Vehicle seat self-adaptive adjusting method, device, equipment and medium

Abstract

The invention discloses a vehicle seat adaptive adjustment method and device, equipment and a medium. The method comprises the steps of obtaining current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle; determining road condition data of a road section in front of the target vehicle according to the navigation information; according to the road condition data and the historical driving behavior data, determining estimated driving behavior data of the driver on the front road section through a trained prediction model; according to the estimated driving behavior data, determining vehicle body posture change data of the target vehicle on the front road section; and on the basis of the vehicle body posture change data, seat regulation and control parameters corresponding to the target vehicle are determined, and according to the seat regulation and control parameters, seats of the target vehicle are subjected to self-adaptive regulation. Compared with traditional application, the seat adjusting method and device can effectively improve the timeliness and adaptability of seat adjustment. The method can be widely applied to the technical field of vehicles.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, equipment and medium for adaptive adjustment of a vehicle seat. Background Art

[0002] Vehicles are the main means of transportation in modern society and are widely used in daily commuting, long-distance travel and cargo transportation. Vehicles are generally equipped with vehicle seats, which are parts inside the car for passengers and drivers to sit on. Their main function is to provide a comfortable and safe riding experience. Traditional fixed vehicle seats can easily cause back pain after long-term driving, especially for professional drivers or long-distance driving scenarios. Therefore, some current vehicle seats are often equipped with corresponding adjustment mechanisms for adjusting the seat position, including front and back, height, backrest angle, etc.

[0003] In related technologies, in order to further improve the ease of use of vehicle seats, some high-end vehicles also use adaptive adjustment functions, that is, automatically adjusting the seat position through sensors, motors and intelligent algorithms. It can dynamically adjust the parameters of the vehicle seat according to the driver's body shape, sitting posture preference, real-time driving status (such as acceleration, turning) or health needs to improve comfort and safety. However, in actual applications, it is found that the current adaptive adjustment strategies are mostly based on sensor parameters. For example, when the vehicle is detected to be bumpy, the seat is adaptively adjusted, but it is likely that the vehicle has passed through a bumpy road section during the adjustment, and there is a certain lag, which affects the user's driving experience.

[0004] In summary, the problems existing in related technologies need to be solved urgently. Summary of the invention

[0005] The purpose of this application is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, an object of embodiments of the present application is to provide a vehicle seat adaptive adjustment method, device, equipment and medium.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present application include:

[0008] In one aspect, an embodiment of the present application provides a vehicle seat adaptive adjustment method, the method comprising:

[0009] Obtaining current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle;

[0010] Determining the road condition data of the road section ahead of the target vehicle according to the navigation information;

[0011] Determining the estimated driving behavior data of the driver on the road ahead by using a trained prediction model according to the road condition data and the historical driving behavior data;

[0012] Determining body posture change data of the target vehicle on the road ahead according to the estimated driving behavior data;

[0013] Based on the vehicle body posture change data, seat adjustment parameters corresponding to the target vehicle are determined, and the seat of the target vehicle is adaptively adjusted according to the seat adjustment parameters.

[0014] In addition, the vehicle seat adaptive adjustment method according to the above embodiment of the present application may also have the following additional technical features:

[0015] Further, in one embodiment of the present application, determining the seat adjustment parameters corresponding to the target vehicle based on the vehicle body posture change data includes:

[0016] Acquiring current seat setting parameters of the target vehicle and the body shape data of the driver;

[0017] Determining target setting parameters corresponding to seats of the target vehicle based on the vehicle body posture change data and the body shape data;

[0018] The seat adjustment parameters corresponding to the target vehicle are determined according to the target setting parameters and the seat setting parameters.

[0019] Further, in one embodiment of the present application, determining the seat adjustment parameters corresponding to the target vehicle according to the target setting parameters and the seat setting parameters includes:

[0020] Acquiring several historical preference parameters of the driver for the seat of the target vehicle;

[0021] Determining a similarity between the target setting parameter and each of the historical preference parameters;

[0022] If the similarity between the target setting parameter and any one of the historical preference parameters is greater than a preset threshold, a difference between the target setting parameter and the seat setting parameter is calculated, and the seat adjustment parameter is determined according to the difference.

[0023] Further, in one embodiment of the present application, the determining the similarity between the target setting parameter and each of the historical preference parameters includes:

[0024] Extracting a first feature vector corresponding to the target setting parameter and a second feature vector corresponding to the historical preference parameter;

[0025] determining a first length of the first eigenvector and a second length of the second eigenvector;

[0026] A first value is obtained according to the product of the first length and the second length, and a second value is obtained according to the inner product of the first eigenvector and the second eigenvector;

[0027] The similarity between the target setting parameter and the historical preference parameter is obtained according to the quotient of the second value and the first value.

