Vehicle chassis control method, electronic equipment, storage medium and program product

Abstract

The invention discloses a vehicle chassis control method, electronic equipment, a storage medium and a program product, and belongs to the field of chassis control. The method comprises the following steps: judging whether the current road is a driving road or not according to the real-time position information; if yes, acquiring historical road condition information of the current road; generating chassis feedforward control information according to the historical road condition information and the real-time position information; and controlling a chassis suspension of the vehicle to carry out feedforward adjustment according to the chassis feedforward control information, so that the chassis suspension adapts to the road condition of the current road in advance. According to the method, the historical road condition information is obtained and utilized in advance, so that the chassis suspension system can perform more accurate feed-forward adjustment to adapt to the upcoming road condition, the adaptive capacity of the vehicle to different road conditions is improved, and therefore the riding comfort, the vehicle controllability and the safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chassis control, and in particular to a vehicle chassis control method, electronic equipment, storage medium and program product. Background Art

[0002] Vehicle chassis control is crucial to ensuring driving safety, comfort and handling. It adapts to different driving conditions and road conditions by precisely adjusting the responses of key components such as the suspension system, braking system and power system, thereby reducing vehicle bumps and roll during driving, improving vehicle stability and handling response, and enhancing the driver's sense of control over the vehicle. At the same time, it provides necessary safety protection in emergency situations, reduces accident risks, and improves the overall driving experience.

[0003] In related technologies, chassis control technology mainly relies on feedback adjustment mechanism, that is, monitoring the current state of the vehicle, such as vehicle speed, acceleration, braking depth, etc., through real-time sensor data, and then adjusting chassis parameters such as suspension stiffness and damping according to these data.

[0004] This control method usually responds only after the vehicle has encountered a change in the road surface, resulting in less than ideal vehicle handling, ride comfort and safety in certain situations, such as when making sharp turns or suddenly encountering uneven roads. Summary of the invention

[0005] In view of the above-mentioned technical problems and defects, the purpose of the present invention is to provide a vehicle chassis control method, electronic equipment, storage medium and program product, which improve the safety, comfort and controllability of vehicle driving through an intelligent feedforward adjustment mechanism.

[0006] To achieve the above-mentioned purpose, in a first aspect, the present invention provides a vehicle chassis control method, comprising: obtaining real-time position information of a vehicle when traveling on a current road; determining whether the current road is a road that has been traveled on based on the real-time position information; if so, obtaining historical road condition information of the current road; generating chassis feedforward control information based on the historical road condition information and the real-time position information; and controlling the chassis suspension of the vehicle to perform feedforward adjustment based on the chassis feedforward control information, so that the chassis suspension can adapt to the road condition of the current road in advance.

[0007] By acquiring and utilizing historical road condition information in advance, the present invention allows the chassis suspension system to perform more accurate feedforward adjustments to adapt to upcoming road conditions, thereby improving the vehicle's ability to adapt to different road conditions, thereby improving ride comfort and vehicle handling. This approach helps to improve driving safety, especially in complex or harsh road conditions. By adjusting the chassis suspension in advance, the risk of vehicle loss of control due to sudden changes in the road surface can be reduced. At the same time, the present invention can also enhance the user's driving experience. Through intelligent chassis adjustment, the vehicle can travel more smoothly and reduce discomfort during driving, especially in long-distance driving or high-frequency commuting. This advantage is particularly obvious.

[0008] In combination with some embodiments of the first aspect, in some embodiments, after determining whether the current road is a road that has been traveled on based on the real-time location information, it also includes: if the current road is a road that has not been traveled on, sending a road condition information sharing request to a cloud server, and the road condition information sharing request includes the real-time location information; when the cloud server determines that the historical road condition information has been shared in the cloud based on the road condition information sharing request, receiving the cloud sharing data of the historical road condition information returned by the cloud server.

[0009] The technical solution of the above embodiment is adopted to provide an intelligent road condition information sharing mechanism. When a vehicle is traveling on a new unrecorded route, the system can actively communicate with the cloud server to obtain road condition information shared by other vehicles. This mechanism not only enhances the vehicle's ability to adapt to unknown road conditions, but also enriches the road condition database through cloud sharing, thereby improving the intelligence level of the entire transportation system. As more vehicles participate in information sharing, the system can continuously learn and optimize, providing drivers with more accurate chassis feedforward control, thereby improving driving safety and comfort.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after receiving the cloud-shared data of the historical road condition information returned by the cloud server, it also includes: obtaining the driving preference information of the driver of the vehicle; determining the predicted driving behavior of the driver based on the driving preference information and the historical road condition information; determining predicted feedforward control information based on the predicted driving behavior and the historical road condition information; and controlling the chassis suspension to perform feedforward adjustment based on the predicted feedforward control information.

[0011] The technical solution of the above embodiment is adopted to realize personalized chassis feedforward control by combining the driver's personal driving preferences and historical road condition information. This method can more accurately predict the driver's behavior under specific road conditions, so as to adjust the chassis suspension system in advance to adapt to the driver's driving style. This not only improves the driving experience, but also may improve fuel efficiency because the system can make more reasonable adjustments based on the driver's habits. In addition, personalized chassis control helps reduce driver fatigue and makes long-term driving more comfortable.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after controlling the chassis suspension of the vehicle to perform feedforward adjustment according to the chassis feedforward control information, it also includes: obtaining preview road condition information of the current road through the preview sensing system of the vehicle, the preview sensing system including a vehicle-mounted camera or a lidar; determining whether the preview road condition information matches the historical road condition information; if not, generating preview feedforward control information according to the preview road condition information; and controlling the chassis suspension to perform feedforward adjustment according to the preview feedforward control information.

