A vehicle-end-based adaptive piloting intelligent driving assistance method and system

Abstract

The application discloses a kind of based on the self-adapting navigation intelligent driving auxiliary method and system of car end, this method includes: every first preset time actively obtains driving data, driving data includes face recognition information, scene information and road information;According to driving data, establish multidimensional table, and based on road information, scene information is evaluated, and evaluation data is recorded in multidimensional table;According to the evaluation data in multidimensional table, obtain the value of regulation and control adjustment parameter, and the value of regulation and control adjustment parameter includes speed compensation value, acceleration compensation value;It is judged whether the value of regulation and control adjustment parameter satisfies adjustment condition, if satisfying, the value of regulation and control adjustment parameter is sent to chassis executor, to make chassis executor control target vehicle according to the value of regulation and control adjustment parameter and adjust.The application solves the problem that intelligent driving function does not meet the style and habit of driver in prior art, and can make vehicle more in line with the style and habit of driver.

Description

Technical Field

[0001] This invention relates to the field of automotive control technology, and in particular to an adaptive navigation intelligent driving assistance method and system based on the vehicle. Background Technology

[0002] With the rapid development of the automotive industry, cars have become increasingly common in people's daily lives, and with the continuous advancement of technology, people have also placed higher demands on the various performance aspects of cars.

[0003] Currently, L2 and L2+ level ADAS assisted driving technologies are relatively mature, and the vehicle penetration rate has reached 25%. L2+ level autonomous driving is characterized by the navigation function (NGP). However, the existing NGP function has not taken into account the driving habits of different drivers. For example, whether the driver is aggressive or conservative when changing lanes, whether the driver is aggressive or gentle when braking after following another vehicle, whether the driver veers to the left or right when driving in the highway lane, etc. This leads to a large discrepancy when many drivers use the intelligent driving function, resulting in discomfort. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide an adaptive navigation intelligent driving assistance method and system based on the vehicle end, in order to solve the problem in the prior art that the intelligent driving function does not conform to the driver's style and habits, thus causing driver discomfort.

[0005] A first aspect of this invention provides a vehicle-based adaptive navigation intelligent driving assistance method, applied to the vehicle's adaptive control module, comprising:

[0006] Driving data is actively acquired every first preset time interval. The driving data includes facial recognition information, scene information, and road information. The scene information includes the vehicle's driving position, braking status information, and following distance to the vehicle in front. The road information includes the center line position.

[0007] A multidimensional table is created based on the driving data, and the scene information is evaluated based on the road information. The evaluation data is then recorded in the multidimensional table.

[0008] The control adjustment parameter values ​​are obtained based on the evaluation data in the multidimensional table. The control adjustment parameter values ​​include velocity compensation value and acceleration compensation value.

[0009] Determine whether the regulation control adjustment parameter value meets the adjustment conditions. If it does, send the regulation control adjustment parameter value to the chassis actuator so that the chassis actuator controls the target vehicle to make adjustments according to the regulation control adjustment parameter value.

[0010] In summary, the aforementioned vehicle-based adaptive cruise control intelligent driving assistance method adds an adaptive control module to the vehicle to record the driver's driving data each time. This module learns and trains on the driving information to obtain different control parameters suitable for different drivers. When different drivers drive the vehicle, the module matches their corresponding control parameters, making the vehicle more compatible with their driving style and habits. Specifically, when the driver is driving, the adaptive control module actively acquires driving data and evaluates the scene information based on road information. It records specific deviation values, specific acceleration values, and following distances to the vehicle ahead in a multi-dimensional table. Based on the data in the multi-dimensional table, it calculates control adjustment parameter values ​​and determines whether these values ​​meet the adjustment conditions. To ensure driving safety, the control parameters need to be kept within a certain range. If the adjustment conditions are met, the control adjustment parameter values ​​are sent to the chassis actuator, which then controls the target vehicle to make adjustments, thereby improving driver comfort.

[0011] Furthermore, the steps of establishing a multidimensional table based on the driving data, evaluating the scene information based on the road information, and recording the evaluation data into the multidimensional table include:

[0012] The facial recognition information in the driving data is numbered, and a driver number column is defined in the multidimensional table based on the numbering results;

[0013] The driving position of the vehicle in the scene information is compared with the center line position in the road information. If the driving position of the vehicle in the scene information deviates from the center line position of the road, the specific deviation value is recorded.

