Vehicle driving parameter calibration method and device, equipment and storage medium

Abstract

The invention discloses a vehicle driving parameter calibration method and device, equipment and a storage medium. The method comprises the steps that driving experience feedback information of a calibrated vehicle is acquired; extracting evaluation information of each driving experience dimension in the driving experience feedback information, and quantifying the evaluation information to obtain a score of each driving experience dimension; and adjusting the vehicle driving parameters based on the driving experience dimension score and a preset mapping relation between the vehicle driving parameters and the driving experience dimension score. According to the method, evaluation information of each driving experience dimension in subjective feeling of driving experience of a driver is extracted and quantified, and parameter adjustment is directly carried out on a calibrated vehicle faster and better according to a quantified result.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of autonomous driving technology, and in particular to a method, device, equipment and storage medium for calibrating vehicle driving parameters. Background Art

[0002] With the rapid development of electric vehicle intelligence, high-level intelligent driving is becoming more and more popular. The intelligent driving system realizes autonomous driving of vehicles by integrating multiple modules such as perception, prediction, planning and control. However, the performance of the system is highly dependent on the accuracy and adaptability of its regulatory algorithm model. Parameter calibration of vehicles using regulatory algorithm models is a key link to ensure stable operation of the system and adaptability to complex environments.

[0003] Existing driving parameter calibration methods mostly rely on manual adjustments by developers, which is inefficient. In addition, existing calibration methods are difficult to adjust parameters directly based on the driver's subjective feelings. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, equipment and storage medium for calibrating vehicle driving parameters, which extracts and quantifies the evaluation information of each driving experience dimension in the driver's subjective feeling of driving experience, and directly adjusts the parameters of the calibrated vehicle faster and better according to the quantified results.

[0005] In a first aspect, an embodiment of the present invention provides a method for calibrating vehicle driving parameters, the method comprising:

[0006] Obtain driving experience feedback information of the calibration vehicle;

[0007] Extracting evaluation information of each driving experience dimension from the driving experience feedback information, and quantifying the evaluation information to obtain a score for each driving experience dimension;

[0008] The vehicle driving parameters are adjusted based on the driving experience dimension score and a mapping relationship between a preset vehicle driving parameter and the driving experience dimension score.

[0009] In a second aspect, an embodiment of the present invention further provides a device for calibrating vehicle driving parameters, the device comprising:

[0010] An information acquisition module, used to obtain driving experience feedback information of the calibration vehicle;

[0011] A feature extraction module, used to extract evaluation information of each driving experience dimension in the driving experience feedback information, and quantify the evaluation information to obtain a score for each driving experience dimension;

[0012] A parameter adjustment module is used to adjust the vehicle driving parameters based on the driving experience dimension score and a mapping relationship between a preset vehicle driving parameter and the driving experience dimension score.

[0013] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0014] one or more processors;

[0015] a storage device for storing one or more programs,

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle driving parameter calibration method provided by the embodiment of the present disclosure.

[0017] In a fourth aspect, the embodiments of the present disclosure further provide a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to implement the vehicle driving parameter calibration method provided by the embodiments of the present disclosure.

[0018] In a fifth aspect, the embodiments of the present disclosure further provide a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the vehicle driving parameter calibration method provided by the embodiments of the present disclosure.

[0019] The present invention discloses a method, device, equipment and storage medium for calibrating vehicle driving parameters, the method comprising: obtaining driving experience feedback information of the calibrated vehicle; extracting evaluation information of each driving experience dimension in the driving experience feedback information, and quantifying the evaluation information to obtain each driving experience dimension score; adjusting the vehicle driving parameters based on the mapping relationship between the driving experience dimension score and the preset vehicle driving parameters and the driving experience dimension score. By using this method: by extracting and quantifying the evaluation information of each driving experience dimension in the driver's subjective feeling of driving experience, the parameters of the calibrated vehicle can be adjusted faster and better according to the quantified results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0021] Figure 1 A flow chart of a method for calibrating vehicle driving parameters provided by an embodiment of the present disclosure;

[0022] Figure 2A schematic diagram of the structure of a vehicle driving parameter calibration device provided by an embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0025] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0026] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0027] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0028] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0029] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0030] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0031] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0032] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0033] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0034] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0035] Embodiment 1

[0036] Figure 1 A flow chart of a vehicle driving parameter calibration method provided in an embodiment of the present disclosure. The embodiment of the present disclosure is suitable for providing a solution to the problem that the driving parameter calibration method mostly relies on manual adjustment by developers and is inefficient. The method can be executed by a vehicle driving parameter calibration method device, which can be implemented in the form of software and / or hardware, and optionally, by an electronic device, which can be a mobile terminal, a PC or a server, etc.