[0013] The technical solution of the above embodiment is adopted, and a preview sensing system is introduced to obtain the road condition information ahead in real time and match it with the historical road condition information. This double verification mechanism ensures the accuracy and real-time performance of the chassis feedforward control. When the preview information does not match the historical information, the system can quickly adjust and generate new feedforward control information, so that the chassis suspension system can respond to the actual road condition changes in a timely manner. This improves the vehicle's adaptability to sudden road conditions and enhances the stability and safety of driving.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after determining whether the preview road condition information matches the historical road condition information, it also includes: if the preview road condition information matches the historical road condition information, then while maintaining chassis feedforward control based on the historical road condition information, obtaining the current driving state of the vehicle through the vehicle's acceleration sensor and suspension height sensor; determining whether the chassis feedforward control information needs to be corrected based on the current driving state; if so, generating feedforward control correction information based on the current driving state and the preview road condition information; and controlling the chassis suspension for feedforward adjustment based on the feedforward control correction information.

[0015] The technical solution of the above embodiment is adopted to monitor the driving state of the vehicle in real time and make dynamic adjustments according to the deviation between the current state and the preset chassis feedforward control information. This method ensures that the chassis suspension system is always in the best state to adapt to the ever-changing driving conditions. Through the data obtained by the acceleration sensor and the suspension height sensor, the system can accurately determine whether the chassis suspension needs to be corrected, thereby improving the vehicle's handling and ride comfort. This adaptive adjustment mechanism enables the vehicle to cope with complex road conditions more intelligently and improve the driving experience.

[0016] In combination with some embodiments of the first aspect, in some embodiments, obtaining the historical road condition information of the current road specifically includes: determining whether the historical road condition information is in the storage module of the vehicle; if so, extracting the historical road condition information from the storage module; if not, sending a road condition information sharing request to the cloud server to obtain the historical road condition information returned by the cloud server.

[0017] The technical solution of the above embodiment provides a flexible way to obtain road condition information. The system first checks whether there is historical road condition information in the local storage module. If not, it requests the cloud server. This method reduces the dependence on the cloud server and reduces the delay and cost of data transmission. At the same time, it also ensures that when there is no network connection or insufficient cloud data, the vehicle can still use the locally stored information for chassis feedforward control, improving the reliability and robustness of the system.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the method also includes: obtaining an intelligent driving planned route for the vehicle; determining an estimated road condition of the passing road based on the intelligent driving planned route; and performing feed-forward adjustment on the chassis suspension based on the estimated road condition.

[0019] By adopting the technical solution of the above embodiment, the intelligent driving planning route is combined with the chassis feedforward control to optimize the entire vehicle driving process. By obtaining the intelligent driving planning route in advance, the system can estimate the road conditions along the way and make feedforward adjustments to the chassis suspension accordingly. This method allows the vehicle to be prepared before entering different sections of the road, thereby improving driving smoothness and fuel efficiency. In addition, it also helps to improve the decision-making quality of the intelligent driving system, allowing the vehicle to switch between different road conditions more smoothly and enhance the overall driving experience.

[0020] In a second aspect, an embodiment of the present invention provides an electronic device, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the electronic device to execute the method described in the first aspect or the second aspect, and any possible implementation method of the first aspect or the second aspect.

[0021] In a third aspect, the present invention provides a computer-readable storage medium comprising instructions, which, when executed on the electronic device, causes the electronic device to execute the method described in the first aspect or the second aspect, and any possible implementation of the first aspect or the second aspect.

[0022] In a fourth aspect, the present invention provides a computer program product comprising instructions, which, when the computer program product is run on the electronic device, enables the electronic device to execute the method described in the first aspect or the second aspect, and any possible implementation of the first aspect or the second aspect.

[0023] It is understandable that the electronic device provided in the second aspect, the storage medium provided in the third aspect, and the computer program product provided in the fourth aspect are all used to execute the method provided in the present invention. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided by the present invention have at least the following technical effects or advantages: 1. Improve driving safety and comfort: The present invention achieves feedforward adjustment of the vehicle chassis suspension system by acquiring vehicle position information in real time, judging road familiarity, and combining historical road conditions and preview sensor data. This intelligent adjustment mechanism enables the chassis suspension to adapt to the upcoming road conditions in advance, improving the vehicle's stability and ride comfort, especially in the face of complex or harsh road conditions, and can reduce the risk of vehicle loss of control caused by sudden changes in the road surface, thereby improving driving safety.

[0025] 2. Enhance the adaptability of vehicles to unknown road conditions: The cloud server road condition information sharing mechanism in the present invention allows vehicles to obtain road condition information shared by other vehicles through the cloud when driving on new unrecorded routes. This information sharing not only enhances the adaptability of vehicles to unknown road conditions, but also improves the intelligence level of the entire transportation system through the continuous enrichment of the road condition database, allowing vehicles to respond more intelligently to road changes and improve driving safety and efficiency.

[0026] 3. Personalized and intelligent chassis control: By collecting the driver's driving preference information and combining it with historical road conditions and intelligent driving planning routes, the present invention can predict the driver's driving behavior and generate personalized feedforward control information accordingly. This personalized chassis control not only improves the driving experience, but also may improve fuel efficiency because the system can make more reasonable adjustments based on the driver's habits. At the same time, the integration of intelligent driving planning routes allows the vehicle to be prepared before entering different sections of the road, thereby improving driving smoothness and fuel efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings herein are incorporated into and constitute a part of the specification, showing embodiments consistent with the present invention, and together with the specification, are used to explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 is a flow chart of a vehicle chassis control method according to an embodiment of the present invention; Figure 2 is a flow chart of another vehicle chassis control method according to an embodiment of the present invention; Figure 3 It is a schematic diagram of the architecture of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to be limiting of the present invention. As used in the specification of the present invention, the singular expressions "a", "a", "above", "the" and "this" are intended to also include plural expressions, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present invention refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood as implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, unless otherwise specified, the meaning of "plurality" is two or more.

[0030] It should also be noted that, unless otherwise clearly specified and limited, in the embodiments of the present invention, the terms such as "setting" and "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two components; it can be a wired communication connection or a wireless communication connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The embodiments of the present invention are described in detail below.

