Personalized vehicle driving style optimization method and device

Abstract

The invention relates to the technical field of intelligent driving, in particular to a personalized vehicle driving style optimization method and device, and the method comprises the steps: collecting the emotional state data of a driver and passengers and the driving state data of a driving vehicle; analyzing the emotional state data and the driving state data to determine an emotional change rule of the driver and passengers in each driving scene; determining the optimal driving state of the driver and the passenger in each driving scene according to the emotion change rule of the driver and the passenger in each driving scene; training a pre-constructed machine learning model by using the emotion change rule of the driver and the passenger in each driving scene and the optimal driving state of the driver and the passenger in each driving scene to obtain a user emotion response model; the user emotion response model is used for predicting the optimal driving state of the driver and passengers, and vehicle driving is adjusted according to the optimal driving state. Therefore, the problem that a traditional driving mode cannot comprehensively meet the requirements of drivers and passengers for personalized experience is solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving technology, and in particular to a personalized vehicle driving style optimization method and device based on emotion perception and big data analysis. Background Art

[0002] The current development of intelligent driving technology has greatly improved the safety and comfort of vehicles. However, in the actual driving process, there are individual differences in the emotions, driving habits and scene experiences of drivers and passengers. The traditional driving method cannot fully meet the needs of drivers and passengers for personalized experience. Especially in different driving scenarios, how to let the vehicle automatically adjust the driving style according to the emotional changes, habit feedback and driving environment of the drivers and passengers to improve the overall ride satisfaction of users has become a technical problem that needs to be solved urgently.

[0003] In addition, the current mainstream technical solution to this problem focuses on collecting user emotional data and analyzing the correlation between current emotions and the driving posture of the car. It has the following main shortcomings: in terms of collecting emotional data of drivers and passengers, the collection method needs to be expanded; in terms of generating the user's personalized driving style, it is still unable to automatically match the appropriate driving style according to different scenarios and real-time environmental perception, and continuously optimize and upgrade. Summary of the invention

[0004] The present invention provides a personalized vehicle driving style optimization method and device to solve the problem that the traditional driving method cannot fully meet the needs of drivers and passengers for personalized experience.

[0005] The first aspect of the present invention provides a personalized vehicle driving style optimization method, comprising the following steps: collecting emotional state data of the driver and passengers and driving state data of the driving vehicle; analyzing the emotional state data and the driving state data to determine the emotional change pattern of the driver and passengers in each driving scenario; determining the optimal driving state of the driver and passengers in each driving scenario according to the emotional change pattern of the driver and passengers in each driving scenario; training a pre-constructed machine learning model using the emotional change pattern of the driver and passengers in each driving scenario and the optimal driving state of the driver and passengers in each driving scenario to obtain a user emotional response model; predicting the optimal driving state of the driver and passengers using the user emotional response model, and adjusting the driving vehicle according to the optimal driving state.

[0006] Optionally, the collecting of emotional state data of the driver and the driving state data of the vehicle includes:

[0007] Collect the facial expressions, voice content and physiological data of the driver and passengers, perform expression recognition on the facial expressions to obtain the facial emotional state, extract the intonation, speech speed and content of the voice content to obtain the voice emotional state, perform emotional inference on the physiological data to obtain physiological emotional changes, wherein the emotional state data includes facial emotional state, voice emotional state and physiological emotional changes

[0008] The operating parameters and environmental conditions of the driving vehicle are collected, and the operating parameters and the environmental conditions are used as the driving state data, wherein the driving state data includes at least one driving state data of driving speed, acceleration, deceleration, steering wheel steering angle, vehicle vibration, and scene data.

[0009] Optionally, the analyzing the emotional state data and the driving state data to determine the emotional change pattern of the driver and passenger in each driving scenario includes:

[0010] fusing the emotional state data and the driving state data in a time-synchronous manner to obtain fused state data;

[0011] The fusion state data is analyzed using a multi-dimensional data analysis model to generate the emotion change pattern of the driver and passengers in each driving scenario.