[0028] Further, in one embodiment of the present application, after determining the body posture change data of the target vehicle on the road ahead according to the estimated driving behavior data, the method further includes:

[0029] The estimated driving behavior data is filtered through a low-pass filter to obtain filtered vehicle body posture change data.

[0030] Further, in one embodiment of the present application, determining the road condition data of the road section ahead of the target vehicle according to the navigation information includes:

[0031] obtaining an adjustment sensitivity level set by the driver;

[0032] Determining the length of the road section ahead according to the adjustment sensitivity level;

[0033] When the last determined front road section is completed, a front road section of the section length is determined in the driving direction of the target vehicle according to the navigation information, and the road condition data of the currently determined front road section is obtained.

[0034] Further, in one embodiment of the present application, the vehicle body posture change data includes at least one of a yaw angle, a roll angle, a pitch angle, a yaw angular velocity, and a lateral acceleration of the target vehicle.

[0035] On the other hand, an embodiment of the present application provides a vehicle seat adaptive adjustment device, the device comprising:

[0036] An acquisition unit, used to acquire current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle;

[0037] A query unit, used to determine the road condition data of the road section ahead of the target vehicle according to the navigation information;

[0038] A prediction unit, configured to determine the estimated driving behavior data of the driver on the road ahead by using a trained prediction model according to the road condition data and the historical driving behavior data;

[0039] A processing unit, configured to determine body posture change data of the target vehicle on the road ahead according to the estimated driving behavior data;

[0040] An execution unit is used to determine a seat adjustment parameter corresponding to the target vehicle based on the vehicle body posture change data, and adaptively adjust the seat of the target vehicle according to the seat adjustment parameter.

[0041] On the other hand, an embodiment of the present application provides an electronic device, including:

[0042] at least one processor;

[0043] at least one memory for storing at least one program;

[0044] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned vehicle seat adaptive adjustment method.

[0045] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the above-mentioned vehicle seat adaptive adjustment method.

[0046] The advantages and benefits of the present application will be partially given in the following description, and partially become apparent from the following description, or be understood through the practice of the present application:

[0047] A vehicle seat adaptive adjustment method, device, equipment and medium disclosed in the embodiments of the present application obtain the current navigation information of the target vehicle and the historical driving behavior data of the driver of the target vehicle; determine the road condition data of the road section ahead of the target vehicle based on the navigation information; determine the estimated driving behavior data of the driver on the road section ahead through a trained prediction model based on the road condition data and the historical driving behavior data; determine the body posture change data of the target vehicle on the road section ahead based on the estimated driving behavior data; determine the seat control parameters corresponding to the target vehicle based on the body posture change data, and adaptively adjust the seat of the target vehicle based on the seat control parameters. Compared with traditional applications, the present application can pre-adjust the seat based on the road condition data of the road section ahead, which can effectively improve the timeliness and adaptability of the seat adjustment, and is conducive to improving the user's driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present application. For technical personnel in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A schematic diagram of an implementation environment of a vehicle seat adaptive adjustment method provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of a process flow of a vehicle seat adaptive adjustment method provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of the structure of a vehicle seat adaptive adjustment device provided in an embodiment of the present application;

[0052] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The present application is further described below in conjunction with the accompanying drawings and specific embodiments. The described embodiments should not be regarded as limiting the present application, and all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0054] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0056] 1) Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0057] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. Basic artificial intelligence technologies generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, pre-trained model technology, operation / interaction systems, mechatronics, etc. Among them, pre-trained models are also called large models and basic models. After fine-tuning, they can be widely used in downstream tasks in various major directions of artificial intelligence. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0058] 2) Machine Learning (ML) is a multi-disciplinary interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in studying how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are spread across all areas of artificial intelligence. Machine learning and deep learning usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and self-learning. The pre-trained model is the latest development in deep learning, which integrates the above technologies.

[0059] 3) LSTM (Long Short-Term Memory) is a special recurrent neural network (RNN) designed specifically for processing time series data. It can effectively capture long-term dependencies by introducing memory units and gating mechanisms, solving the gradient vanishing problem of traditional RNN when processing long sequences.

