Vehicle seat adaptive adjustment method, device, equipment and medium

Abstract

The application discloses a vehicle seat adaptive adjustment method, device, equipment and medium, obtains current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle; determines road condition data of a front road section of the target vehicle according to the navigation information; determines estimated driving behavior data of the driver on the front road section through a trained prediction model according to the road condition data and the historical driving behavior data; determines vehicle body posture change data of the target vehicle on the front road section according to the estimated driving behavior data; determines seat control parameters corresponding to the target vehicle based on the vehicle body posture change data, and adaptively adjusts a seat of the target vehicle according to the seat control parameters. Compared with the traditional application, the application can effectively improve the timeliness and adaptability of seat adjustment. The application can be widely applied in the technical field of vehicles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, and in particular to a vehicle seat adaptive adjustment method, device, equipment and medium. BACKGROUND

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

[0003] In related technologies, in order to further improve the convenience of using vehicle seats, some high-end vehicles also apply adaptive adjustment functions, that is, through sensors, motors and intelligent algorithms, the seat position is automatically adjusted. It can dynamically adjust the parameters of the vehicle seat according to the driver's body size, sitting posture preference, real-time driving state (such as acceleration, turning) or health needs, and improve comfort and safety. However, in actual application, it is found that the current adaptive adjustment strategy is mostly based on sensor parameters to achieve adjustment, such as detecting vehicle bumps to adaptively adjust the seat, but the adjustment may have already passed the bump section, which has a certain hysteresis, affecting the user's driving experience.

[0004] In summary, the problems in related technologies need to be solved. SUMMARY

[0005] The present application aims to at least partly solve one of the technical problems in related technologies.

[0006] To this end, an object of the 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 purpose, the technical solutions adopted by the embodiments of the present application include:

[0008] On the one hand, the embodiments of the present application provide a vehicle seat adaptive adjustment method, which includes:

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

[0010] determining road condition data of a front road section of the target vehicle according to the navigation information;

[0011] According to the road condition data and the historical driving behavior data, estimated driving behavior data of the driver on the front road section is determined through a trained prediction model;

[0012] According to the estimated driving behavior data, vehicle body posture change data of the target vehicle on the front road section is determined;

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

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

[0015] Further, in an embodiment of the present application, the determination of the seat control parameters corresponding to the target vehicle based on the vehicle body posture change data comprises:

[0016] The current seat setting parameters of the target vehicle and the body size data of the driver are obtained;

[0017] Based on the vehicle body posture change data and the body size data, target setting parameters corresponding to the seats of the target vehicle are determined;

[0018] According to the target setting parameters and the seat setting parameters, the seat control parameters corresponding to the target vehicle are determined.

[0019] Further, in an embodiment of the present application, the determination of the seat control parameters corresponding to the target vehicle according to the target setting parameters and the seat setting parameters comprises:

[0020] The historical preference parameters of the driver for the seats of the target vehicle are obtained;

[0021] The similarity between the target setting parameters and each of the historical preference parameters is determined;

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

[0023] Further, in an embodiment of the present application, the determination of the similarity between the target setting parameters and each of the historical preference parameters comprises:

[0024] The first feature vector corresponding to the target setting parameters and the second feature vector corresponding to the historical preference parameters are extracted;

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

[0026] obtaining a first value according to a product of the first length and the second length, and obtaining a second value according to an inner product of the first feature vector and the second feature vector;

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

[0028] Further, in an embodiment of the present application, after the step of determining the body posture change data of the target vehicle on the front road section according to the predicted driving behavior data, the method further comprises:

[0029] filtering the predicted driving behavior data through a low-pass filter to obtain filtered body posture change data.

[0030] Further, in an embodiment of the present application, the step of determining the road condition data of the front road section of the target vehicle according to the navigation information comprises:

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

[0032] determining a road section length of the front road section according to the adjustment sensitivity level;

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

[0034] Further, in an embodiment of the present application, the body posture change data comprises 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] In another aspect, an embodiment of the present application provides a vehicle seat adaptive adjustment device, the device comprising:

[0036] an obtaining unit configured to obtain current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle;

[0037] a querying unit configured to determine road condition data of a front road section of the target vehicle according to the navigation information;

[0038] a predicting unit configured to determine predicted driving behavior data of the driver on the front road section by 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 front road segment according to the estimated driving behavior data;

[0040] an execution unit configured to determine a seat control parameter corresponding to the target vehicle based on the body posture change data, and to perform adaptive adjustment on a seat of the target vehicle according to the seat control parameter.