[0012] Optionally, adjusting the driving of the vehicle according to the optimal driving style comprises:

[0013] At least one of the acceleration, deceleration, steering amplitude, steering smoothness, driving speed and suspension system of the driving vehicle is adjusted according to the optimal driving style.

[0014] A second aspect of the present invention provides a personalized vehicle driving style optimization device, comprising:

[0015] A collection module is used to collect emotional state data of the driver and passengers and driving state data of the vehicle;

[0016] An analysis module, used to analyze the emotional state data and the driving state data to determine the emotional change pattern of the driver and passengers in each driving scenario;

[0017] A determination module, used to determine the optimal driving state of the driver and passenger in each driving scenario according to the emotion change rules of the driver and passenger in each driving scenario;

[0018] A training module, used to train a pre-built machine learning model using the emotion change rules of the driver and passenger in each driving scenario and the optimal driving state of the driver and passenger in each driving scenario to obtain a user emotion response model;

[0019] The adjustment module is used to predict the optimal driving state of the driver and passenger using the user emotional response model, and adjust the driving vehicle according to the optimal driving state.

[0020] Optionally, the acquisition module includes:

[0021] A first collection unit is used to collect facial expressions, voice content and physiological data of the driver and passenger, and perform expression recognition on the facial expressions to obtain facial emotional state, extract intonation, speech speed and content from the voice content to obtain voice emotional state, and perform emotion inference on the physiological data to obtain physiological emotional changes, wherein the emotional state data includes facial emotional state, voice emotional state and physiological emotional changes;

[0022] The second acquisition unit is used to collect operating parameters and environmental conditions of driving the vehicle, and use the operating parameters and the environmental conditions as the driving state data, wherein the driving state data includes at least one driving state data of driving speed, acceleration, deceleration, steering wheel steering angle, vehicle vibration, and scene data.

[0023] Optionally, the analysis module includes:

[0024] a fusion unit, configured to perform time-synchronous fusion of the emotional state data and the driving state data to obtain fused state data;

[0025] An analysis unit is used to analyze the fusion state data using a multi-dimensional data analysis model to generate a pattern of emotional changes of the driver and passengers in each driving scenario.

[0026] Optionally, in the adjustment module, at least one of the acceleration, deceleration, steering amplitude, steering smoothness, driving speed and suspension system of the driving vehicle is adjusted according to the optimal driving style.

[0027] A third aspect of the present invention provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the personalized vehicle driving style optimization method as described in the above embodiment.

[0028] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned personalized vehicle driving style optimization method.

[0029] The personalized vehicle driving style optimization method and device proposed in the embodiment of the present invention collects the emotional data of the driver and passengers through multiple channels, combines it with the driving status data of the vehicle, correlates and analyzes the emotional changes of the driver and passengers in different scenarios, and extracts the driving posture that is most satisfactory to the user, so as to form an automated driving mode that adapts to different scenarios and individual needs, thereby improving the driving experience of the driver and passengers in different scenarios.

[0030] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0032] Figure 1 A flowchart of a personalized vehicle driving style optimization method provided by an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of the composition of emotional state data provided by an embodiment of the present invention;

[0034] Figure 3 A schematic diagram of the composition of driving status data provided by an embodiment of the present invention;

[0035] Figure 4 A block diagram of a personalized vehicle driving style optimization device provided by an embodiment of the present invention;

[0036] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0038] The personalized vehicle driving style optimization method and device according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

[0039] Figure 1 A schematic flow chart of a personalized vehicle driving style optimization method provided by an embodiment of the present invention.

[0040] like Figure 1 As shown, the personalized vehicle driving style optimization method includes the following steps:

[0041] In step S101, the emotional state data of the driver and the driving state data of the vehicle are collected.

[0042] In some embodiments, collecting emotional state data of the driver and the driving state data of the vehicle includes:

[0043] Collect the facial expressions, voice content and physiological data of the driver and passengers, and perform expression recognition on the facial expressions to obtain the facial emotional state, extract the intonation, speech speed and content of the voice content to obtain the voice emotional state, and perform emotional inference on the physiological data to obtain physiological emotional changes. Among them, the emotional state data includes facial emotional state, voice emotional state and physiological emotional changes

[0044] The operating parameters and environmental conditions of the driving vehicle are collected and used as driving state data, wherein the driving state data includes but is not limited to at least one of driving state data including driving speed, acceleration, deceleration, steering wheel steering angle, vehicle vibration, and scene data.

