Intelligent cabin based on driver characteristics and intelligent driving system control method

Abstract

The invention discloses an intelligent cabin and intelligent driving system control method based on driver characteristics, and the method comprises the following steps: S1, collecting input signals through various sensors in a vehicle, and completing the extraction of the physiological characteristics and driving characteristics of a driver; s2, integrating the user identity information and the extracted driver characteristics to complete matching, and memorizing and storing the digital characteristics; s3, confirming the user identity, correspondingly adjusting the cabin and the intelligent driving system if the user identity is confirmed, and maintaining the current situation and the default value if the user identity is not confirmed; s4, in the new driving process, the system continues to learn the characteristics of the driver, the previous memory characteristics are updated, and normally-open and normally-new states are kept all the time; the intelligent cabin is endowed with a multi-dimensional memory function, and the intelligent driving system designs parameters based on user characteristics; the user identity is confirmed in multiple modes, the requirements of multiple users are met, the user experience and satisfaction degree are improved, and the personalized driving feeling is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent driving and intelligent cockpit control, and in particular to an intelligent cockpit and intelligent driving system control method based on driver characteristics. Background Art

[0002] With the rapid development of information technology, the automotive industry has undergone profound changes, with smart cockpits and smart driving systems becoming the focus. In addition, consumer demand has increased, and is no longer limited to traditional transportation functions. Consumers expect cars to provide comfortable, convenient, safe and personalized driving experiences. Smart cockpits meet comfort and entertainment needs, and smart driving systems reduce the burden on drivers and improve safety.

[0003] However, there are some problems with the existing smart cockpit and smart driving systems. At the level of smart driving, for the L2 assisted driving function with the highest installation rate, the fixed design requires that lateral control must be carried out along the center line of the lane, and the position error must not exceed 10cm on a straight road and 20cm on a curve; the longitudinal fixed following distance, braking distance, etc. make it difficult for the system to meet the personalized needs of different users, because people drive more casually, and do not require to be centered, and must control a fixed distance from the front car. In order to improve the above problems, existing solutions often use driving style classification and design corresponding thresholds to improve the adaptability of the system. The patent with announcement number CN201910017822.3 discloses a vehicle assisted driving control method, that is, by obtaining information including dynamic acceleration and deceleration of the vehicle to determine the driver type, and mapping the obtained driver type to the driving parameters of the assisted driving system; thereby providing the driver with a personalized anthropomorphic driving experience and improving the experience brought to the driver by the vehicle's assisted driving function.

[0004] Although the above scheme has certain improvements compared to the fixed threshold, it also has the following problems: First, currently there is no unified standard for the definition, classification, and identification method of driving style or type in academia or industry; Second, driving style / type is greatly affected by internal and external environments, such as driving scenes, driver emotions, etc. Whether it is offline or online identification, can the accuracy be guaranteed and whether the identification results are universal for the same user.

[0005] Correspondingly, in the field of intelligent cockpit control, the existing improvements are more focused on the design of human-computer interaction, mainly including voice control and gesture control, which are more used for the driving experience during the current driving task; and with the continuous improvement of cockpit intelligence, there are more and more controllable components and systems in the cockpit, but most of them are limited to functions such as seat memory and cannot support multi-user information memory and matching. For example, the patent with announcement number CN202410536950.X discloses a control method, device, computer equipment and storage medium for an intelligent cockpit. The method includes: when it is detected that the user is performing an unlocking operation, the user's biometric information is collected, and the biometric information is compared with the pre-stored information in the pre-stored information library, the target control scene is determined based on the comparison result, and the cockpit configuration parameters of the target vehicle are adjusted according to the target control scene, which is used to solve the problem that passengers need to enter the car to adjust the cockpit configuration.