[0037] like Figure 1 As shown, a method for calibrating vehicle driving parameters provided by an embodiment of the present disclosure may specifically include the following steps:

[0038] S110: Obtain driving experience feedback information of the calibration vehicle.

[0039] In this embodiment, the calibrated vehicle may be a vehicle whose driving data is to be calibrated, and the driving experience feedback information may be a description of the driver's subjective feelings during driving collected by a data acquisition module of the intelligent driving system, including text feedback on comfort, safety, response speed, etc.

[0040] S120: extracting evaluation information of each driving experience dimension from the driving experience feedback information, and quantifying the evaluation information to obtain a score for each driving experience dimension.

[0041] Among them, the driving experience dimensions include at least one of the following: braking experience, acceleration experience, starting experience, following experience, ramp experience, cornering experience, and braking experience.

[0042] In this embodiment, the driving experience dimension may be a dimension for evaluating the driving experience. The driving experience dimension includes at least one of the following: braking experience, acceleration experience, starting experience, following experience, ramp experience, curve experience, and brake stop experience.

[0043] Specifically, the information irrelevant to the driving experience is deleted from the driving feedback information, and the text format is standardized, and the processed driving feedback information is input into the pre-trained long short-term memory network model to output the evaluation information of each driving experience dimension. The preset evaluation information corresponding score table is queried, and the score corresponding to the evaluation information is used as the score of each driving experience dimension.

[0044] Based on the above embodiment, extracting evaluation information of each driving experience dimension in driving feedback information includes the following steps:

[0045] a1) deleting driving experience-irrelevant information in the driving feedback information, and standardizing the text format of the driving feedback information to obtain standard driving feedback information.

[0046] The driving experience irrelevant information includes characters and stop words irrelevant to the driving experience.

[0047] b1) Input the standard driving feedback information into the pre-trained long short-term memory network model and output the evaluation information of each driving experience dimension.

[0048] Based on the above embodiment, inputting the standard driving feedback information into the pre-trained long short-term memory network model and outputting the evaluation information of each driving experience dimension includes the following steps:

[0049] b11) Obtain a pre-trained LSTM network model.

[0050] Among them, the long short-term memory network model includes: embedding layer and feature extraction layer.

[0051] b12) Inputting the standard driving feedback information into the embedding layer, converting each word in the standard driving feedback information into a word vector of a set dimension, and obtaining low-dimensional driving feedback information.

[0052] b13) Inputting the low-dimensional driving feedback information into the feature extraction layer to extract the emotional features of each driving experience dimension, and outputting the evaluation information of each driving experience dimension of the calibrated vehicle.

[0053] In this embodiment, the long short-term memory network model consists of an input layer, an embedding layer, a feature extraction layer, and an output layer. The input layer is used to receive the word vector of the driver's text feedback after preprocessing, the embedding layer is used to convert each word in the driver's feedback into a word vector of fixed dimension, the feature extraction layer uses multiple long short-term memory network units to extract features and transfer information from the input text sequence, and the output layer is used to output the evaluation information of each driving experience dimension of the calibrated vehicle.

[0054] On the basis of this embodiment, the training process of the model also includes: performing data enhancement operations on the original training data, performing synonym replacement on the text feedback, randomly inserting or deleting some words without changing the original meaning, and other operations to increase the diversity of the training data. Taking into account different driving scenarios and individual differences of drivers, a dynamic adjustment mechanism is adopted. The mapping relationship between the sentiment analysis results and the calibration parameters can be dynamically optimized based on the driver's historical sentiment analysis results and actual driving behavior data. For example, if a driver often maintains good driving habits when the sentiment analysis results are negative, then the calibration adjustment for the driver can be relatively conservative.

[0055] On the basis of the above embodiment, quantifying the evaluation information to obtain the scores of each driving experience dimension includes: querying a preset score table corresponding to the evaluation information, and using the scores corresponding to the evaluation information as the scores of each driving experience dimension.

[0056] In this embodiment, the score of each driving experience dimension may be a value between -1 and 1, where -1 represents an extremely negative emotion, 0 represents a neutral emotion, and 1 represents an extremely positive emotion.

