Seat control method and device, vehicle and storage medium

Abstract

The application discloses a seat control method and device, a vehicle and a storage medium, and belongs to the field of vehicle control. The server is used to acquire bumping characteristics and slippery characteristics of a road section and generate comprehensive seat control parameters, so that a target vehicle can dynamically adjust seat response characteristics according to the parameters, and the target vehicle has the advantages of considering ride comfort and driving safety under the composite road conditions of the bumping and slippery characteristics of the road.

Description

Technical Field

[0001] This application relates to the field of vehicle control, and in particular to a seat control method, device, vehicle, and storage medium. Background Technology

[0002] In the field of vehicle seat control, related technologies typically formulate seat control strategies based on a single dimension of road characteristics. For example, some solutions adjust the damping of the seat shock absorbers based on road bump information to improve ride comfort; others focus on the impact of road adhesion conditions on vehicle stability. However, the road characteristics relied upon by these solutions are singular, and their control objectives are also independent.

[0003] In real-world road environments, the same road segment often exhibits both bumpy and low-adhesion characteristics. These two characteristics place different, or even conflicting, demands on seat control. Current technologies lack a mechanism to generate seat control parameters when dealing with road segments exhibiting both of these characteristics.

[0004] Therefore, how to generate seat control parameters that can meet different control needs when road sections have both bumpy and low-adhesion characteristics has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a seat control method, device, vehicle, and storage medium, which can improve seat control performance. The technical solution is as follows: On the one hand, a seat control method is provided, executed by a cloud server, the method comprising: Obtain the bump and slip features of the road section ahead of the target vehicle in the seat control cloud map. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip features. The bump features are used to indicate the intensity of seat vibration caused by the road section ahead, and the slip features are used to indicate the road surface adhesion of the road section ahead. Based on the bumpy characteristics and the slippery characteristics, seat control parameters corresponding to the road segment ahead are generated. The seat control parameters are used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead. The seat control parameters are sent to the target vehicle so that the target vehicle can control the seat based on the seat control parameters.

[0006] On the one hand, another seat control method is provided, characterized in that it is executed by a target vehicle, the method comprising: The system receives seat control parameters from a cloud server. These parameters are generated based on bump and slip features of the road ahead of the target vehicle, which are marked in the seat control cloud map. The bump features represent the intensity of seat vibration caused by the road ahead, and the slip features represent the road surface adhesion of the road ahead. The seat control cloud map is cloud map data that simultaneously marks the road segment with bump and slip features. Based on the seat control parameters, the dynamic response characteristics of the target vehicle's seat on the road section ahead are determined, and the target vehicle's seat is controlled according to the dynamic response characteristics.

[0007] On one hand, a seat control device is provided, the device comprising: The acquisition module is used to acquire the bump and slip features of the road section ahead of the target vehicle in the seat control cloud map. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip features. The bump features are used to represent the intensity of seat vibration caused by the road section ahead, and the slip features are used to represent the road surface adhesion of the road section ahead. The generation module is used to generate seat control parameters corresponding to the road segment ahead based on the bump characteristics and the slippery characteristics. The seat control parameters are used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead. The sending module is used to send the seat control parameters to the target vehicle so that the target vehicle can control the seat based on the seat control parameters.

[0008] On the one hand, another seat control device is provided, the device comprising: The receiving module is used to receive seat control parameters sent by the cloud server. The seat control parameters are generated based on the bump features and slippery features of the road section ahead of the target vehicle marked in the seat control cloud map. The bump features are used to represent the intensity of seat vibration caused by the road section ahead, and the slippery features are used to represent the road surface adhesion of the road section ahead. The seat control cloud map is cloud map data that simultaneously marks the road section with bump features and slippery features. The control module is used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead based on the seat control parameters, and to control the target vehicle's seat according to the dynamic response characteristics.

[0009] On one hand, a vehicle is provided, the vehicle including one or more processors and one or more memories, the one or more memories storing at least one computer program, the computer program being loaded and executed by the one or more processors to implement the seat control method.

[0010] On one hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, the computer program being loaded and executed by a processor to implement the seat control method.

[0011] On one hand, a computer program product or computer program is provided, which includes program code stored in a computer-readable storage medium. The vehicle's processor reads the program code from the computer-readable storage medium and executes the program code, causing the vehicle to perform the aforementioned seat control method. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the implementation environment of a seat control method provided in an embodiment of this application; Figure 2 This is a flowchart of a seat control method provided in an embodiment of this application; Figure 3 This is a flowchart of another seat control method provided in the embodiments of this application; Figure 4 This is a flowchart illustrating the annotation and updating of a seat control cloud map, as provided in an embodiment of this application. Figure 5 This is a flowchart of another seat control method provided in the embodiments of this application; Figure 6 This is a flowchart of another seat control method provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of a seat control device provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a seat control device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a cloud server provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0015] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "n," nor are they limited in quantity or execution order.

[0016] In the field of vehicle seat control, related technologies typically formulate control strategies based on single-dimensional road characteristics. This leads to a failure to generate seat control parameters that coordinate vibration comfort and driving safety requirements when road segments exhibit both bumpy and low-adhesion characteristics. The essence of this problem lies in the lack of a mechanism for integrating multi-dimensional road characteristics in the control strategy. This forces the generation of seat control parameters to make decisions based on isolated features, resulting in the seat's dynamic response characteristics failing to simultaneously meet different control objectives on road segments with complex characteristics. Specifically, bumpy characteristics require the seat dampers to reduce damping to suppress vibration transmission, while low-adhesion characteristics require the seat to enhance lateral support to maintain vehicle stability. These two needs conflict and cannot be prioritized using existing mechanisms.

[0017] For example, on slippery, winding road sections, the road surface exhibits both bumpy and low-traction conditions. The section ahead of the target vehicle is marked with bumpy and slippery characteristics. Bumpy characteristics indicate high seat vibration intensity, while slippery characteristics indicate low road surface adhesion. Existing technical solutions can only generate control parameters based on a single characteristic, leading to contradictory seat control effects on this road section: if the shock absorber damping is adjusted based on the bumpy characteristics, insufficient lateral seat support affects vehicle cornering stability. If seat support is enhanced based on the slippery characteristics, vibration suppression is weakened, reducing ride comfort. Furthermore, this problem is particularly pronounced on continuous curved sections of mountain highways. Because bumpy and slippery characteristics coexist, the seat control system cannot dynamically determine the dominant characteristic, causing the dynamic response characteristics to deviate from the optimal state.

[0018] If the aforementioned issues are not addressed, vehicles will continuously face the dilemma of conflicting control objectives on road sections with complex characteristics, potentially leading to decreased ride comfort and reduced vehicle handling stability, thereby affecting the overall performance and user acceptance of intelligent driving assistance systems. Consequently, the lack of adaptability in the generation mechanism of seat control parameters to multi-dimensional road characteristics will hinder the reliable application of vehicle seat control technology in complex road environments.

[0019] Based on this, the technical solution provided in the embodiments of this application is proposed.

[0020] Figure 1This is a schematic diagram illustrating the implementation environment of a seat control method provided in this application embodiment. See also... Figure 1 The implementation environment may include a cloud server 110, a satellite positioning system 120, at least one vehicle 130, a magnetorheological seat 131 and an on-board terminal 132 equipped in the vehicle 130, and the vehicle 130 is capable of collecting road environment and its bump characteristics 140 and uploading them to the cloud server 110. The cloud server 110 serves as the data processing center, responsible for data aggregation, processing, and model training; the vehicle 130 serves as the execution terminal, responsible for data collection and real-time seat control; and the satellite positioning system 120 provides the vehicle's real-time location information.

[0021] Figure 2 This is a flowchart of a seat control method provided in an embodiment of this application. See also... Figure 2 Taking a cloud server as the executing entity as an example, the methods include: 201. The cloud server obtains the bump and slip characteristics of the road section ahead of the target vehicle in the seat control cloud map. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip characteristics. The bump characteristics are used to indicate the intensity of seat vibration caused by the road section ahead, and the slip characteristics are used to indicate the road surface adhesion of the road section ahead.

[0022] The seat control cloud map is a cloud-based map data feature that simultaneously marks road segments with both bump and slippery characteristics. This cloud map provides comprehensive road condition information for vehicle seat control. Bump features indicate the intensity of vibrations that the road ahead may cause to the vehicle seat. These features quantify the unevenness of the road surface, such as potholes and undulations, thus providing a basis for seat vibration damping control. Slippery characteristics indicate the road surface's adhesion to the road ahead. These features reflect the road surface's coefficient of friction, such as wetness, ice, and gravel, providing a reference for seat support control in terms of vehicle stability.

[0023] 202. Based on the bumpy and slippery characteristics, the cloud server generates seat control parameters corresponding to the road segment ahead. These seat control parameters are used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead.

[0024] The seat control parameters are generated based on the bumpy and slippery characteristics of the road ahead, and are used to determine the dynamic response characteristics of the target vehicle's seat on that road section. These parameters guide the target vehicle on how to adjust the seat's operating state to adapt to different road conditions. Dynamic response characteristics refer to the kinematic and mechanical behavior of the seat when subjected to external stimuli. For seats, this includes their vertical damping capacity, lateral support force, and longitudinal support force; dynamic response characteristics directly affect ride comfort and safety.

[0025] 203. The cloud server sends the seat control parameters to the target vehicle so that the target vehicle can control the seat based on the seat control parameters.

[0026] Once the target vehicle receives the seat control parameters, the seat control unit will analyze the seat control parameters and adjust the seat's shock absorber damping, side wing support force, or seat cushion hardness accordingly to change the seat's dynamic response characteristics.

[0027] The technical solution provided in this application avoids the limitations of a single-feature control strategy, enabling the seat's dynamic response characteristics to more comprehensively adapt to complex road environments. By sending these parameters to the target vehicle, the vehicle seat can make pre-adaptive adjustments based on the actual conditions of the road ahead, thereby improving ride comfort while ensuring occupant safety.

[0028] Steps 201-203 above are a brief description of the technical solutions provided in the embodiments of this application. The technical solutions provided in the embodiments of this application will be described more clearly below with reference to some examples. See [link to relevant documentation]. Figure 3 Taking a cloud server as the executing entity as an example, the methods include: 301. The cloud server obtains the bump and slip characteristics of the road section ahead of the target vehicle in the seat control cloud map. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip characteristics. The bump characteristics are used to indicate the intensity of seat vibration caused by the road section ahead, and the slip characteristics are used to indicate the road surface adhesion of the road section ahead.

[0029] In one possible implementation, the cloud server determines the road segment ahead of the target vehicle based on the location information reported by the target vehicle. The cloud server then obtains the bump characteristics and slippery characteristics at the location corresponding to the road segment ahead from the seat control cloud map.

[0030] Based on the location information reported by the target vehicle, the system determines the road segment ahead of the target vehicle. This aims to identify the road area the target vehicle is about to travel on, providing a clear geographical range for subsequently acquiring the bump and slippery characteristics of that road area. By utilizing the real-time location data reported by the vehicle, the cloud server can dynamically track the vehicle's trajectory and predict its future path, thereby ensuring that the acquired road features are highly relevant to the vehicle's actual needs. In some embodiments, the cloud server can receive real-time location information such as latitude and longitude coordinates and altitude from the target vehicle via positioning devices such as a Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), or Inertial Navigation System (INS). Based on this location information, combined with the target vehicle's driving direction and the length of the road segment ahead (e.g., 500 meters to 2 kilometers ahead), the cloud server delineates a continuous road area on a digital map as the road segment ahead. Alternatively, the cloud server can receive a driving data packet reported by the target vehicle containing location information, vehicle speed, driving direction, timestamps, and other data. In some embodiments, the location reported by the target vehicle is determined via satellite positioning. The cloud server uses this data, along with a path planning algorithm or prediction model and high-precision map data, to predict the path the target vehicle may take in the future, and designates one or more segments of this predicted path as the upcoming road segment. From the seat control cloud map, the bump and slip characteristics corresponding to the upcoming road segment are obtained. This aims to retrieve the unique bump and slip characteristics of the determined upcoming road segment from the pre-built seat control cloud map. These bump and slip characteristics form the basis for subsequent seat control parameters, directly affecting the seat's dynamic response characteristics, thereby improving ride comfort and driving safety. After determining the upcoming road segment, the cloud server can send its geographic coordinate range or road segment ID (Identification) as query conditions to the seat control cloud map database. The seat control cloud map database matches and extracts all bump and slip characteristics corresponding to the upcoming road segment based on these query conditions. Bump and slippery features can be stored in a structured format, such as JSON or XML, containing specific information such as bump level and slippery level. Alternatively, a cloud server can use Geographic Information System (GIS) technology to perform a spatial query on the seat control cloud map of the road ahead. Using spatial indexing (such as R-trees or quadtrees) or geocoding techniques, the cloud map area covered by the road ahead can be located, and all marked bump and slippery features within that area can be extracted.

[0031] Through the above implementation method, the cloud server can dynamically and accurately determine the road segment ahead of the vehicle based on the real-time location information reported by the target vehicle. Based on this, the cloud server can obtain the bump and slippery features corresponding to that specific road segment from the seat control cloud map. This mechanism overcomes the problems of inaccurate road segment identification or low feature acquisition efficiency that may exist in traditional methods, ensuring that the cloud server can timely and accurately grasp detailed road condition information of the road segment ahead of the vehicle. Therefore, it lays the foundation for subsequently generating highly adaptive and real-time seat control parameters based on these accurate features.

[0032] For example, a target vehicle is traveling on a highway. Its onboard positioning module (e.g., a high-precision GNSS receiver) reports its location information, including latitude and longitude, speed, and direction of travel, to a cloud server every second. Upon receiving this location information, the cloud server, based on the vehicle's current location and direction of travel, and combined with a preset forward detection distance (e.g., 2 kilometers ahead), determines a continuous road segment on its high-precision digital map as the forward road segment. For instance, if the vehicle is currently at a highway milestone, the cloud server will identify a specific highway segment extending 2 kilometers forward from that milestone. The cloud server uses the geographic coordinates of the forward road segment or its unique identifier in the map database as the query key to access a pre-stored seat control cloud map database. This database stores road segment information from various locations, and each segment is associated with its bump characteristics (e.g., a bump level of "moderate") and slippery characteristics (e.g., a road surface adhesion level of "good"). The cloud server uses a spatial query algorithm to extract the bump and slip characteristics corresponding to the forward road segment from the seat control cloud map. For example, if the road ahead includes a construction zone, its bumpy characteristics might be labeled as "high bumpy," while if the road ahead has just rained, its slippery characteristics might be labeled as "low grip." This acquired feature data will serve as input for the subsequent generation of seat control parameters.

[0033] To provide a clearer explanation of the above embodiments, the method for generating the seat control cloud map involved in the above embodiments will be described below.

[0034] In one possible implementation, a cloud server receives driving data reported by at least one vehicle as it travels through a road segment. This driving data includes seat vibration information and road surface adhesion coefficient information. Based on the seat vibration information, the cloud server generates a bump feature corresponding to the road segment. Based on the road surface adhesion coefficient information, the cloud server generates a slippery feature corresponding to the road segment. The cloud server simultaneously labels the bump feature and the slippery feature in the cloud map data at the locations corresponding to the road segment, thus obtaining the seat control cloud map.

[0035] The seat vibration information is a quantitative data set of the vibrations experienced by the vehicle seat during driving. For example, it can be data such as vibration acceleration, vibration frequency, and vibration amplitude collected in real time by devices such as acceleration sensors and vibration sensors installed on the seat or chassis. Seat vibration information directly reflects the impact of road surface roughness on ride comfort. The road surface adhesion coefficient information is an indicator of the friction between the vehicle's tires and the road surface, reflecting the road's slipperiness. Seat vibration information can be obtained through data acquired by the vehicle's ABS (Anti-lock Braking System) / ESP (Electronic Stability Program) system, tire slip ratio sensors, or dedicated road surface adhesion coefficient sensors. For example, it can be indirectly calculated from the tire slip ratio and wheel speed differences during braking, acceleration, or steering of the target vehicle. The target vehicle can encrypt and upload the collected data to the cloud server via an onboard communication module (such as a 4G / 5G or V2X communication module), or store the data in local storage and upload it to the cloud server in batches via wired or wireless means when the vehicle connects to a charging pile or service station. Bump characteristics are indicators used to represent the intensity of seat vibration caused by the road section. In some embodiments, the cloud server can perform signal processing such as filtering and Fourier transform on the raw vibration data to extract characteristic parameters such as vibration intensity and dominant frequency. Combined with factors such as the target vehicle's speed and load, it can calculate the bump level or bump type of the road section using a preset algorithm model (such as a machine learning model or empirical formula). Alternatively, the cloud server can also generate feature values ​​representing the overall bump situation of the road section by performing weighted averaging or cluster analysis based on statistical quantities such as peak acceleration and root mean square (RMS) of the seat vibration information, combined with the number of times the target vehicle passes through the road section and feedback from different vehicles. Slippery characteristics are indicators used to represent the road surface adhesion of the road section. In some embodiments, the cloud server can compare the adhesion coefficient with a preset adhesion threshold. When the adhesion coefficient is below a certain threshold, it is determined to be a slippery road section, and the slipperiness level is classified according to the specific value of the adhesion coefficient (e.g., slightly slippery, moderately slippery, severely slippery). In addition, the cloud server can also combine road surface adhesion coefficient information, environmental sensor (e.g., rain sensor, temperature sensor) data, and historical weather data, and use a fusion algorithm to determine road surface conditions such as icing and water accumulation, thereby generating more refined slipperiness characteristics, such as a range of adhesion coefficients, slipperiness level, or a specific description of road surface conditions.The cloud server matches the generated bump and slippery features with the vehicle's reported location information (such as GPS coordinates and high-precision positioning data), storing these features as attribute data in the cloud map database and associating them with the corresponding road segment geometry information (such as segment ID, start coordinates, and end coordinates). The cloud server can employ a layered map structure, overlaying bump and slippery features as independent layers or attribute fields onto the base high-precision map data to form a multi-dimensional seat control cloud map. When a vehicle queries a specific road segment, it can simultaneously obtain the segment's geographic information, bump and slippery features.