[0006] In response to the above-mentioned problems existing in intelligent driving and intelligent cabin, the present invention proposes a control method of an intelligent cockpit and an intelligent driving system based on driver characteristics. Summary of the invention

[0007] To solve the above problems, the present invention proposes a smart cockpit and smart driving system control method based on driver characteristics. With regard to the anthropomorphic design level of the smart driving system, the present invention abandons the idea based on driver style and type identification, and instead adopts the extraction and storage of driving characteristics based on the driver's dynamic driving process to more directly adjust the smart driving system. In terms of smart cockpit control, the present invention extracts and stores the user's physiological characteristics, and can adapt multiple software and hardware systems in the cockpit to user habits when in use, thereby opening up a comfortable driving experience for users. At the same time, during new driving tasks, the driver's characteristics will be continuously updated and memorized, allowing users to always have new experiences.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for controlling an intelligent cockpit and an intelligent driving system based on driver characteristics, comprising the following steps: S1: Use various sensors in the car to collect input signals and extract the driver's physiological characteristics and driving characteristics; S2: Combine the user identity information and the extracted driver characteristics to complete the matching, and memorize and store the digitized characteristics; S3: User identity confirmation. If confirmed, the cockpit and intelligent driving system will be adjusted accordingly. If not confirmed, the status quo and default values ​​will be maintained or user-defined adjustment parameters will be used. S4: During a new driving process, the system will continue to learn the driver's characteristics and update the previously remembered characteristics to keep it always up to date.

[0009] Preferably, a smart cockpit and smart driving system based on driver characteristics includes a driver physiological characteristics extraction module, a driving characteristics extraction module, a characteristics storage memory module, a user identity confirmation module, and a driver characteristics calling module.

[0010] Preferably, the driver physiological characteristic extraction module counts the driver's cabin settings based on probability from three different time periods, morning, noon and evening, and two dimensions of mileage, and determines the setting items corresponding to the maximum probability event.

[0011] Preferably, the setting items include seat position, seat back angle, rearview mirror angle, steering wheel adjustment position, music type, volume setting, interior ambient light color and brightness, fragrance system setting, and navigation mode.

[0012] Preferably, the driving characteristics extraction module, that is, in terms of longitudinal following, collects the driving parameters of dynamically following the front vehicle at different speeds (i.e., dynamic driving parameters) through millimeter-wave radar; in terms of lateral aspect: based on the front-view camera, collects the driver's dynamic parameters at different speeds and different curvature radii; the collected data after filtering is matched with the lateral and longitudinal driving scenes and dynamic parameters based on the scene classifier and probability statistical analysis.

[0013] Preferably, the dynamic driving parameters include lateral and longitudinal driving parameters, wherein the longitudinal driving parameters include: stable following distance, following stopping distance, braking timing, longitudinal acceleration, and longitudinal acceleration change rate; the lateral signals include: posture error, steering wheel correction amplitude, steering wheel correction frequency, lane departure warning timing, lane departure correction timing, curve curvature, and lateral acceleration.

[0014] Preferably, the user identity confirmation module matches and stores the user characteristics; the user identity confirmation module can confirm the user information based on any of the methods such as the driver's face recognition system, fingerprint recognition system or mobile phone QR code verification.

[0015] Preferably, when the user starts a new driving task, after the user identity is confirmed, the user characteristics stored in memory can be called up by the user, and the various components and software systems in the cockpit are automatically adjusted to the memory settings; the driving characteristic parameters are imported into the intelligent driving system to replace the default values; if the identity confirmation fails, the cockpit settings remain the same, and the intelligent driving system parameters will remain the default values.

[0016] Preferably, when the user starts a new driving task, new feature extraction and memory storage are started.

[0017] The beneficial effects of the present invention are as follows: 1. The present invention endows the cockpit with more setting memory functions from multiple dimensions; by extracting the physiological characteristics of the driver, statistical analysis is performed from different time periods of morning, noon and evening and from two dimensions of maintaining mileage, and setting items such as seat position, backrest angle, rearview mirror angle, music type, etc. are determined, thereby reducing the tedious operations of the user every time after getting on the car; improving the user experience, meeting the user's needs for convenience and personalization, and thus increasing user stickiness.

[0018] 2. The present invention designs dynamic intelligent driving parameters based on the user's driving characteristics; by collecting and analyzing longitudinal following and lateral driving parameters, including stable following distance, braking timing, steering wheel correction amplitude, etc., the intelligent driving system is more in line with the user's driving habits; this design, while meeting regulations, gives the intelligent driving function more humanized characteristics. It is no longer a single default setting, but can be adjusted according to the driving characteristics of different users; it can better adapt to the driving styles of different users, meet the diverse needs of customers, and improve user satisfaction.