[0060] Vehicles are the main means of transportation in modern society and are widely used in daily commuting, long-distance travel and cargo transportation. Vehicles are generally equipped with vehicle seats, which are parts inside the car for passengers and drivers to sit on. Their main function is to provide a comfortable and safe riding experience. Traditional fixed vehicle seats can easily cause back pain after long-term driving, especially for professional drivers or long-distance driving scenarios. Therefore, some current vehicle seats are often equipped with corresponding adjustment mechanisms for adjusting the seat position, including front and back, height, backrest angle, etc.

[0061] In related technologies, in order to further improve the ease of use of vehicle seats, some high-end vehicles also use adaptive adjustment functions, that is, automatically adjusting the seat position through sensors, motors and intelligent algorithms. It can dynamically adjust the parameters of the vehicle seat according to the driver's body shape, sitting posture preference, real-time driving status (such as acceleration, turning) or health needs to improve comfort and safety. However, in actual applications, it is found that the current adaptive adjustment strategies are mostly based on sensor parameters. For example, when the vehicle is detected to be bumpy, the seat is adaptively adjusted, but it is likely that the vehicle has passed through a bumpy road section during the adjustment, and there is a certain lag, which affects the user's driving experience.

[0062] In view of this, a method for adaptively adjusting a vehicle seat is provided in an embodiment of the present application, which obtains the current navigation information of a target vehicle and the historical driving behavior data of the driver of the target vehicle; determines the road condition data of the road section ahead of the target vehicle based on the navigation information; determines the estimated driving behavior data of the driver on the road section ahead through a trained prediction model based on the road condition data and the historical driving behavior data; determines the body posture change data of the target vehicle on the road section ahead based on the estimated driving behavior data; determines the seat control parameters corresponding to the target vehicle based on the body posture change data, and adaptively adjusts the seat of the target vehicle based on the seat control parameters. Compared with traditional applications, the present application can pre-adjust the seat based on the road condition data of the road section ahead, which can effectively improve the timeliness and adaptability of the seat adjustment, and is conducive to improving the user's driving experience.

[0063] Please refer to Figure 1 , Figure 1 The schematic diagram of the implementation environment of a vehicle seat adaptive adjustment method provided in the embodiment of the present application is shown. In the implementation environment, the main hardware and software entities involved include a terminal device 110 and a backend server 120. The terminal device 110 and the backend server 120 are connected in communication.

[0064] Specifically, the vehicle seat adaptive adjustment method provided in the embodiment of the present application can be executed separately on the terminal device 110 side, or based on the data interaction between the terminal device 110 and the background server 120. The terminal device 110 can be a vehicle-mounted terminal; the background server 120 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0065] The terminal device 110 and the backend server 120 may establish a communication connection via a wireless network or a wired network. The wireless network or wired network uses standard communication technology and / or protocols, and the network may be set to the Internet or any other network, such as but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network.

[0066] Of course, it is understandable that Figure 1 The implementation environment in the embodiment of the present application is only some optional application scenarios of the vehicle seat adaptive adjustment method provided in the embodiment of the present application, and the actual application is not fixed. Figure 1 The hardware and software environment shown.

[0067] Below, in combination with the introduction of the aforementioned implementation environment, a vehicle seat adaptive adjustment method provided in an embodiment of the present application is introduced and illustrated.

[0068] Please refer to Figure 2 , Figure 2 : is a schematic diagram of a vehicle seat adaptive adjustment method provided by an embodiment of the present application, and the vehicle seat adaptive adjustment method includes but is not limited to:

[0069] Step 210: obtaining current navigation information of the target vehicle and historical driving behavior data of the driver of the target vehicle;

[0070] Step 220: determining the road condition data of the road section ahead of the target vehicle according to the navigation information;

[0071] Step 230: Determine the estimated driving behavior data of the driver on the road ahead by using a trained prediction model according to the road condition data and the historical driving behavior data;

[0072] Step 240: determining the body posture change data of the target vehicle on the road ahead according to the estimated driving behavior data;

[0073] Step 250: Determine seat control parameters corresponding to the target vehicle based on the vehicle body posture change data, and adaptively adjust the seat of the target vehicle according to the seat control parameters.

[0074] In an embodiment of the present application, a vehicle seat adaptive adjustment method is provided. The method can adjust the seat in advance according to the road condition data of the road section ahead, which can effectively improve the timeliness and adaptability of the seat adjustment and is conducive to improving the user's driving experience.

[0075] Specifically, in an embodiment of the present application, when the seats of a vehicle are adaptively adjusted, the vehicle whose seats need to be adjusted can be recorded as a target vehicle. In an embodiment of the present application, there is no restriction on the specific type of the target vehicle. For the target vehicle, its navigation information at the current time point and the historical driving behavior data of the driver of the target vehicle can be obtained. Generally speaking, when using the navigation function, the user will enter the real-time location and destination of the vehicle in the relevant navigation service, and then the navigation service will provide some alternative paths, and the user will select one from the alternative paths as the target path. In an embodiment of the present application, the target path currently used by the target vehicle can be obtained as navigation information based on the navigation service.