[0036] The above implementation method aggregates driving data from multiple vehicles and transforms it into meaningful bump and slip characteristics, thereby constructing a high-precision, high-time-efficiency seat control cloud map. This provides a solid data foundation for intelligent seat control on the cloud server, enabling the seat control system to adaptively adjust the seat's dynamic response characteristics based on the actual bumps and slippage of the road ahead, thereby improving ride comfort and driving safety. This solves the technical problem that traditional seat control methods cannot perceive and respond to complex road conditions in real time.

[0037] To provide a clearer explanation of the above embodiments, the following description is divided into several parts.

[0038] Part 1: The cloud server generates the bump characteristics corresponding to the road segment based on the seat vibration information.

[0039] In one possible implementation, the cloud server determines the seat vibration intensity of the road segment based on the seat vibration information. The cloud server then normalizes the seat vibration intensity based on the speed of the at least one vehicle traveling through the road segment, obtaining a normalized vibration intensity. Based on this normalized vibration intensity, the cloud server determines the bump characteristics corresponding to the road segment.

[0040] Seat vibration information is typically collected by sensors (such as accelerometers and gyroscopes) installed on the vehicle seat or chassis, reflecting vibrations caused by uneven road surfaces during vehicle operation. Seat vibration intensity can be determined by performing time-domain or frequency-domain analysis on the collected seat vibration signals. In some embodiments, vibration intensity can be determined by performing time-domain or frequency-domain analysis on the collected seat vibration signals. For example, calculating the root mean square value, peak acceleration, and vibration power spectral density of the vibration signal can intuitively reflect the severity of the vibration. Alternatively, specific vibration comfort evaluation criteria can be used to weight the seat vibration information to obtain a vibration intensity index that conforms to human comfort. Normalization processing is then performed to obtain normalized vibration intensity, aiming to eliminate or reduce the influence of vehicle speed on seat vibration intensity, allowing vibration data collected at different speeds to be compared and evaluated under a unified benchmark. Normalization processing more accurately reflects the bumpy characteristics of the road itself, rather than differences caused by vehicle speed. Based on normalized vibration intensity, the bump characteristics corresponding to road segments are determined. The aim is to transform the seat vibration intensity after speed normalization into bump characteristics with practical significance and operability, so as to facilitate annotation in the seat control cloud map and subsequent generation of seat control parameters. Bump characteristics are usually represented in discrete level or continuous numerical form.

[0041] In some embodiments, the above-mentioned seat vibration intensity can be determined by the following formula (1): (1) in, Let a be the seat vibration intensity, which is the root mean square value of the vertical acceleration of the mass on the seat spring. i N represents the sampled value of the vertical acceleration of the seat sprung mass, and N is the number of data points collected through this road segment.

[0042] The above implementation method aggregates driving data from multiple vehicles and transforms it into meaningful bump and slip characteristics, thereby constructing a high-precision, high-time-efficiency seat control cloud map. This provides a data foundation for intelligent seat control on a cloud server, enabling the target vehicle to adaptively adjust the seat's dynamic response characteristics based on the actual bump and slip conditions of the road ahead. This improves ride comfort and driving safety, solving the technical problem that traditional seat control methods cannot perceive and respond to complex road conditions in real time.

[0043] To provide a clearer explanation of the above embodiments, the method described below for normalizing the vibration intensity of the seat based on the driving speed of the at least one vehicle when it passes through the road segment, and obtaining the normalized vibration intensity, will be explained below.

[0044] In one possible implementation, the cloud server determines the ratio of the speed of the at least one vehicle traveling through the road segment to a reference speed, which is a reference value used to uniformly convert the seat vibration intensity at different speeds to the same speed benchmark. Based on this speed ratio, the cloud server normalizes the seat vibration intensity to obtain the normalized vibration intensity.

[0045] The process involves determining the ratio of the speed of at least one vehicle traveling through the road segment to a reference speed. This aims to quantify the relative relationship between the actual speed of the target vehicle and a preset benchmark speed. Calculating this speed ratio provides a unified scaling factor for subsequent normalization, eliminating differences in seat vibration intensity between different vehicles traveling at different speeds through the same road segment, making seat vibration intensity more comparable. This speed ratio can be obtained by acquiring the vehicle's speed in real time using onboard sensors (such as GPS modules and wheel speed sensors) and dividing the speed by the preset reference speed. Alternatively, it can be achieved by consulting a preset speed ratio lookup table, which pre-stores the relationship between different speed ranges and their corresponding speed ratios. The cloud server directly retrieves the corresponding ratio based on the current speed. The reference speed is a standardized speed value, serving as a common basis for normalizing seat vibration intensity. By converting all collected vibration intensity data to the equivalent value at this reference speed, the direct influence of speed on vibration intensity can be eliminated, thus more accurately assessing the bump characteristics of the road segment itself. The reference speed can be set to the legal speed limit of a country or region, such as the average speed limit on highways or urban roads, to reflect common driving scenarios. Alternatively, it can be a typical or average driving speed derived from statistical analysis of a large amount of historical driving data to ensure its representativeness and practicality. Based on this speed ratio, the seat vibration intensity is normalized to obtain the normalized vibration intensity. This is achieved by adjusting the original seat vibration intensity data using the previously determined speed ratio to eliminate the influence of speed differences, thus obtaining a normalized vibration intensity that is independent of speed and better reflects the actual bumpiness of the road segment. The normalization process can use linear scaling, such as multiplying the original seat vibration intensity by the reciprocal of the speed ratio (or some function related to the speed ratio), to compensate for the influence of speed on vibration intensity. Alternatively, a nonlinear model can be used, for example, by using a pre-established nonlinear model, taking the original vibration intensity and the speed ratio as input, and outputting the normalized vibration intensity.

[0046] Through the above implementation method, by introducing a reference vehicle speed and calculating the speed ratio, the original seat vibration intensity is precisely normalized, enabling vibration data collected at different speeds to be evaluated on a common speed benchmark. This improves the accuracy and reliability of bump characteristics, ensuring that the seat control cloud map more realistically reflects the bump conditions of the road segment. Therefore, the seat control parameters generated based on these bump characteristics will be more accurate, thereby improving the comfort and effectiveness of the target vehicle seat's dynamic response control on the road ahead.

[0047] To provide a clearer explanation of the above embodiments, the normalized vibration intensity of the seat based on the speed ratio will be explained below.

[0048] In one possible implementation, the cloud server obtains a speed impact index corresponding to the bump type of the road segment. This speed impact index represents the degree of influence of driving speed on seat vibration intensity under different bump types. Based on this speed ratio and the speed impact index, the cloud server normalizes the seat vibration intensity to obtain the normalized vibration intensity.

[0049] The vehicle speed influence index is a parameter used to quantify the impact of vehicle speed changes on seat vibration intensity under different bump types. Its function is to provide a correction factor, enabling more accurate reflection of the true vibration characteristics of a specific bump type at different vehicle speeds during seat vibration intensity normalization. For example, for some bump types, a small change in vehicle speed may lead to a significant change in vibration intensity, while for others, the impact of speed changes on vibration intensity is relatively small. This vehicle speed influence index can be obtained in advance through statistical analysis and modeling of a large amount of real-vehicle test data, or determined by simulating the vehicle's dynamic response under different vehicle speeds and bump types. The degree of influence of driving speed on seat vibration intensity under different bump types refers to the combined effect of the physical characteristics of road bumps (such as the shape, height, and spacing of bumps) and vehicle speed on seat vibration. For example, when a vehicle passes over a speed bump at low speed, the seat vibration may manifest as a large impact. However, when passing over a washboard road at high speed, the seat vibration may manifest as high-frequency continuous vibration. The difference in the degree of influence is a key consideration in normalization. This can be achieved by conducting multi-speed, multi-vehicle tests on different bumpy road sections, collecting seat vibration data, and analyzing this data using road feature recognition technology. This establishes a quantitative relationship between speed, bump type, and vibration intensity. Based on this speed ratio and the vehicle speed influence index, the seat vibration intensity is normalized to eliminate or reduce the influence of vehicle speed on the measured seat vibration intensity, resulting in a normalized vibration intensity that is independent of or weakly correlated with speed and better reflects the bumpy characteristics of the road itself. In some embodiments, the original seat vibration intensity can be divided by a power of the speed ratio; the exponent of this power is the vehicle speed influence index. Alternatively, the seat vibration intensity can be corrected using a function model that includes the speed ratio and the vehicle speed influence index. In this way, even if different vehicles travel through the same road section at different speeds, the normalized seat vibration intensity can maintain a high degree of consistency, thereby improving the accuracy and reliability of the seat control cloud map data.

[0050] In some embodiments, the above normalization process can be achieved by the following formula (2): (2) in, To measure the root mean square value of the vertical acceleration of the mass on the seat spring, which is also the seat vibration intensity, The normalized root mean square value after eliminating the effect of velocity, v ref For reference vehicle speed, v actual The actual vehicle speed is represented by k, which is the speed impact index related to the type of bumps. This speed impact index can be continuously corrected as multi-vehicle data accumulates in the cloud.

[0051] Through the above implementation method, the different impacts of driving speed on seat vibration intensity under different bump types are fully considered when normalizing the seat vibration intensity. This allows the normalized seat vibration intensity to more accurately reflect the bump characteristics of the road segment itself, rather than simply being a function of vehicle speed. Therefore, the bump characteristics generated based on this more accurate normalized vibration intensity have improved accuracy and reliability. This improvement directly optimizes the quality of seat control cloud map data, thereby enabling the cloud server to generate more accurate and adaptive seat control parameters. After receiving these parameters, the target vehicle can control the seat based on more realistic road condition information, thereby ensuring passenger comfort while also improving riding safety in complex road conditions.

[0052] In some embodiments, the mapping relationship between normalized vibration intensity and bump level can be shown in Table 1 below: Table 1

[0053] For example, when generating a seat control cloud map, the seat vibration intensity of a certain road segment needs to be normalized. This segment is identified as a "speed bump" type bump. The cloud server queries a preset mapping table or model to obtain the vehicle speed influence index corresponding to the "speed bump" type bump. For example, this vehicle speed influence index can be 1.5. This means that for speed bumps, vehicle speed has a relatively large impact on vibration intensity. If a vehicle passes over the speed bump at a speed of 1.5 m / s², and the reference vehicle speed is 1 m / s², then the speed ratio is 1.5. At this time, the original seat vibration intensity (e.g., the RMS value measured by an accelerometer) will be normalized based on this speed ratio of 1.5 and the vehicle speed influence index of 1.5. The specific normalization formula can be: Normalized vibration intensity = Original vibration intensity / (Speed ​​ratio ^ Vehicle speed influence index). That is, Normalized vibration intensity = Original vibration intensity / (1.5) 1.5 As another specific implementation, if the road section is identified as a "slightly washboard road" type of bumpy road, its corresponding speed influence index can be 0.8, indicating that the influence of vehicle speed on vibration intensity is relatively small. If a vehicle passes through at a speed of 1.25 m / s², the reference speed is still 1 m / s², and the speed ratio is 1.25. Then, the normalized vibration intensity = original vibration intensity / (1.25) 0.8 In this way, the normalization of seat vibration intensity under different types of bumps can be adjusted according to its unique speed sensitivity, making the final normalized vibration intensity more representative.

[0054] The second part involves the cloud server generating the slippery feature corresponding to the road segment based on the road surface adhesion coefficient information.

[0055] In one possible implementation, the cloud server obtains the road surface adhesion coefficient of the road segment based on the road surface adhesion coefficient information. If the road surface adhesion coefficient is greater than or equal to a preset adhesion threshold, the cloud server generates a slippery feature indicating that the road segment is a non-slippery segment. If the road surface adhesion coefficient is less than the preset adhesion threshold, the cloud server determines the slippery level corresponding to the road segment based on the road surface adhesion coefficient and generates a slippery feature carrying that slippery level.

[0056] The road surface adhesion coefficient information refers to data reflecting the magnitude of friction between the road surface and the tire. This information can be obtained from various sources, such as inertial measurement unit (IMU) data from vehicle wheel speed sensors, accelerometers, gyroscopes, etc., estimated using vehicle dynamics models. Alternatively, it can be obtained by using visual sensors such as onboard cameras and lidar to identify road texture, water accumulation, icing, etc., and then predicting using machine learning algorithms. Real-time weather and road condition data can also be obtained from external weather stations and road management departments. The road surface adhesion coefficient is a quantitative indicator used to describe the ratio of the maximum friction force between the road surface and the tire to the vertical load, usually expressed as the friction coefficient or adhesion coefficient. This coefficient can be obtained through real-time processing and calculation of the aforementioned road surface adhesion coefficient information, for example, by dynamically estimating it using wheel slip ratio and wheel torque data during vehicle braking or acceleration using algorithms such as Kalman filtering. The preset adhesion threshold is a pre-set critical value used to distinguish whether the road surface is slippery. The preset adhesion threshold can be set based on various factors, such as vehicle type, tire performance, driving mode (e.g., Sport mode, Snow mode), and safety standards, through empirical settings. Alternatively, it can be calibrated using extensive road test data and expert experience to ensure effective differentiation between slippery and non-slippery road sections under different conditions. For example, it can be set to 0.4 or 0.5; values ​​below this are considered slippery. When the road surface adhesion coefficient is greater than or equal to the preset adhesion threshold, it indicates that the road surface has sufficient adhesion and is unlikely to slip. The resulting slippery characteristic can be a simple Boolean flag (e.g., "is..."). slipperyThe slipperiness level can be either a "false" value or a specific numerical value (e.g., a slipperiness level of 0) to explicitly indicate that the road segment is not slippery. This slipperiness feature is generated to simplify subsequent seat control decisions, avoiding the triggering of unnecessary safety control modes on non-slippery road segments, thereby ensuring ride comfort. This slipperiness level is a quantitative indicator that finely categorizes the degree of road slipperiness. When the road surface adhesion coefficient is less than a preset adhesion threshold, its slipperiness level needs further evaluation. The slipperiness level can be determined using various methods, such as dividing the range of the road surface adhesion coefficient into multiple intervals. Each interval corresponds to a slipperiness level (e.g., 0.3-0.4 is slightly slippery, 0.2-0.3 is moderately slippery, and below 0.2 is severely slippery). Alternatively, the road adhesion coefficient can be mapped to a 0-N level system using a continuous function, where N represents the highest slipperiness level. This grading process provides more granular input to the target vehicle's seat controller, enabling more targeted safety control strategies. The slipperiness feature carrying the slipperiness level can be a data structure containing level values ​​(e.g., a JSON object, such as {"slippery");} level (":2}), or a specific code that uniquely identifies a certain slip level. By directly embedding the slip level into the slip feature, the seat controller of the target vehicle can directly obtain the specific slip level of the road surface when it receives the slip feature, thereby quickly and accurately adjusting the dynamic response characteristics of the seat to cope with different levels of slip risk.

[0057] In some embodiments, the mapping relationship between the road surface adhesion coefficient and the skid resistance level can be shown in Table 2 below: Table 2

[0058] Through the above implementation method, when generating slippery features in the seat control cloud map, it is possible to not only determine whether a road segment is slippery based on the actual magnitude of the road surface adhesion coefficient, but also to further refine its slipperiness level, generating slippery features with slipperiness grades. The slippery features generated in this way can more accurately reflect the actual road conditions, avoiding information gaps that may result from simple binary judgments. Therefore, when generating seat control parameters subsequently, the cloud server can formulate more targeted and adaptive seat safety control strategies based on more discriminative slippery features, thereby improving the riding safety of the target vehicle when driving on slippery road segments and optimizing the riding comfort on non-slippery road segments, avoiding unnecessary safety interventions.

[0059] For example, a cloud server receives driving data from a vehicle, including road surface adhesion coefficient (BPCC) information. Based on this BPCC information, such as by analyzing wheel speed differences and braking pressure data, the cloud server calculates the BPCC of the current road segment, for example, 0.35. A preset adhesion threshold is set to 0.4. Since the calculated BPCC of 0.35 is less than the preset adhesion threshold of 0.4, the cloud server determines that the road segment is slippery. To further refine the slipperiness level, the cloud server maps the BPCC of 0.35 to a corresponding slipperiness level according to a preset mapping relationship. For example, a BPCC between 0.3 and 0.4 can be set to correspond to "slightly slippery" level 1, between 0.2 and 0.3 to "moderately slippery" level 2, and below 0.2 to "severely slippery" level 3. In this example, the BPCC of 0.35 will correspond to slipperiness level 1. The cloud server will generate a slippery feature carrying the slipperiness level 1, such as a data packet containing the field "slippery". level "and assign it a value of 1. If the road surface adhesion coefficient of another road segment is calculated to be 0.5, which is greater than the preset adhesion threshold of 0.4, the cloud server will generate a slippery feature representing a non-slippery road segment, such as a data packet containing the field "is slippery And assign it the value false.