[0014] Obtain braking status information from the scene information. If the braking status is detected as depressed, record the specific acceleration value.

[0015] Record the following distance between the target vehicle and the vehicle in front;

[0016] Based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, evaluation data is generated and recorded in a multidimensional table. The evaluation data corresponds to the driver number in the driver number column.

[0017] Furthermore, the process of generating evaluation data based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, and recording this evaluation data in a multidimensional table, further includes the following after the evaluation data is correlated with the driver ID in the driver ID column:

[0018] Every second preset time interval, all records with the same driver number in the multidimensional table are summarized, and when the mileage corresponding to the driver number reaches the first preset value, the average value of all evaluation data corresponding to the driver number is calculated to obtain the average value of evaluation data.

[0019] The style learning module of the adaptive control module obtains new control adjustment parameter values ​​based on the mean of the evaluation data.

[0020] Further, the step of determining whether the regulation control adjustment parameter value meets the adjustment conditions, and if so, sending the regulation control adjustment parameter value to the chassis actuator so that the chassis actuator controls the target vehicle to make adjustments according to the regulation control adjustment parameter value includes:

[0021] Based on the aforementioned regulation adjustment parameter values, the regulation parameters of the target vehicle are pre-calculated to obtain the adjusted regulation parameters of the target vehicle.

[0022] After the adjustment is completed, determine whether the control parameters of the target vehicle are within the upper and lower limits of the control parameters of the factory control module. If they are, the adjustment conditions are met, and the control parameters of the target vehicle are adjusted.

[0023] Furthermore, the step of establishing a multidimensional table based on the driving data, evaluating the scene information based on the road information, and recording the evaluation data into the multidimensional table further includes:

[0024] Obtain the facial recognition information from the facial recognition device and compare the facial recognition information with the driver number in the driver number column of the multidimensional table;

[0025] If the comparison is successful, the regulation control adjustment parameter value corresponding to the driver number is sent to the chassis actuator, so that the chassis actuator can control the target vehicle to make adjustments according to the regulation control adjustment parameter value.

[0026] A second aspect of this invention provides a vehicle-based adaptive navigation intelligent driving assistance system, applied to the adaptive control module of a vehicle, comprising:

[0027] Data acquisition module: used to actively acquire driving data every first preset time interval. The driving data includes facial recognition information, scene information and road information. The scene information includes the vehicle's driving position, braking status information and following distance to the vehicle in front. The road information includes the center line position.

[0028] Table creation module: used to create a multidimensional table based on the driving data, evaluate the scene information based on the road information, and record the evaluation data into the multidimensional table;

[0029] Adjustment value calculation module: used to obtain the control adjustment parameter values ​​based on the evaluation data in the multidimensional table, the control adjustment parameter values ​​including velocity compensation value and acceleration compensation value;

[0030] Chassis execution module: used to determine whether the regulation control adjustment parameter value meets the adjustment conditions. If it does, the regulation control adjustment parameter value is sent to the chassis actuator so that the chassis actuator controls the target vehicle to make adjustments according to the regulation control adjustment parameter value.

[0031] Furthermore, the steps of the table creation module include:

[0032] The facial recognition information in the driving data is numbered, and a driver number column is defined in the multidimensional table based on the numbering results;

[0033] The driving position of the vehicle in the scene information is compared with the center line position in the road information. If the driving position of the vehicle in the scene information deviates from the center line position of the road, the specific deviation value is recorded.

[0034] Obtain braking status information from the scene information. If the braking status is detected as depressed, record the specific acceleration value.

[0035] Record the following distance between the target vehicle and the vehicle in front;

[0036] Based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, evaluation data is generated and recorded in a multidimensional table. The evaluation data corresponds to the driver number in the driver number column.

[0037] Furthermore, the process of generating evaluation data based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, and recording this evaluation data in a multidimensional table, further includes the following after the evaluation data is correlated with the driver ID in the driver ID column:

[0038] Every second preset time interval, all records with the same driver number in the multidimensional table are summarized, and when the mileage corresponding to the driver number reaches the first preset value, the average value of all evaluation data corresponding to the driver number is calculated to obtain the average value of evaluation data.