[0041] In another aspect, an electronic device is provided, comprising:

[0042] at least one processor;

[0043] at least one memory configured to store at least one program;

[0044] the at least one program, when executed by the at least one processor, causing the at least one processor to implement the vehicle seat adaptive adjustment method described above.

[0045] In another aspect, an electronic device is provided, comprising:

[0046] The advantages and beneficial effects of the present application will be partially given in the following description, partially will become obvious from the following description, or will be learned through the practice of the present application:

[0047] The vehicle seat adaptive adjustment method, device, equipment and medium disclosed by the embodiments of the present application obtain the current navigation information of a target vehicle and the historical driving behavior data of a driver of the target vehicle; determine road condition data of a front road segment of the target vehicle according to the navigation information; determine estimated driving behavior data of the driver on the front road segment through a trained prediction model according to the road condition data and the historical driving behavior data; determine body posture change data of the target vehicle on the front road segment according to the estimated driving behavior data; determine a seat control parameter corresponding to the target vehicle based on the body posture change data, and perform adaptive adjustment on a seat of the target vehicle according to the seat control parameter. Compared with the traditional application, the present application can perform seat adjustment in advance according to the road condition data of the front road segment, which can effectively improve the timeliness and adaptability of seat adjustment, and is conducive to improving the driving experience of users. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present application or the prior art. It should be understood that the drawings in the following introduction are only for the convenience of describing part of the embodiments of the technical solutions in the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the premise of the drawings.

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

[0050] Figure 2 A flowchart schematic diagram of a vehicle seat adaptive adjustment method provided in the embodiments of the present application;

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

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

[0053] The present application will be further described below in conjunction with the drawings of the specification and specific embodiments. The described embodiments should not be regarded as limiting the present application, and all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] In the following description, "some embodiments" are related to a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

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

[0056] 1) Artificial Intelligence (AI) is the theory, method, technology and application system that use digital computers or digital computer controlled machines 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 of computer science, which tries to understand the essence of intelligence and produce a new intelligent machine that can react 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, involving a wide range of fields, both hardware and software technologies. Artificial intelligence basic technologies generally include sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, pre-training model technology, operation / interaction system, mechatronics, etc. Among them, the pre-training model is also called large model, basic model, which can be widely applied to downstream tasks in various directions of artificial intelligence after fine-tuning. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology and machine learning / deep learning, etc.

[0058] 2) Machine Learning (ML) is a multi-disciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is a branch of computer science that studies how computers simulate or implement human learning behavior to acquire new knowledge or skills, and reorganize existing knowledge structure to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental approach to making computers intelligent, and its applications are widespread in various fields of artificial intelligence. Machine learning and deep learning usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and rule-based learning. Pre-training model is the latest development of deep learning, which integrates the above technologies.

[0059] 3) LSTM (Long Short-Term Memory) is a special kind of recurrent neural network (RNN) designed specifically for processing time series data. It can effectively capture long-term dependencies by introducing memory cells 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, widely used in daily commuting, long-distance travel and cargo transportation. A vehicle seat is generally configured in a vehicle, which is a component for passengers and drivers to sit in the car interior, and its main function is to provide a comfortable and safe riding experience. Traditional fixed vehicle seats can easily cause back pain after a long drive, 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 forward and backward, high and low, backrest angle, etc.

[0061] In related technologies, in order to further improve the convenience of vehicle seats, some high-end vehicles also apply adaptive adjustment functions, that is, through sensors, motors and intelligent algorithms, automatically adjust the seat position. It can dynamically adjust the parameters of the vehicle seat according to the driver's body size, sitting posture preference, real-time driving state (such as acceleration, turning) or health needs, to improve comfort and safety. However, in actual application, it is found that the current adaptive adjustment strategy is mostly based on sensor parameters for adjustment, such as detecting vehicle bumps and adjusting the seat accordingly. However, the adjustment may have already passed the bumping section, which has a certain hysteresis, affecting the user's driving experience.