[0060] Optionally, before obtaining the road surface adhesion coefficient of the road segment based on the road surface adhesion coefficient information, the following steps can also be performed.

[0061] In one possible implementation, a cloud server acquires road surface images captured by at least one vehicle as it travels through the road segment. The cloud server performs visual feature recognition on the road surface images to determine whether the road segment contains suspected slippery visual features. If the suspected slippery visual feature is identified, the cloud server triggers the step of obtaining the road surface adhesion coefficient of the road segment based on the road surface adhesion coefficient information.

[0062] Acquiring at least one road surface image captured by a vehicle while driving through the road segment refers to the real-time or periodic capture of image data of the road surface in front of or below the vehicle by its onboard visual sensors (such as vehicle-mounted cameras) during actual driving. Road surface images contain visual information about the road surface conditions and form the basis for initial judgment. For example, a vehicle may be equipped with a high-resolution wide-angle camera to continuously record road surface conditions along its driving path. Visual feature recognition of the road surface image to determine whether the road segment contains suspected slippery visual features refers to analyzing and processing the acquired road surface image to identify visual cues that may lead to a decrease in road surface adhesion. This can be achieved using various image processing and pattern recognition techniques. For example, deep learning models, such as Convolutional Neural Networks (CNNs), can be used to train and recognize images to detect water stains, ice and snow, oil stains, mud, and unique texture or color changes of wet and slippery road surfaces. Alternatively, traditional image processing algorithms, such as edge detection, color space analysis, and texture analysis, can be used to identify preset slippery visual patterns. When a road segment is identified as containing visually suggestive slippery features, the step of obtaining the road surface adhesion coefficient (LPC) based on the identified LPC information is triggered. This means that when visual recognition indicates a potential slippery road surface risk, the cloud server issues a command to initiate or activate the mechanism for obtaining the LPC. This is a conditionally triggered mechanism designed to avoid performing accurate but potentially resource-intensive LPC measurements on all road segments. For example, when a large puddle is detected in a road image, vehicle sensors (such as wheel speed sensors and accelerometers) are triggered to combine with the vehicle dynamics model to begin calculating or requesting the real-time LPC for that road segment.

[0063] In some embodiments, the correspondence between identifiable visual features and identifiable content in road surface images acquired by the vehicle-mounted vision sensor can be shown in Table 3 below: Table 3

[0064] By implementing the above methods, it is possible to avoid continuously acquiring road surface adhesion coefficient information on all road sections, thereby reducing the computational resources and data transmission volume required during operation. This intelligent predictive mechanism makes the generation process of slippery features more efficient and economical, while ensuring that key data can be acquired in a timely manner when it is truly necessary to accurately determine slippery road sections, thus improving the generation efficiency and accuracy of seat control cloud maps.

[0065] 302. The cloud server determines the dominant feature from the bumpy feature and the slippery feature.

[0066] In one possible implementation, the cloud server obtains the vibration intensity level corresponding to the bump feature and the road surface adhesion level corresponding to the slippery feature. Based on a preset priority comparison rule between the vibration intensity level and the road surface adhesion level, the cloud server determines the one with higher priority between the bump feature and the slippery feature as the dominant feature.

[0067] The acquisition of vibration intensity level corresponding to bump characteristics is a quantitative representation of the bumpiness of the road section ahead. Vibration intensity level can be a discrete value; for example, the bumpiness can be divided into several levels such as slight, moderate, and severe, and assigned corresponding numerical values. This simplifies complex bump characteristics into standardized values ​​that are easy to compare and process. For example, the raw data of bump characteristics stored in the seat control cloud map, such as the peak value or root mean square (RMS) value of vibration acceleration, can be graded through a preset threshold range to obtain the corresponding vibration intensity level. Alternatively, a machine learning model can be trained to directly output the corresponding vibration intensity level based on bump data. The acquisition of road surface adhesion level corresponding to slippery characteristics is a quantitative representation of the road surface friction coefficient or slipperiness of the road section ahead. This road surface adhesion level can also be a discrete value; for example, road surface adhesion can be divided into several levels such as good, average, and poor, and assigned corresponding numerical values. This simplifies complex slippery characteristics into standardized values ​​that are easy to compare and process. For example, the road surface adhesion coefficient (μ value) can be graded based on the raw data of slippery features stored in the seat control cloud map, through a preset threshold range, to obtain the corresponding road surface adhesion level. Alternatively, the road surface image recognition results (such as the detection of water accumulation, ice, sand, etc.) and environmental sensor data (such as temperature and humidity) can be combined to comprehensively judge and output the corresponding road surface adhesion level. This preset priority comparison rule is a set of logic or algorithms used to prioritize bumpy features and slippery features. Its purpose is to provide the cloud server with a clear decision-making basis, selecting the feature with a more significant impact on vehicle driving and passenger experience as the dominant feature when both features exist simultaneously. For example, a priority matrix or lookup table can be defined, where the horizontal axis represents the vibration intensity level, the vertical axis represents the road surface adhesion level, and the table content represents the corresponding priority result (bumpy feature priority or slippery feature priority). Rules can also be defined through a series of conditional statements. For example, if the road surface adhesion level reaches a certain danger threshold, the slippery feature takes priority regardless of the vibration intensity level. Otherwise, if the vibration intensity level reaches a certain discomfort threshold, the bumpy feature takes priority. The feature with the higher priority determined by this comparison rule will be explicitly identified as the dominant feature, thereby guiding the generation of subsequent seat control parameters.

[0068] In some embodiments, based on a preset priority comparison rule between vibration intensity level and road surface adhesion level, the priority arbitration between bumpy characteristics and slippery characteristics can be as shown in Table 4 below: Table 4

[0069] The above implementation method avoids subjective or experiential judgments, enabling objective and intelligent selection of dominant features. This allows for prioritizing the response to the most critical characteristics of the current road segment (whether comfort or safety) in complex road conditions, resulting in more targeted seat control parameters, improved seat control decision-making efficiency and accuracy, and ultimately optimized passenger experience and driving safety.

[0070] To provide a clearer explanation of the above embodiments, the following describes how the dominant feature is determined based on a preset priority comparison rule between the vibration intensity level and the road surface adhesion level. This rule determines the higher priority of the bumpy feature and the slippery feature as the dominant feature.

[0071] In one possible implementation, the cloud server determines, based on the vibration intensity level and the road surface adhesion level, whether a preset condition is met, indicating that the priority of the bumpy feature is higher than that of the slippery feature. If the preset condition is met, the cloud server identifies the bumpy feature as the dominant feature. If the preset condition is not met, the cloud server identifies the slippery feature as the dominant feature.

[0072] The method of determining whether a preset condition is met based on the vibration intensity level and road surface adhesion level aims to introduce a dynamic and conditional judgment mechanism to decide whether bump characteristics should take precedence over slippery characteristics in a specific context. This avoids the limitations of fixed priority rules, making the determination of the dominant characteristic more flexible and intelligent. For example, preset conditions can be defined using a combination of preset thresholds; when the vibration intensity level reaches a high level (e.g., "severe bumpiness") and the road surface adhesion level is within the normal range, the preset conditions are met. Alternatively, a decision tree or machine learning model can be used to determine the preset conditions. This model can learn, based on historical data and expert experience, under what combinations of vibration intensity and road surface adhesion levels the bump characteristics should be considered higher priority. When it is determined that the bump characteristics are more critical in the current context, they are established as the dominant factor guiding the generation of subsequent seat control parameters. When the priority of the bump characteristics does not meet the preset conditions, it means that the slippery characteristics may be more important or at least as important as the bump characteristics in the current context; in this case, the slippery characteristics are established as the dominant factor guiding the generation of subsequent seat control parameters.

[0073] Through the above implementation method, the relative priority of bumpy and slippery characteristics can be dynamically determined based on the actual vibration intensity level and road surface adhesion level, thereby intelligently identifying the dominant characteristic. This avoids judgment errors that may be caused by traditional fixed priority rules, ensuring that in complex and changing road conditions, the factors with the greatest impact on ride comfort or driving safety can be prioritized, thereby generating more targeted and effective seat control parameters, improving the adaptability and accuracy of seat control, while taking into account both ride comfort and driving safety.

[0074] To provide a clearer explanation of the above embodiments, the following describes the method used in the above embodiments to determine whether the preset condition of prioritizing the bumpy feature as higher than the slippery feature is met based on the vibration intensity level and the road surface adhesion level.

[0075] In one possible implementation, the cloud server determines whether the vibration intensity level reaches a preset intensity level and whether the road surface adhesion level is within a preset adhesion threshold range. If the vibration intensity level reaches the preset intensity level and the road surface adhesion level is within the preset adhesion threshold range, the cloud server determines that the preset condition is met. If the vibration intensity level does not reach the preset intensity level, or the road surface adhesion level is not within the preset adhesion threshold range, the cloud server determines that the preset condition is not met.

[0076] The preset intensity level is a pre-defined critical value for vibration intensity, used to determine whether the bumpiness of the road ahead reaches a level requiring special attention. The preset intensity level can be set based on factors such as vehicle type, passenger comfort standards, road grade, or historical data statistical analysis. For example, when the vibration intensity level exceeds a specific acceleration peak or frequency range, it is considered to have reached the preset intensity level. The preset adhesion threshold range is a pre-defined critical interval for road surface adhesion, used to determine whether the road surface adhesion of the road ahead is at a safe or non-hazardous level. This preset adhesion threshold range can be a numerical range of the adhesion coefficient; for example, an adhesion coefficient between 0.6 and 0.9 is considered normal, while a coefficient below 0.6 is considered poor adhesion. This preset adhesion threshold range can also be dynamically adjusted based on weather conditions (such as rain, snow, and ice) and road surface type (such as asphalt, cement, and mud) to reflect safe adhesion under different conditions. Determining whether the vibration intensity level reaches the preset intensity level aims to assess the severity of the bumpiness characteristics. In practical implementation, the vibration intensity level of the current road segment can be numerically compared with a preset intensity level. For example, if the vibration intensity level is higher than or equal to the preset intensity level, it is considered to have been reached. Alternatively, fuzzy logic or interval judgment can be used; that is, when the vibration intensity level falls into a certain high vibration interval, it is considered to have reached the threshold. Determining whether the road surface adhesion level is within the preset adhesion threshold range aims to assess the safety of the slippery characteristics. When the vibration intensity level reaches the preset intensity level and the road surface adhesion level is within the preset adhesion threshold range, determining that the preset condition is met is a logical judgment used to clarify one of the conditions under which the bumpy characteristics have a higher priority than the slippery characteristics. When both sub-conditions—that the vibration intensity level reaches the preset intensity level and the road surface adhesion level is within the preset adhesion threshold range—are true, the entire preset condition is determined to be met. When the vibration intensity level does not reach the preset intensity level, or the road surface adhesion level is not within the preset adhesion threshold range, determining that the preset condition is not met is also a logical judgment used to clarify the condition under which the bumpy characteristics have a lower priority than the slippery characteristics. If either of the two sub-conditions, namely, the vibration intensity level not reaching the preset intensity level or the road surface adhesion ability level not being within the preset adhesion threshold range, is true, then the entire preset condition is determined to be unsatisfied.

[0077] By simultaneously considering whether the vibration intensity level reaches a preset intensity level and whether the road surface adhesion level is within a preset adhesion threshold range, the ambiguity in determining the dominant feature under complex road conditions is avoided. This allows the target vehicle to improve ride comfort (prioritizing bump features when the road surface is safe but severely bumpy) while ensuring driving safety (especially prioritizing slippery features when road surface adhesion is poor). This intelligent prioritization improves the adaptability and effectiveness of the seat control strategy, thereby optimizing the user's driving experience.

[0078] For example, if the preset intensity level is set to "moderate bumpiness" or higher, and the preset adhesion threshold range is set to "normal adhesion" or "good adhesion," when the cloud server obtains that the vibration intensity level corresponding to the bumpiness feature of the road section ahead of the target vehicle is "severe bumpiness," and the road adhesion level corresponding to the slippery feature is "normal adhesion," the cloud server determines that the priority of the bumpiness feature is higher than that of the slippery feature because the vibration intensity level "severe bumpiness" reaches the preset intensity level and the road adhesion level "normal adhesion" is within the preset adhesion threshold range. Therefore, the bumpiness feature is identified as the dominant feature. Conversely, if the cloud server obtains that the vibration intensity level of the road section ahead is "slight bumpiness," but the road adhesion level is "normal adhesion," the cloud server determines that the priority of the bumpiness feature is not higher than that of the slippery feature because the vibration intensity level "slight bumpiness" does not reach the preset intensity level. In this case, the slippery feature may be identified as the dominant feature. One specific implementation example is as follows: When the cloud server obtains that the vibration intensity level of the road section ahead is "severe bumps" but the road surface adhesion level is "low adhesion" (for example, the road surface is icy), since the road surface adhesion level "low adhesion" is not within the preset adhesion threshold range, the cloud server will also determine that the preset condition that the priority of the bump feature is higher than that of the slippery feature is not met. At this time, the slippery feature will be determined as the dominant feature.

[0079] In some embodiments, the input parameters received by the above-described seat prediction model may be as shown in Table 5 below: Table 5

[0080] 303. The cloud server generates the seat control parameters corresponding to the dominant feature so that the dynamic response characteristics of the target vehicle's seat on the road segment ahead match the dominant feature. The seat control parameters are used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead.

[0081] In one possible implementation, if the dominant feature is the bumpy feature, the cloud server generates a first type of seat control parameters corresponding to the bumpy feature. These first type of seat control parameters are used to enable the seat of the target vehicle to operate in a comfort control mode on the road segment ahead. If the dominant feature is the slippery feature, the cloud server generates a second type of seat control parameters corresponding to the slippery feature. These second type of seat control parameters are used to enable the seat of the target vehicle to operate in a safety control mode on the road segment ahead.

[0082] The first type of seat control parameters consists of a set of control commands or settings specifically generated for the bumpy characteristics of the road ahead. Their main function is to prioritize ride comfort when the vehicle's seat controller encounters bumpy roads. These parameters may include, but are not limited to, the damping coefficient and stiffness setting of the seat dampers, the control strategy parameters of the active suspension, or the inflation / deflation pressure threshold of the seat's internal airbags. The aim is to effectively absorb and isolate road vibrations, reducing the impact and sway felt by the occupants. Comfort control mode is a seat adjustment strategy focused on enhancing the occupant's riding experience. In comfort control mode, the seat system actively or passively adjusts its dynamic response characteristics based on the first type of seat control parameters. For example, it may soften the seat suspension, optimize damping characteristics, or activate active damping functions to minimize the transmission of vibrations caused by uneven road surfaces, thereby providing a smooth and comfortable riding environment for the occupants. The second type of seat control parameters consists of a set of control commands or settings specifically generated for the slippery characteristics of the road ahead. Their function is to guide the seat system to prioritize occupant safety and stability when encountering slippery roads. The second type of seat control parameters may include, but are not limited to, the stiffness of the seat side bolsters, the pressure of the lumbar support, the friction adjustment of the seat cushion, or the trigger threshold for seatbelt pretensioning. These parameters are designed to effectively secure the occupant's body and prevent unnecessary displacement in the seat when the vehicle may skid or experience rapid acceleration / deceleration. The safety control mode is a seat adjustment strategy centered on ensuring occupant safety and body stability. In safety control mode, the target vehicle's seat controller actively or passively adjusts its dynamic response characteristics based on the second type of seat control parameters. For example, it may increase the stiffness of the seat side bolsters and lumbar support, adjust the seat cushion shape to provide better support, or pretension the seatbelt when necessary. This provides stronger body support and fixation for the occupant when the vehicle is traveling on slippery roads, thereby improving occupant safety in emergency situations.

[0083] Through the above implementation method, the system can intelligently determine the main risks or needs of the current road conditions based on the actual road conditions ahead, and generate targeted seat control parameters accordingly. This differentiated parameter generation mechanism allows the target vehicle's seat controller to switch to a comfort control mode (aimed at comfort) or a safety control mode (aimed at safety) depending on whether the dominant characteristic is bumpy or slippery. This solves the problem that traditional seat control solutions struggle to balance comfort and safety when facing complex and changing road conditions, or can only provide a single response with poor versatility. Therefore, the above implementation method can improve the passenger experience under different road conditions, providing better comfort on bumpy roads and stronger body support and safety on slippery roads, thus achieving intelligent and refined seat control strategies.

[0084] To provide a clearer explanation of the above embodiments, the following description is divided into several parts.

[0085] Part 1: When the dominant feature is the bump feature, the cloud server generates a first type of seat control parameters corresponding to the bump feature.