[0076] In the embodiment of the present application, the current navigation information of the target vehicle is obtained, and the purpose is to determine the relevant information of the road section that the target vehicle will travel next. Specifically, the road condition data of the road section ahead of the target vehicle can be determined according to the navigation information. Here, the road section ahead of the target vehicle refers to a section on the driving forward direction of the target vehicle in the navigation path. For the specific length of the road section ahead, this application does not limit it, for example, it can be 100m or 1000m, etc., or it can be other numerical values. The road condition data of the road section ahead can include but is not limited to traffic index data (such as traffic flow data, traffic congestion data, speed limit data, etc.), construction data of the road section (such as road surface flatness, curvature, inclination, intersection position, etc.), environmental data (such as construction status of road surface, meteorological correlation status of road surface, etc.), but are not limited to this.

[0077] In the embodiment of the present application, the driver's historical driving behavior data is also obtained. Specifically, in some embodiments, the historical driving behavior data may include the driver's driving operation data, such as acceleration / deceleration related records, steering behavior records, such as the number of times the driver performs rapid acceleration, curve data of brake pedal force, and speed when turning. In some embodiments, the historical driving behavior data may include the driver's driving time and space distribution data, such as the economic speed ratio (the proportion of time maintained in the 60-90km / h interval), speed fluctuation entropy (i.e., the disorder degree of speed change). Of course, it is understandable that the type of actual historical driving behavior data is not limited to the examples given above, and the present application does not limit this.

[0078] It should be noted that in the embodiment of the present application, when obtaining the driver's historical driving behavior data, the historical driving behavior data can be associated with the relevant historical road condition data, and the historical road condition data can be integrated into the historical driving behavior data. For example, the two can be associated by time point, so that it is convenient to record the historical driving behavior data and record the road condition data under which the driver made the corresponding driving behavior. This is to facilitate the subsequent prediction model to analyze the driver's driving characteristics and improve the accuracy of predicting driving behavior.

[0079] In the embodiment of the present application, after determining the road condition data of the road section ahead and the driver's historical driving behavior data, the driver's estimated driving behavior data in the road section ahead can be determined by a trained prediction model. Specifically, here, the prediction model refers to a model that analyzes and predicts the driver's driving behavior data based on machine learning or deep learning technology and combines multi-source data. In the embodiment of the present application, there is no restriction on the specific type of the prediction model, for example, it can adopt a decision tree model, a neural network model or a time series model. Exemplarily, since the driver's historical driving behavior data is a time series type of data, recording the driver's driving behavior over a long period of time, in the embodiment of the present application, LSTM (Long Short-Term Memory, long short-term memory network) can be used for analysis, which is a special recurrent neural network (RNN) specially designed for processing time series data. It can effectively capture long-term dependencies by introducing memory units and gating mechanisms. Based on the prediction model, the driver's historical driving behavior data can be used to learn the decisions he made under different historical road condition data. In this way, the prediction model can predict its driving behavior data in the road section ahead based on the road condition data of the road section ahead, and in the embodiment of the present application, it is recorded as estimated driving behavior data. It is understandable that the data type of the estimated driving behavior data may be consistent with the aforementioned historical driving behavior data, or may contain less information than the historical driving behavior data, and this application does not impose any limitation on this.

[0080] It should be noted that for the prediction model, large-scale training is generally required before use to improve the performance of the model. In the embodiment of the present application, when training the prediction model, the driving behavior data of multiple sample personnel driving the vehicle and the corresponding road condition data when the driving behavior data is made can be obtained, and then these data are processed in time segments, and the data of one segment and the road condition data of the next segment are used as input. The driving behavior data of the next segment is predicted by the prediction model, and then compared with the actual data, so as to determine the loss value, update the parameters of the prediction model, and obtain a trained prediction model. Of course, in some embodiments, the existing model in the relevant field can also be directly selected as the prediction model, and this application does not limit this.