[0039] The style learning module of the adaptive control module obtains new control adjustment parameter values ​​based on the mean of the evaluation data.

[0040] Furthermore, the steps of the chassis execution module include:

[0041] Based on the aforementioned regulation adjustment parameter values, the regulation parameters of the target vehicle are pre-calculated to obtain the adjusted regulation parameters of the target vehicle.

[0042] After the adjustment is completed, determine whether the control parameters of the target vehicle are within the upper and lower limits of the control parameters of the factory control module. If they are, the adjustment conditions are met, and the control parameters of the target vehicle are adjusted.

[0043] Furthermore, the table creation module is also used for:

[0044] Regulation parameter matching module: used to obtain the facial recognition information from the facial recognition device and compare the facial recognition information with the driver number in the driver number column of the multidimensional table;

[0045] If the comparison is successful, the regulation control adjustment parameter value corresponding to the driver number is sent to the chassis actuator, so that the chassis actuator can control the target vehicle to make adjustments according to the regulation control adjustment parameter value. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the implementation of an adaptive navigation intelligent driving assistance method based on the vehicle side, as provided in an embodiment of the present invention.

[0048] Figure 2 This is a structural block diagram of an adaptive navigation intelligent driving assistance system based on a vehicle, provided in an embodiment of the present invention. Detailed Implementation

[0049] The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0050] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects and not to describe a particular order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, it may include a series of steps or units, or optionally, steps or units not listed, or other steps or units inherent to these processes, methods, products, or devices.

[0051] Please see Figure 1 , Figure 1 The diagram illustrates the implementation flowchart of an adaptive navigation intelligent driving assistance method based on the vehicle side, provided by an embodiment of the present invention.

[0052] Step S10: Actively acquire driving data every first preset time interval. The driving data includes facial recognition information, scene information, and road information. The scene information includes the vehicle's driving position, braking status information, and following distance to the vehicle in front. The road information includes the centerline position.

[0053] It should be noted that the facial recognition information is obtained by a facial recognition device, the scene information is obtained by a scene perception sensor, and the road information is obtained by a forward-facing camera.

[0054] Step S20: Establish a multidimensional table based on the driving data, evaluate the scene information based on the road information, and record the evaluation data in the multidimensional table.

[0055] The specific steps for establishing a multidimensional table are as follows: number the facial recognition information in the driving data, and define a driver number column in the multidimensional table based on the numbering results;

[0056] The driving position of the vehicle in the scene information is compared with the center line position in the road information. If the driving position of the vehicle in the scene information deviates from the center line position of the road, the specific deviation value is recorded.

[0057] Obtain braking status information from the scene information. If the braking status is detected as depressed, record the specific acceleration value.

[0058] It should be noted that if the vehicle's position deviates to the left of the road centerline, the deviation value is recorded as negative; if the vehicle's position deviates to the right of the road centerline, the deviation value is recorded as positive. This recording method is used to specifically record the deviation position and deviation value. Based on the specific acceleration value, the braking severity can be extracted and classified. There are many ways to define braking severity, which will not be specifically described in this invention.

[0059] Record the following distance between the target vehicle and the vehicle in front;

[0060] Based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, evaluation data is generated and recorded in a multidimensional table. The evaluation data corresponds to the driver number in the driver number column.

[0061] Specifically, every second preset time interval, all records with the same driver number in the multidimensional table are summarized, and when the mileage corresponding to the driver number reaches the first preset value, the average value of all evaluation data corresponding to the driver number is calculated to obtain the average value of evaluation data.

[0062] The style learning module of the adaptive control module obtains new control adjustment parameter values ​​based on the mean of the evaluation data.

[0063] Obtain the facial recognition information from the facial recognition device and compare the facial recognition information with the driver number in the driver number column of the multidimensional table;

[0064] If the comparison is successful, the regulation control adjustment parameter value corresponding to the driver number is sent to the chassis actuator, so that the chassis actuator can control the target vehicle to make adjustments according to the regulation control adjustment parameter value.