[0086] In one possible implementation, when the dominant characteristic is the bumpy feature, the cloud server determines a continuous ceiling damping coefficient based on the bumpy feature and the vehicle speed information of the road segment ahead. Based on this continuous ceiling damping coefficient, the cloud server determines a lower comfort current limit and an upper comfort current limit, which are used to define the range of control current applied to the seat of the target vehicle in comfort control mode. The cloud server uses the continuous ceiling damping coefficient, the lower comfort current limit, and the upper comfort current limit as the first type of seat control parameters.

[0087] Vehicle speed information refers to data reflecting the current operating conditions of the road ahead, such as predicted vehicle speed. The continuous ceiling damping coefficient is a variable damping coefficient that can continuously change within a certain range based on external inputs (such as road bumpiness and vehicle speed) to control seat vibration. It simulates the characteristics of an ideal "ceiling" damper, providing a smaller damping force when the seat moves upward to allow for a rapid response, and a larger damping force when the seat moves downward to absorb impact energy, thus suppressing vibration while ensuring comfort. The continuous ceiling damping coefficient can be determined using a preset lookup table or mathematical model. This lookup table or mathematical model takes bump characteristics (such as bump level and bump type) and vehicle speed information of the road ahead (such as predicted vehicle speed) as input and outputs the corresponding continuous ceiling damping coefficient value. Alternatively, it can be calculated in real time using adaptive control algorithms, such as fuzzy logic controllers or neural networks, to dynamically adjust the damping coefficient based on bump information and vehicle dynamic information collected by sensors, thereby optimizing the seat's vibration reduction effect. The lower and upper limits of the comfort current are used to define the minimum and maximum permissible values ​​of the drive current of the seat dampers or active suspension system when operating in comfort control mode. The setting of the lower and upper limits of the comfort current aims to ensure that the seat provides comfortable damping while avoiding instability or exceeding design performance due to excessive or insufficient current output. The lower and upper limits of the comfort current can be determined by mapping the continuous ceiling damping coefficient to a preset current range, or by calculating a target control current value based on the continuous ceiling damping coefficient and then adding or subtracting a preset current margin from this target control current value. The function of the lower and upper limits of the comfort current is to ensure that the actual applied control current always remains within this limited range by setting a current limiter in the seat controller, and to clamp it to the upper or lower limit when the calculated control current exceeds this range.

[0088] In some embodiments, when the dominant feature is the bump feature, the first type of seat control parameters (i.e., parameters in the comfort control mode) output by the above seat prediction model can be shown in Table 6 below: Table 6

[0089] Through the above implementation method, by dynamically determining the continuous ceiling damping coefficient and combining it with the setting of the lower and upper limits of the comfort current, the seat can achieve more precise and stable vibration suppression in comfort control mode. This adaptive and constrained parameter generation method improves ride comfort while ensuring the reliability of seat control.

[0090] To provide a clearer explanation of the above embodiments, the following description will be divided into several parts.

[0091] A. The cloud server determines the continuous ceiling damping coefficient based on the bump characteristics and the vehicle speed information of the road ahead.

[0092] In one possible implementation, the vehicle speed information includes the predicted vehicle speed for the road segment ahead. The cloud server determines the bump type and bump level corresponding to the bump feature. Based on the bump type and bump level, the cloud server determines the basic damping coefficient. Based on the predicted vehicle speed for the road segment ahead, the cloud server corrects the basic damping coefficient to obtain the continuous ceiling damping coefficient.

[0093] The predicted vehicle speed directly affects the vibration frequency and amplitude of the seat when the vehicle travels over bumpy road sections. For example, the higher the predicted vehicle speed, the shorter the time the target vehicle takes to traverse the same bumpy road section, potentially resulting in a higher seat vibration frequency and different damping requirements. The predicted vehicle speed can be based on the target vehicle's current speed and acceleration, or it can be the average speed of multiple vehicles traversing the preceding road section. Bump type refers to the unevenness of the road surface, such as continuous small undulations, individual potholes, speed bumps, etc. Bump level indicates the severity or intensity of the bump type, categorized as mild, moderate, severe, etc. Bump type and bump level can be pre-stored in the seat control cloud map or obtained through analysis and classification of raw bump data. The base damping coefficient is a preset damping value based on the inherent bump characteristics (type and level) of the road section, without considering the vehicle's real-time driving status. Different bump types and bump levels have different effects on seat vibration, therefore a corresponding base damping coefficient is needed to provide initial vibration reduction. For example, a lower base damping coefficient is suitable for minor bumps, while a higher base damping coefficient is needed for severe potholes. This base damping coefficient can be established through experimental testing, simulation, or expert experience, creating a mapping table or function model between bump type / level combinations and the base damping coefficient. Correcting the base damping coefficient involves adjusting it based on the predicted vehicle speed of the upcoming road segment, resulting in a continuous ceiling damping coefficient that better suits the current driving conditions. Since the seat vibration response varies when the target vehicle passes the same bumpy road segment at different speeds, a dynamic adjustment based on vehicle speed is necessary. The predicted vehicle speed of the upcoming road segment is used for adjustment because the ceiling damping coefficient controls the vehicle's response characteristics when passing bumpy road features over a future period. The predicted vehicle speed represents the future situation of the target vehicle passing the upcoming road segment, thus providing a better correction effect. The correction process can employ multiplicative correction, additive correction, or a lookup table-based method.

[0094] By incorporating the predicted vehicle speed ahead as part of the vehicle speed information and using it to correct the base damping coefficient, the generated continuous ceiling damping coefficient more accurately reflects the actual vibration response requirements of the seat when the vehicle travels over bumpy roads at different speeds. This avoids the inaccurate control issues caused by relying solely on the road bump characteristics while ignoring vehicle dynamics. Therefore, the seat's vibration damping effect in comfort control mode is improved, better adapting to the vehicle's real-time driving conditions and providing passengers with a more refined and personalized riding comfort experience.

[0095] To provide a clearer explanation of the above implementation methods, the method for determining the base damping coefficient based on the bump type and bump level in the above implementation methods will be described below.

[0096] In one possible implementation, the cloud server obtains the mapping relationship between the combination of bump type and bump level and the damping coefficient. Based on the bump type and bump level, the cloud server determines the basic damping coefficient through this mapping relationship.

[0097] The process of obtaining the mapping relationship between the combination of bump type and bump level and the damping coefficient involves establishing a pre-defined association rule or data structure. This rule or structure maps different combinations of bump types (e.g., speed bumps, potholes, continuous undulating road surfaces) and bump levels (e.g., slight, moderate, severe) to a recommended base damping coefficient value. This mapping relationship can serve as a knowledge base or lookup table, providing a basis for subsequent determination of the base damping coefficient. This mapping relationship can be established in various ways. For example, it can be constructed by collecting and analyzing a large amount of road test data, combined with driver or passenger comfort feedback, using statistical methods or machine learning algorithms. Alternatively, it can be based on vehicle dynamics models and seat damper characteristics, simulating and calculating the optimal damping coefficient under different bump scenarios, and then organizing these calculation results into a mapping table. Determining the base damping coefficient based on the bump type and bump level through this mapping relationship means using the established mapping relationship to obtain the corresponding base damping coefficient value based on the bump type and bump level of the current road segment ahead. The above determination can be a direct lookup operation. Once the bump type and bump level of the upcoming road segment are obtained, the cloud server directly queries a pre-defined mapping table to find the damping coefficient value that matches the combination, which serves as the base damping coefficient. Alternatively, if the mapping relationship is based on a continuous function or a multidimensional interpolation table, the corresponding base damping coefficient can also be calculated using interpolation algorithms (such as linear interpolation, cubic spline interpolation, etc.) based on the current bump type and bump level. The purpose of determining the base damping coefficient is to provide an initial, uncorrected damping value for subsequent corrections to the continuous roof damping coefficient.

[0098] Through the above implementation method, by establishing and utilizing the mapping relationship between bump type and bump level combinations and damping coefficients, the cloud server can determine the basic damping coefficient for a given road segment. This ensures that the initial damping setting is highly correlated with actual road conditions, thereby making the calculation of the continuous ceiling damping coefficient more accurate and effective. The seat's comfort control mode can be optimized to provide occupants with a smoother and more comfortable riding experience, especially in mitigating vibrations caused by various road surface irregularities.

[0099] For example, the cloud server stores a mapping table that associates different bump types and levels with corresponding base damping coefficients. This mapping might include: a base damping coefficient of 0.7 for a "speed bump" with a "moderate" bump level; a base damping coefficient of 1.0 for a "pothole" with a "severe" bump level; a base damping coefficient of 0.4 for a "slight" bump with a "continuous undulation" bump level; and so on. When the cloud server detects that the bump type ahead of the target vehicle is "pothole" with a "moderate" bump level, it queries the mapping table. Based on the query result, the cloud server determines that the corresponding base damping coefficient for this road segment is 0.8. This base damping coefficient of 0.8 will then be used to further calculate the continuous ceiling damping coefficient.

[0100] To provide a clearer explanation of the above implementation method, the following describes how the basic damping coefficient is corrected based on the predicted driving speed of the road segment ahead to obtain the continuous ceiling damping coefficient.

[0101] In one possible implementation, the cloud server determines a correction factor corresponding to the predicted vehicle speed. Based on this correction factor, the cloud server corrects the base damping coefficient to obtain the continuous ceiling damping coefficient.

[0102] The correction coefficient is a numerical factor used to adjust the base damping coefficient. Its determination aims to reflect the influence of predicted vehicle speed on the seat vibration response. The correction coefficient can be a dimensionless scaling factor or a compensation value with specific units. It can be determined based on a pre-established lookup table, which provides corresponding correction coefficients for different speed ranges or specific speed values. Alternatively, it can be calculated using a mathematical function model that takes the predicted vehicle speed as input and outputs the corresponding correction coefficient. Correcting the base damping coefficient means adjusting the initially determined base damping coefficient according to the influence of the predicted vehicle speed to more accurately reflect the damping requirements under actual driving conditions. The correction operation can employ various methods. For example, the base damping coefficient can be multiplied by the correction coefficient. Alternatively, addition or subtraction can be performed, adding or subtracting the correction coefficient from the base damping coefficient. More complex nonlinear functional relationships can also be used for correction to finely adjust the damping characteristics. The obtained continuous ceiling damping coefficient refers to the damping coefficient after correction for predicted vehicle speed. This continuous ceiling damping coefficient is a parameter used to describe the proportional relationship between the damping force and the speed of movement of the seat sprung mass in comfort control mode. Through correction, the obtained continuous ceiling damping coefficient can more accurately adapt to the actual vehicle speed, thereby providing the seat with a damping effect more suitable for the current operating conditions and ensuring good ride comfort at different vehicle speeds.

[0103] Through the above implementation method, the generation of seat control parameters not only considers the road's bump characteristics but also incorporates the target vehicle's real-time speed. By dynamically correcting the base damping coefficient based on the predicted vehicle speed, the final generated continuous ceiling damping coefficient more accurately reflects the vehicle's actual vibration requirements under current driving conditions. This avoids control deviations that may result from relying solely on static bump characteristics, improving the adaptability and accuracy of seat comfort control. Therefore, the seat provides more appropriate vibration damping at different vehicle speeds, enhancing passenger comfort, especially in complex road conditions with frequent speed changes, better suppressing unnecessary vibrations and providing passengers with a smoother and more comfortable riding experience.

[0104] For example, when determining the continuous ceiling damping coefficient, the cloud server looks up a base damping coefficient from a pre-defined mapping table based on the type of bumps (e.g., minor bumps, large undulations) and bump level (e.g., slight, moderate, severe) of the road ahead. To correct this base damping coefficient, the cloud server obtains the predicted vehicle speed for the road ahead. For example, if the predicted vehicle speed for the road ahead is 60 km / h, the cloud server calculates a corresponding correction coefficient from a pre-stored speed-correction coefficient lookup table or through a speed function (e.g., a piecewise linear function or a polynomial function). If this correction coefficient is a scaling factor, such as 0.9 or 1.1, the cloud server multiplies the base damping coefficient by the determined correction coefficient to obtain the final continuous ceiling damping coefficient. For example, if the base damping coefficient is C... base The correction factor is K. speed The continuous ceiling damping coefficient C skyhook =C base ×K speed This correction method allows the seat's damping characteristics to be dynamically adjusted according to the vehicle's actual speed, thus providing more precise comfort control at different speeds.

[0105] B. The cloud server determines the lower and upper limits of the comfort current based on the continuous ceiling damping coefficient.

[0106] In one possible implementation, the cloud server determines the target control current value based on the continuous ceiling damping coefficient. Based on this target control current value, the cloud server determines a preset current margin, which is a current regulation allowance reserved for the target control current value in this comfort control mode. The difference between the target control current value and the preset current margin is determined as the lower limit of the comfort current, and the sum of the target control current value and the preset current margin is determined as the upper limit of the comfort current.

[0107] The target control current value is a reference control current that the seat damper should apply in the ideal comfort control mode, calculated based on the continuous ceiling damping coefficient. It represents the theoretical current required to achieve the desired damping effect under specific road conditions and driving conditions. The target control current value can be obtained by multiplying the continuous ceiling damping coefficient by the moving speed of the seat sprung mass to obtain the target damping force, and then inversely calculating it based on the damping force-current mapping relationship of the damper. The preset current margin is a current adjustment allowance reserved for the target control current value in comfort control mode. Its purpose is to allow the seat control system to adjust the current within a certain range around the target control current value during actual control to cope with subtle changes in road conditions, sensor errors, or actuator response delays, thereby ensuring the stability and robustness of the seat's damping effect. The preset current margin can be a fixed value or a dynamic value determined proportionally to the target control current value or the continuous ceiling damping coefficient. The comfort current lower limit is the minimum control current applied to the seat in comfort control mode. It is obtained by subtracting a preset current margin from the target control current value, and is used to ensure that the control current is not too low even when minimum damping force is required, thereby avoiding damper failure or discomfort. The comfort current upper limit is the maximum control current applied to the seat in comfort control mode, which is obtained by adding a preset current margin to the target control current value, and is used to limit the control current from being too high, thereby avoiding damper overload, excessive energy consumption, or excessively stiff damping effect.

[0108] In some embodiments, the target control current value can be determined by the following formula (3): (3) To ensure that the output current is within a safe and effective range, the actual output control current can be limited using the following formula (4): (4) in, c is the actual output control current. skyhook v is the damping coefficient of the continuous ceiling. sprung Let f be the velocity of the sprung mass of the seat (which can be obtained by integrating the acceleration information). -1 I is the inverse mapping function of damping force-current for a magnetorheological damper. min I max These are the lower limit and upper limit of the comfort current, respectively.

[0109] Through the above implementation method, when the seat is in comfort control mode, a target control current value can be dynamically determined based on the continuous ceiling damping coefficient, and a preset current margin is introduced to set the upper and lower limits of the comfort current. This allows the seat's control current to not only respond to the bumps in the road ahead, but also to have a reasonable adjustment margin near the target value, thereby coping with various uncertainties in actual driving and avoiding discomfort or insufficient damping effect caused by excessively high or low control current. While ensuring ride comfort, the above implementation method improves the flexibility and robustness of seat damping control, enabling the seat's dynamic response characteristics to match road conditions more accurately and stably, thus optimizing the driving experience.

[0110] For example, the cloud server determines a target control current value based on the continuous ceiling damping coefficient. For instance, by consulting a preset damping coefficient-current mapping table or through real-time calculation, the target control current value is determined to be 2.5A. Based on this target control current value, the cloud server can determine a preset current margin. This preset current margin can be a fixed value, such as 0.3A, or a percentage of the target control current value, such as 10% of the target control current value, i.e., 0.25A. With a preset current margin of 0.3A, the lower limit of the comfort current can be calculated as the target control current value of 2.5A minus the preset current margin of 0.3A, resulting in 2.2A. The upper limit of the comfort current can be calculated as the target control current value of 2.5A plus the preset current margin of 0.3A, resulting in 2.8A.

[0111] The second part involves generating a second type of seat control parameters corresponding to the slippery feature when the dominant feature is the slippery feature.

[0112] In one possible implementation, when the dominant feature is the slippery feature, the cloud server determines a safe base current and a safe maximum allowable current based on the slippery feature. The safe base current serves as a reference value for the control current of the target vehicle's seat in the safe control mode, and the safe maximum allowable current limits the upper limit of the control current applied to the target vehicle's seat in the safe control mode. The cloud server uses the safe base current and the safe maximum allowable current as the second type of seat control parameters.

[0113] The safety baseline current refers to the benchmark value of the seat control current in the target vehicle under safety control mode. Its function is to provide a stable, minimum support or response for the seat to address the potential risks posed by slippery road surfaces. The safety maximum allowable current refers to the upper limit of the control current applied to the target vehicle seat under safety control mode. Its function is to limit the seat's response intensity, preventing vehicle instability or driver discomfort that may result from over-response, while ensuring that the seat still provides necessary support in emergency situations. Based on this slippery characteristic, determining the safety baseline current and the safety maximum allowable current aims to quantify the appropriate control range of the seat in safety mode according to the slipperiness of the road section ahead. This ensures that the seat's dynamic response characteristics match the road surface adhesion, thereby improving driving safety. Using the safety baseline current and the safety maximum allowable current as the second type of seat control parameters means that these two parameters together constitute the core instructions for seat safety control on slippery road sections. They are packaged into second-type seat control parameters so that the cloud server can send them to the target vehicle, guiding the target vehicle's seat controller to make accurate and limited current adjustments in safety control mode to achieve the expected safety response characteristics.