[0045] like Figure 2 and 3 As shown in the figure, in the actual implementation process, the emotional state of the driver and passengers is monitored in real time through various channels, such as capturing the facial expressions of the driver and passengers in real time through the camera installed in the car, analyzing the emotional state of the driver and passengers through facial expression recognition technology, and identifying various emotions such as happiness, tension, anger, fatigue, etc. The camera is trained by artificial intelligence algorithms and can accurately identify emotions based on facial features and muscle movement changes. The in-vehicle voice assistant collects the voice signals of the driver and passengers through a microphone, and uses natural language processing and emotion analysis algorithms to extract features such as intonation, speech speed, and voice rhythm from the voice to judge the emotional state of the driver and passengers. The emotions of the driver and passengers such as calmness, excitement, anxiety, etc. are recognized through voice data. The wearable devices (such as smart bracelets and watches) worn by the passengers communicate with the vehicle system through Bluetooth or wireless connection to monitor the physiological data of the driver and passengers such as heart rate, blood pressure, and skin electricity in real time. These physiological data can further reflect the emotional state, such as judging whether the driver and passengers are in a state of tension or relaxation through changes in heart rate and blood pressure.

[0046] In addition, drivers and passengers can be allowed to manually input emotional feedback through the in-vehicle display or mobile phone application to obtain user emotional feedback to enhance the accuracy of the data.

[0047] At the same time, the vehicle's operating parameters and environmental conditions are also collected as vehicle driving status data, including real-time collection of vehicle speed, acceleration and deceleration through the on-board ECU (electronic control unit) and speed sensor, analysis of vehicle acceleration and deceleration behavior; recording of steering wheel angle and steering control when the vehicle turns through the steering angle sensor, analysis of vehicle handling stability; recording of vehicle vibration during driving through the vibration sensor or accelerometer of the suspension system, especially when the road is uneven or when turning sharply, analysis of ride comfort; collection of relevant data about the vehicle's environment, such as road conditions, weather, traffic congestion, etc., through on-board sensors or network interfaces. These external conditions will directly affect the vehicle's driving style and driving experience.

[0048] In step S102, the emotional state data and the driving state data are analyzed to determine the emotional change pattern of the driver and passengers in each driving scenario.

[0049] In some embodiments, the emotional state data and driving state data are analyzed to determine the emotional change patterns of the driver and passengers in each driving scenario, including:

[0050] fusing the emotional state data and the driving state data in time synchronization to obtain fused state data;

[0051] The fusion state data is analyzed using a multi-dimensional data analysis model to generate the emotional change patterns of drivers and passengers in each driving scenario.

[0052] In the actual implementation process, the emotional state data and driving state data are synchronized in time and analyzed in combination with driving scenarios. The correlation between emotional data and driving state data can be calculated through a multi-dimensional data analysis model to calculate the degree of correlation between various emotions and driving behaviors. For example, when the vehicle accelerates or brakes suddenly, how do the emotions of the driver and passengers fluctuate, and whether these emotional changes can reflect their riding comfort and satisfaction; different driving scenarios are classified through on-board sensors and external environmental data. Common scenarios include: high-speed driving, urban road driving, rural road conditions, bad weather, traffic jams, etc. The degree of correlation between various emotions and driving behaviors is associated with the classified scenarios to generate the emotional change patterns of the driver and passengers in each driving scenario.

[0053] In step S103, the optimal driving state of the driver and passenger in each driving scenario is determined according to the emotion change rule of the driver and passenger in each driving scenario.

[0054] In step S104, the pre-built machine learning model is trained using the emotion change patterns of the driver and passengers in each driving scenario and the optimal driving state of the driver and passengers in each driving scenario to obtain a user emotion response model.