[0019] 3. The present invention can confirm user information based on face recognition, fingerprint recognition or mobile phone QR code verification through the user identity confirmation module; it can store and call respective feature settings for different users to meet the needs of multiple users using the same vehicle; whether it is a family shared vehicle or a car rental scenario, it can provide personalized driving experience for different users. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a method for controlling an intelligent cockpit and an intelligent driving system based on driver characteristics; Figure 2 This is a schematic diagram of the structure of the smart cockpit and smart driving system; Figure 3 This is a schematic diagram of the structure of the smart cockpit and smart driving system steps; Figure 4 Schematic diagram of the scene classifier structure. DETAILED DESCRIPTION

[0021] Because there is no unified standard for the definition, classification and identification of driving style or type in the existing technology in intelligent driving; driving style and type are greatly affected by internal and external environment, the accuracy of offline and online identification is difficult to guarantee, and the universality of identification results for the same user is questionable; simple driver type and intelligent driving system design parameters are a single mapping relationship, which is not adaptable enough; In terms of smart cockpit control, existing technologies focus on human-computer interaction design (voice, gesture control), which is mainly used for the driving experience of current driving tasks. With the improvement of cockpit intelligence, the number of controllable components and systems has increased, but most of them are limited to functions such as seat memory and cannot support multi-user information memory and matching.

[0022] Therefore, if Figure 1 As shown, the present invention proposes a method for controlling an intelligent cockpit and an intelligent driving system based on driver characteristics, comprising the following steps: S1: Use various sensors in the car to collect input signals and extract the driver's physiological characteristics and driving characteristics; Use various sensors in the car to collect input signals; for the extraction of the driver's physiological characteristics, use pressure sensors and bioelectric sensors installed in the seat, steering wheel and other parts to monitor the driver's body pressure distribution, heart rate and other physiological indicators in real time; at the same time, use the camera in the car to capture the driver's facial expressions, eye conditions and other information; For driving characteristics extraction, the vehicle's own sensors, such as speed sensors, acceleration sensors, steering angle sensors, millimeter-wave radars, and front-view cameras, are used to collect data such as the vehicle's speed, acceleration, steering angle, and distance to the vehicle in front during driving. The driver's physiological characteristics extraction process is to collect statistics on the driver's cockpit settings based on probability from three different time periods: morning, noon, and evening, as well as two dimensions: mileage. For example, in the morning, the driver's settings for seat position, seat back angle, rearview mirror angle, steering wheel adjustment posture, etc. when entering the vehicle for the first time are recorded. As the driving mileage increases, observe whether the driver will adjust these settings, and record the frequency and magnitude of the adjustments. Repeat the same process in the noon and evening periods. By statistically analyzing the cabin settings at different time periods and mileages, the settings corresponding to the highest probability event are determined; these settings include seat position, seat back angle, rearview mirror angle, steering wheel adjustment position, music type, volume setting, interior ambient light color and brightness, fragrance system setting, navigation mode, etc.; for example, if in most morning driving, the driver adjusts the seat to a specific position and angle, and selects a specific music type and volume, then this setting combination is considered to be the physiological characteristic setting corresponding to the highest probability event of the driver in the morning period; In terms of longitudinal following, the millimeter-wave radar continuously collects driving parameters of the dynamic following vehicle in front at different vehicle speeds; at different vehicle speeds, the stable following distance, following stopping distance, braking timing, longitudinal acceleration, longitudinal acceleration change rate and other parameters between the vehicle and the vehicle in front are recorded; for example, when driving at low speeds, the driver's choice of following distance and braking timing when following the vehicle in front is observed; when driving at high speeds, the changes in longitudinal acceleration and acceleration change rate are recorded; In the horizontal direction, the driver's dynamic parameters at different vehicle speeds and different curvature radii are collected based on the front-view camera; including posture error, steering wheel correction amplitude, steering wheel correction frequency, lane departure warning timing, lane departure correction timing, curve curvature, lateral acceleration, etc.; for example, when driving on a curve, the driver's steering wheel correction amplitude and frequency, as well as the choice of lane departure warning and correction timing are recorded; The collected longitudinal and lateral driving parameters are filtered to remove noise and abnormal data; then, based on the scene classifier and probability statistical analysis, the lateral and longitudinal driving scenes are matched with the dynamic parameters; for example, according to different road types (highways, urban roads, mountain roads, etc.) and driving scenes (straight driving, curve driving, following a vehicle, etc.), the driving parameters are classified and counted to determine the typical driving characteristics of the driver in different scenes.