[0114] In some embodiments, when the dominant feature is the slippery feature, the second type of seat control parameters (i.e., parameters in the safety control mode) output by the above seat prediction model can be shown in Table 7 below: Table 7

[0115] Through the above implementation method, the cloud server can determine the control current range of the seat in the safety control mode based on the slippery characteristics of the road ahead. Setting a safe baseline current ensures that the seat provides continuous and necessary support on slippery roads, improving driver stability. Simultaneously, the introduction of a safe maximum allowable current limits the upper limit of the seat's response, avoiding potential vehicle instability or driver discomfort caused by over-adjustment. This optimizes the seat's dynamic response characteristics while ensuring driving safety. This method of dynamically adjusting safety control parameters based on slippery characteristics allows the seat control system to adapt more intelligently and accurately to complex road conditions, improving overall vehicle safety and ride comfort when driving on slippery roads.

[0116] To provide a clearer explanation of the above embodiments, the method for determining the safe base current and the safe maximum allowable current based on this slippery characteristic in the above embodiments will be explained below.

[0117] In one possible implementation, the cloud server determines the slipperiness level corresponding to the slipperiness feature. Based on the slipperiness level, the cloud server determines the safe base current and the safe maximum allowable current.

[0118] The slipperiness level refers to a quantified classification of road surface adhesion capacity characterized by slipperiness features. Its function is to transform continuous or complex slipperiness information into discrete, manageable level values, facilitating subsequent current parameter matching. This can be achieved by classifying levels based on the threshold range of the road surface adhesion coefficient. For example, a road surface adhesion coefficient between 0.5 and 0.7 could be classified as slippery level 1, between 0.3 and 0.5 as slippery level 2, and less than 0.3 as slippery level 3. Alternatively, a pre-defined level mapping table can be used to directly classify levels based on the original descriptions of slipperiness features (such as "wet," "water accumulation," or "ice surface"). Determining the safe basic current and the safe maximum allowable current based on the slipperiness level aims to establish an ordered correspondence between slipperiness features and current parameters through this intermediate quantification layer. This ensures that road sections with different slipperiness levels correspond to different levels of current parameters, thereby achieving graded control of seat support under safe control modes.

[0119] Through the above implementation method, the cloud server can systematically extract the information needed to determine the safe current parameters from the slipperiness characteristics using the standardized intermediate value of slipperiness level. The introduction of slipperiness level transforms complex road surface adhesion information into discrete, manageable level identifiers, giving the determination of the safe base current and the safe maximum permissible current a clear hierarchy and explicit basis. Different slipperiness levels correspond to different magnitudes of current parameters, enabling the seat to provide differentiated support responses in safety control mode based on the actual slipperiness of the road section ahead, thereby achieving precise safety control of road surfaces with different slipperiness risk levels.

[0120] To provide a clearer explanation of the above implementation methods, the method for determining the safe base current and the safe maximum allowable current based on the slipperiness level in the above implementation methods will be described below.

[0121] In one possible implementation, the cloud server determines the safe base current based on the slipperiness level and a first mapping relationship, wherein the first mapping relationship is a mapping between the slipperiness level and the safe base current. The cloud server then determines the safe maximum permissible current based on the slipperiness level and a second mapping relationship, wherein the second mapping relationship is a mapping between the slipperiness level and the safe maximum permissible current.

[0122] The first mapping relationship is the correspondence between slip level and safe base current. This first mapping relationship can take several forms. For example, it could be a lookup table pre-stored in a cloud server, where each slip level corresponds to a specific safe base current value. Alternatively, it could be a mathematical function that calculates the corresponding safe base current by taking the slip level as input. The second mapping relationship is the correspondence between slip level and safe maximum permissible current. Similar to the first mapping relationship, the second mapping relationship can also be a lookup table or a mathematical function.

[0123] Through the above implementation method, the cloud server can determine the safe base current and safe maximum allowable current of the seat in the safety control mode based on the slippery characteristics of the road section ahead. This parameter generation method based on slipperiness level and preset mapping relationship allows the seat control parameters to be dynamically matched with the slipperiness of the road surface, avoiding the inaccurate control problems caused by single fixed parameters or fuzzy judgments. Therefore, the target vehicle's seat can obtain a more reasonable and precise support response on slippery road sections, improving the occupant's riding stability and safety when facing changes in road adhesion during vehicle operation.

[0124] For example, slippery road conditions are classified into three levels: mild slippery (Level 1), moderate slippery (Level 2), and severe slippery (Level 3). The cloud server pre-defines a first mapping relationship; for instance, when the slippery level is 1, the safe base current is set to 0.5A. When the slippery level is 2, the safe base current is set to 1.0A. When the slippery level is 3, the safe base current is set to 1.5A. The cloud server also pre-defines a second mapping relationship; for instance, when the slippery level is 1, the safe maximum allowable current is set to 1.0A. When the slippery level is 2, the safe maximum allowable current is set to 1.8A. When the slippery level is 3, the safe maximum allowable current is set to 2.5A. When the cloud server identifies a slippery feature on the road ahead corresponding to a slippery level of moderate slippery (Level 2), it will determine the safe base current to be 1.0A based on the first mapping relationship and the safe maximum allowable current to be 1.8A based on the second mapping relationship.

[0125] 304. The cloud server sends the seat control parameters to the target vehicle so that the target vehicle can control the seat based on the seat control parameters.

[0126] In one implementation, the cloud server can encapsulate the generated seat control parameters into data packets via a standard wireless communication network (such as 4G or 5G) and send them to the target vehicle's onboard communication module. Upon receiving the seat control parameters, the target vehicle's seat controller parses them and adjusts the seat's shock absorber damping, side bolster support, or cushion firmness accordingly to alter the seat's dynamic response characteristics. Alternatively, the cloud server can upload the seat control parameters to a shared cloud platform. Before entering the next road segment, the target vehicle actively downloads the corresponding seat control parameters from this platform. For example, when the cloud server generates a set of seat control parameters for an upcoming curve, it sends them wirelessly to the target vehicle about to enter the curve. Upon receiving the parameters, the target vehicle immediately applies them to adjust the seat.

[0127] In some embodiments, the server may also perform the following steps.

[0128] In one possible implementation, the server obtains seat vibration feedback information and road surface adhesion feedback information reported by the target vehicle after it has passed the road segment ahead. Based on the seat vibration feedback information, the server updates the bump feature corresponding to the road segment ahead in the seat control cloud map, and based on the road surface adhesion feedback information, updates the slippery feature corresponding to the road segment ahead in the seat control cloud map.

[0129] The seat vibration feedback information refers to data from the target vehicle regarding the vibration experienced by its seat during actual driving. This feedback information serves as the basis for evaluating the current seat control effectiveness and road surface roughness. For example, the target vehicle can be equipped with vibration sensors (such as accelerometers) installed on the seat or key parts of the vehicle body to monitor the seat's vibration amplitude, frequency, or acceleration in real time. This raw vibration data can be preliminarily processed at the vehicle end, such as calculating the root mean square (RMS) value, peak value, or performing spectral analysis to extract key indicators characterizing the seat vibration intensity. This processed seat vibration feedback information, along with the vehicle's position information, is reported to the server. Alternatively, the vehicle's seat controller can record the actual control current or damping force applied by the seat actuators on a specific road segment, as well as the actual displacement response of the seat, and report this data as feedback information for the server to analyze the effectiveness of the current control strategy. The road surface adhesion feedback information refers to data from the target vehicle regarding its actual road surface adhesion capability detected on the road segment ahead. This feedback information serves as the basis for evaluating the accuracy of the slippery feature markings in the current seat control cloud map. For example, a target vehicle can use its chassis control system, wheel speed sensors, inertial measurement units, and other sensors to estimate the road surface adhesion coefficient in real time during driving, and report this road surface adhesion coefficient as road surface adhesion feedback information, along with the vehicle's position information, to the server. Alternatively, the vehicle can report indirect indicators such as the triggering frequency of the anti-lock braking system or traction control system on slippery road sections, and the percentage increase in braking distance, as road surface adhesion feedback information, so that the server can assess the actual slipperiness of that road section. Updating the bump feature corresponding to the road section ahead based on this seat vibration feedback information means that the server uses the received actual seat vibration feedback data to correct or adjust the bump feature stored in the seat control cloud map related to the road section ahead, aiming to improve the accuracy and real-time performance of the seat control cloud map data. For example, the server can compare the reported seat vibration feedback information with the bump feature of that road section in the current seat control cloud map. If the feedback information shows that the actual vibration intensity is significantly higher or lower than the intensity expected by the bump feature in the cloud map, the server will adjust the bump feature of that road section accordingly. For example, a weighted average algorithm can be used to fuse new seat vibration feedback information with historical data to smoothly update the bump characteristics. Alternatively, the server can set a threshold; when the difference between the seat vibration feedback information and the existing bump characteristics exceeds this threshold, adjustments to the bump characteristics are triggered, such as increasing or decreasing the bump level. Furthermore, the update process can consider the confidence level of the feedback information; for example, feedback information from multiple vehicles or verified multiple times over different time periods may have higher weighting.Based on the road surface adhesion feedback information, updating the slippery feature corresponding to the road segment ahead in the seat control cloud map means that the server uses the received actual road surface adhesion feedback data to correct or adjust the slippery feature stored in the seat control cloud map related to the road segment ahead. This aims to maintain the slippery feature in sync with the actual road surface conditions and ensure the effectiveness of the safety control mode. For example, the server can compare the reported road surface adhesion feedback information with the slippery feature of the road segment in the current seat control cloud map. If the feedback information shows that the actual road surface adhesion coefficient does not match the adhesion coefficient range corresponding to the slippery feature in the cloud map, the server will adjust the slippery feature of the road segment accordingly. For instance, when the road surface adhesion feedback information indicates that the actual adhesion coefficient of the road segment has returned to a normal level (e.g., greater than or equal to a preset adhesion threshold), but the cloud map still marks it as a slippery road segment, the server can update the slippery feature of the road segment to a non-slippery road segment. Alternatively, when the road surface adhesion feedback information indicates that the actual slipperiness of the road segment is more severe than the slipperiness level marked in the cloud map, the server can raise the slipperiness level to a level that matches the actual adhesion coefficient. The update process can also employ methods such as weighted averaging, threshold triggering, or confidence assessment to ensure the reliability and stability of the update results.

[0130] Through the above implementation methods, the bump characteristics in the seat control cloud map can be continuously calibrated and optimized, and the slippery characteristics can be updated synchronously with the actual road conditions, overcoming the lag and inaccuracy that may exist in static map data. This enables the server to generate more accurate and real-time seat control parameters, thereby ensuring that the target vehicle's seat provides optimal dynamic response characteristics on different road sections, improving passenger comfort, ensuring timely switching to safety control mode on low-adhesion road sections, and enhancing safety control performance in complex road conditions.

[0131] For example, when a target vehicle is traveling on a certain road segment, the server generates and sends out corresponding seat control parameters based on the initial bump characteristics of that segment in the seat control cloud map (e.g., marked as "slight bump"). During the journey, the accelerometer inside the seat detects that the actual vibration intensity is much higher than the expected level for "slight bump," for example, reaching the level of "moderate bump." The target vehicle reports seat vibration feedback information (including actual vibration intensity data and corresponding location information) to the server. Upon receiving this feedback, the server compares it with the original "slight bump" characteristic of that segment in the seat control cloud map. Because the actual feedback shows a higher vibration intensity, the server updates the bump characteristics of that segment to "moderate bump" according to a preset update strategy. When other vehicles or the same vehicle travel through that segment again, the server obtains the updated "moderate bump" characteristic and generates more aggressive vibration damping control parameters accordingly, such as adjusting the continuous ceiling damping coefficient, lower comfort current limit, and upper comfort current limit to provide more effective vibration suppression and thus improve ride comfort. Meanwhile, when the target vehicle travels through the preceding road section, its chassis control system detects an actual road surface adhesion coefficient of 0.6, which is within the normal level for dry roads. However, this section is marked as a slippery section in the seat control cloud map. The target vehicle reports road surface adhesion feedback information (including the actual road surface adhesion coefficient of 0.6 and the corresponding location information) to the server. After receiving this feedback information, the server compares it with a preset adhesion threshold. Finding that the actual adhesion coefficient is greater than the threshold, it indicates that the road section no longer meets the slippery conditions. Therefore, the slippery characteristic of this road section is updated to non-slippery. When other vehicles pass through this road section again, the server will no longer generate the second type of seat control parameters under the safety control mode for this road section. Instead, it will adopt the comfort control mode or the normal mode according to actual needs, thereby avoiding unnecessary safety control intervention and improving the accuracy and rationality of seat control.

[0132] The following is combined with Figure 4 This section explains how to build and update the seat control cloud map on a cloud server.

[0133] See Figure 4 The seat control cloud map is not a traditional navigation map, but a dynamic map that integrates data from multiple vehicles and features road bumps and slippery conditions. Its construction and updating process includes the following steps: 401. Data Acquisition: The cloud server collects driving data from other vehicles passing through road sections. This driving data is also known as historical driving data. Historical driving data is rich in content, including but not limited to: vehicle location information when passing through specific road sections, vehicle motion status information (such as vertical acceleration impact response), road surface information identified by onboard environmental perception sensors, seat vibration information, such as vertical acceleration at the seat rail, etc.

[0134] 402. Feature Extraction: The cloud server utilizes big data analytics to mine this massive amount of data. Through big data analysis of vehicle motion status and seat vibration information, combined with image / point cloud recognition results, road bump features and slippery features are extracted. Bump features include bump type (e.g., "speed bump," "pothole," "wavy road surface," "fly slope"), bump location (latitude and longitude coordinates and range of influence), and bump feature level (e.g., potholes / continuous bumpy road sections can be divided into level one, level two, and level three potholes / continuous bumpy road sections). When more than a certain number of vehicles record abnormal bump features or slippery features near the same location, that location can be identified as a feature point with high confidence. When both bump features and slippery features exist near a location point, the seat control cloud map only outputs the slippery features until the slippery features disappear. 403. Cloud Map Annotation and Updates: The cloud server continuously annotates and updates the seat control cloud map using multi-source data analysis results. Newly collected data is constantly compared, integrated, and verified with existing features in the cloud map, ensuring that the bump and slip characteristics of the cloud map have both high accuracy and strong timeliness, achieving the effect of "becoming more accurate with use".

[0135] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0136] Steps 301-304 above describe the technical solution for the cloud server to generate and send seat control parameters to the target vehicle. The following describes the technical solution for the target vehicle to use these seat control parameters to achieve seat control. (See below for more details.) Figure 5 and Figure 6 Taking the target vehicle as the executing entity as an example, the steps include the following.

[0137] 501. The target vehicle receives seat control parameters sent by the cloud server. These seat control parameters are generated based on the bump and slip features of the road section ahead of the target vehicle marked in the seat control cloud map. The bump features are used to indicate the intensity of seat vibration caused by the road section ahead, and the slip features are used to indicate the road surface adhesion of the road section ahead. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip features.

[0138] The generation and distribution process of seat control parameters are described in steps 301-304 above and will not be repeated here. The target vehicle is equipped with an on-board terminal, and the technical solution on the target vehicle side can be executed by the on-board terminal. In some embodiments, the on-board terminal is the vehicle controller, intelligent driving controller, or seat controller of the target vehicle, etc., and this application embodiment does not limit this.

[0139] 502. Based on the seat control parameters, the target vehicle determines the dynamic response characteristics of the seat on the road segment ahead.

[0140] In one possible implementation, the target vehicle acquires seat control feature information corresponding to the road segment ahead. This seat control feature information indicates the feature type corresponding to the road segment ahead, such as a bumpy or slippery feature. Based on the feature type indicated by the seat control feature information, the target vehicle determines the control mode type to be used by its seat on the road segment ahead. This control mode type includes a comfort control mode and a safety control mode. Based on the control mode type and the seat control parameters, the target vehicle determines the dynamic response characteristics of its seat on the road segment ahead.

[0141] The seat control feature information is data used to indicate the main feature type of the road segment ahead. This information can be an identifier or data field generated by the cloud server when generating seat control parameters, based on the dominant feature (bumpy or slippery). For example, the seat control feature information can be a Boolean flag indicating whether the seat control parameters are optimized for bumpy or slippery roads, or an enumerated value directly representing the feature type. The seat control feature information can be sent as part of the seat control parameters or as a separate control signal to the target vehicle; this application does not limit this. The feature type is a classification of the main characteristics of the road segment ahead, specifically including bumpy or slippery features. Bumpy features indicate the intensity of seat vibration caused by the road segment, while slippery features indicate road surface adhesion. By clearly distinguishing these two feature types, the seat controller can be guided to adopt different response strategies. Determining the control mode type adopted by the target vehicle's seat on the road segment ahead aims to intelligently select the control strategy that the seat should execute on the current road segment ahead, based on the received seat control feature information. For example, if the feature type indicates a bumpy feature, the comfort control mode should be selected. If the feature type indicates slippery conditions, the safety control mode should be selected. Control mode type refers to the two main operating modes adopted by the seat control system based on the characteristics of the road ahead: comfort control mode and safety control mode. Comfort control mode typically focuses on reducing vibration and improving ride comfort. Safety control mode focuses on providing additional support to address the potential risk of vehicle instability on slippery surfaces. Based on this control mode type and the seat control parameters, determining the dynamic response characteristics of the target vehicle's seat on the road ahead involves combining the determined control mode type with the seat control parameters sent from the cloud server to specifically set the seat's dynamic response behavior. For example, in comfort control mode, seat control parameters might be used to adjust the seat's damping performance to optimize ride comfort. In safety control mode, seat control parameters might be used to adjust the seat's support performance to enhance occupant stability.