[0055] In the actual implementation process, the emotional fluctuations and satisfaction data of drivers and passengers in different driving scenarios are analyzed according to the emotional changes of drivers and passengers in each driving scenario, and the best driving status of drivers and passengers in various scenarios is extracted. For example, through analysis, it can be concluded that a driver prefers a relatively stable and slow driving style when the city road is congested; while on the highway, he may prefer a slightly faster and stable driving mode.

[0056] Furthermore, a large amount of emotional change patterns of drivers and passengers in each driving scenario and the optimal driving state of drivers and passengers in each driving scenario are collected to train pre-built machine learning models (such as decision trees, random forests, etc.) to obtain a personalized driving style model of drivers and passengers (i.e., a user emotional response model), so that the most suitable driving style can be recommended or automatically adjusted for users in different driving scenarios.

[0057] In step S105, the user emotional response model is used to predict the optimal driving state of the driver and passenger, and the driving of the vehicle is adjusted according to the optimal driving state.

[0058] In some embodiments, adjusting driving of the vehicle according to an optimal driving style includes:

[0059] At least one of the acceleration, deceleration, steering amplitude, steering smoothness, driving speed and suspension system of the driving vehicle is adjusted according to the optimal driving style.

[0060] In the actual implementation process, the user emotional response model is used to predict the optimal driving state of the driver and passengers, and the most suitable driving style is recommended or automatically adjusted for the user based on the optimal driving state, including the following items:

[0061] Acceleration and deceleration optimization: According to the emotional response of the driver and passengers, the acceleration and deceleration of the vehicle are adjusted to avoid excessive acceleration or sudden braking, thereby improving driving comfort;

[0062] Steering Smoothness Adjustment: When turning, the steering sensitivity and turning smoothness are optimized according to the user's preferences, ensuring that the turning operation can meet the control requirements without excessively affecting the ride comfort;

[0063] Speed ​​control: Under the premise of meeting road safety requirements, the vehicle's speed is adjusted according to different driving scenarios and emotional reactions to make it consistent with the speed preferences of the driver and passengers;

[0064] Suspension system adjustment: By adjusting the hardness or softness of the vehicle's suspension system, the vibration and bumps of the vehicle can be reduced under rugged or uneven road conditions to ensure a smooth riding experience.

[0065] In some embodiments, it also includes:

[0066] During driving, the vehicle's driving style is dynamically adjusted based on real-time emotional state data, driving state data, and external environmental conditions to ensure that the user's riding experience is always in the best state, including:

[0067] Real-time emotional feedback: The system can monitor the emotional state of the driver and passengers in real time through cameras, voice and physiological monitoring devices. If the system detects significant fluctuations in the user's emotions (such as anxiety or dissatisfaction), it will automatically adjust the driving style to improve the user's experience.

[0068] Automatic scene matching: It can automatically match the best driving style analyzed previously according to the current driving scene (such as highways, urban roads, etc.) and emotional data. For example, on the highway, the system may choose a relatively stable and fast driving style, while in congested urban roads, the system will choose a slower and more stable driving style.

[0069] Feedback and learning: The system will continuously optimize the personalized driving model based on the user's manual feedback or emotion monitoring data. Through the self-learning mechanism, it will gradually improve the prediction accuracy of the user's driving preferences, so as to better meet the needs of different users in different scenarios.

[0070] In summary, the personalized vehicle driving style optimization method proposed in the embodiment of the present invention can effectively capture the emotional changes of drivers and passengers in different driving scenarios through real-time correlation analysis of emotion perception and driving data, and generate a personalized driving style based on this; in actual driving, the vehicle can automatically match the driving mode that best suits the preferences of the drivers and passengers according to the current driving scenario, thereby significantly improving the driving comfort and satisfaction; it has high flexibility, adapts to the driving habits and emotional needs of different users, and can continuously optimize the user experience; in addition, the method can be applied to various types of intelligent driving cars, semi-automatic or fully automatic driving systems, and vehicle-mounted systems with emotion perception and intelligent control capabilities, and is widely applicable to advanced driver assistance systems (ADAS) and automatic driving scenarios.