[0031] The embodiment of the present invention provides a vehicle chassis control method, which is applied to a chassis control system (hereinafter referred to as the system), such as Figure 1 As shown, the specific steps include: Step 201, obtaining real-time position information of a vehicle when traveling on a current road.

[0032] Specifically, this can be achieved through the global positioning system (GPS) or similar positioning technology such as the Beidou satellite navigation system integrated in the vehicle. Using the satellite network, the precise geographic location of the vehicle can be provided, whether on city streets or country roads. The vehicle's GPS receiver receives signals from satellites and determines the longitude, latitude and altitude of the vehicle by calculating the propagation time difference of the signals.

[0033] In addition, in order to improve the accuracy and reliability of positioning, other sensor data, such as inertial navigation systems (INS), wheel encoders or cameras, may be combined to assist in positioning. The inertial navigation system can provide short-term positioning information and maintain the continuity of positioning even when the GPS signal is weak or lost. The wheel encoder can measure the number of rotations and speed of the wheel to infer the distance the vehicle has moved. The camera can identify road signs and lane lines to assist in determining the specific position of the vehicle on the road. Through the combined use of these technologies, the vehicle chassis control system can obtain and update the vehicle's precise position information in real time, providing the necessary data support for the chassis's feedforward adjustment.

[0034] Step 202: Determine whether the current road is a road that has been traveled on based on the real-time location information.

[0035] Specifically, the system will compare the real-time location information obtained through GPS or other positioning technologies with the historical driving data stored in the vehicle. These historical data include the geographic location information of the routes that the vehicle has previously traveled, usually stored in the vehicle's memory or cloud server in the form of a map database. The system will analyze the similarity between the current GPS coordinates and the coordinates of the historical records, and use certain algorithms, such as geo-fencing technology or coordinate matching algorithms, to identify whether the vehicle is traveling on a recorded route. If the current GPS coordinates are close enough to the coordinates in the historical records, and this proximity appears continuously in a certain time series, the system will determine that the current road the vehicle is traveling on is one that has been traveled on before.

[0036] In addition, in order to improve the accuracy of judgment, the system may also combine other information such as the vehicle's speed, driving direction, and road geometry to ensure the accuracy of recognition. Once it is confirmed that the vehicle is traveling on a known road, the system can call up the historical road condition information of the road to provide data support for the chassis' feedforward control.

[0037] If the current road is a road that has been traveled on, proceed to step 203 .

[0038] If the current road is a road that has not been traveled on, a traffic information sharing request can be sent to the cloud server to obtain the historical traffic information of the current road shared by the cloud.

[0039] Step 203, obtaining historical traffic information of the current road.

[0040] Specifically, the stored road condition database can be accessed through the vehicle's data processing center, which contains the road condition data collected when the vehicle was previously driving on the current road. These data may include information such as the flatness, potholes, speed bumps, curve inclination angles, friction coefficients, etc. The system uses the vehicle's real-time location information, such as GPS coordinates, to quickly retrieve and extract historical road condition records that match the current location, providing basic data for the next step of feedforward control.

[0041] Step 204: Generate chassis feedforward control information according to the historical road condition information and the real-time position information.

[0042] Specifically, the system will first conduct a detailed analysis and quantitative processing of historical road condition information. For example, for road surface flatness, the size, depth, length and other information of potholes and bumps are converted into specific numerical indicators; for road slope, the slope angle value of each road section is determined; for curve curvature, the curvature radius of different curves is accurately calculated.

[0043] At the same time, combined with high-precision real-time location information, the system can accurately determine what kind of road condition the vehicle is about to enter. If the real-time location shows that the vehicle is about to enter a road with multiple consecutive potholes, and the historical road condition information provides the specific size and distribution of these potholes, the system will calculate the optimal state required for the chassis suspension based on the pre-set algorithm and vehicle dynamics model. This algorithm takes into account factors such as the vehicle's speed and weight to generate chassis feedforward control information.

[0044] Among them, chassis feedforward control information includes suspension stiffness parameters, damping coefficients, suspension height settings, shock absorber preload values, active anti-roll bar adjustment instructions, tire pressure adjustment instructions, and electronic stability program (ESP) adjustment instructions, etc. This information and data integrates road feature predictions, such as road type, bumps, potholes, speed bumps, and curve inclination angles, as well as vehicle dynamic predictions, including current speed, acceleration, and braking status.

[0045] Step 205: Control the chassis suspension of the vehicle to perform feedforward adjustment according to the chassis feedforward control information, so that the chassis suspension can adapt to the road condition of the current road in advance.

[0046] Specifically, the system sends these chassis feedforward control information to the actuators of the chassis suspension. These actuators may include electric control valves, hydraulic control units or pneumatic control systems, which are responsible for adjusting various parameters of the suspension system in real time.

[0047] For example, if the feedforward control information indicates that the suspension damping needs to be increased, the electric regulating valve may adjust the flow rate of the suspension oil to change the damping coefficient. If the suspension height needs to be adjusted, the air pressure control system may increase or decrease the pressure of the suspension airbag. These adjustments are automatic and real-time, ensuring that the vehicle's chassis suspension is in the best condition before the vehicle actually enters a specific road section, thereby realizing the feedforward adjustment of the chassis.

[0048] In this way, when the vehicle travels to this section of road, the chassis suspension can absorb the impact of the road more smoothly, reduce the shaking of the vehicle body, and provide a more stable and comfortable riding experience. In this way, the chassis suspension can adapt to the current road conditions in advance, improving the vehicle's handling and riding comfort.