[0142] Through the above implementation method, when the target vehicle receives seat control parameters, it can simultaneously acquire seat control characteristic information indicating the main characteristics of the road segment ahead. This allows the target vehicle to intelligently select the most suitable control mode type (comfort control mode or safety control mode) based on the dominant characteristics of the actual road conditions (bumpy or slippery), and determine the seat's dynamic response characteristics based on this mode and seat control parameters. This approach avoids the simple application of a single control strategy to all road conditions and solves the problem of potential mismatches in seat control strategies under different road conditions. Therefore, it can improve the intelligence and adaptability of seat control, providing better ride comfort on bumpy roads and more reliable occupant support and protection on slippery roads, thereby comprehensively improving the driving experience and driving safety.

[0143] To provide a clearer explanation of the above implementation methods, the following describes how the target vehicle determines the dynamic response characteristics of its seat on the road segment ahead based on the control mode type and the seat control parameters.

[0144] In one possible implementation, when the control mode type is the comfort control mode, the target vehicle determines the vertical damping response characteristics of the target vehicle's seat on the road segment ahead, based on the seat control parameters. When the control mode type is the safety control mode, the target vehicle determines the lateral support response characteristics and longitudinal support response characteristics of the target vehicle's seat on the road segment ahead, based on the seat control parameters.

[0145] The Comfort Control mode aims to enhance passenger comfort during vehicle operation. Its core function is to absorb and mitigate vibrations and impacts from the road surface by adjusting the seat's responsiveness, thereby reducing the perceived bumps. Comfort Control mode can be achieved by adjusting the seat's damping and stiffness through active or semi-active suspension systems, or by altering the seat's support characteristics via pneumatic or hydraulic systems to accommodate different body types and postures. Vertical damping response characteristics describe the seat's ability to suppress vibrations in the vertical direction and its response pattern. It primarily focuses on how the seat absorbs and dissipates vertical impact energy from the road surface to reduce the vertical vibrations transmitted to the passengers. Vertical damping response characteristics can be achieved by adjusting the damping coefficient of the seat's shock absorbers to control the seat's vertical movement, or by adjusting the stiffness of the seat's internal elastic elements (such as springs and airbags) to change its response to vertical loads. Safety control modes are designed to enhance occupant safety during vehicle operation, especially on slippery surfaces or when the vehicle is dynamically unstable. The core principle is to enhance seat support and restraint to prevent unnecessary displacement during rapid acceleration, deceleration, sharp turns, or skidding, thus maintaining occupant stability. Safety control modes can be implemented by increasing the support of the seat side wings and lumbar region through airbags or electric adjustment mechanisms, or by pre-tensioning seat belts or adjusting the seat back angle to provide stronger longitudinal support. Lateral support response characteristics describe the seat's ability to provide lateral support and restraint to occupants during lateral movements of the vehicle (such as cornering or skidding). They primarily focus on how the seat limits the lateral swaying of the occupant's body to maintain a stable position within the seat. Lateral support response characteristics can be increased by adjusting the inflation level of the seat side wings or the electric clamping force, or by enhancing lateral restraint through the friction of the seat surface material or special shape design. Longitudinal support response characteristics describe the seat's ability to provide longitudinal support and restraint to occupants during longitudinal movements of the vehicle (such as acceleration and deceleration). It primarily focuses on how the seat limits the occupant's fore-and-aft movement to maintain a stable position within the seat. Longitudinal support response characteristics can be improved by adjusting the seat back tilt angle or the degree of lumbar support protrusion to provide stronger back support, or by increasing friction and support for the buttocks through the shape design or material selection of the seat cushion.

[0146] Through the above implementation methods, the target vehicle can intelligently select either a comfort control mode or a safety control mode based on the actual characteristics of the road ahead, and specifically determine the dynamic response characteristics of the seat. On bumpy roads, the seat can focus on providing vertical damping, enhancing the comfort of the occupants. On slippery roads or in scenarios requiring higher safety, the seat can promptly provide reinforced lateral and longitudinal support, limiting the body displacement of the occupants and thus improving driving safety. This dynamically adaptive seat control strategy avoids the problem of traditional seat control systems struggling to balance comfort and safety, allowing the seat's response characteristics to be highly matched with road conditions and driving needs, thereby providing the best riding experience and safety assurance for occupants under different driving conditions.

[0147] To provide a clearer explanation of the above embodiments, the embodiments will be described in several parts below.

[0148] Part 1: When the control mode type is the comfort control mode, the target vehicle determines the vertical damping response characteristics of the target vehicle's seat on the road segment ahead based on the seat control parameters.

[0149] In one possible implementation, when the control mode type is the comfort control mode, the target vehicle obtains the continuous ceiling damping coefficient, lower limit of comfort current, and upper limit of comfort current from the seat control parameters. The continuous ceiling damping coefficient represents the proportional relationship between damping force and seat sprung mass velocity in the comfort control mode. The lower limit of comfort current limits the minimum value of the control current applied in the comfort control mode, and the upper limit of comfort current limits the maximum value of the control current applied in the comfort control mode. The target vehicle obtains the sprung mass velocity of the target vehicle's seat. Based on the continuous ceiling damping coefficient and the sprung mass velocity, the target vehicle determines the target control current. Based on the target control current, the lower limit of comfort current, and the upper limit of comfort current, the target vehicle determines the control current applied to the seat of the target vehicle, which is used to determine the vertical damping response characteristics.

[0150] The continuous ceiling damping coefficient represents the proportional relationship between seat damping force and seat sprung mass movement speed in comfort control mode. The continuous ceiling damping coefficient is an ideal damping control strategy designed to simulate a damper connected to a fixed ceiling, effectively isolating road vibrations. The lower and upper limits of the comfort current limit define the range of control current applied to the seat in comfort control mode. They ensure that the current output of the seat actuator in comfort mode is neither too low (leading to insufficient damping) nor too high (leading to over-suppression or damage). In some embodiments, these can be preset fixed values, for example, a minimum allowable current value and a maximum allowable current value set according to the physical characteristics and safe operating range of the seat actuator. Alternatively, the lower and upper limits of the comfort current limit can be dynamically adjusted based on more refined comfort levels or user preferences included in the seat control parameters to provide a more personalized comfort experience. The sprung mass movement speed of the target vehicle's seat refers to the vertical speed of the passenger-carrying portion of the seat relative to the vehicle body or ground. The sprung mass velocity can be obtained in real time through displacement or acceleration sensors installed on the sprung mass portion of the seat, combined with integral calculations. Alternatively, it can be estimated using suspension sensor data from the vehicle chassis, combined with the seat's dynamic model. The target control current is the theoretically optimal current value that provides ideal damping, calculated based on the continuous ceiling damping coefficient and the sprung mass velocity. The target control current represents the ideal control quantity required to achieve ceiling damping under the current motion state. It can be determined by multiplying the continuous ceiling damping coefficient by the sprung mass velocity to obtain the target damping force, and then deriving the target control current based on the damping force-current mapping relationship of the seat damper. Alternatively, a pre-trained neural network model can be used, inputting the continuous ceiling damping coefficient and the sprung mass velocity, to directly output the target control current. The target control current applied to the seat of the target vehicle is the final actual current applied to the seat actuator, which determines the magnitude of the seat damping force, thus affecting the seat's vertical damping response characteristics. The target control current is obtained by limiting and adjusting the target control current in conjunction with the lower and upper limits of the comfort current. It can be determined by comparing the target control current with the lower and upper limits of the comfort current to limit the target control current within an effective range. Alternatively, a feedback control algorithm such as a PID controller can be used, with the target control current as the setpoint, to adjust the actual applied current in real time to achieve precise damping control.

[0151] Through the above implementation method, in comfort control mode, the target control current can be accurately calculated based on the continuous ceiling damping coefficient and the speed of seat sprung mass movement, and the control current can be limited by combining the lower limit and upper limit of the comfort current. This allows the seat damper to provide ideal vertical damping effect while avoiding instability or discomfort caused by excessive or insufficient control current. Thus, in complex and changing road conditions, it provides passengers with a more stable, smooth, and controlled riding experience, improving ride comfort.

[0152] To provide a clearer explanation of the above implementation methods, the method by which the target vehicle determines the target control current based on the continuous ceiling damping coefficient and the sprung mass movement speed in the above implementation methods will be described below.

[0153] In one possible implementation, the target vehicle determines the target damping force based on the continuous ceiling damping coefficient and the sprung mass movement velocity. The target vehicle acquires a mapping relationship between the damping force and current of the seat damper of the target vehicle. Based on the target damping force and the mapping relationship, the target vehicle determines the target control current.

[0154] The determination of the target damping force refers to the ideal damping force calculated based on the continuous ceiling damping coefficient and the sprung mass velocity. This represents the magnitude of the force that the seat damper should provide under the current comfort control mode to effectively suppress vibrations and improve ride comfort. This target damping force can be determined by directly multiplying the continuous ceiling damping coefficient by the sprung mass velocity, or by consulting a pre-established lookup table. This lookup table takes the continuous ceiling damping coefficient and the sprung mass velocity as input and outputs the corresponding target damping force value. Obtaining the mapping relationship between the damping force and current of the seat damper in the target vehicle refers to obtaining the relationship describing the magnitude of the damping force that the seat damper can generate under different control currents. This mapping relationship can be a mathematical function model, obtained by fitting experimental data, which can calculate the corresponding damping force based on the input current, or vice versa. Alternatively, this mapping relationship can also be a lookup table stored in memory, containing data points on the damping force that the damper can generate under different control current values, obtained through interpolation or direct lookup. Determining the target control current based on the target damping force and the mapping relationship means converting the theoretical damping force requirement into an actually applicable electrical signal to drive the vibration damper to generate the required damping force. If the mapping relationship is a mathematical function, the target control current can be directly calculated by solving the inverse function of the function, using the target damping force as input. If the mapping relationship is a lookup table, the target control current can be obtained by searching for the value closest to the target damping force in the lookup table, or by performing linear or nonlinear interpolation on the data in the lookup table to determine a more accurate target control current.

[0155] The above implementation method can fully consider the actual working characteristics of the seat damper, including its nonlinear response, so that the vertical damping response characteristics of the seat can more accurately match the expected comfort control mode, thereby improving the comfort experience of passengers on bumpy roads.

[0156] For example, in comfort control mode, the continuous ceiling damping coefficient sent by the cloud server is C, and the target vehicle's seat sprung mass movement speed sensor detects a current speed of V. The target vehicle then uses formula F... damping The required target damping force F can be calculated using C × V. damping For example, if C is 1000 Ns / m and V is 0.1 m / s, then the target damping force F damping The target damping force is 100 N. The cloud server queries a pre-stored mapping relationship between the seat shock absorber's damping force and current. This mapping relationship can be a two-dimensional lookup table. By consulting this lookup table, the cloud server determines that when the control current is 0.2A, the damping force generated by the shock absorber is exactly 100N. The cloud server sets 0.2A as the target control current. If the target damping force falls between two values ​​in the lookup table, the cloud server uses linear interpolation or other interpolation methods to determine a more precise control current. For example, if the target damping force is 120N, the cloud server interpolates between 0.2A and 0.3A to obtain a target control current between 0.2A and 0.3A.

[0157] Part Two: When the control mode type is the safety control mode, the target vehicle determines the lateral support response characteristics and longitudinal support response characteristics of the target vehicle's seat on the road segment ahead based on the seat control parameters.

[0158] In one possible implementation, when the control mode type is the safety control mode, the target vehicle obtains the safe base current and the safe maximum allowable current from the seat control parameters. The safe base current serves as a reference value for the control current in the safety control mode, and the safe maximum allowable current limits the upper limit of the control current applied in the safety control mode. Based on the safe base current, the target vehicle determines the basic support response characteristics of the target vehicle's seat on the road segment ahead. These basic support response characteristics include basic lateral support response and basic longitudinal support response. Based on these basic support response characteristics, the target vehicle determines the lateral support response characteristics and the longitudinal support response characteristics.

[0159] The safety baseline current refers to the reference control current value used to drive the seat support mechanism in safety control mode. Its function is to provide a preset, stable basic support force for the seat to cope with potential lateral or longitudinal instability when the vehicle is traveling on slippery roads. The maximum permissible safety current refers to the upper limit of the control current applied to the seat support mechanism in safety control mode. Its function is to limit the output intensity of the seat support mechanism, preventing overload, damage, or discomfort to the occupants due to excessive current, thereby ensuring the safety and reliability of the seat. This maximum permissible safety current can be determined by the hardware design of the seat support mechanism or configured via software according to the vehicle's overall safety strategy or the comfort needs of the occupants. The basic support response characteristic refers to the initial or minimum support effect provided by the seat based on the safety baseline current in safety control mode. This basic support response characteristic is the basis for the seat to provide support when responding to dynamic changes in the vehicle, allowing the occupants to obtain a basic sense of stability even without severe external disturbances. The basic support response characteristic can be manifested as the preset position, preset inflation pressure, or preset support force output of the seat side wings, lumbar support, or leg support components. The basic lateral support response refers to the support effect the seat provides to the occupant against lateral forces, as part of the basic support response characteristics. For example, the support provided by the seat side wings to the occupant's sides when the vehicle is cornering or skidding. This basic lateral support response can be achieved by adjusting the stiffness, position, or inflation level of the seat side wings. The basic longitudinal support response refers to the support effect the seat provides to the occupant against longitudinal forces, as part of the basic support response characteristics. For example, the support provided by the seat's lumbar or leg supports to the occupant's fore-and-aft position when the vehicle is accelerating or braking. This basic longitudinal support response can be achieved by adjusting the position or stiffness of the seat's lumbar and leg supports. Determining these lateral and longitudinal support response characteristics means, based on the basic support response characteristics, ultimately determining the specific lateral and longitudinal support performance that the seat should provide under the current safety control mode. This can be achieved by directly using the basic support response characteristics as the final support characteristics, or by further adjusting and optimizing them based on other real-time information (such as vehicle dynamic parameters) to create a support effect more adapted to the current driving conditions.

[0160] Through the above implementation method, when the target vehicle is traveling on a slippery road section and enters the safety control mode, the safe basic current and the safe maximum allowable current can be extracted from the seat control parameters, and based on these, the basic support response characteristics of the seat can be determined. This allows the seat to provide stable, controllable, and safe initial support, solving the technical problem of how to establish the basic support level of the seat on slippery road sections. At the same time, by setting the safe maximum allowable current, the safety of the support process is ensured, avoiding discomfort to the occupants or damage to the seat mechanism, thereby improving the safety and comfort of the occupants in complex road conditions.

[0161] To provide a clearer explanation of the above embodiments, the method for determining the lateral support response characteristics and the longitudinal support response characteristics based on the basic support response characteristics in the above embodiments will be described below.

[0162] In one possible implementation, the target vehicle acquires its driving dynamic parameters. Based on these driving dynamic parameters, the target vehicle determines enhanced support response characteristics, including enhanced lateral support response and enhanced longitudinal support response. Based on the basic support response characteristics and the enhanced support response characteristics, the target vehicle determines the lateral support response characteristics and the longitudinal support response characteristics.

[0163] Among them, driving dynamic parameters refer to the physical quantities that change in real time during vehicle operation, reflecting the vehicle's motion state and force conditions. The role of driving dynamic parameters is to assess the vehicle's dynamic behavior in real time, providing a basis for dynamic adjustments to the seat's support response. For example, driving dynamic parameters can be collected in real time through the vehicle's built-in inertial measurement unit (IMU), wheel speed sensors, steering angle sensors, and other sensor systems, or by obtaining relevant data from modules such as the vehicle's Electronic Stability Program (ESP) or Anti-lock Braking System (ABS) via the vehicle's Controller Area Network (CAN) bus. Enhanced support response characteristics refer to support characteristics that are dynamically adjusted and strengthened based on the vehicle's real-time driving dynamic parameters, building upon the basic support response characteristics. Their function is to provide additional, adaptive support force when the vehicle undergoes drastic dynamic changes, better securing occupants and improving safety and comfort. Enhanced support response characteristics can be achieved by adjusting the inflation and deflation of the seat's internal airbags, the extension and retraction of the motor-driven side wings or lumbar support mechanisms, or by changing the damping force or stiffness of the seat's shock absorbers to provide stronger support in specific directions. Enhanced lateral support response refers to the additional lateral support the seat provides to occupants when the vehicle undergoes lateral movement (such as cornering or skidding), designed to prevent excessive displacement of the occupants under lateral forces and maintain body stability. Enhanced longitudinal support response refers to the additional longitudinal support the seat provides to occupants when the vehicle undergoes longitudinal movement (such as acceleration or braking), designed to prevent excessive forward or backward leaning of the occupants under longitudinal forces and maintain body stability. These two enhanced responses are typically achieved through tightening the seat side bolsters or inflating the airbags, as well as adjusting the seat's lumbar or shoulder support.