[0062] Therefore, in the embodiments of the present application, a vehicle seat adaptive adjustment method is provided, which obtains the current navigation information of the target vehicle and the historical driving behavior data of the driver of the target vehicle; according to the navigation information, determines the road condition data of the front road section of the target vehicle; according to the road condition data and the historical driving behavior data, determines the estimated driving behavior data of the driver on the front road section through a trained prediction model; according to the estimated driving behavior data, determines the body posture change data of the target vehicle on the front road section; based on the body posture change data, determines the corresponding seat control parameters of the target vehicle, and adjusts the seat of the target vehicle adaptively according to the seat control parameters. Compared with the traditional application, the present application can adjust the seat in advance according to the road condition data of the front road section, which can effectively improve the timeliness and adaptability of seat adjustment, and is beneficial to improve the user's driving experience.

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

[0064] Specifically, the vehicle seat adaptive adjustment method provided in the embodiments of the present application can be executed independently on the terminal device 110 side or based on 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 a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms.

[0065] The terminal device 110 and the background server 120 can establish a communication connection through a wireless network or a wired network. The wireless network or the wired network uses standard communication technology and / or protocols, and the network can be set as the Internet or any other network, for example, any combination of 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 a virtual private network, but is not limited to these.

[0066] Of course, it can be understood that Figure 1 The implementation environment in the foregoing merely is some optional application scenarios of the vehicle seat adaptive adjustment method provided in the embodiments of the present application, and the actual application is not fixed to the hardware and software environment shown in Figure 1 .

[0067] Next, the vehicle seat adaptive adjustment method provided in the embodiments of the present application is introduced and described in combination with the foregoing introduction of the implementation environment.

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

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

[0070] Step 220, determining road condition data of a front road section of the target vehicle according to the navigation information;

[0071] Step 230, determining estimated driving behavior data of the driver on the front road section through a trained prediction model according to the road condition data and the historical driving behavior data.

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

[0073] Step 250, determining a seat adjustment parameter corresponding to the target vehicle based on the vehicle body posture change data, and adaptively adjusting the seat of the target vehicle according to the seat adjustment parameter.

[0074] In the embodiments of the present application, a vehicle seat adaptive adjustment method is provided, which can pre-adjust the seat according to the road condition data of the front road section, effectively improve the timeliness and adaptability of seat adjustment, and be beneficial to improve the driving experience of users.

[0075] Specifically, in the control of adaptive adjustment of the seat of the vehicle, the vehicle whose seat needs to be adjusted is recorded as a target vehicle, and the specific type of the target vehicle is not limited in the embodiments of the present application. 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, when using the navigation function, the user inputs the real-time position and destination of the vehicle in the related navigation service, and then the navigation service gives some alternative paths, and the user selects one as the target path. In the embodiments of the present application, the target path currently used by the target vehicle can be obtained as the navigation information based on the navigation service.

[0076] In the embodiments of the present application, the current navigation information of the target vehicle is obtained, and the purpose is to determine the related information of the road section to be traveled by the target vehicle next. Specifically, the road condition data of the front road section of the target vehicle can be determined according to the navigation information. Here, the front road section of the target vehicle refers to a section of road in the navigation path in the forward direction of travel of the target vehicle. The specific length of the front road section is not limited in the present application, which can be 100m or 1000m, etc., or other values. The road condition data of the front road section can include but is not limited to traffic index data (such as traffic flow data, congestion data of traffic flow, speed limit data, etc.), construction data of the road section (such as road surface flatness, curvature, inclination, intersection position, etc.), environmental data (such as road construction state, road weather-related state, etc.), but is not limited thereto.