[0049] This embodiment adopts the above steps, and by acquiring the vehicle position information in real time, the system can accurately identify the road where the vehicle is currently located. When it is determined that the vehicle is traveling on a known road, the system will call the historical road condition data of the road, which may include the flatness of the road surface, the inclination angle of the curve, and the feedback information of the passing vehicles. Combining the real-time position information and the historical road condition information, the system can predict the road condition changes that the vehicle is about to encounter, and generate chassis feedforward control information accordingly. The core advantage of this process is its foresight, that is, the chassis suspension system of the vehicle can be pre-adjusted according to the chassis feedforward control information before the actual road condition changes. For example, before the vehicle is about to enter a bumpy section, the system can increase the damping of the suspension in advance to reduce the bumpy feeling; before entering a curve, the stiffness of the suspension can be adjusted to improve the stability and handling of the vehicle. At the same time, this intelligent chassis control method of the present embodiment not only improves the driving performance of the vehicle, but also helps to reduce driving errors caused by sudden changes in road conditions, thereby reducing the risk of traffic accidents and improving safety. At the same time, it can also improve the vehicle's fuel efficiency, as intelligent adjustment of the suspension system can reduce unnecessary energy consumption.

[0050] Combine the following Figure 2 To further explain the method of this embodiment, the method specifically includes the following steps: Step 301, obtaining real-time position information of a vehicle when traveling on a current road.

[0051] Step 302: Determine whether the current road is a road that has been traveled on based on the real-time location information.

[0052] If yes, proceed to step 303 ; if no, proceed to step 304 .

[0053] Step 303, determining whether the storage module of the vehicle has the historical road condition information.

[0054] If so, the historical traffic information is extracted from the storage module, and then the process proceeds to step 306 .

[0055] If not, proceed to step 304 to send a traffic information sharing request to the cloud server to obtain the historical traffic information returned by the cloud server.

[0056] Step 304: Send a traffic information sharing request to the cloud server.

[0057] The traffic information sharing request includes the real-time location information. The system sends a traffic information sharing request containing the real-time location information of the vehicle to the cloud server through the vehicle communication module, such as 4G / 5G or Wi-Fi connection. The traffic information sharing request is intended to query the cloud server whether there is traffic information about the location shared by other vehicles. The real-time location information may include precise GPS coordinates, vehicle speed, driving direction, etc., which are used to match possible traffic data in the cloud database.

[0058] Step 305 , when the cloud server determines that the historical road condition information has been shared in the cloud based on the road condition information sharing request, receiving the cloud sharing data of the historical road condition information returned by the cloud server.

[0059] Specifically, after receiving the request from the vehicle, the cloud server will search its database to determine whether there is any road condition information about the real-time location previously shared by other vehicles. If the cloud server finds matching shared data, it will package the data into a response message and send it back to the vehicle through the vehicle's communication module, and the system will obtain the historical road condition information from the cloud server. Then, it goes to step 306.

[0060] Among them, the cloud-shared data may include driving records of other vehicles on the same road section, road conditions, traffic conditions, and adjustment suggestions for the chassis control system, etc., providing additional information support for the vehicle to facilitate more precise chassis feedforward adjustment.

[0061] In some embodiments, if no shared road condition information matching the current real-time location of the vehicle is found on the cloud server, the cloud server will send an indicative response to the vehicle, informing the system that there is currently no road condition information available. In this case, the chassis control system of the vehicle will switch to another data acquisition mode, activating advanced sensors such as on-board cameras and lidar. These sensors will scan and analyze the environment around the vehicle in real time, collecting key information such as road conditions, obstacles, traffic signs, and lane lines. Through the data obtained by these sensors, the chassis control system can make a real-time assessment of the road conditions of the current road, and generate chassis feedforward control information based on this, so as to make necessary feedforward adjustments to the chassis suspension of the vehicle to ensure that the vehicle can adapt to the current road conditions and maintain driving safety and comfort. At the same time, these newly collected road condition information may also be uploaded by the vehicle to the cloud server to contribute data for future road condition information sharing and community.

[0062] In some embodiments, after step 305, the following steps are also included: obtaining driving preference information of the driver of the vehicle; determining the predicted driving behavior of the driver based on the driving preference information and the historical road condition information; determining predicted feedforward control information based on the predicted driving behavior and the historical road condition information; and controlling the chassis suspension to perform feedforward adjustment based on the predicted feedforward control information.

[0063] Specifically, first, the system collects and analyzes the driver's driving habits and preferences under different road conditions through built-in driving behavior analysis software. This may include the driver's preferences for acceleration, braking, steering, and personalized settings for vehicle dynamic response. This information can be accumulated through long-term monitoring of the driver's operating habits, or obtained through the driver's customized driving mode in the vehicle settings.

[0064] The system then combines the driver's driving preferences with the road conditions information on which the vehicle is about to travel to predict the driving actions that the driver may take under specific road conditions. For example, if the historical road condition information shows that there is a sharp turn ahead, and the driver's preference information indicates that he prefers to drive smoothly, the system may predict that the driver will slow down and smoothly pass the curve. Specifically, the system uses algorithms such as machine learning to conduct in-depth analysis of the collected driving preference information to identify the driver's habitual reactions under different road conditions, such as the tendency to slow down when turning or the preference for stable cruising on straight sections. The system combines these personalized driving habits with historical road condition information to simulate the driver's possible driving behavior through a prediction model. The model takes into account the upcoming road condition characteristics, such as curves, slopes, road surface materials, etc., as well as the driver's historical driving data under similar road conditions. The system then evaluates the current traffic conditions, vehicle dynamics (such as speed, acceleration), and environmental factors (such as weather, time) to further refine the prediction. Through this comprehensive analysis, the chassis control system is able to predict the driver's behavior under specific road conditions, thereby adjusting the vehicle's chassis suspension system in advance to adapt to the driver's expected operations and road conditions to ensure driving safety and comfort. This process is dynamic, allowing the system to continuously learn and adapt based on real-time feedback to improve the accuracy of predictions.

[0065] The system then uses the predicted driving behavior and historical road condition data to generate a series of feedforward control instructions. The feedforward control instructions will guide the vehicle's chassis suspension system on how to adjust to the driver's expected operation and upcoming road conditions. For example, if it is predicted that the driver will slow down in a curve, the system may adjust the suspension stiffness and damping in advance to provide better lateral support and stability.