[0164] Through the above implementation methods, in the safety control mode, the lateral and longitudinal support response characteristics of the seat not only provide basic support based on a preset safety baseline current, but can also be dynamically adjusted according to the real-time driving dynamic parameters of the target vehicle. For example, by acquiring driving dynamic parameters and determining enhanced support response characteristics, the seat can provide additional, adaptive support force according to the actual movement state of the vehicle (such as sharp turns, rapid acceleration, and rapid deceleration). This makes the seat's support response no longer static and fixed, but able to adapt to the dynamic changes of the vehicle in real time, thereby improving the body stability of the occupants in complex or emergency driving situations, preventing excessive body swaying or displacement, and enhancing the safety and comfort of the ride.

[0165] To provide a clearer explanation of the above implementation methods, the following describes the method for determining the enhanced support response characteristics based on the driving dynamic parameters in the above implementation methods.

[0166] In one possible implementation, the driving dynamics parameters include at least one of lateral acceleration, longitudinal acceleration, and yaw rate. If the lateral acceleration exceeds a preset lateral threshold, the target vehicle determines a first enhanced current corresponding to the lateral acceleration. If the longitudinal acceleration exceeds a preset longitudinal threshold, the target vehicle determines a second enhanced current corresponding to the longitudinal acceleration. If the yaw rate exceeds a preset yaw threshold, the target vehicle determines a third enhanced current corresponding to the yaw rate. The target vehicle determines the enhanced support response characteristics based on at least one of the first, second, and third enhanced currents.

[0167] Lateral acceleration refers to the acceleration component of the target vehicle in the horizontal direction perpendicular to its forward direction, reflecting the vehicle's lateral movement tendency, such as turning or sideslipping. When lateral acceleration exceeds a certain threshold, it indicates that the target vehicle is experiencing a large lateral force, and the occupants' bodies may undergo lateral displacement. In this case, the seat needs to provide lateral support to stabilize the occupants. Lateral acceleration can be directly measured by the vehicle's inertial measurement unit (IMU) or calculated and output by the vehicle's chassis control system (such as ESP / ESC). Longitudinal acceleration refers to the acceleration component of the target vehicle in the forward direction, reflecting the vehicle's acceleration or deceleration tendency. When longitudinal acceleration exceeds a certain threshold, it indicates that the target vehicle is experiencing rapid acceleration or deceleration, and the occupants' bodies may undergo fore-and-aft displacement. In this case, the seat needs to provide longitudinal support to stabilize the occupants. Longitudinal acceleration can be directly measured by the vehicle's inertial measurement unit (IMU) or calculated and output by the vehicle's engine management system (EMS) or braking system (ABS). Yaw rate refers to the angular velocity of the target vehicle rotating about its vertical axis, reflecting the vehicle's steering or rotation tendency. When the yaw rate exceeds a certain threshold, it indicates that the target vehicle is undergoing rapid steering or may be spinning uncontrollably. The occupants' bodies may be affected by rotational inertial forces, requiring the seat to provide comprehensive support to stabilize them. The yaw rate can be directly measured by the vehicle's inertial measurement unit (IMU) or calculated and output by the vehicle's steering or chassis control system. The preset lateral threshold is a pre-set lateral acceleration value. When the actual lateral acceleration exceeds the preset lateral threshold, the seat's lateral reinforcement support mechanism is triggered, serving as a critical condition for determining whether the seat intervention is needed to enhance lateral support in the vehicle's lateral dynamics. The preset lateral threshold can be calibrated based on vehicle type, seat characteristics, and safety and comfort requirements through experimental testing, driver subjective evaluation, or simulation analysis. It can also be dynamically adjusted according to the vehicle's driving mode (e.g., Sport mode, Comfort mode) or driver preferences. The preset longitudinal threshold is a pre-set longitudinal acceleration value. When the actual longitudinal acceleration exceeds the preset longitudinal threshold, the seat's longitudinal reinforcement support mechanism is triggered, serving as a critical condition for determining whether the seat intervention is needed to enhance lateral support in the vehicle's longitudinal dynamics. The preset longitudinal threshold can be calibrated based on vehicle type, seat characteristics, and safety and comfort requirements through experimental testing, driver subjective evaluation, or simulation analysis. It can also be dynamically adjusted according to the vehicle's driving mode or driver preferences. The preset yaw threshold is a pre-set yaw rate value. When the actual yaw rate exceeds the preset threshold, the seat's enhanced support mechanism is triggered, serving as a critical condition for determining whether seat intervention is needed to enhance support during vehicle rotation. This preset yaw threshold can also be calibrated based on vehicle type, seat characteristics, and safety and comfort requirements through experimental testing, driver subjective evaluation, or simulation analysis. It can also be dynamically adjusted according to the vehicle's driving mode or driver preferences.The first enhancement current is an additional control current provided to the seat support mechanism (such as airbags, motors, etc.) when the lateral acceleration exceeds a preset lateral threshold. It enhances the lateral support force, directly driving the seat's actuators to provide lateral support matching the intensity of the lateral acceleration. The first enhancement current can be calculated based on the amplitude of the lateral acceleration using a preset mapping curve or functional relationship, or it can be a fixed value. The second enhancement current is an additional control current provided to the seat support mechanism when the longitudinal acceleration exceeds a preset longitudinal threshold. It enhances the longitudinal support force, directly driving the seat's actuators to provide longitudinal support matching the intensity of the longitudinal acceleration. The second enhancement current can be calculated based on the amplitude of the longitudinal acceleration using a preset mapping curve or functional relationship, or it can be a fixed value. The third enhancement current is an additional control current provided to the seat support mechanism when the yaw rate exceeds a preset yaw threshold. It enhances the overall support force, directly driving the seat's actuators to provide overall support matching the intensity of the yaw rate. The third enhancement current can be calculated based on the amplitude of the yaw rate using a preset mapping curve or functional relationship, or it can be a fixed value. Enhanced support response characteristics refer to the additional support force or posture adjustment generated by the seat's support structure (such as side wings, lumbar support, leg rests, etc.) in response to dynamic changes in the vehicle after receiving enhanced current. Building upon basic support response, enhanced support response characteristics further improve the fixation and protection of the occupant's body to cope with drastic dynamic changes in the vehicle. Enhanced support response characteristics can be achieved by adjusting the inflation volume of airbags or the position of motors in the seat's side wings, lumbar support, and leg rests, changing the shape and stiffness of the support surface, or by increasing the restraint force on the occupant's body through the seat's internal hydraulic or electromagnetic damping system.

[0168] In some embodiments, under the safety control mode, the enhanced current triggered based on vehicle driving dynamic parameters can be as shown in Table 8 below: Table 8

[0169] Through the above implementation methods, it is possible to identify high-dynamic situations of the target vehicle, such as sharp turns, rapid acceleration, rapid deceleration, or instability. Based on these dynamic parameters, the corresponding enhanced current can be determined, thereby enabling the enhanced support response characteristics of the seat to match the actual dynamic changes of the vehicle in a timely, accurate, and quantitative manner. This avoids the possibility of insufficient or excessive support that may result from judging solely based on vague "driving dynamic parameters," improving the seat's support effect and safety for occupants under complex or emergency driving conditions. It allows occupants to maintain a stable posture even during violent vehicle movements, reducing body displacement and discomfort caused by inertial forces.

[0170] To provide a clearer explanation of the above embodiments, the method for determining the enhanced support response characteristics based on at least one of the first enhanced current, the second enhanced current, and the third enhanced current in the above embodiments will be described below.

[0171] In one possible implementation, the target vehicle sums the triggering terms from the first, second, and third enhanced currents to obtain a total enhanced current. If the total enhanced current does not exceed the maximum permissible safe current, the target vehicle determines the enhanced support response characteristics based on the total enhanced current. If the total enhanced current exceeds the maximum permissible safe current, the target vehicle determines the enhanced support response characteristics based on the maximum permissible safe current.

[0172] The first, second, and third enhanced currents are calculated based on the vehicle's lateral acceleration, longitudinal acceleration, and yaw rate, respectively, when these parameters exceed preset thresholds. They represent the incremental control current required to provide additional support under specific dynamic conditions. The triggering term refers to which of the first, second, and third enhanced currents are actually calculated and considered because their corresponding dynamic parameters exceed preset thresholds. For example, if only lateral acceleration exceeds the threshold, only the first enhanced current is considered a triggering term, ensuring that the corresponding enhanced current is considered only when actually needed, avoiding unnecessary calculations and controls. The total enhanced current is obtained by summing all triggered first, second, and third enhanced currents. This total enhanced current reflects the total control current requirement for providing enhanced support under the current vehicle dynamic conditions. This summation can be a simple arithmetic sum or a weighted sum based on the weights of different dynamic parameters' impact on the occupants. The maximum permissible safe current is a preset upper limit used to restrict the control current applied to the seat in safe control mode. The enhanced support response characteristics are determined based on the total enhanced current. When the accumulated total enhanced current does not exceed the maximum permissible safe current, this total enhanced current is directly used to determine the seat's enhanced support response characteristics. This means the seat can provide the required support exactly according to the current dynamic needs, such as by adjusting the seat side wings, lumbar support, or seat cushion inflation to enhance the support effect. This determination method is usually achieved through a preset current-support response mapping relationship. The enhanced support response characteristics are also determined based on the maximum permissible safe current. When the accumulated total enhanced current exceeds the maximum permissible safe current, the total enhanced current is no longer used; instead, the maximum permissible safe current is used as a reference to determine the enhanced support response characteristics. This is a safety protection mechanism that ensures the seat's control current does not exceed the safe range, even under extreme driving conditions. In this case, the seat will provide its maximum possible support within safety limits to maximize occupant safety and comfort while avoiding overload.

[0173] In some embodiments, the total enhanced current corresponding to the enhanced support response described above can be determined by the following formula (5): (5) The actual control current in safe mode can be determined by the following formula (6), where the maximum allowable safe current is used as the upper limit: (6) in, This is the actual control current in safety mode. For the total boost current, For the safety mode base current, ΔI side ΔI long ΔI yaw These are the enhanced currents triggered by lateral acceleration, longitudinal acceleration, and yaw rate, respectively. This is the maximum permissible current in safe mode. Exceeding the maximum permissible current in safe mode may cause magnetic saturation of the damper, and further increasing the current will no longer significantly improve the damping force.

[0174] Through the above implementation method, by accumulating all triggered enhanced currents and comparing and limiting them with the safe maximum permissible current, it is ensured that the control current of the seat is always within a safe and acceptable range when providing enhanced support response. This not only protects the seat actuator from overload damage and extends its service life, but more importantly, it avoids discomfort or potential safety risks to occupants caused by excessive support. While maintaining vehicle driving safety, the occupant's riding experience is optimized, enabling the seat's dynamic response to respond promptly and effectively to changes in vehicle posture while also considering occupant comfort and safety, thereby improving the overall riding quality.

[0175] For example, when a target vehicle performs an emergency avoidance maneuver, a lateral acceleration of 0.6g is detected, exceeding a preset lateral acceleration threshold (e.g., 0.4g). Simultaneously, due to vehicle deceleration, the longitudinal acceleration also reaches -0.3g, exceeding a preset longitudinal acceleration threshold (e.g., -0.2g), while the yaw rate does not reach its preset threshold. Based on a preset mapping relationship, the target vehicle calculates a first enhanced current of 500mA corresponding to the 0.6g lateral acceleration and a second enhanced current of 300mA corresponding to the -0.3g longitudinal acceleration. Since the yaw rate is not triggered, the third enhanced current is 0mA. The target vehicle accumulates the triggered first, second, and third enhanced currents, resulting in a total enhanced current of 500mA + 300mA + 0mA = 800mA. With a maximum permissible safe current of 700mA, since the calculated total enhanced current of 800mA exceeds the maximum permissible safe current of 700mA, the target vehicle will no longer use 800mA. Instead, the enhanced support response characteristics of the seat will be determined based on the maximum permissible safe current of 700mA. This means the seat will adjust its lateral and longitudinal support to provide the maximum safe support corresponding to the 700mA current. For example, the side wings and lumbar support will be inflated to the maximum safe level corresponding to 700mA, rather than the stronger but potentially unsafe support that 800mA might theoretically result in. In this way, even when multiple dynamic parameters simultaneously trigger high-intensity support demands, the seat can provide effective and safe support, avoiding over-control.

[0176] 503. The target vehicle controls the seat of the target vehicle according to the dynamic response characteristics.

[0177] The target vehicle seat is a magnetorheological seat or other seat that can be actively adjusted; however, this application embodiment does not limit this.

[0178] Through the above implementation method, the target vehicle can pre-adjust the seat's dynamic response characteristics based on comprehensive road condition information of the road segment ahead. When the road segment has both bumpy and slippery characteristics, the control mode corresponding to the dominant characteristic is intelligently selected based on the seat control parameters issued by the cloud server, achieving the best balance between comfort and safety. For example, on a moderately bumpy and low-adhesion road segment, a safety control mode is prioritized, while damping parameters are appropriately adjusted to ensure vehicle stability without excessively sacrificing comfort. Compared to traditional solutions that rely solely on a single dimension of road condition information, the seat control parameters generated by comprehensively considering bumpy and slippery characteristics enable the seat's dynamic response characteristics to fully adapt to complex road environments, resolving the parameter conflict problem caused by independent control objectives in related technologies.

[0179] Figure 7 This is a schematic diagram of the structure of a seat control device provided in an embodiment of this application. See also... Figure 7 The device includes: The acquisition module 701 is used to acquire the bump and slip features of the road section ahead of the target vehicle in the seat control cloud map. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip features. The bump feature is used to represent the intensity of seat vibration caused by the road section ahead, and the slip feature is used to represent the road surface adhesion of the road section ahead.

[0180] The generation module 702 is used to generate seat control parameters corresponding to the road segment ahead based on the bumpy characteristics and the slippery characteristics. The seat control parameters are used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead.

[0181] The sending module 703 is used to send the seat control parameters to the target vehicle so that the target vehicle can control the seat based on the seat control parameters.

[0182] It should be noted that the seat control device provided in the above embodiments is only illustrated by the division of the above functional modules when controlling the seat. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the vehicle can be divided into different functional modules to complete all or part of the functions described above. In addition, the seat control device and the seat control method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0183] Figure 8 This is a schematic diagram of another seat control device provided in an embodiment of this application. See also... Figure 8 The device includes: The receiving module 801 is used to receive seat control parameters sent by the cloud server. These seat control parameters are generated based on the bump and slip features of the road section ahead of the target vehicle marked in the seat control cloud map. The bump features are used to indicate the intensity of seat vibration caused by the road section ahead, and the slip features are used to indicate the road surface adhesion of the road section ahead. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip features.

[0184] The control module 802 is used to determine the dynamic response characteristics of the target vehicle's seat on the road section ahead based on the seat control parameters, and to control the target vehicle's seat according to the dynamic response characteristics.

[0185] It should be noted that the seat control device provided in the above embodiments is only illustrated by the division of the above functional modules when controlling the seat. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the vehicle can be divided into different functional modules to complete all or part of the functions described above. In addition, the seat control device and the seat control method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0186] Figure 9 This is a schematic diagram of a cloud server structure provided in an embodiment of this application. The cloud server 900 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 901 and one or more memories 902. The one or more memories 902 store at least one computer program, which is loaded and executed by the one or more processors 901 to implement the methods provided in the various method embodiments described above. Of course, the cloud server 900 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The cloud server 900 may also include other components for implementing device functions, which will not be elaborated upon here.

[0187] Figure 10 This is a structural block diagram of a vehicle provided in an embodiment of this application, such as... Figure 10 As shown, the vehicle includes: processor 1010, memory 1020, communication interface 1030 and bus 1040.

[0188] Processor 1010 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. Processor 1010 is the control center of the vehicle, connecting various parts of the vehicle through various interfaces and wiring.

[0189] The memory 1020 may include read-only memory (ROM) and random access memory (RAM), and provides instructions and data to the processor 1010. A portion of the memory 1020 may also include non-volatile random access memory (NVRAM). For example, the memory 1020 may also store device type information, operating system, applications, etc. The memory 1020 stores a computer program that can be executed by the processor 1010. When the computer program 1021 is executed by the processor 1010, it implements the method steps or functional modules described in the above embodiments of this application.

[0190] The communication interface 1030 is used for communication with other devices. For example, when vehicle 1000 acts as an in-vehicle terminal, the communication interface 1030 is used to interact with a cloud server via a mobile communication network (such as 4G / 5G), upload vehicle status information and sensor data, and receive bump events and seat control model configuration parameters from the cloud. When vehicle 1000 acts as a cloud server, the communication interface 1030 is used to communicate with multiple in-vehicle terminals via the Internet, collect driving data from all vehicles, and issue predictive control commands.

[0191] Bus 1040 is used to connect and communicate between processor 1010, memory 1020, and communication interface 1030. In addition to the data bus, bus 1040 may also include a power bus, control bus, and status signal bus, etc. However, for clarity, all buses are labeled as bus 1040 in the figure.