[0077] In the embodiments of the present application, the historical driving behavior data of the driver is also acquired. Specifically, in some embodiments, the historical driving behavior data can include the driving operation data of the driver, such as the records related to acceleration / deceleration, the records of steering behavior, such as the number of times of sudden acceleration performed by the driver, the curve data of the brake pedal force and the speed at the time of steering, and the like. In some embodiments, the historical driving behavior data can include the spatiotemporal distribution data of the driver, such as the proportion of economic speed (the proportion of time maintained in the interval of 60-90 km / h), the speed fluctuation entropy value (i.e., the degree of disorder of speed change). It can be understood 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 embodiments of the present application, when the historical driving behavior data of the driver is acquired, the historical driving behavior data can be associated with the related historical road condition data, and the historical road condition data is integrated into the historical driving behavior data. For example, the two can be associated by time point, so that it can be recorded that the historical driving behavior data records the driving behavior of the driver under which road condition data. This facilitates subsequent prediction model analysis of the driving characteristics of the driver and improves the accuracy of predicting driving behavior.

[0079] In the embodiments of the present application, after the road condition data of the front road section and the historical driving behavior data of the driver are determined, the prediction model trained can be used to determine the estimated driving behavior data of the driver on the front road section. Specifically, here, the prediction model refers to a model based on machine learning or deep learning technology, which analyzes and predicts the driving behavior data of the driver in combination with multi-source data. In the embodiments of the present application, the specific type of the prediction model is not limited, for example, it can adopt a decision tree model, a neural network model or a time series model. For example, since the historical driving behavior data of the driver is time series type data, which records the driving behavior of the driver over a long period of time, in the embodiments 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) designed specifically for processing time series data. It can effectively capture long-term dependencies by introducing memory cells and gating mechanisms. Based on the prediction model, the driver's decisions made under different historical road condition data can be learned from the historical driving behavior data of the driver. In this way, the prediction model can predict the driving behavior data of the driver on the front road section based on the road condition data of the front road section, which is recorded as estimated driving behavior data in the embodiments of the present application. It can be understood that the data type of the estimated driving behavior data can be consistent with the historical driving behavior data described above, or there can be less information than the historical driving behavior data, and the present application does not limit this.

[0080] It should be noted that for the prediction model, generally a large-scale training is performed before use to improve the performance of the model. In the embodiment of the present application, when the prediction model is trained, a plurality of sample personnel driving behavior data of a vehicle and corresponding road condition data when the driving behavior data is made can be obtained, and then the data is time segmented, the data of a segment and the road condition data of the next segment are taken as input, the next segment of driving behavior data is predicted by the prediction model, and then compared with the actual data to determine the loss value, and the parameters of the prediction model are updated to obtain the trained prediction model. Of course, in some embodiments, an existing model in the related field can also be directly selected as the prediction model, and the present application does not make any limitation.

[0081] After the estimated driving behavior data of the driver in the front road segment is determined, the vehicle body posture change data of the target vehicle in the front road segment can be determined according to the estimated driving behavior data. In the embodiment of the present application, the vehicle body posture change data can include the yaw angle (yaw angle), roll angle, pitch angle, yaw angular velocity, lateral acceleration and other parameters, which can reflect the dynamic situation of the vehicle when turning, changing lanes or passing through complex road conditions. Specifically, when the estimated driving behavior data is determined, the vehicle body posture change data can be calculated by a dynamic model. For example, the current front road segment is a curve. After analyzing the historical driving behavior data of the driver, the estimated driving behavior data output by the prediction model is that the driver will pass through the front road segment at a speed of 60 km per hour, the steering angle is 25 degrees, and the speed is basically maintained. 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 front road segment can also be considered, such as whether the road condition exists steep slope, low friction and the like, and the present application does not make any limitation.

[0083] It should be noted that in the embodiment of the present application, the vehicle body posture change data of the target vehicle in the front road segment can be a continuous data changing with time, that is, the vehicle body posture change data of the target vehicle at each time point can be determined, and the data is collected as a set.

[0084] After the body posture change data is determined, the seat control parameter corresponding to the target vehicle can be determined according to the body posture change data, and the seat of the target vehicle is adaptively adjusted according to the seat control parameter. For example, when it is determined that the inclination angle of the target vehicle on the front road section is greater than 3 degrees, the support angle and intensity of the seat back and the seat cushion to the driver can be appropriately adjusted to improve the driving experience of the user. In the embodiments of the present application, the determination of the seat control parameter can be that one seat control parameter is determined according to the body posture change data of the target vehicle on the front road section, and then the adjustment is made at the current time point, or multiple seat control parameters are determined, and adaptive adjustment is made before the target vehicle reaches the corresponding position of the front road section, which is not limited in the present application.