[0057] S130: Adjust the vehicle driving parameters based on the driving experience dimension score and a preset mapping relationship between the vehicle driving parameters and the driving experience dimension score.

[0058] Specifically, an adjustment scheme for the vehicle driving parameters corresponding to the driving experience dimension score in the mapping relationship is searched, and the vehicle driving parameters are adjusted according to the adjustment scheme.

[0059] On the basis of the above embodiment, adjusting the vehicle driving parameters based on the driving experience dimension score and the mapping relationship between the preset vehicle driving parameters and the driving experience dimension score includes the following steps:

[0060] a2) Finding the adjustment scheme of the vehicle driving parameters corresponding to the driving experience dimension score in the mapping relationship. b2) Adjusting the vehicle driving parameters according to the adjustment scheme.

[0061]

[0062] Table 1

[0063] In this embodiment, the mapping relationship between the vehicle driving parameters and the driving experience dimension scores is shown in Table 1. The vehicle driving parameters are adjusted according to the driving experience dimension scores. The adjustment scheme may be the adjustment direction and adjustment amount of the vehicle driving parameters.

[0064] On the basis of the above embodiment, after obtaining the score of each driving experience dimension, it also includes: when the score of each driving experience dimension is greater than a set threshold, stopping adjusting the vehicle driving parameters.

[0065] In this embodiment, the set threshold may be a preset threshold, which is set according to actual conditions and is not specifically limited in the present invention. The set threshold may be 90 points.

[0066] Specifically, when the scores of each driving experience dimension are greater than a set threshold, the adjustment of the vehicle driving parameters is stopped.

[0067] The present invention discloses a method for calibrating vehicle driving parameters, including: obtaining driving experience feedback information of a calibrated vehicle; extracting evaluation information of each driving experience dimension in the driving experience feedback information, and quantifying the evaluation information to obtain scores of each driving experience dimension; adjusting the vehicle driving parameters based on the driving experience dimension scores and the mapping relationship between the preset vehicle driving parameters and the driving experience dimension scores. By using this method: by extracting and quantifying the evaluation information of each driving experience dimension in the driver's subjective feeling of driving experience, the parameters of the calibrated vehicle can be adjusted faster and better according to the quantified results.

[0068] Figure 2 The present invention also provides a schematic diagram of a vehicle driving parameter calibration method and device structure. Figure 2 As shown, an information acquisition module 210, a feature extraction module 220 and a parameter adjustment module 230.

[0069] An information acquisition module 210 is used to obtain driving experience feedback information of the calibration vehicle;

[0070] A feature extraction module 220 is used to extract evaluation information of each driving experience dimension in the driving experience feedback information, and quantify the evaluation information to obtain a score of each driving experience dimension;

[0071] The parameter adjustment module 230 is used to adjust the vehicle driving parameters based on the driving experience dimension score and a mapping relationship between the preset vehicle driving parameters and the driving experience dimension score.

[0072] The technical solution provided by the embodiment of the present disclosure utilizes this method: by extracting and quantifying the evaluation information of each driving experience dimension in the driver's subjective feeling of driving experience, the parameters of the calibrated vehicle can be adjusted faster and better directly according to the quantified results.

[0073] Furthermore, the feature extraction module 220 may be used to:

[0074] Deleting information irrelevant to driving experience in the driving feedback information, and standardizing the text format of the driving feedback information to obtain standard driving feedback information; the information irrelevant to driving experience includes: characters and stop words irrelevant to driving experience;

[0075] The standard driving feedback information is input into a pre-trained long short-term memory network model, and the evaluation information of each driving experience dimension is output.

[0076] Furthermore, the feature extraction module 220 may also be used for:

[0077] Acquire the pre-trained long short-term memory network model; wherein the long short-term memory network model includes: an embedding layer and a feature extraction layer;

[0078] Inputting the standard driving feedback information into the embedding layer, converting each word in the standard driving feedback information into a word vector of a set dimension, and obtaining low-dimensional driving feedback information;

[0079] The low-dimensional driving feedback information is input into the feature extraction layer to extract the emotional features of each driving experience dimension, and the evaluation information of each driving experience dimension of the calibrated vehicle is output.

[0080] Furthermore, the feature extraction module 220 may also be used for:

[0081] A preset scoring table corresponding to the evaluation information is queried, and the score corresponding to the evaluation information is used as the score of each driving experience dimension.