[0081] After determining the estimated driving behavior data of the driver in the road section ahead, the body posture change data of the target vehicle in the road section ahead can be determined according to the estimated driving behavior data. In the embodiment of the present application, the body posture change data may include parameters such as the yaw angle (yaw angle), roll angle, pitch angle, yaw angular velocity, lateral acceleration, etc. of the vehicle, which can reflect the dynamic situation of the vehicle when turning, changing lanes or passing through complex road conditions. Specifically, when determining the body posture change data according to the estimated driving behavior data, it can be calculated by a dynamic model. Exemplarily, for example, the current road section ahead is a bend. After analyzing the historical driving behavior data of the driver, the estimated driving behavior data output by the prediction model is: the driver will pass the road section ahead at a speed of 60km per hour, with a steering angle of 25 degrees, and basically maintain a uniform speed. In the embodiment of the present application, the yaw angular velocity and lateral acceleration can be calculated based on the steering angle and vehicle speed.

[0082] Of course, in some embodiments, when determining the vehicle body posture change data, some factors of the road condition data of the road section ahead may also be considered, such as whether there are steep slopes, low friction, etc. in the road condition, and this application does not impose any restrictions on this.

[0083] It should be noted that, in the embodiment of the present application, the body posture change data of the target vehicle on the road ahead is determined, which can be a continuous data that changes with time, that is, the body posture change data of the target vehicle at each time point can be determined and summarized into a set.

[0084] After determining the body posture change data, the seat control parameters corresponding to the target vehicle can be determined according to the body posture change data, and the seat of the target vehicle can be adaptively adjusted according to the seat control parameters. Exemplarily, for example, when it is determined that the inclination angle of the target vehicle in the road section ahead is greater than 3 degrees, the support angle and strength of the seat back and seat cushion for the driver can be appropriately adjusted to improve the user's driving experience. In the embodiment of the present application, for the determination of the seat control parameters, it can be to determine a seat control parameter based on the body posture change data of the target vehicle in the road section ahead, and then adjust it at the current time point, or it can be to determine multiple seat control parameters and adaptively adjust them before the target vehicle reaches the corresponding position of the road section ahead. This application does not limit this.

[0085] It can be understood that a method for adaptively adjusting a vehicle seat is provided in an embodiment of the present application, which obtains the current navigation information of a target vehicle and the historical driving behavior data of the driver of the target vehicle; determines the road condition data of the road section ahead of the target vehicle based on the navigation information; determines the estimated driving behavior data of the driver on the road section ahead through a trained prediction model based on the road condition data and the historical driving behavior data; determines the body posture change data of the target vehicle on the road section ahead based on the estimated driving behavior data; determines the seat control parameters corresponding to the target vehicle based on the body posture change data, and adaptively adjusts the seat of the target vehicle based on the seat control parameters. Compared with traditional applications, the present application can pre-adjust the seat based on the road condition data of the road section ahead, which can effectively improve the timeliness and adaptability of the seat adjustment, and is conducive to improving the user's driving experience.

[0086] In the embodiment of the present application, when determining the seat adjustment parameters corresponding to the target vehicle according to the body posture change data, in some cases, the seat adjustment parameters that need to be adjusted can be directly determined according to the body posture change data, for example, whether the height of the seat needs to be raised, the angle of the seat needs to be increased, etc. In other embodiments, the target setting parameters currently applicable to the driver can be determined based on the body posture change data, and then the seat adjustment parameters can be determined.

[0087] Specifically, in some embodiments, determining the seat control parameters corresponding to the target vehicle based on the vehicle body posture change data includes:

[0088] Acquiring current seat setting parameters of the target vehicle and the body shape data of the driver;

[0089] Determining target setting parameters corresponding to seats of the target vehicle based on the vehicle body posture change data and the body shape data;

[0090] The seat adjustment parameters corresponding to the target vehicle are determined according to the target setting parameters and the seat setting parameters.

[0091] In an embodiment of the present application, when determining the seat adjustment parameters corresponding to the target vehicle, the seat setting parameters of the current target vehicle and the body shape data of the driver can be obtained. Then, the target setting parameters corresponding to the target vehicle can be determined based on the body posture change data and the body shape data. It can be understood that the target setting parameters can be a characterization of what setting the seat of the target vehicle is adjusted to when the corresponding body posture change data appears to be most suitable for the driver. When determining the target setting parameters, the correspondence between the body posture change data and the optimal setting parameters can be stored according to a related database, and the database is also divided into different situations based on the body shape data of the driver. In this way, the target setting parameters corresponding to the seat of the target vehicle can be determined more quickly.

[0092] In the embodiment of the present application, after the target setting parameters are determined, the corresponding seat adjustment parameters can be determined according to the target setting parameters and the seat setting parameters.