[0085] It can be understood that the embodiments of the present application provide a vehicle seat adaptive adjustment method, which obtains the current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle; determines road condition data of a front road section of the target vehicle according to the navigation information; determines estimated driving behavior data of the driver on the front road section through a trained prediction model according to the road condition data and the historical driving behavior data; determines body posture change data of the target vehicle on the front road section according to the estimated driving behavior data; and determines a seat control parameter corresponding to the target vehicle based on the body posture change data, and adaptively adjusts the seat of the target vehicle according to the seat control parameter. Compared with the traditional application, the present application can pre-adjust the seat according to the road condition data of the front road section, which can effectively improve the timeliness and adaptability of seat adjustment, and is beneficial to improve the driving experience of the user.

[0086] In the embodiments of the present application, when the seat control parameter corresponding to the target vehicle is determined according to the body posture change data, in some cases, the seat control parameter that needs to be adjusted can be directly determined according to the body posture change data, such as specifically needing to adjust the height of the seat, increase the angle of the seat, etc. In other embodiments, the target setting parameter currently applicable to the driver can be determined based on the body posture change data, and then the seat control parameter is determined.

[0087] Specifically, in some embodiments, the determination of the seat control parameter corresponding to the target vehicle based on the body posture change data comprises:

[0088] obtaining the current seat setting parameter of the target vehicle and the body size data of the driver;

[0089] determining the target setting parameter corresponding to the seat of the target vehicle based on the body posture change data and the body size data;

[0090] According to the target setting parameter and the seat setting parameter, a seat control parameter corresponding to the target vehicle is determined.

[0091] In the embodiments of the present application, when determining the seat control parameter corresponding to the target vehicle, the seat setting parameter of the current target vehicle and the body size data of the driver can be obtained. Then, the target setting parameter corresponding to the target vehicle can be determined based on the body posture change data and the body size data. It can be understood that the target setting parameter can be a parameter representing that in the case of the corresponding body posture change data, the seat of the target vehicle is adjusted to what setting is most suitable for the driver. When determining the target setting parameter, the corresponding relationship between the body posture change data and the optimal setting parameter can be stored according to the related database, and the database also divides different cases based on the body size data of the driver. In this way, the target setting parameter corresponding to the seat of the target vehicle can be determined more quickly.

[0092] In the embodiments of the present application, after the target setting parameter is determined, the seat control parameter corresponding to the target setting parameter and the seat setting parameter can be determined.

[0093] Specifically, in some embodiments, the determining the seat control parameter corresponding to the target vehicle according to the target setting parameter and the seat setting parameter comprises:

[0094] Obtaining a plurality of historical preference parameters of the driver for the seat of the target vehicle;

[0095] Determining the 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, calculating the difference between the target setting parameter and the seat setting parameter, and determining the seat control parameter according to the difference.

[0097] It should be noted that in the embodiments of the present application, the target setting parameter determined according to the body posture change data is the optimal setting parameter in an ideal case, and for each individual driver, their preferences can be different. When determining the seat control parameter, after the target setting parameter is determined, the target setting parameter can be first detected for personalized preference. If it is determined that the target setting parameter conforms to the preference and driving habit of the current driver, it is considered that the control mode determined by the self-adaptive adjustment is reasonable, and the seat control parameter can be determined according to the target setting parameter; otherwise, if it is determined that the target setting parameter does not conform to the preference and driving habit of the current driver, it is considered that the control mode determined by the self-adaptive adjustment is unreasonable, and at this time, the seat control parameter can be determined by other ways.