[0066] Finally, the system sends the generated feedforward control instructions to the vehicle's suspension system actuators, such as electric regulating valves or hydraulic control units. These actuators adjust the parameters of the suspension system in real time to ensure that the vehicle's chassis suspension is in the best condition before the vehicle actually enters a specific road section, thereby achieving feedforward adjustment of the chassis. In this way, the vehicle can adapt to the driver's driving style and upcoming road conditions more smoothly, improving driving safety, comfort and controllability.

[0067] Step 306: Generate chassis feedforward control information according to the historical road condition information and the real-time position information.

[0068] Step 307: Control the chassis suspension of the vehicle to perform feedforward adjustment according to the chassis feedforward control information.

[0069] Step 308, obtaining preview road condition information of the current road through the preview sensor system of the vehicle.

[0070] The preview sensing system includes a vehicle-mounted camera or a lidar. The vehicle-mounted camera can identify road signs, lane lines, traffic signals, and the texture and unevenness of the road surface, while the lidar constructs a high-precision three-dimensional environmental map by emitting a laser beam and measuring the time it takes for it to reflect back, including detecting obstacles, depressions, bumps, or other abnormalities on the road surface.

[0071] Preview road condition information includes but is not limited to the flatness of the road surface, potholes, speed bumps, the inclination angle of the curve, the location of the lane line, traffic signs, and possible obstacles. The preview sensing system can provide road condition details beyond the visual range of human drivers by scanning and analyzing the environment in front of the vehicle in real time, so that the chassis control system of the vehicle can make feedforward adjustments based on this data.

[0072] The preview sensor system transmits this data to the chassis control system in real time. The chassis control system can use image processing and data analysis algorithms to parse and understand road conditions in real time.

[0073] In some embodiments, under certain special circumstances, if the preview sensing system cannot normally collect available road condition information, the system maintains the current chassis feedforward adjustment based on historical road condition information.

[0074] Step 309, determining whether the previewed traffic condition information matches the historical traffic condition information.

[0075] Specifically, the system first compares the real-time data in the preview traffic information with the records of the corresponding positions of the historical data in the historical traffic information through the built-in algorithm, and analyzes the consistency of the key features between the two. For example, the system may check whether the previewed road depression is located in the same position as the depression in the historical record, whether the size is similar, and whether the position of the lane line is consistent with the historical data. In addition, the system may also consider the real-time location information of the vehicle, such as GPS coordinates, to ensure that the data of the same section of road is compared.

[0076] Among them, the preview road condition information and historical road condition information are two data sets containing road condition feature data, which come from the vehicle's preview sensor system (such as an on-board camera or lidar) and the historical data collected and stored during the previous driving process. In order to ensure that the two data sets can be accurately compared, they need to be sorted and aligned according to the vehicle's real-time positioning information. This process involves matching the feature data in the two data sets according to the geographical location on the vehicle's driving path, ensuring that each preview road condition information point corresponds to the road condition information at the same location in the historical data. This is usually achieved by comparing the geographical location coordinates of each data point in the two data sets, such as longitude and latitude, and possible timestamp information. After sorting and alignment, it can be ensured that when the vehicle travels to a specific location, the preview road condition information and the historical road condition information are consistent in space or time, allowing the system to perform effective comparison and analysis, and provide accurate feedforward adjustment basis for the chassis control system. This sorting and alignment method is crucial to improving the accuracy of road condition information and the adaptability of chassis control strategies.

[0077] If the real-time data and the historical data are highly consistent in key features, the system considers that the two match, which indicates that the road section that the vehicle is about to travel is similar to the road conditions recorded in the history, so the historical road condition information can be used to optimize the chassis control strategy. Then, the process proceeds to step 312.

[0078] If not, the system considers that the two do not match, and proceeds to step 310. The system may need to rely on real-time data to adjust chassis control, or update new real-time data into a historical database for future reference.

[0079] This matching process is dynamic. As the vehicle continues to travel and collect new data, the system will continuously update and optimize its judgment algorithm to improve the accuracy of matching.

[0080] In some embodiments, a statistical method, such as a chi-square test or a Pearson correlation coefficient, may be used to evaluate the similarity between the preview traffic condition information and the historical traffic condition information.

[0081] Specifically, this embodiment combines the characteristics of preview traffic information and historical traffic information, and specifically improves the algorithms of chi-square statistics and Pearson correlation coefficient, so as to more accurately evaluate the similarity between the two.

[0082] 1. Improved Chi-square statistic: For preview road condition information and historical road condition information, road condition features (such as flatness, potholes, curve inclination angle, etc.) can be regarded as categorical variables. The improved chi-square statistic formula may need to consider the importance weight of the feature, because different features have different degrees of influence on vehicle driving. The improved chi-square statistic formula can be expressed as:

[0083] in, is the weighted chi-square statistic.

[0084] n is the total number of observed categories.

[0085] w i is the weight of the ith road condition feature, which can be assigned according to the importance of the road condition feature on vehicle driving. For example, flatness may be more important than road surface color.

[0086] δ(t) is a time- or environment-dependent adjustment factor used to adjust the weight w i To reflect the impact of time or environmental changes on the importance of road characteristics. Adjustment factor δ(t) It can be defined based on a variety of factors, such as: Time dependence: if the importance of a road condition feature changes over time (such as the impact of traffic flow during peak hours in the morning and evening), then δ(t) It can be a time function that reflects this change; Environmental conditions, if weather or road conditions (such as rain, snow, ice) affect the importance of road characteristics, δ(t) Can be a function of environmental conditions; Real-time traffic conditions, if real-time traffic data is available, δ(t) It can reflect the impact of current traffic conditions on the importance of road condition features.

[0087] O i is the number of observations of the ith road condition feature, that is, the value in the preview road condition information.

[0088] E i is the expected number of the ith road condition feature, that is, the value in the historical road condition information.

[0089] By comparing the difference between the observed frequency and the expected frequency, we can determine whether the two data sets (preview road condition information and historical road condition information) are statistically significantly different. If the observed number is very different from the expected number, this may indicate that the current road condition is significantly different from the historical record, and the chassis control system needs to be adjusted accordingly.