[0065] It should be noted that when a driver drives the vehicle for the first time, the adaptive control module's multidimensional table does not contain a corresponding driver ID. Therefore, the adaptive control module generates a corresponding driver ID in the multidimensional table and records the driver's driving data, thereby generating the corresponding control parameters. When the driver drives the vehicle for the second time, the adaptive control module compares the driver's facial recognition information with the driver ID in the multidimensional table. If the comparison is successful, the corresponding control parameters are sent to the chassis actuator, so that the chassis actuator can control the target vehicle to make adjustments based on the control adjustment parameter values.

[0066] Step S30: Obtain the control adjustment parameter values ​​based on the evaluation data in the multidimensional table. The control adjustment parameter values ​​include velocity compensation values ​​and acceleration compensation values.

[0067] It should be noted that the control parameter values ​​are adjustments to the uniform control parameters set when the vehicle leaves the factory, resulting in control parameters that are more in line with the current driving habits of drivers. The control adjustment parameters include, but are not limited to, the curvature of the planned trajectory curve, the turning angle command, the deceleration command, and the acceleration command. By adjusting the control parameters in multiple aspects, the driving needs of drivers can be better met.

[0068] Step S40: Determine whether the regulation control adjustment parameter value meets the adjustment conditions. If it does, send the regulation control adjustment parameter value to the chassis actuator so that the chassis actuator controls the target vehicle to make adjustments according to the regulation control adjustment parameter value.

[0069] Specifically, the regulation control parameters of the target vehicle are pre-calculated based on the regulation control adjustment parameter values ​​to obtain the adjusted regulation control parameters of the target vehicle;

[0070] After the adjustment is completed, determine whether the control parameters of the target vehicle are within the upper and lower limits of the control parameters of the factory control module. If they are, the adjustment conditions are met, and the control parameters of the target vehicle are adjusted.

[0071] Understandably, when a vehicle leaves the factory, it is set with uniform factory control parameters. These factory control parameters have an upper and lower limit range. If the vehicle's control parameters are not within this range, the driver's driving safety cannot be guaranteed. Therefore, when adjusting the control parameters, it is necessary to ensure that the control parameters after the adjustment meet the upper and lower limit range of the factory control parameters.

[0072] Please see Figure 2 , Figure 2 This is a structural block diagram of a vehicle-based adaptive cruise control intelligent driving assistance system provided in an embodiment of the present invention. The modules included in this vehicle-based adaptive cruise control intelligent driving assistance system are used to execute... Figure 1 The steps in the corresponding embodiments. Please refer to the details. Figure 1 as well as Figure 1 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 2 An adaptive cruise control intelligent driving assistance system based on the vehicle includes: a data acquisition module 10, a table creation module 11, an adjustment value calculation module 12, and a chassis execution module 13, wherein:

[0073] The data acquisition module is used to actively acquire driving data every first preset time interval. The driving data includes facial recognition information, scene information, and road information. The scene information includes the vehicle's driving position, braking status information, and following distance to the vehicle in front. The road information includes the centerline position.

[0074] It should be noted that the facial recognition information is obtained by a facial recognition device, the scene information is obtained by a scene perception sensor, and the road information is obtained by a forward-facing camera.

[0075] The table creation module is used to create a multidimensional table based on the driving data, evaluate the scene information based on the road information, and record the evaluation data into the multidimensional table.

[0076] Specifically, the facial recognition information in the driving data is numbered, and a driver number column is defined in a multidimensional table based on the numbering results;

[0077] The driving position of the vehicle in the scene information is compared with the center line position in the road information. If the driving position of the vehicle in the scene information deviates from the center line position of the road, the specific deviation value is recorded.

[0078] Obtain braking status information from the scene information. If the braking status is detected as depressed, record the specific acceleration value.

[0079] It should be noted that if the vehicle's position deviates to the left of the road centerline, the deviation value is recorded as negative; if the vehicle's position deviates to the right of the road centerline, the deviation value is recorded as positive. This recording method is used to specifically record the deviation position and deviation value. Based on the specific acceleration value, the braking severity can be extracted and classified. There are many ways to define braking severity, which will not be specifically described in this invention.

[0080] Record the following distance between the target vehicle and the vehicle in front;

[0081] Based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, evaluation data is generated and recorded in a multidimensional table. The evaluation data corresponds to the driver number in the driver number column.