[0098] Specifically, a plurality of historical preference parameters of the driver for the seat of the target vehicle can be acquired, each of which can be a set of seat setting parameters (such as seat front-back position, backrest inclination angle, seat height, etc.), and each set of historical preference parameters can be a set of parameters frequently used by the driver. For example, in some embodiments, all setting parameters of the seat of the target vehicle can be acquired, and then a plurality of setting parameters with the highest frequency of use can be selected from the acquired setting parameters as the historical preference parameters, where the plurality of setting parameters can be one or more, such as 10, which is not limited in the present application.

[0099] Then, the similarity between the target setting parameter and each historical preference parameter can be determined, and a preset threshold value can be set for the similarity. Here, the similarity is an index for measuring the similarity of two targets, and the value of the similarity is between 0 and 1. The greater the value of the similarity, the closer the two targets are. The smaller the value of the similarity, the less close the two targets are. It can be understood that the type of the similarity algorithm used in the embodiments of the present application is not limited, for example, any one of the following can be used: Levenshtein distance coefficient, Cosine similarity, Jaccard Similarity Coefficient, Euclidean distance, Manhattan distance, etc.

[0100] If the similarity between the target setting parameter and any one of the historical preference parameters is greater than the preset threshold value, it means that the target setting parameter meets the preference and driving habit of the current driver, and the seat control parameter can be determined according to the difference between the target setting parameter and the seat setting parameter. Conversely, if the similarity between the target setting parameter and all historical preference parameters is less than or equal to the preset threshold value, it means that the target setting parameter is likely to not meet the preference and driving habit of the current driver, and at this time, the historical preference parameter with the highest similarity to the target setting parameter can be selected, and the seat control parameter can be determined according to the difference between the historical preference parameter and the seat setting parameter.

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

[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 feature vector and a second length of the second feature vector;

[0104] a first value is obtained according to a product of the first length and the second length, and a second value is obtained according to an inner product of the first feature vector and the second feature vector;

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

[0106] In the embodiments of the present application, the similarity between the target setting parameter and the historical preference parameter can be determined by using a cosine similarity algorithm. Specifically, a feature vector corresponding to the target setting parameter is extracted, denoted as a first feature vector, and a feature vector corresponding to the historical preference parameter is extracted, denoted as a second feature vector. Then, the length of the first feature vector is determined, denoted as a first length, and the length of the second feature vector is determined, denoted as a second length. Next, the product of the first length and the second length is calculated as a first value, and the inner product of the first feature vector and the second feature vector is calculated as a second value. Then, the quotient of the first value and the second value is calculated as the similarity between the first feature vector and the second feature vector, that is, the similarity between the target setting parameter and the historical preference parameter.

[0107] In the embodiments of the present application, for the body posture change data, when the target vehicle is driving in a scene with more curves or frequent acceleration and deceleration, frequent changes may occur. If the determination of the seat setting parameter is performed on the body posture change data of multiple time points, frequent seat adjustment may occur, which is easy to cause bad driving experience. Therefore, in the embodiments of the present application, a regulated buffer area can be set, for example, when the body posture change data is small, it can be ignored. For example, when the roll angle or acceleration of the target vehicle changes within a certain range (for example, the roll angle changes less than 3°, and the acceleration changes less than 0.3 m / s 2 ), the system does not adjust the seat of the target vehicle, at this time, the corresponding body posture change data can be ignored.

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

[0109] Of course, in some embodiments, a self-adaptive adjustment function button can also be set for the driver. If the driver feels that the automatic adjustment of the seat support is too frequent and affects the driving experience, the self-adaptive adjustment function can be turned off. The present application does not limit this.

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

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

[0112] determining the road section length of the front road section according to the adjustment sensitivity level;

[0113] when the last determined front road section is driven through, determining the front road section of the road section length in the driving direction of the target vehicle according to the navigation information, and obtaining the road condition data of the currently determined front road section.

[0114] In the embodiments of the present application, the driver can also set an adjustment sensitivity level, which can be used to set the adjustment frequency. In the embodiments of the present application, a road section length corresponding to the adjustment sensitivity level can be determined, and then the seat is adjusted once every time the target vehicle drives through the road section length.