[0023] S2: Combine the user identity information and the extracted driver characteristics to complete the matching, and memorize and store the digitized characteristics; The user's identity information is obtained by connecting the vehicle system with the user's mobile phone to obtain the user's mobile phone QR code information; when the user gets on the vehicle, the vehicle system scans the QR code on the user's mobile phone and reads the user's unique identification information; Using the DMS (Driver Monitoring System) facial recognition function in the car, the driver's facial image is captured through the camera and compared with the pre-stored user's facial features to determine the user's identity; Alternatively, fingerprint recognition technology can be used to set fingerprint recognition sensors on the vehicle's start button, steering wheel, etc., so that users can confirm their identity by pressing their fingerprints when starting the vehicle; The feature matching process matches the acquired user identity information with the extracted driver features. For example, if the driver is determined to be user A through face recognition, the system will search for the historical driving feature records corresponding to user A. If there are historical records, the currently extracted driving features will be compared with the historical records to check whether there are significant changes. If the changes are within an acceptable range, it is considered that the features of the same user are successfully matched. If the changes are significant, it may be necessary to further confirm the user's identity or relearn the user's driving features. Memory storage method, storing the digital driver characteristics in the memory of the vehicle system; a database can be used to create an independent record for each user, including the user's identity information, physiological characteristic setting items, driving characteristic parameters, etc.; for physiological characteristic setting items, specific numerical values ​​can be used to represent them, such as the coordinates of the seat position, the degree of the seat back angle, the volume of the music, etc.; for driving characteristic parameters, a set of numerical values ​​or curves can be used to represent them, such as the stable following distance curve at different vehicle speeds, the relationship curve between the steering wheel correction amplitude and the vehicle speed, etc.; In order to improve storage efficiency and data security, data compression and encryption technology can be used; the stored data should be backed up regularly to prevent data loss; at the same time, data access permissions should be set so that only authorized users and systems can access and modify the stored driver characteristic data.

[0024] S3: When the user gets in the car, the vehicle system confirms the user's identity through mobile phone QR code, DMS face recognition or fingerprint recognition. If the confirmation is successful and matches the memory information, the cockpit automatically switches to the user's memory settings, and the intelligent driving system imports the memory driving characteristic parameters; if the identity confirmation fails, the cockpit settings remain the same, the user customizes the settings, and the intelligent driving system parameters remain the default values; User identity confirmation process: When the user gets on the vehicle, the vehicle system automatically starts the user identity confirmation procedure. This can be triggered by the user's operation, such as opening the door, starting the vehicle, etc. The system also starts multiple methods such as mobile phone QR code scanning, DMS face recognition and fingerprint recognition to improve the accuracy and speed of identity confirmation. If the mobile phone QR code scan is successful, the system reads the user information in the QR code and compares it with the user record in the memory bank; if face recognition or fingerprint recognition is successful, the system also compares the recognition result with the memory bank; if the confirmation results of multiple methods are consistent, the user identity is considered to be confirmed; Automatic cockpit switching settings: if the user's identity is confirmed and matches the memory information, the cockpit system automatically switches to the user's memory settings; for example, the positions of seats, rearview mirrors and other components are automatically adjusted according to the stored settings such as seat position, seat back angle, and rearview mirror angle; at the same time, the corresponding system parameters are automatically adjusted according to the user's preferences such as music type, volume setting, interior ambient light color and brightness, and fragrance system settings; the intelligent driving system imports memory driving characteristic parameters; for example, according to the stored stable following distance, braking timing, steering wheel correction range and other parameters, the settings of the intelligent driving assistance system are adjusted to adapt to the user's driving habits; Identity confirmation failure processing: If the user identity confirmation fails, the cabin settings remain the same and the user needs to adjust the settings by himself; for example, components such as seats and rearview mirrors remain in the current position, and system parameters such as music and ambient lights also remain in the default state.