[0090] Based on the weighted chi-square statistic, it can be used to evaluate the similarity between preview road condition information and historical road condition information. By assigning different weights to different road condition characteristics, the system can more accurately reflect which characteristics have a greater impact on vehicle driving performance, thereby giving these characteristics more consideration in chassis feedforward adjustment. This method helps to improve the adaptability and responsiveness of the chassis control system, ensuring that the vehicle can better adapt to actual road conditions.

[0091] 2. Improved Pearson correlation coefficient: The Pearson correlation coefficient is usually used to measure the linear relationship between two continuous variables. In the comparison between the preview traffic information and the historical traffic information, this embodiment introduces a dynamic adjustment factor to reflect the change in the correlation of the variables under different traffic conditions. The improved Pearson correlation coefficient formula can be expressed as:

[0092] in, r d It represents the dynamically adjusted Pearson correlation coefficient, which reflects the linear correlation between the two variable sets (preview traffic information and historical traffic information) after considering the dynamic adjustment factor.

[0093] X i Indicates i The value of a data point in the first variable set (preview traffic condition information), such as a feature value in the preview traffic condition information.

[0094] represents the average value of the first set of variables, that is, all X i The arithmetic mean of the values.

[0095] Y i Indicates i The value of the data point in the second variable set (historical traffic condition information), such as the corresponding feature value in the historical traffic condition information.

[0096] represents the average value of the second variable set, that is, all Y i The arithmetic mean of the values.

[0097] di Indicates i This is a value adjusted based on the complexity of the road conditions or other relevant factors to reflect the importance or changes in the relationship between variables under specific road conditions. For example, in complex road conditions, such as sharp turns or steep slopes, you can increase d i to improve the accuracy of the correlation assessment under these conditions.

[0098] n is the number of data points in the preview traffic information and historical traffic information used to calculate the correlation.

[0099] In the improved Pearson correlation coefficient formula, the numerator measures the degree of covariance between the two sets of variables, while the denominator standardizes this covariance, making the correlation coefficient r d The value of is between -1 and 1. r d When it is close to 1 or -1, it means there is a strong positive or negative correlation between the two sets of variables; when r d When it is close to 0, it means that there is almost no linear relationship between the two sets of variables. By introducing a dynamic adjustment factor d i , which can more accurately reflect the changes in the correlation between variables under different road conditions.

[0100] In some embodiments, a similarity measure of road condition features, such as cosine similarity, may also be introduced to evaluate the similarity between preview road condition information and historical road condition information in a multidimensional feature space. In this embodiment, the cosine similarity formula is as follows:

[0101] Among them, sim stands for cosine similarity, which indicates the degree of similarity between two vectors (preview traffic information and historical traffic information), and its value ranges from -1 to 1. 1 means complete similarity, -1 means complete dissimilarity, and 0 means no correlation. i Indicates i The value of a data point in the preview road condition information vector represents the observation data of a specific feature. i Indicates iThe value of a data point in the historical road condition information vector represents the observation data of the same feature. n represents the total number of features in the data set, that is, the number of elements in the vector. The cosine similarity of this embodiment is obtained by calculating the dot product of two vectors and dividing it by the product of their norms. This ratio reflects the degree of similarity between the two vectors in direction, regardless of their size. In the vehicle chassis control system, if the cosine similarity between the preview road condition information and the historical road condition information is high, then it can be considered that the two are very similar in features, and the chassis control system can use the historical road condition information to adjust the suspension system with more confidence. This method helps to improve the adaptability and accuracy of the chassis control system.

[0102] In some embodiments, in order to provide a more comprehensive similarity evaluation, the weighted chi-square statistic, the Pearson correlation coefficient and the cosine similarity may be combined to determine whether the preview traffic condition information matches the historical traffic condition information.

[0103] For example, the weighted chi-square statistic, Pearson correlation coefficient, and cosine similarity are first normalized to the same range (e.g., [0, 1]).

[0104] The comprehensive score S is then calculated using the following comprehensive evaluation formula:

[0105] Among them, α, β, and γ are weight parameters used to adjust the influence of each statistic in the comprehensive score; χ w,norm represents the standardized value of the weighted chi-square statistic, r norm Represents the standardized value of the Pearson correlation coefficient, sim norm A normalized value representing the cosine similarity.

[0106] If S is greater than a preset threshold (e.g., 0.8), the preview road condition information is considered to match the historical road condition information. If S is less than the threshold, the two are considered to not match. This embodiment can more accurately evaluate the matching degree between the preview road condition information and the historical road condition information by comprehensively considering multiple statistics, thereby improving the feedforward adjustment accuracy of the chassis control system.

[0107] Step 310: Generate preview feedforward control information according to the preview road condition information.

[0108] Specifically, the vehicle's chassis control system receives real-time road condition data (i.e., preview road condition information) provided by a preview sensor system such as an on-board camera or lidar, and uses this data for analysis and processing. The system will evaluate the upcoming road condition characteristics, such as the flatness of the road surface, the location of obstacles, the inclination angle of the curve, etc., and predict the possible impact of these characteristics on the vehicle's driving.

[0109] Based on these predictions, the system will generate a series of feedforward control instructions, including adjusting the stiffness, damping coefficient, suspension height and other parameters of the suspension system to optimize the vehicle's adaptability to upcoming road conditions. These preview feedforward control information is designed to prepare the chassis suspension system before the vehicle actually encounters changes in road conditions, thereby improving the vehicle's handling and ride comfort.

[0110] Step 311, controlling the chassis suspension to perform feedforward adjustment according to the preview feedforward control information.

[0111] The vehicle's chassis control system sends the generated preview feedforward control instructions to the actuators of the suspension system, such as the electric control valve, hydraulic control unit, or air pressure control system. These actuators adjust the various parameters of the suspension system in real time to achieve feedforward adjustment of the chassis. For example, if the preview feedforward control information indicates that the suspension damping needs to be increased to cope with the upcoming bumpy road section, the electric control valve may adjust the flow rate of the suspension oil to change the damping coefficient. If the suspension height needs to be adjusted to adapt to the change in road height, the air pressure control system may increase or decrease the pressure of the suspension airbag. These adjustments are automatic and real-time, ensuring that the vehicle's chassis suspension is in the best condition before the vehicle actually enters a specific road section, thereby achieving feedforward adjustment of the chassis and improving the vehicle's driving stability and ride comfort.