[0093] Specifically, in some embodiments, determining the seat adjustment parameters corresponding to the target vehicle according to the target setting parameters and the seat setting parameters includes:

[0094] Acquiring several historical preference parameters of the driver for the seat of the target vehicle;

[0095] Determining a similarity between the target setting parameter and each of the historical preference parameters;

[0096] If the similarity between the target setting parameter and any one of the historical preference parameters is greater than a preset threshold, a difference between the target setting parameter and the seat setting parameter is calculated, and the seat adjustment parameter is determined according to the difference.

[0097] It should be noted that in the embodiment of the present application, the target setting parameters determined based on the vehicle body posture change data are the optimal setting parameters under ideal conditions, and for each individual driver, their preferences may be different. When determining the seat control parameters, after the target setting parameters are determined, the target setting parameters can be first tested for personalized preferences. If it is determined that the target setting parameters are in line with the current driver's preferences and driving habits, it is considered that the control method determined by the adaptive adjustment is reasonable, and the seat control parameters can be determined based on the target setting parameters; conversely, if it is determined that the target setting parameters do not meet the current driver's preferences and driving habits, it is considered that the control method determined by the adaptive adjustment is unreasonable. At this time, the seat control parameters can be determined by other methods.

[0098] Specifically, several historical preference parameters of the driver for the seat of the target vehicle can be obtained, where each historical preference parameter can be a set of seat setting parameters (such as seat front and rear position, backrest tilt angle, seat height and other dimensional data), and each (group) of historical preference parameters can be a set of parameters used by the driver and with a high frequency of use. Exemplarily, in some embodiments, all setting parameters of the driver for the seat of the target vehicle can be obtained, and then several of the most frequently used ones are selected as historical preference parameters, where several can be one or more, for example, can be set to 10, and this application does not limit this.

[0099] Then, the similarity between the target setting parameters and each historical preference parameter can be determined, and a preset threshold value can be set for the similarity. Here, similarity is an indicator for measuring the similarity between two targets, and its value is between 0 and 1. The larger the similarity value, the closer the two targets are, and the smaller the similarity value, the less close the two targets are. It can be understood that in the embodiment of the present application, there is no restriction on the type of similarity algorithm used. For example, it can be implemented based on any one of the edit distance (Levenshtein distance) coefficient, cosine similarity (Cosinesimilarity), Jaccard Similarity Coefficient (Jaccard Similarity Coefficient), Euclidean distance (Euclideandistance), Manhattan distance, etc.

[0100] If the similarity between the target setting parameter and any of the historical preference parameters is greater than a preset threshold, it means that the target setting parameter meets the current driver's preferences and driving habits, and the seat adjustment parameters can be determined based on the difference between the target setting parameter and the seat setting parameter. Conversely, if the similarity between the target setting parameter and all the historical preference parameters is less than or equal to the preset threshold, it means that the target setting parameter is likely to not meet the current driver's preferences and driving habits. At this time, the historical preference parameter with the highest similarity to the target setting parameter can be selected, and the seat adjustment parameters can be determined based on the difference between it and the seat setting parameter.

[0101] Specifically, in some embodiments, determining the similarity between the target setting parameter and each of the historical preference parameters includes:

[0102] Extracting a first feature vector corresponding to the target setting parameter and a second feature vector corresponding to the historical preference parameter;

[0103] determining a first length of the first eigenvector and a second length of the second eigenvector;

[0104] A first value is obtained according to the product of the first length and the second length, and a second value is obtained according to the inner product of the first eigenvector and the second eigenvector;

[0105] The similarity between the target setting parameter and the historical preference parameter is obtained according to the quotient of the second value and the first value.

[0106] In an embodiment of the present application, the similarity between the target setting parameters and the historical preference parameters can be determined by a cosine similarity algorithm. Specifically, the feature vector corresponding to the target setting parameters can be extracted, recorded as the first feature vector, and the feature vector corresponding to the historical preference parameters can be extracted, recorded as the second feature vector. Then, the length of the first feature vector, recorded as the first length, and the length of the second feature vector, recorded as the second length, can be determined. Then, the product of the first length and the second length is calculated as the first numerical value, and the inner product of the first feature vector and the second feature vector is calculated as the second numerical value, and then the quotient of the first numerical value and the second numerical value is calculated as the similarity between the first feature vector and the second feature vector, that is, the similarity between the target setting parameters and the historical preference parameters.

[0107] In the embodiment of the present application, for the body posture change data, when the target vehicle is traveling in a scene with many bends or frequent acceleration and deceleration, frequent changes may occur. If the seat setting parameters are determined based on the body posture change data at multiple time points, frequent seat adjustments may occur, which may easily lead to a poor driving experience. Therefore, in the embodiment of the present application, an adjustment buffer area can be set. For example, when the body posture change data is small, it can be ignored. For example, when the change in the roll angle or acceleration of the target vehicle is within a certain range (such as the roll angle change is less than 3°, and the acceleration change is less than 0.3m / s 2 ), the system does not adjust the seat of the target vehicle, and the corresponding body posture change data can be ignored.