[0082] Furthermore, the parameter adjustment module 230 may also be used to:

[0083] Searching for an adjustment scheme for the vehicle driving parameter corresponding to the driving experience dimension score in the mapping relationship;

[0084] The vehicle driving parameters are adjusted according to the adjustment scheme.

[0085] Furthermore, the device can also be used for:

[0086] When the scores of the driving experience dimensions are all greater than a set threshold, the adjustment of the vehicle driving parameters is stopped.

[0087] Furthermore, the device can also be used for:

[0088] The driving experience dimension includes at least one of the following: braking experience, acceleration experience, starting experience, following vehicle experience, ramp experience, curve experience, and brake stop experience.

[0089] The above device can execute the methods provided by all the above embodiments of the present invention, and has the corresponding functional modules and beneficial effects of executing the above methods. For technical details not described in detail in this embodiment, please refer to the methods provided by all the above embodiments of the present invention.

[0090] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is given. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0091] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0092] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0093] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for calibrating vehicle driving parameters.

[0094] In some embodiments, the calibration method of the vehicle driving parameters may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the calibration method of the vehicle driving parameters described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the calibration method of the vehicle driving parameters in any other appropriate manner (e.g., by means of firmware).

[0095] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0097] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0100] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0101] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0102] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calibrating vehicle driving parameters, characterized in that: include: Obtain driving experience feedback information of the calibration vehicle; Extracting evaluation information of each driving experience dimension from the driving experience feedback information, and quantifying the evaluation information to obtain a score for each driving experience dimension; The vehicle driving parameters are adjusted based on the driving experience dimension score and a mapping relationship between a preset vehicle driving parameter and the driving experience dimension score.

2. The method according to claim 1, characterized in that The extracting evaluation information of each driving experience dimension from the driving feedback information includes: Deleting information irrelevant to driving experience in the driving feedback information, and standardizing the text format of the driving feedback information to obtain standard driving feedback information; the information irrelevant to driving experience includes: characters and stop words irrelevant to driving experience; The standard driving feedback information is input into a pre-trained long short-term memory network model, and the evaluation information of each driving experience dimension is output.

3. The method according to claim 2, characterized in that The step of inputting the standard driving feedback information into a pre-trained long short-term memory network model and outputting the evaluation information of each driving experience dimension includes: Acquire the pre-trained long short-term memory network model; wherein the long short-term memory network model includes: an embedding layer and a feature extraction layer; Inputting the standard driving feedback information into the embedding layer, converting each word in the standard driving feedback information into a word vector of a set dimension, and obtaining low-dimensional driving feedback information; The low-dimensional driving feedback information is input into the feature extraction layer to extract the emotional features of each driving experience dimension, and the evaluation information of each driving experience dimension of the calibrated vehicle is output.

4. The method according to claim 1, characterized in that The step of quantifying the evaluation information to obtain scores for each driving experience dimension includes: A preset scoring table corresponding to the evaluation information is queried, and the score corresponding to the evaluation information is used as the score of each driving experience dimension.

5. The method according to claim 1, characterized in that The adjusting the vehicle driving parameter based on the driving experience dimension score and a mapping relationship between a preset vehicle driving parameter and the driving experience dimension score includes: Searching for an adjustment scheme for the vehicle driving parameter corresponding to the driving experience dimension score in the mapping relationship; The vehicle driving parameters are adjusted according to the adjustment scheme.

6. The method according to claim 1, characterized in that After obtaining the scores of the driving experience dimensions, the method further includes: When the scores of the driving experience dimensions are all greater than a set threshold, the adjustment of the vehicle driving parameters is stopped.

7. The method according to claim 1, characterized in that The driving experience dimension includes at least one of the following: braking experience, acceleration experience, starting experience, following vehicle experience, ramp experience, curve experience, and brake stop experience.

8. A vehicle driving parameter calibration device, characterized in that: include: An information acquisition module, used to obtain driving experience feedback information of the calibration vehicle; A feature extraction module, used to extract evaluation information of each driving experience dimension in the driving experience feedback information, and quantify the evaluation information to obtain a score for each driving experience dimension; A parameter adjustment module is used to adjust the vehicle driving parameters based on the driving experience dimension score and a mapping relationship between a preset vehicle driving parameter and the driving experience dimension score.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle driving parameter calibration method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle driving parameter calibration method according to any one of claims 1 to 7 when executed.

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