[0112] Step 312, while maintaining chassis feedforward control based on the historical road condition information, obtain the current driving state of the vehicle through the acceleration sensor and suspension height sensor of the vehicle.

[0113] Among them, after the vehicle has adjusted the chassis suspension system according to historical road condition information, the system continues to monitor the acceleration changes of the vehicle in the vertical direction through the acceleration sensor, which can reflect the vehicle's response to uneven road surfaces, such as bumps or jumps. At the same time, the suspension height sensor provides the current height information of the suspension system relative to the vehicle chassis, which helps to understand the vehicle's load changes and road height changes. These sensor data together provide the control system with dynamic information of the vehicle in actual driving, ensuring continuous optimization and adjustment of the chassis feedforward control.

[0114] Step 313: Determine whether the chassis feedforward control information needs to be corrected according to the current driving state.

[0115] Specifically, the system analyzes data obtained from acceleration sensors and suspension height sensors to evaluate whether the actual performance of the chassis suspension system meets expectations. If the data shows that the vehicle's vibration exceeds the preset comfort range, or the suspension height is not as expected, this may indicate that the chassis feedforward control needs to be further optimized. Based on these real-time data, combined with the vehicle's driving mode, speed and road conditions, the control system determines whether it is necessary to fine-tune the stiffness, damping or other parameters of the chassis suspension.

[0116] If the chassis feedforward control information needs to be corrected, then step 314 is entered to achieve more precise chassis control to ensure that the vehicle can maintain optimal driving performance and ride comfort under various road conditions. This process is dynamic, allowing the system to continuously learn and adapt based on real-time feedback to improve the accuracy and efficiency of chassis control.

[0117] If the chassis feedforward control information does not need to be corrected, the process returns to step 312 .

[0118] Step 314, generating feedforward control correction information according to the current driving state and the preview road condition information.

[0119] Specifically, after analyzing the real-time driving data of the vehicle collected by the acceleration sensor and the suspension height sensor, the system combines the road condition information ahead provided by the preview sensing system to conduct a comprehensive assessment. This process involves comparing the actual driving data with the preview road condition information to identify the adjustments that may be needed for the chassis suspension system to cope with the upcoming road conditions. The control system will consider the vehicle's current speed, acceleration, braking status, and expected changes in the road surface, such as curves, slopes, obstacles, etc., to generate correction information. These correction information may include fine-tuning of the suspension stiffness, damping coefficient, or suspension height to ensure that the chassis suspension system can more accurately adapt to the road conditions ahead and improve the vehicle's handling and ride comfort.

[0120] Step 315: Control the chassis suspension to perform feedforward adjustment based on the feedforward control correction information.

[0121] Specifically, the system converts the generated correction information into specific control instructions and sends them to the actuators of the chassis suspension. These actuators, such as electric control valves or hydraulic control units, adjust the parameters of the suspension system in real time according to the instructions received. For example, if the correction information indicates that the suspension damping needs to be increased to cope with the bumpy road ahead, the actuator will adjust the damping coefficient accordingly. If the suspension height needs to be adjusted to adapt to the change in road height, the air pressure control system will adjust the pressure of the suspension airbag. These adjustments ensure that the chassis suspension system can be optimized according to the real-time road conditions and vehicle driving status, thereby achieving more precise feedforward control and improving the vehicle's driving stability and ride comfort under various road conditions. In this way, the vehicle can respond to road changes more intelligently and improve driving safety and efficiency.

[0122] In some embodiments, the method of this embodiment also includes: obtaining the intelligent driving planned route of the vehicle; determining the estimated road conditions of the passing roads based on the intelligent driving planned route; and performing feed-forward adjustment on the chassis suspension based on the estimated road conditions.

[0123] Specifically, the system can obtain the intelligent driving planned route in a variety of ways. If the vehicle is equipped with a navigation system, the navigation system will plan an optimal driving route based on the vehicle's current location, destination, and various traffic information (such as road congestion, construction information, etc.). For example, the navigation system may consider factors such as the shortest distance, fastest time, or lowest energy consumption to determine the intelligent driving planned route. In addition, some vehicles can also communicate with cloud servers to obtain real-time traffic data and road conditions information from the server to optimize the planned route. The vehicle's automatic driving system will also continuously adjust and optimize the intelligent driving planned route based on the surrounding environment information obtained by sensors, such as road signs, lane lines, etc.

[0124] Once the intelligent driving planned route is obtained, the system can begin to determine the estimated road conditions of the passing roads. First, the system can use historical road condition data for analysis. If a section of the road in the planned route has often been congested, under construction, or in poor road conditions in a certain period of time in the past, it can be preliminarily estimated that the section may also have similar road conditions at the current time. At the same time, the system can also combine real-time traffic information, such as obtaining road congestion and accident information through communication with the traffic management department, to further improve the estimated road conditions of the passing roads. For example, if it is known that a traffic accident has occurred on a road on the planned route, it can be estimated that traffic congestion may occur on that section of the road, and some damage may be caused to the road surface. In addition, the vehicle can also use information such as road grade, slope, curves, etc. in the map data to estimate the driving conditions of the vehicle on different sections of the road.

[0125] Finally, based on the estimated road conditions, the system can perform feedforward adjustments to the chassis suspension. If the estimated road conditions show that the vehicle is about to enter a bumpy road, the system can reduce the stiffness of the suspension and increase the damping coefficient in advance to better filter road vibrations and improve ride comfort. If there are continuous curves in the estimated route, the system can increase the roll stiffness of the suspension to improve the stability of the vehicle in the curve. For example, when the system estimates that the vehicle is about to enter a section with a small curve radius, it will adjust the spring stiffness and shock absorber damping characteristics in the suspension to prepare the vehicle before entering the curve and improve handling performance. Through this feedforward adjustment method, the vehicle can adjust the chassis suspension in advance before actually encountering different road conditions to better adapt to the upcoming driving environment.

