that can run on the processor 502.

[0100] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0101] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0102] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0103] Processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of this application.

[0104] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described adaptive adjustment method for vehicle suspension.

[0105] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0109] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0112] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. An adaptive adjustment method for vehicle suspension, characterized in that, Includes the following steps: Obtain at least one of the following: the vehicle's current location, image information of the road ahead, and environmental perception information; Obtaining road condition information for the road ahead based on at least one of the current location, image information of the road ahead, and environmental perception information, and uploading it to a server, includes: uploading the current location to the server to receive the road condition information sent by the server based on the current location, wherein the road condition information of the server is obtained or updated based on uploads from other vehicles; and Based on the current road condition information, a target adjustment action for the vehicle suspension system is generated to adapt to the current road condition, and the vehicle suspension system is driven to execute the target adjustment action until the road segment corresponding to the current road condition information is passed. After passing through the road segment, a rating result of the driving experience and safety index of the target adjustment action is obtained, and the target adjustment action is optimized based on the rating result. The optimization of the target adjustment action based on the rating result includes: The scoring results are uploaded to the server, so that the server generates the optimal adjustment action as the target adjustment action based on the vehicle's scoring results and the scoring results of at least one other vehicle. The optimal adjustment action is uploaded to the server for priority application by vehicles that have no experience of passing through the current road conditions or have not evaluated the experience of passing through them.

2. The method according to claim 1, characterized in that, The specific step of obtaining road condition information of the road ahead based on at least one of the vehicle's current location, image information of the road ahead, and environmental perception information is replaced by: At least one road condition feature is extracted from the image information and / or the environmental perception information, and the road condition information is generated based on the at least one road condition feature.

3. The method according to claim 1, characterized in that, The step of generating a target adjustment action for the vehicle suspension system adapted to the current road conditions based on the current road condition information includes: Obtain at least one of the driver's actual preferences and the actual weather conditions of the current environment; The target adjustment action is generated by combining at least one of the driver's actual preferences, the actual weather conditions of the current environment, and the current road condition information.

4. A suspension controller, characterized in that, include: The first acquisition module is used to acquire at least one of the following: the vehicle's current location, image information of the road ahead, and environmental perception information; The second acquisition module is used to obtain road condition information of the road ahead based on at least one of the current location, image information of the road ahead, and environmental perception information, and upload it to the server. The second acquisition module includes: a communication module, used to upload the current location to the server, and to receive the road condition information sent by the server based on the current location, wherein the road condition information of the server is obtained or updated based on uploads from other vehicles; and The control module is used to generate a target adjustment action of the vehicle suspension system adapted to the current road conditions based on the current road condition information, and drive the vehicle suspension system to execute the target adjustment action until it passes through the road segment corresponding to the current road condition information. An evaluation module is used to obtain a rating of the driving experience and safety index of the target adjustment action after passing through the road segment, so as to optimize the target adjustment action based on the rating results. The evaluation module includes: A first generation component is used to upload the scoring results to the server, so that the server generates the best adjustment action as the target adjustment action based on the vehicle's scoring results and the scoring results of at least one other vehicle. The optimal adjustment action is uploaded to the server for priority application by vehicles that have no experience of passing through the current road conditions or have not evaluated the experience of passing through them.

5. The suspension controller according to claim 4, characterized in that, The communication module in the second acquisition module can be replaced with: The generation module is used to extract at least one road condition feature from the image information and / or the environmental perception information, and generate the road condition information based on the at least one road condition feature.

6. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the adaptive adjustment method for vehicle suspension as described in any one of claims 1-3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the adaptive adjustment method for vehicle suspension as described in any one of claims 1-3.

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