[0071] Next, the personalized vehicle driving style optimization device proposed according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0072] Figure 4 It is a block diagram of a personalized vehicle driving style optimization device according to an embodiment of the present invention.

[0073] like Figure 4 As shown, the personalized vehicle driving style optimization device 40 includes: a collection module 401, an analysis module 402, a determination module 403, a training module 404 and an adjustment module 405.

[0074] Among them, the acquisition module 401 is used to collect the emotional state data of the driver and the driving state data of the driving vehicle. The analysis module 402 is used to analyze the emotional state data and the driving state data to determine the emotional change pattern of the driver and the passenger in each driving scenario. The determination module 403 is used to determine the optimal driving state of the driver and the passenger in each driving scenario according to the emotional change pattern of the driver and the passenger in each driving scenario. The training module 404 is used to train the pre-built machine learning model using the emotional change pattern of the driver and the passenger in each driving scenario and the optimal driving state of the driver and the passenger in each driving scenario to obtain a user emotional response model. The adjustment module 405 is used to predict the optimal driving state of the driver and the passenger using the user emotional response model, and adjust the driving of the vehicle according to the optimal driving state.

[0075] In some embodiments, the acquisition module 401 includes:

[0076] The first collection unit is used to collect facial expressions, voice content and physiological data of the driver and passenger, and perform expression recognition on the facial expressions to obtain the facial emotional state, extract the intonation, speech speed and content of the voice content to obtain the voice emotional state, and perform emotion inference on the physiological data to obtain physiological emotional changes, wherein the emotional state data includes facial emotional state, voice emotional state and physiological emotional changes;

[0077] The second acquisition unit is used to collect operating parameters and environmental conditions of driving the vehicle, and use the operating parameters and environmental conditions as driving state data, wherein the driving state data includes at least one driving state data of driving speed, acceleration, deceleration, steering wheel steering angle, vehicle vibration, and scene data.

[0078] In some embodiments, the analysis module 402 includes:

[0079] A fusion unit, used for time-synchronously fusing the emotional state data and the driving state data to obtain fused state data;

[0080] The analysis unit is used to analyze the fusion state data using a multi-dimensional data analysis model to generate the emotion change pattern of the driver and passengers in each driving scenario.

[0081] In some embodiments, in the adjustment module 405, at least one of the acceleration, deceleration, steering amplitude, steering smoothness, driving speed and suspension system of the driving vehicle is adjusted according to the optimal driving style.

[0082] It should be noted that the aforementioned explanation of the embodiment of the personalized vehicle driving style optimization method is also applicable to the personalized vehicle driving style optimization device of this embodiment, and will not be repeated here.

[0083] According to the personalized vehicle driving style optimization device proposed in the embodiment of the present invention, through real-time correlation analysis of emotion perception and driving data, it is possible to effectively capture the emotional changes of the driver and passengers in different driving scenarios, and generate a personalized driving style based on this; in actual driving, the vehicle can automatically match the driving mode that best suits the driver and passengers' preferences according to the current driving scenario, thereby significantly improving the driving comfort and satisfaction; it has high flexibility, can adapt to the driving habits and emotional needs of different users, and can continuously optimize the user experience; in addition, the device can be applied to various types of intelligent driving cars, semi-automatic or fully automatic driving systems, and vehicle-mounted systems with emotion perception and intelligent control capabilities, and is widely applicable to advanced driver assistance systems (ADAS) and automatic driving scenarios.

[0084] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0085] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0086] When the processor 502 executes the program, the personalized vehicle driving style optimization method provided in the above embodiment is implemented.

[0087] Furthermore, the electronic device further comprises:

[0088] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0089] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0090] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0091] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0092] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0093] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0094] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the above-mentioned personalized vehicle driving style optimization method is implemented.

[0095] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0096] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0097] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0099] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0100] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0101] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0102] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A personalized vehicle driving style optimization method, characterized in that: The following steps are involved: Collect emotional state data of drivers and passengers and driving state data of the vehicle; Analyzing the emotional state data and the driving state data to determine the emotional change pattern of the driver and passenger in each driving scenario; Determining the optimal driving state of the driver and passenger in each driving scenario according to the emotional change rules of the driver and passenger in each driving scenario; The pre-built machine learning model is trained using the emotion change rules of the driver and passenger in each driving scenario and the optimal driving state of the driver and passenger in each driving scenario to obtain a user emotion response model; The user emotional response model is used to predict the optimal driving state of the driver and passenger, and the driving vehicle is adjusted according to the optimal driving state.