[0126] In summary, this embodiment proposes an intelligent vehicle chassis control method, which aims to achieve real-time feedforward adjustment of the vehicle chassis suspension system through accurate road condition information and driving behavior prediction, so as to improve driving safety, comfort and controllability. This embodiment first obtains real-time location information through the vehicle's positioning system, and determines whether the current road is a road that has been traveled. If so, the system will retrieve historical road condition information from local storage or cloud servers; if not, the system will send a road condition information sharing request to the cloud and receive historical road condition data shared by the cloud.

[0127] After obtaining historical road condition information, the system will further collect the driver's driving preference information, combine historical road condition data, and predict the driver's driving behavior. Based on these predictions, the system generates feedforward control information to adjust the chassis suspension system to adapt to the upcoming road conditions. In addition, the vehicle's preview sensing system, including on-board cameras or lidar, will obtain preview road condition information of the road ahead in real time. The system compares this preview information with historical road condition information to determine whether it is necessary to generate new feedforward control information based on the preview information, and make corresponding feedforward adjustments to the chassis suspension.

[0128] After the chassis suspension is feedforward adjusted, the system will also monitor the current driving state of the vehicle through the acceleration sensor and suspension height sensor. If the current driving state does not match the preset chassis feedforward control information, the system will generate correction information to further optimize the adjustment of the chassis suspension. In addition, this technical solution also takes into account the intelligent driving planning route. By obtaining the vehicle's intelligent driving planning route, the road conditions along the way are estimated, and the chassis suspension is feedforward adjusted accordingly.

[0129] Overall, this embodiment realizes a comprehensive and dynamic vehicle chassis control system by integrating real-time location information, historical road condition information, driver preferences, preview road condition information, and intelligent driving planning routes. The system can make comprehensive judgments and adjustments based on multiple information sources to ensure that the vehicle can maintain optimal driving performance under various road conditions. Through this intelligent chassis control, the vehicle can respond to road changes more intelligently, improve driving safety and efficiency, and also provide the driver with a more personalized and comfortable driving experience.

[0130] The method provided in the above embodiment can be executed by a chassis control system, which can include an electronic device. The electronic device in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of an electronic device in an embodiment of the present invention.

[0131] It should be noted that Figure 3 The structure of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0132] like Figure 3 As shown, the electronic device includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In RAM 403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0133] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a button switch, etc.; an output section 407 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0134] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the present invention are performed.

[0135] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0137] Specifically, the electronic device of this embodiment includes a processor and a memory, the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the electronic device to execute the method provided by the above embodiment.

[0138] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the electronic device, the electronic device implements the method provided in the above embodiment.

[0139] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

[0140] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0141] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A vehicle chassis control method, characterized in that: include: Get the real-time location information of the vehicle when it is traveling on the current road; Determining whether the current road is a road that has been traveled on according to the real-time location information; If yes, obtaining historical traffic information of the current road; generating chassis feedforward control information according to the historical road condition information and the real-time position information; The chassis suspension of the vehicle is controlled to perform feedforward adjustment according to the chassis feedforward control information, so that the chassis suspension adapts to the road condition of the current road in advance.

2. The method according to claim 1, characterized in that After determining whether the current road is a road that has been traveled on according to the real-time location information, the method further includes: If the current road is a road that has not been traveled on, a traffic information sharing request is sent to a cloud server, wherein the traffic information sharing request includes the real-time location information; When the cloud server determines that the historical road condition information is shared with the cloud based on the road condition information sharing request, the cloud server receives the cloud sharing data of the historical road condition information returned by the cloud server.

3. The method according to claim 2, characterized in that After receiving the cloud-shared data of the historical road condition information returned by the cloud server, the method further includes: obtaining driving preference information of a driver of the vehicle; determining a predicted driving behavior of the driver according to the driving preference information and the historical road condition information; Determining predicted feedforward control information according to the predicted driving behavior and the historical road condition information; The chassis suspension is controlled to perform feedforward adjustment according to the predicted feedforward control information.

4. The method according to any one of claims 1 to 3, characterized in that: After controlling the chassis suspension of the vehicle to perform feedforward adjustment according to the chassis feedforward control information, the method further includes: Acquiring preview road condition information of the current road through a preview sensing system of the vehicle, wherein the preview sensing system includes a vehicle-mounted camera or a laser radar; Determining whether the previewed traffic condition information matches the historical traffic condition information; If not, generating preview feedforward control information according to the preview road condition information; The chassis suspension is controlled to perform feedforward adjustment according to the preview feedforward control information.

5. The method according to claim 4, characterized in that After determining whether the previewed road condition information matches the historical road condition information, the method further includes: If the preview road condition information matches the historical road condition information, the current driving state of the vehicle is obtained through an acceleration sensor and a suspension height sensor of the vehicle while maintaining chassis feedforward control based on the historical road condition information; Determining whether the chassis feedforward control information needs to be corrected according to the current driving state; If yes, generating feedforward control correction information according to the current driving state and the preview road condition information; The chassis suspension is controlled to perform feedforward adjustment based on the feedforward control correction information.

6. The method according to claim 1, characterized in that The obtaining of the historical traffic information of the current road specifically includes: Determining whether the historical road condition information is in the storage module of the vehicle; If yes, extracting the historical road condition information from the storage module; If not, a traffic information sharing request is sent to the cloud server to obtain the historical traffic information returned by the cloud server.

7. The method according to claim 1, characterized in that The method further comprises: Obtaining the intelligent driving planned route of the vehicle; Determine the estimated road conditions of the passing roads according to the intelligent driving planned route; The chassis suspension is feed-forward adjusted according to the estimated road condition.

8. An electronic device, characterized in that: including one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the electronic device to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed on an electronic device, the electronic device is caused to execute the method as claimed in any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is executed on an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 7.

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