[0025] S4: During a new driving process, the system will continue to learn the driver's characteristics and update the previously memorized characteristics to always keep it up to date; While the vehicle is driving, the system continuously monitors the driver's physiological state (such as heart rate, body pressure distribution, facial expressions and eye conditions) and driving behavior (vehicle speed, acceleration, steering angle, distance to the vehicle in front, etc.) through in-vehicle sensors and cameras; after the driving trip, the system compares the new data with the memory characteristics, finds the differences, and analyzes whether the differences are temporary factors or changes in habits; if it is determined that the driver's habits have changed, the system updates the memory characteristics, including driving characteristic parameters (such as following distance) and physiological characteristic settings (such as seat position, music volume); the system introduces a feedback mechanism, and at appropriate times, asks the driver about his satisfaction with the current settings through voice prompts or display screens, and further adjusts the characteristic parameters based on the feedback; regularly organizes and optimizes the learned driver characteristics, removes abnormal data caused by accidental factors, retains representative data, and continuously optimizes the learning algorithm to maintain a constantly open and new state, providing the driver with a personalized and comfortable driving experience.

[0026] The main function of the feature extraction module is to extract the driver's dynamic driving characteristics based on the state signal of the driver's feedback on the cockpit operation behavior and the output signal of the intelligent driving system. The driver's characteristics mainly include physiological characteristics related to the intelligent cabin and driving characteristics related to intelligent driving. The input of this module mainly includes but is not limited to the following cockpit signals and intelligent driving system output signals: After the above signal is input, the physiological characteristic module counts the driver's physiological characteristics based on probability from two dimensions: time period (morning, noon, evening) and mileage, and determines the event with the highest probability. Taking volume adjustment as an example, the description is as follows: in a driving task in time period T1, the driving mileage is Z (not less than 10km), and within Z1 mileage, the volume is set to V11; within Z2 mileage, the volume is set to V12; within Z3 mileage, the volume is set to V13; During the T1 period: Case 1: If the difference between Z1, Z2 and Z3 is large, then the volume memory value is the volume setting corresponding to the maximum mileage; Case 2: If the difference between Z1, Z2, and Z3 is small, the memory value is the average volume of each mileage segment and rounded: memory value = round((V11+V12+V13) / 3); Case 3: If there is a small mileage value, it should be eliminated first, and then processed according to Case 1 and Case 2; finally, the volume memory value of the user in the T1 time period is obtained; similarly, the corresponding volume memory values ​​of the corresponding T2 and T3 time periods can be obtained; other physiological characteristics are obtained according to the above method and will not be repeated.

[0027] Driving characteristics module: At present, as the cost of intelligent driving systems gradually decreases and the installation rate increases year by year, the rich on-board sensor signals can not only be used as input during the normal operation of the intelligent driving system, but also can be used to collect dynamic data during normal manual driving to evaluate the driving characteristics of different drivers.

[0028] In terms of longitudinal following, the present invention uses millimeter-wave radar to collect driving parameters of dynamically following the leading vehicle at different vehicle speeds: the speed of the own vehicle, the stable following distance between the own vehicle and the leading vehicle, the following stop distance, the relative speed, the timing of starting braking of the own vehicle, the acceleration and deceleration of the own vehicle and its rate of change, etc.

[0029] Lateral aspect: Based on the forward-looking camera, the system collects signals such as lateral position error and heading error, curvature radius, steering wheel correction amplitude and frequency during the driver's dynamic driving process at different vehicle speeds and different curvature radii.

[0030] The collected data is first classified by the scene classifier, as follows: Figure 4 As shown; The present invention does not restrict the specific algorithm of the classifier, and clustering algorithms such as K-mean and support vector machine can be used; after classification, the relevant dynamic driving parameters are recorded multiple times for each scene, and the mean and variance are statistically analyzed and stored.

[0031] Characteristic memory storage module: The extracted physiological characteristics and driving characteristics of the driver are memorized and stored. During this process, the driver user's identity needs to be confirmed, which can be done through face recognition (which can be shared with the driver attention monitoring system DMS) or fingerprint confirmation or through mobile phone text messages, QR codes, etc.; ultimately, the characteristic memory storage of the user's driving characteristics and physiological characteristics is achieved.

[0032] Part of the horizontal and vertical system parameters recorded above belong to system design parameters, and part of them belong to system performance constraint parameters. Through the dynamic adjustment of the above parameters, the overall performance of the system can be changed to better suit the user's driving habits.

[0033] Driver feature call module: When the user gets on the car, the user identity recognition system will confirm the user information. If the confirmation is passed and it matches the information in the memory bank, the cockpit software and hardware systems will automatically adapt to the user's stored settings. For example, the seat will automatically restore the previous position; the music will also restore the memory settings and be in standby mode, waiting for the user to turn it on and use it to create a comfortable cabin environment; accordingly, the intelligent driving system parameters will import the previous memory parameters to make the actual performance of the intelligent driving function more in line with the user's driving habits. On the contrary, if the identity confirmation fails, the cockpit settings will remain the same, and the user can adjust and set them according to their own needs; and the intelligent driving system parameters will also remain at the default values.