[0115] Specifically, in the embodiments of the present application, the road section length of the front road section 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, and the shorter the corresponding road section length, so that the seat is adjusted once every time the target vehicle drives through a very short road section; the lower the adjustment sensitivity level, the less frequent the adjustment, and the longer the corresponding road section length, so that the seat is adjusted once every time the target vehicle drives through a very long road section. Therefore, the adjustment sensitivity level and the road section length are negatively correlated in the embodiments of the present application, and the specific relationship between the two is not limited in the present application. When the road condition data of the front road section of the target vehicle is obtained to determine the seat control parameter used for the current adjustment, it can be detected whether the last determined front road section is driven through. If not, the adjustment is temporarily not performed; if driven through, a new front road section of a road section length can be determined according to the navigation information, and the corresponding road condition data is obtained.

[0116] In some embodiments, the present application can also set a feedback channel in the central control system of the target vehicle, so that the driver can timely feedback the experience of the seat support adaptive adjustment in different scenarios. For example, the driver can score the satisfaction degree of the seat adjustment function in the just ended driving process after driving, and simply describe the unsatisfied scenario through the multimedia system in the vehicle. The system can collect these feedback data for subsequent algorithm optimization.

[0117] In the embodiments of the present application, the system can learn the seat adjustment strategy individually according to the feedback of the driver and the long-term driving habit data. If the driver often drives on mountain roads with many curves and has fed back many times that the automatic adjustment is too frequent, the system can automatically adjust the adjustment strategy in this scenario, such as further increasing the adjustment threshold or reducing the adjustment sensitivity, to provide a driving experience that is more in line with the expectations of the driver.

[0118] With reference to Figure 3 The embodiments of the present application also provide a vehicle seat adaptive adjustment device, comprising:

[0119] An acquisition unit 310 is configured 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 is configured to determine road condition data of a front road section of the target vehicle according to the navigation information;

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

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

[0123] An execution unit 350 is configured to determine seat adjustment parameters corresponding to the target vehicle based on the vehicle body posture change data, and to perform adaptive adjustment on a seat of the target vehicle according to the seat adjustment parameters.

[0124] It can be understood that the contents in the above method embodiments are all applicable to the present device embodiments, the present device embodiments specifically implement the same functions as the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0125] With reference to Figure 4 The embodiments of the present application provide an electronic device, comprising:

[0126] At least one processor 410;

[0127] At least one memory 420 is configured to store at least one program;

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

[0129] Similarly, the contents in the method embodiments described above are applicable to the electronic device embodiments, the electronic device embodiments specifically implement the same functions as the method embodiments, and achieve the same beneficial effects as the method embodiments.

[0130] The electronic device embodiment also provides a computer readable storage medium, which stores a program executable by the processor 410, and the program executable by the processor 410 is used for executing the vehicle seat adaptive adjustment method described above when executed by the processor 410.

[0131] Similarly, the contents in the method embodiments described above are applicable to the computer readable storage medium embodiments, the computer readable storage medium embodiments specifically implement the same functions as the method embodiments, and achieve the same beneficial effects as the method embodiments.

[0132] In some alternative embodiments, the functions / operations mentioned in the block diagram can not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two blocks shown in succession can actually be executed substantially simultaneously or the blocks can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flowcharts of the present application are provided by way of example, and the purpose is to provide a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of a larger operation are independently executed.

[0133] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It can also be understood that a detailed discussion of the actual implementation of each module is unnecessary for an understanding of the present application. Rather, given the properties, functions and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be within the routine skill of an engineer, given the teachings of the present application. Therefore, a person skilled in the art can implement the present application as set forth in the claims without undue experimentation, using ordinary skill. It can also be understood that the disclosed specific concepts are merely illustrative and are not intended to limit the scope of the present application, the scope of the present application being determined by the full scope of the appended claims and their equivalents.

[0134] If the functions are implemented in software, the functions can be stored in or implemented as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium can be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, or twisted pair, then the coaxial cable, fiber optic cable, or twisted pair are included in the definition of medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), and Blu-Ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0135] In other words, like a human driver of a vehicle, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on. In some embodiments, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment using a machine learning algorithm. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on using a machine learning algorithm.

[0136] In other words, like a human driver of a vehicle, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on. In some embodiments, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment using a machine learning algorithm. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on using a machine learning algorithm.