[0108] In the embodiment of the present application, after obtaining the vehicle posture change data, a relevant filtering algorithm can be used to filter it, such as using a low-pass filter to filter out high-frequency and small-amplitude fluctuation signals. In this way, when the vehicle posture change data is subsequently used, only the signals exceeding a certain amplitude or duration will be adjusted, which can reduce the frequent seat adjustments caused by slight vehicle shaking or short-term posture changes, and is conducive to improving the comfort of vehicle use.

[0109] Of course, in some embodiments, an adaptive adjustment function button may be provided for the driver, and if the driver feels that the automatic adjustment of the seat support is too frequent and affects the driving experience, the adaptive adjustment function may be turned off. This application does not limit this.

[0110] In some embodiments, determining the road condition data of the road section ahead of the target vehicle according to the navigation information includes:

[0111] obtaining an adjustment sensitivity level set by the driver;

[0112] Determining the length of the road section ahead according to the adjustment sensitivity level;

[0113] When the last determined front road section is completed, a front road section of the section length is determined in the driving direction of the target vehicle according to the navigation information, and the road condition data of the currently determined front road section is obtained.

[0114] In the embodiment of the present application, the driver can also set an adjustment sensitivity level, which can be used to set the frequency of adjustment. In the embodiment of the present application, a corresponding length of a road section can be determined according to the adjustment sensitivity level, and then the seat can be adaptively adjusted once after each travel of the road section.

[0115] Specifically, in the embodiment of the present application, the length of the road section ahead can be determined according to the adjustment sensitivity level. It can be understood that the higher the adjustment sensitivity level, the more frequent the adjustment, the shorter the corresponding road section length can be, and the effect achieved is that the seat adjustment is performed once after every short road section; the lower the adjustment sensitivity level, the less frequent the adjustment, the longer the corresponding road section length can be, and the effect achieved is that the seat adjustment is performed once after every long road section. Therefore, the adjustment sensitivity level and the road section length in the embodiment of the present application are negatively correlated, and the present application does not limit the specific relationship between the two. When obtaining the road condition data of the road section ahead of the target vehicle to determine the seat control parameters used for the current adjustment, it can be detected whether the road section ahead determined last time has been completed. If not, no adjustment is temporarily performed; if the travel is completed, the road section ahead of a new section length can be determined according to the navigation information, and the corresponding road condition data can be obtained.

[0116] In some embodiments, the present application may also set a feedback channel in the central control system of the target vehicle, so that the driver can promptly provide feedback on the experience of the adaptive adjustment of the seat support in different scenarios. For example, after the driver finishes driving, he can use the multimedia system in the car to rate his satisfaction with the seat adjustment function during the driving process that just ended, and briefly describe the unsatisfactory scenes. The system can collect these feedback data for subsequent algorithm optimization.

[0117] In the embodiment of the present application, the system can learn the seat adjustment strategy in a personalized way based on the driver's feedback and long-term driving habit data. If the driver often drives on mountain roads with many curves and the automatic adjustment is too frequent, the system can automatically adjust its adjustment strategy in this scenario, such as further increasing the adjustment threshold or reducing the adjustment sensitivity, to provide a driving experience that better meets the driver's expectations.

[0118] Reference Figure 3 In an embodiment of the present application, a vehicle seat adaptive adjustment device is also provided, comprising:

[0119] An acquisition unit 310 is used to acquire current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle;

[0120] A query unit 320, configured to determine the road condition data of the road section ahead of the target vehicle according to the navigation information;

[0121] A prediction unit 330, configured to determine the estimated driving behavior data of the driver on the road ahead by using a trained prediction model according to the road condition data and the historical driving behavior data;

[0122] A processing unit 340 is used to determine the body posture change data of the target vehicle on the front road section according to the estimated driving behavior data;

[0123] The execution unit 350 is used to determine the seat adjustment parameters corresponding to the target vehicle based on the vehicle body posture change data, and adaptively adjust the seat of the target vehicle according to the seat adjustment parameters.

[0124] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0125] Reference Figure 4 , an embodiment of the present application provides an electronic device, including:

[0126] at least one processor 410;

[0127] At least one memory 420, used to store at least one program;

[0128] When at least one program is executed by at least one processor 410 , at least one processor 410 implements the above-mentioned vehicle seat adaptive adjustment method.