2. The personalized vehicle driving style optimization method according to claim 1, characterized in that: The collecting of emotional state data of the driver and the driving state data of the vehicle includes: Collect the facial expressions, voice content and physiological data of the driver and passengers, perform expression recognition on the facial expressions to obtain the facial emotional state, extract the intonation, speech speed and content of the voice content to obtain the voice emotional state, perform emotional inference on the physiological data to obtain physiological emotional changes, wherein the emotional state data includes facial emotional state, voice emotional state and physiological emotional changes The operating parameters and environmental conditions of the driving vehicle are collected, and the operating parameters and the environmental conditions are used as the driving state data, wherein the driving state data includes at least one driving state data of driving speed, acceleration, deceleration, steering wheel steering angle, vehicle vibration, and scene data.

3. The personalized vehicle driving style optimization method according to claim 1, characterized in that: The analyzing the emotional state data and the driving state data to determine the emotional change pattern of the driver and passenger in each driving scenario includes: fusing the emotional state data and the driving state data in a time-synchronous manner to obtain fused state data; The fusion state data is analyzed using a multi-dimensional data analysis model to generate the emotion change pattern of the driver and passengers in each driving scenario.

4. The personalized vehicle driving style optimization method according to claim 1, characterized in that: The adjusting the driving vehicle according to the optimal driving style comprises: At least one of the acceleration, deceleration, steering amplitude, steering smoothness, driving speed and suspension system of the driving vehicle is adjusted according to the optimal driving style.

5. A personalized vehicle driving style optimization device, characterized in that: include: A collection module is used to collect emotional state data of the driver and passengers and driving state data of the vehicle; An analysis module, used to analyze the emotional state data and the driving state data to determine the emotional change pattern of the driver and passengers in each driving scenario; A determination module, used to determine the optimal driving state of the driver and passenger in each driving scenario according to the emotion change rules of the driver and passenger in each driving scenario; A training module, used to train a pre-built machine learning model using the emotion change rules of the driver and passenger in each driving scenario and the optimal driving state of the driver and passenger in each driving scenario to obtain a user emotion response model; The adjustment module is used to predict the optimal driving state of the driver and passenger using the user emotional response model, and adjust the driving vehicle according to the optimal driving state.

6. The personalized vehicle driving style optimization device according to claim 5, characterized in that: The acquisition module comprises: A first collection unit is used to collect facial expressions, voice content and physiological data of the driver and passenger, and perform expression recognition on the facial expressions to obtain facial emotional state, extract intonation, speech speed and content from the voice content to obtain voice emotional state, and perform emotion inference on the physiological data to obtain physiological emotional changes, wherein the emotional state data includes facial emotional state, voice emotional state and physiological emotional changes; The second acquisition unit is used to collect operating parameters and environmental conditions of driving the vehicle, and use the operating parameters and the environmental conditions as the driving state data, wherein the driving state data includes at least one driving state data of driving speed, acceleration, deceleration, steering wheel steering angle, vehicle vibration, and scene data.

7. The personalized vehicle driving style optimization device according to claim 5, characterized in that: The analysis module comprises: a fusion unit, configured to perform time-synchronous fusion of the emotional state data and the driving state data to obtain fused state data; An analysis unit is used to analyze the fusion state data using a multi-dimensional data analysis model to generate a pattern of emotional changes of the driver and passengers in each driving scenario.

8. The personalized vehicle driving style optimization device according to claim 5, characterized in that: In the adjustment module, at least one of the acceleration, deceleration, steering amplitude, steering smoothness, driving speed and suspension system of the driving vehicle is adjusted according to the optimal driving style.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the personalized vehicle driving style optimization method according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the personalized vehicle driving style optimization method as described in any one of claims 1 to 4.

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