[0137] It should be understood that portions of the application can be realized with hardware, software, firmware or a combination thereof. In the foregoing description, multiple steps or methods can be realized as software or firmware to be executed by a suitable instruction executing system. For example, if realized with hardware, and as in another embodiment, any one or a combination of the following technologies known in the art can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0138] In the above description of the present specification, the description of the terms "one embodiment", "another embodiment", or "certain embodiments" or the like means that the specific features, structures, materials or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can 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, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the claims and their equivalents.

[0140] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and these equivalent modifications or substitutions are included in the scope defined by the claims of the present application.

Claims

1. A method for adaptive adjustment of a vehicle seat, 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 road condition data of a front road section of the target vehicle according to the navigation information; determining estimated driving behavior data of the driver on the front road section by a trained prediction model according to the road condition data and the historical driving behavior data; determining vehicle body posture change data of the target vehicle on the front road section according to the estimated driving behavior data; determining seat control parameters corresponding to the target vehicle based on the vehicle body posture change data, and adaptively adjusting a seat of the target vehicle according to the seat control parameters; The method further comprises: obtaining an adjustment sensitivity level set by the driver; determining a road section length according to the adjustment sensitivity level; when the last determined front road section is driven, determining a front road section of the road section length in the driving direction of the target vehicle according to the navigation information, and obtaining road condition data of the currently determined front road section.

2. The method of claim 1, wherein, The method further comprises: obtaining current seat setting parameters of the target vehicle and body size data of the driver; determining target setting parameters corresponding to the seat of the target vehicle based on the vehicle body posture change data and the body size data; determining the seat control parameters corresponding to the target vehicle according to the target setting parameters and the seat setting parameters.

3. The method of claim 2, wherein, The method further comprises: obtaining a plurality of historical preference parameters of the driver for the seat of the target vehicle; determining the similarity between the target setting parameters and each of the historical preference parameters; if the similarity between the target setting parameters and any one of the historical preference parameters is greater than a preset threshold, calculating the difference between the target setting parameters and the seat setting parameters, and determining the seat control parameters according to the difference.

4. The method of claim 3, wherein, The method further comprises: extracting a first feature vector corresponding to the target setting parameters and a second feature vector corresponding to the historical preference parameters; determining a first length of the first feature vector and a second length of the second feature vector; obtaining a first value according to the product of the first length and the second length, and obtaining a second value according to the inner product of the first feature vector and the second feature vector; obtaining the similarity between the target setting parameters and the historical preference parameters according to the quotient of the second value and the first value.

5. The method of claim 1, wherein, The method further comprises: filtering the estimated driving behavior data by a low-pass filter to obtain filtered vehicle body posture change data.

6. The method of claim 1-5, wherein, The vehicle body posture change data comprises 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.

7. A vehicle seat self-adapting adjustment device, characterized by, The device comprises: an acquisition unit configured to acquire current navigation information of a target vehicle and historical driving behavior data of a driver of the target vehicle; a query unit configured to determine road condition data of a front road section of the target vehicle according to the navigation information; a prediction unit configured to determine estimated driving behavior data of the driver on the front road section according to the road condition data and the historical driving behavior data by using a trained prediction model; a processing unit configured to determine vehicle body posture change data of the target vehicle on the front road section according to the estimated driving behavior data; an execution unit configured to determine seat control parameters corresponding to the target vehicle based on the vehicle body posture change data, and to adaptively adjust a seat of the target vehicle according to the seat control parameters; The determination of the road condition data of the front road section of the target vehicle according to the navigation information comprises: acquiring an adjustment sensitivity level set by the driver; determining a road section length according to the adjustment sensitivity level; when a previously determined front road section is driven through, determining a front road section of the road section length in the driving direction of the target vehicle according to the navigation information, and acquiring road condition data of the currently determined front road section.

8. An electronic device, comprising: comprise: at least one processor; at least one memory configured to store 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 according to any one of claims 1-6.

9. A computer readable storage medium having stored therein a program that is executable by a processor, characterized in that, The program executable by the processor, when executed by the processor, is used to implement a vehicle seat adaptive adjustment method according to any one of claims 1-6.

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