[0034] While the user is driving, the system continues to repeat the previous steps, extracts the driver's characteristics, and updates before powering off, so the entire system is always up to date and can better meet user needs.

[0035] It should not be understood as limiting the scope of the invention patent, nor is it any form of limitation on the structure of the present invention; it should be pointed out that for ordinary technicians in this field, several changes and improvements can be made without departing from the concept of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. A method for controlling an intelligent cockpit and an intelligent driving system based on driver characteristics, characterized in that: The following steps are involved: S1: Use various sensors in the car to collect input signals and extract the driver's physiological characteristics and driving characteristics; S2: Combine the user identity information and the extracted driver characteristics to complete the matching, and memorize and store the digitized characteristics; S3: User identity confirmation. If confirmed, the cockpit and intelligent driving system will be adjusted accordingly. If not confirmed, the status quo and default values ​​will be maintained or user-defined adjustment parameters will be used. S4: During a new driving process, the system will continue to learn the driver's characteristics and update the previously remembered characteristics to keep it always up to date.

2. The method for controlling a smart cockpit and a smart driving system based on driver characteristics according to claim 1, characterized in that: A method for controlling a smart cockpit and a smart driving system based on driver characteristics is implemented based on a smart cockpit and a smart driving system based on driver characteristics. The smart cockpit and the smart driving system based on driver characteristics include a driver physiological characteristics extraction module, a driving characteristics extraction module, a characteristics storage memory module, a user identity confirmation module, and a driver characteristics calling module.

3. The method for controlling a smart cockpit and a smart driving system based on driver characteristics according to claim 2, characterized in that: The driver's physiological characteristics extraction module counts the driver's cabin settings based on probability from three different time periods: morning, noon and evening, and two dimensions: mileage, and determines the setting items corresponding to the event with the highest probability.

4. The method for controlling a smart cockpit and a smart driving system based on driver characteristics according to claim 3, characterized in that: The settings include seat position, seat back angle, rearview mirror angle, steering wheel adjustment position, music type, volume setting, interior ambient light color and brightness, fragrance system setting, and navigation mode.

5. The method for controlling a smart cockpit and a smart driving system based on driver characteristics according to claim 2, characterized in that: The module for extracting driving characteristics, that is, in terms of longitudinal following, uses millimeter-wave radar to collect driving parameters of dynamically following the vehicle in front at different speeds, that is, dynamic driving parameters; in terms of lateral aspect: based on the front-view camera, it collects the driver's dynamic parameters at different speeds and different curvature radii; the collected data after filtering is matched with the lateral and longitudinal driving scenes and dynamic parameters based on the scene classifier and probability statistical analysis.

6. The method for controlling a smart cockpit and a smart driving system based on driver characteristics according to claim 5, characterized in that: Dynamic driving parameters include lateral and longitudinal driving parameters, among which longitudinal driving parameters include: stable following distance, following stopping distance, braking timing, longitudinal acceleration, and longitudinal acceleration change rate; lateral signals include: posture error, steering wheel correction amplitude, steering wheel correction frequency, lane departure warning timing, lane departure correction timing, curve curvature, and lateral acceleration.

7. The method for controlling a smart cockpit and a smart driving system based on driver characteristics according to claim 2, characterized in that: The user identity confirmation module matches and stores user characteristics; the user identity confirmation module can confirm user information based on any of the following methods, such as the driver's face recognition system, fingerprint recognition system, or mobile phone QR code verification.

8. The method for controlling a smart cockpit and a smart driving system based on driver characteristics according to claim 7, characterized in that: When the user starts a new driving task, after the user identity is confirmed, he can call up the user characteristics stored in memory, and the cockpit components and software systems will automatically adjust to the memory settings; the driving characteristic parameters are imported into the intelligent driving system to replace the default values; if the identity confirmation fails, the cockpit settings remain the same and the intelligent driving system parameters will remain the default values.

9. The method for controlling a smart cockpit and a smart driving system based on driver characteristics according to claim 8, characterized in that: When the user starts a new driving task, new features are extracted and memorized.

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