[0192] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including a computer program that can be executed by a processor to perform the seat control method in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0193] In an exemplary embodiment, a computer program product or computer program is also provided, which includes program code stored in a computer-readable storage medium. The vehicle's processor reads the program code from the computer-readable storage medium and executes the program code, causing the vehicle to perform the above-described seat control method.

[0194] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0195] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A seat control method, characterized in that, The method, executed by a cloud server, includes: Obtain the bump and slip features of the road section ahead of the target vehicle in the seat control cloud map. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip features. The bump features are used to indicate the intensity of seat vibration caused by the road section ahead, and the slip features are used to indicate the road surface adhesion of the road section ahead. Based on the bumpy characteristics and the slippery characteristics, seat control parameters corresponding to the road segment ahead are generated. The seat control parameters are used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead. The seat control parameters are sent to the target vehicle so that the target vehicle can control the seat based on the seat control parameters.

2. The method according to claim 1, characterized in that, The acquisition of bump and slippery features of the road section ahead of the target vehicle in the seat control cloud map includes: Based on the location information reported by the target vehicle, the road segment ahead of the target vehicle is determined; From the seat control cloud map, obtain the bump characteristics and slippery characteristics at the location corresponding to the road segment ahead.

3. The method according to claim 1, characterized in that, The step of generating seat control parameters corresponding to the road segment ahead based on the bumpy and slippery characteristics includes: The dominant feature is determined from the bumpy feature and the slippery feature; Generate seat control parameters corresponding to the dominant feature so that the dynamic response characteristics of the target vehicle's seat on the road segment ahead match the dominant feature.

4. The method according to claim 3, characterized in that, The process of determining the dominant feature from the bumpy feature and the slippery feature includes: Obtain the vibration intensity level corresponding to the bumpy feature and the road surface adhesion level corresponding to the slippery feature; Based on a preset priority comparison rule between the vibration intensity level and the road surface adhesion level, the one with higher priority between the bumpy feature and the slippery feature is determined as the dominant feature.

5. The method according to claim 4, characterized in that, The method, based on a preset priority comparison rule between the vibration intensity level and the road surface adhesion level, determines the dominant feature as the one with higher priority between the bumpy feature and the slippery feature, including: Based on the vibration intensity level and the road surface adhesion level, determine whether the preset condition that the priority of the bumpy feature is higher than the priority of the slippery feature is met. Under the condition that the preset conditions are met, the bumpy feature is determined as the dominant feature; If the preset conditions are not met, the slippery feature is determined as the dominant feature.

6. The method according to claim 5, characterized in that, The step of determining whether the preset condition, based on the vibration intensity level and the road surface adhesion level, the priority of the bumpy feature is higher than that of the slippery feature includes: Determine whether the vibration intensity level reaches the preset intensity level, and whether the road surface adhesion level is within the preset adhesion threshold range; If the vibration intensity level reaches the preset intensity level and the road surface adhesion level is within the preset adhesion threshold range, it is determined that the preset condition is met. If the vibration intensity level does not reach the preset intensity level, or the road surface adhesion level is not within the preset adhesion threshold range, it is determined that the preset condition is not met.

7. The method according to claim 3, characterized in that, The generation of the seat control parameters corresponding to the dominant feature includes: When the dominant feature is the bumpy feature, a first type of seat control parameters corresponding to the bumpy feature are generated. The first type of seat control parameters are used to enable the seat of the target vehicle to operate in a comfort control mode on the road section ahead. When the dominant feature is the slippery feature, a second type of seat control parameter corresponding to the slippery feature is generated. The second type of seat control parameter is used to enable the seat of the target vehicle to operate in a safety control mode on the road section ahead.

8. The method according to claim 7, characterized in that, When the dominant feature is the bump feature, generating a first type of seat control parameters corresponding to the bump feature includes: When the dominant feature is the bumpy feature, the continuous canopy damping coefficient is determined based on the bumpy feature and the vehicle speed information of the road section ahead. Based on the continuous ceiling damping coefficient, a lower limit and an upper limit of the comfort current are determined. The lower limit and the upper limit of the comfort current are used to limit the range of control current applied to the seat of the target vehicle in the comfort control mode. The continuous ceiling damping coefficient, the lower limit of the comfort current, and the upper limit of the comfort current are used as the control parameters for the first type of seat.

9. The method according to claim 8, characterized in that, The vehicle speed information includes the predicted vehicle speed of the road segment ahead. The determination of the continuous ceiling damping coefficient based on the bump characteristics and the vehicle speed information of the road segment ahead includes: Determine the bump type and bump level corresponding to the bump characteristics; Based on the bump type and bump level, determine the basic damping coefficient; Based on the predicted vehicle speed of the road ahead, the basic damping coefficient is corrected to obtain the continuous ceiling damping coefficient.

10. The method according to claim 9, characterized in that, The determination of the base damping coefficient based on the bump type and the bump level includes: Obtain the mapping relationship between the combination of bump type and bump level and the damping coefficient; Based on the bump type and the bump level, the basic damping coefficient is determined through the mapping relationship.

11. The method according to claim 9, characterized in that, The method of correcting the basic damping coefficient based on the predicted vehicle speed of the road segment ahead to obtain the continuous ceiling damping coefficient includes: Based on the predicted vehicle speed, determine the correction coefficient corresponding to the predicted vehicle speed; Based on the correction factor, the basic damping coefficient is corrected to obtain the continuous ceiling damping coefficient.

12. The method according to claim 8, characterized in that, The determination of the lower and upper limits of the comfort current based on the continuous ceiling damping coefficient includes: The target control current value is determined based on the continuous ceiling damping coefficient. Based on the target control current value, a preset current margin is determined. The preset current margin is the current adjustment margin reserved for the target control current value in the comfort control mode. The difference between the target control current value and the preset current margin is determined as the lower limit of the comfort current, and the sum of the target control current value and the preset current margin is determined as the upper limit of the comfort current.

13. The method according to claim 7, characterized in that, When the dominant feature is the slippery feature, generating a second type of seat control parameters corresponding to the slippery feature includes: When the dominant feature is the slippery feature, a safe base current and a safe maximum allowable current are determined based on the slippery feature. The safe base current is used as a reference value for the control current of the target vehicle's seat in the safe control mode, and the safe maximum allowable current is used to limit the upper limit of the control current applied to the target vehicle's seat in the safe control mode. The safe base current and the safe maximum allowable current are used as the control parameters for the second type of seat.

14. The method according to claim 13, characterized in that, The determination of the safe base current and the safe maximum permissible current based on the slippery characteristic includes: Determine the slipperiness level corresponding to the slippery feature; Based on the slipperiness level, the safe base current and the safe maximum allowable current are determined.

15. The method according to claim 1, characterized in that, The method for generating the seat control cloud map includes: Receive driving data reported by at least one vehicle when it travels through a road segment, the driving data including seat vibration information and road surface adhesion coefficient information; Based on the seat vibration information, the bump characteristics corresponding to the road segment are generated; Based on the road surface adhesion coefficient information, the slippery feature corresponding to the road segment is generated; The bumpy features and the slippery features are simultaneously marked on the cloud map data at the locations corresponding to the road segments to obtain the seat control cloud map.

16. The method according to claim 15, characterized in that, The step of generating the bump features corresponding to the road segment based on the seat vibration information includes: Based on the seat vibration information, the seat vibration intensity of the road section is determined; Based on the driving speed of the at least one vehicle when it passes through the road section, the vibration intensity of the seat is normalized to obtain the normalized vibration intensity. Based on the normalized vibration intensity, the bump characteristics corresponding to the road segment are determined.

17. The method according to claim 16, characterized in that, The normalization of the seat vibration intensity based on the driving speed of the at least one vehicle when traveling through the road segment, to obtain the normalized vibration intensity, includes: Determine the ratio of the speed of the at least one vehicle when it travels through the road segment to a reference speed, wherein the reference speed is a reference value used to uniformly convert the seat vibration intensity at different driving speeds to the same speed reference. Based on the speed ratio, the vibration intensity of the seat is normalized to obtain the normalized vibration intensity.

18. The method according to claim 17, characterized in that, The normalization of the seat vibration intensity based on the velocity ratio to obtain the normalized vibration intensity includes: Obtain the vehicle speed influence index corresponding to the bump type of the road segment. The vehicle speed influence index is used to represent the degree of influence of driving speed on seat vibration intensity under different bump types. Based on the speed ratio and the vehicle speed influence index, the seat vibration intensity is normalized to obtain the normalized vibration intensity.

19. The method according to claim 15, characterized in that, The step of generating the slippery feature corresponding to the road segment based on the road surface adhesion coefficient information includes: Based on the road surface adhesion coefficient information, the road surface adhesion coefficient of the road segment is obtained; When the road surface adhesion coefficient is greater than or equal to a preset adhesion threshold, a slippery feature is generated to indicate that the road segment is a non-slippery road segment; When the road surface adhesion coefficient is less than the preset adhesion threshold, the slipperiness level of the road segment is determined based on the road surface adhesion coefficient, and the slipperiness feature carrying the slipperiness level is generated.

20. The method according to claim 19, characterized in that, Before obtaining the road surface adhesion coefficient of the road segment based on the road surface adhesion coefficient information, the method further includes: Acquire road surface images captured by at least one vehicle while it is traveling through the road segment; Visual feature recognition is performed on the road surface image to determine whether the road segment contains visual features that suggest it is slippery. If the road segment is identified as containing the suspected slippery visual features, the step of obtaining the road surface adhesion coefficient of the road segment based on the road surface adhesion coefficient information is triggered.

21. The method according to claim 1, characterized in that, The method further includes: Obtain seat vibration feedback information and road surface adhesion feedback information reported by the target vehicle after it has passed the road section ahead; Based on the seat vibration feedback information, the bump features corresponding to the road segment ahead in the seat control cloud map are updated, and based on the road surface adhesion feedback information, the slippery features corresponding to the road segment ahead in the seat control cloud map are updated.

22. A seat control method, characterized in that, Performed by the target vehicle, the method includes: The system receives seat control parameters from a cloud server. These parameters are generated based on bump and slip features of the road ahead of the target vehicle, which are marked in the seat control cloud map. The bump features represent the intensity of seat vibration caused by the road ahead, and the slip features represent the road surface adhesion of the road ahead. The seat control cloud map is cloud map data that simultaneously marks the road segment with bump and slip features. Based on the seat control parameters, the dynamic response characteristics of the target vehicle's seat on the road section ahead are determined, and the target vehicle's seat is controlled according to the dynamic response characteristics.

23. The method according to claim 22, characterized in that, Before determining the dynamic response characteristics of the target vehicle's seat on the road segment ahead based on the seat control parameters, the method further includes: Obtain seat control feature information corresponding to the road segment ahead, wherein the seat control feature information is used to indicate the feature type corresponding to the road segment ahead, and the feature type is bumpy feature or slippery feature; The step of determining the dynamic response characteristics of the target vehicle's seat on the road segment ahead based on the seat control parameters includes: Based on the feature type indicated by the seat control feature information, the control mode type adopted by the seat of the target vehicle on the road segment ahead is determined, and the control mode type includes comfort control mode and safety control mode; Based on the control mode type and the seat control parameters, the dynamic response characteristics of the target vehicle's seat on the road segment ahead are determined.

24. The method according to claim 23, characterized in that, The determination of the dynamic response characteristics of the target vehicle's seat on the road segment ahead, based on the control mode type and the seat control parameters, includes: When the control mode type is the comfort control mode, the vertical damping response characteristics of the target vehicle's seat on the road segment ahead are determined based on the seat control parameters. When the control mode type is the safety control mode, the lateral support response characteristics and longitudinal support response characteristics of the target vehicle's seat on the road segment ahead are determined based on the seat control parameters.

25. The method according to claim 24, characterized in that, When the control mode type is the comfort control mode, determining the vertical damping response characteristics of the target vehicle's seat on the road segment ahead, based on the seat control parameters, includes: When the control mode type is the comfort control mode, the continuous ceiling damping coefficient, the lower limit of the comfort current, and the upper limit of the comfort current are obtained from the seat control parameters. The continuous ceiling damping coefficient is used to represent the proportional relationship between the damping force and the speed of movement of the seat sprung mass in the comfort control mode. The lower limit of the comfort current is used to limit the minimum value of the control current applied in the comfort control mode. The upper limit of the comfort current is used to limit the maximum value of the control current applied in the comfort control mode. Obtain the speed at which the sprung mass of the seat of the target vehicle moves. The target control current is determined based on the continuous ceiling damping coefficient and the sprung mass moving speed; Based on the target control current, the lower limit of the comfort current, and the upper limit of the comfort current, a control current is determined for applying to the seat of the target vehicle, and the control current is used to determine the vertical vibration reduction response characteristics.

26. The method according to claim 25, characterized in that, The determination of the target control current based on the continuous ceiling damping coefficient and the sprung mass moving speed includes: The target damping force is determined based on the continuous ceiling damping coefficient and the sprung mass moving speed. Obtain the mapping relationship between the damping force and the current of the shock absorber of the seat in the target vehicle; The target control current is determined based on the target damping force and the mapping relationship.

27. The method according to claim 24, characterized in that, When the control mode type is the safety control mode, determining the lateral support response characteristics and longitudinal support response characteristics of the target vehicle's seat on the road segment ahead, based on the seat control parameters, includes: When the control mode type is the safety control mode, the safety base current and the safety maximum allowable current are obtained from the seat control parameters. The safety base current is used as a reference value for the control current in the safety control mode, and the safety maximum allowable current is used to limit the upper limit of the control current applied in the safety control mode. Based on the safety base current, the basic support response characteristics of the target vehicle's seat on the road section ahead are determined, and the basic support response characteristics include the basic lateral support response and the basic longitudinal support response. Based on the basic support response characteristics, the lateral support response characteristics and the longitudinal support response characteristics are determined.

28. The method according to claim 27, characterized in that, Before determining the lateral support response characteristics and the longitudinal support response characteristics based on the basic support response characteristics, the method further includes: Obtain the driving dynamic parameters of the target vehicle; The determination of the lateral support response characteristics and the longitudinal support response characteristics based on the basic support response characteristics includes: Based on the driving dynamic parameters, enhanced support response characteristics are determined, including enhanced lateral support response and enhanced longitudinal support response. Based on the basic support response characteristics and the enhanced support response characteristics, the lateral support response characteristics and the longitudinal support response characteristics are determined.

29. The method according to claim 28, characterized in that, The driving dynamic parameters include at least one of lateral acceleration, longitudinal acceleration, and yaw rate. Determining the enhanced support response characteristics based on the driving dynamic parameters includes: If the lateral acceleration exceeds a preset lateral threshold, a first enhanced current corresponding to the lateral acceleration is determined; If the longitudinal acceleration exceeds a preset longitudinal threshold, a second enhanced current corresponding to the longitudinal acceleration is determined; If the yaw rate exceeds a preset yaw threshold, a third enhanced current corresponding to the yaw rate is determined. The enhanced support response characteristics are determined based on at least one of the first enhanced current, the second enhanced current, and the third enhanced current.

30. The method according to claim 29, characterized in that, Determining the enhanced support response characteristics based on at least one of the first enhanced current, the second enhanced current, and the third enhanced current includes: The trigger terms in the first enhanced current, the second enhanced current and the third enhanced current are summed to obtain the total enhanced current; If the total enhanced current does not exceed the maximum permissible safe current, the enhanced support response characteristics are determined based on the total enhanced current. If the total enhanced current exceeds the maximum permissible safe current, the enhanced support response characteristics are determined based on the maximum permissible safe current.

31. A seat control device, characterized in that, The device includes: The acquisition module is used to acquire the bump and slip features of the road section ahead of the target vehicle in the seat control cloud map. The seat control cloud map is cloud map data that simultaneously marks the road section with bump and slip features. The bump features are used to represent the intensity of seat vibration caused by the road section ahead, and the slip features are used to represent the road surface adhesion of the road section ahead. The generation module is used to generate seat control parameters corresponding to the road segment ahead based on the bump characteristics and the slippery characteristics. The seat control parameters are used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead. The sending module is used to send the seat control parameters to the target vehicle so that the target vehicle can control the seat based on the seat control parameters.

32. A seat control device, characterized in that, The device includes: The receiving module is used to receive seat control parameters sent by the cloud server. The seat control parameters are generated based on the bump features and slippery features of the road section ahead of the target vehicle marked in the seat control cloud map. The bump features are used to represent the intensity of seat vibration caused by the road section ahead, and the slippery features are used to represent the road surface adhesion of the road section ahead. The seat control cloud map is cloud map data that simultaneously marks the road section with bump features and slippery features. The control module is used to determine the dynamic response characteristics of the target vehicle's seat on the road segment ahead based on the seat control parameters, and to control the target vehicle's seat according to the dynamic response characteristics.

33. A vehicle, characterized in that, The vehicle includes one or more processors and one or more memories, wherein at least one computer program is stored in the one or more memories, the computer program being loaded and executed by the one or more processors to implement the seat control method as described in any one of claims 1 to 21, or being loaded and executed to implement the seat control method as described in any one of claims 22 to 30.

34. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to implement the seat control method as described in any one of claims 1 to 21, or is loaded and executed to implement the seat control method as described in any one of claims 22 to 30.

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