[0129] Similarly, the contents of the above method embodiments are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0130] The embodiment of the present application also provides a computer-readable storage medium, in which a program executable by the processor 410 is stored. When the program executable by the processor 410 is executed by the processor 410, it is used to execute the above-mentioned vehicle seat adaptive adjustment method.

[0131] Similarly, the contents of the above method embodiments are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0132] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the application is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part described as a larger operation is performed independently.

[0133] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present application. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional techniques of the engineer. Therefore, those skilled in the art can implement the present application set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the attached claims and their equivalents.

[0134] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0136] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0137] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0138] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0139] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0140] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Technical personnel familiar with the field can make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A vehicle seat adaptive adjustment method, characterized in that: The method comprises: Obtaining current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle; Determining the road condition data of the road section ahead of the target vehicle according to the navigation information; Determining the estimated driving behavior data of the driver on the road ahead by using a trained prediction model according to the road condition data and the historical driving behavior data; Determining body posture change data of the target vehicle on the road ahead according to the estimated driving behavior data; Based on the vehicle body posture change data, seat adjustment parameters corresponding to the target vehicle are determined, and the seat of the target vehicle is adaptively adjusted according to the seat adjustment parameters.

2. A vehicle seat adaptive adjustment method according to claim 1, characterized in that: The determining, based on the vehicle body posture change data, the seat adjustment parameters corresponding to the target vehicle includes: Acquiring current seat setting parameters of the target vehicle and the body shape data of the driver; Determining target setting parameters corresponding to seats of the target vehicle based on the vehicle body posture change data and the body shape data; The seat adjustment parameters corresponding to the target vehicle are determined according to the target setting parameters and the seat setting parameters.

3. A vehicle seat adaptive adjustment method according to claim 2, characterized in that: The step of determining the seat adjustment parameters corresponding to the target vehicle according to the target setting parameters and the seat setting parameters includes: Acquiring several historical preference parameters of the driver for the seat of the target vehicle; Determining a similarity between the target setting parameter and each of the historical preference parameters; If the similarity between the target setting parameter and any one of the historical preference parameters is greater than a preset threshold, a difference between the target setting parameter and the seat setting parameter is calculated, and the seat adjustment parameter is determined according to the difference.

4. The vehicle seat adaptive adjustment method according to claim 3, characterized in that: The determining the similarity between the target setting parameter and each of the historical preference parameters comprises: Extracting a first feature vector corresponding to the target setting parameter and a second feature vector corresponding to the historical preference parameter; determining a first length of the first eigenvector and a second length of the second eigenvector; A first value is obtained according to the product of the first length and the second length, and a second value is obtained according to the inner product of the first eigenvector and the second eigenvector; The similarity between the target setting parameter and the historical preference parameter is obtained according to the quotient of the second value and the first value.

5. The vehicle seat adaptive adjustment method according to claim 1, characterized in that: After determining the body posture change data of the target vehicle on the road ahead according to the estimated driving behavior data, the method further includes: The estimated driving behavior data is filtered through a low-pass filter to obtain filtered vehicle body posture change data.

6. The vehicle seat adaptive adjustment method according to claim 1, characterized in that: Determining the road condition data of the road section ahead of the target vehicle according to the navigation information includes: obtaining an adjustment sensitivity level set by the driver; Determining the length of the road section ahead according to the adjustment sensitivity level; When the last determined front road section is completed, a front road section of the section length is determined in the driving direction of the target vehicle according to the navigation information, and the road condition data of the currently determined front road section is obtained.

7. A vehicle seat adaptive adjustment method according to any one of claims 1 to 6, characterized in that: The vehicle body posture change data includes at least one of a yaw angle, a roll angle, a pitch angle, a yaw angular velocity, and a lateral acceleration of the target vehicle.

8. A vehicle seat adaptive adjustment device, characterized in that: The device comprises: An acquisition unit, used to acquire current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle; A query unit, used to determine the road condition data of the road section ahead of the target vehicle according to the navigation information; A prediction unit, configured to determine the estimated driving behavior data of the driver on the road ahead by using a trained prediction model according to the road condition data and the historical driving behavior data; A processing unit, configured to determine body posture change data of the target vehicle on the road ahead according to the estimated driving behavior data; An execution unit is used to determine a seat adjustment parameter corresponding to the target vehicle based on the vehicle body posture change data, and adaptively adjust the seat of the target vehicle according to the seat adjustment parameter.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a vehicle seat adaptive adjustment method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to implement a vehicle seat adaptive adjustment method as described in any one of claims 1 to 7 when executed by the processor.

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