[0082] Specifically, every second preset time interval, all records with the same driver number in the multidimensional table are summarized, and when the mileage corresponding to the driver number reaches the first preset value, the average value of all evaluation data corresponding to the driver number is calculated to obtain the average value of evaluation data.

[0083] The style learning module of the adaptive control module obtains new control adjustment parameter values ​​based on the mean of the evaluation data.

[0084] Obtain the facial recognition information from the facial recognition device and compare the facial recognition information with the driver number in the driver number column of the multidimensional table;

[0085] If the comparison is successful, the regulation control adjustment parameter value corresponding to the driver number is sent to the chassis actuator, so that the chassis actuator can control the target vehicle to make adjustments according to the regulation control adjustment parameter value.

[0086] It should be noted that when a driver drives the vehicle for the first time, the adaptive control module's multidimensional table does not contain a corresponding driver ID. Therefore, the adaptive control module generates a corresponding driver ID in the multidimensional table and records the driver's driving data, thereby generating the corresponding control parameters. When the driver drives the vehicle for the second time, the adaptive control module compares the driver's facial recognition information with the driver ID in the multidimensional table. If the comparison is successful, the corresponding control parameters are sent to the chassis actuator, so that the chassis actuator can control the target vehicle to make adjustments based on the control adjustment parameter values.

[0087] The adjustment value calculation module is used to obtain the control adjustment parameter values ​​based on the evaluation data in the multidimensional table. The control adjustment parameter values ​​include velocity compensation values ​​and acceleration compensation values.

[0088] It should be noted that the control parameter values ​​are adjustments to the uniform control parameters set when the vehicle leaves the factory, resulting in control parameters that are more in line with the current driving habits of drivers. The control adjustment parameters include, but are not limited to, the curvature of the planned trajectory curve, the turning angle command, the deceleration command, and the acceleration command. By adjusting the control parameters in multiple aspects, the driving needs of drivers can be better met.

[0089] The chassis execution module is used to determine whether the regulation control adjustment parameter value meets the adjustment conditions. If it does, the regulation control adjustment parameter value is sent to the chassis actuator so that the chassis actuator controls the target vehicle to make adjustments according to the regulation control adjustment parameter value.

[0090] Specifically, the regulation control parameters of the target vehicle are pre-calculated based on the regulation control adjustment parameter values ​​to obtain the adjusted regulation control parameters of the target vehicle;

[0091] After the adjustment is completed, determine whether the control parameters of the target vehicle are within the upper and lower limits of the control parameters of the factory control module. If they are, the adjustment conditions are met, and the control parameters of the target vehicle are adjusted.

[0092] Understandably, when a vehicle leaves the factory, it is set with uniform factory control parameters. These factory control parameters have an upper and lower limit range. If the vehicle's control parameters are not within this range, the driver's driving safety cannot be guaranteed. Therefore, when adjusting the control parameters, it is necessary to ensure that the control parameters after the adjustment meet the upper and lower limit range of the factory control parameters.

[0093] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0094] Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The reference to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily indicate the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0095] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A vehicle-based adaptive navigation intelligent driving assistance method, characterized in that, An adaptive control module applied to vehicles, the method comprising: Driving data is actively acquired every first preset time interval. The driving data includes facial recognition information, scene information, and road information. The scene information includes the vehicle's driving position, braking status information, and following distance to the vehicle in front. The road information includes the center line position. A multidimensional table is created based on the driving data, and the scene information is evaluated based on the road information. The evaluation data is then recorded in the multidimensional table. The steps of establishing a multidimensional table based on the driving data, evaluating the scene information based on the road information, and recording the evaluation data into the multidimensional table include: The facial recognition information in the driving data is numbered, and a driver number column is defined in the multidimensional table based on the numbering results; The driving position of the vehicle in the scene information is compared with the center line position in the road information. If the driving position of the vehicle in the scene information deviates from the center line position of the road, the specific deviation value is recorded. Obtain braking status information from the scene information. If the braking status is detected as depressed, record the specific acceleration value. Record the following distance between the target vehicle and the vehicle in front; Based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, evaluation data is generated and recorded in a multidimensional table. The evaluation data corresponds to the driver number in the driver number column. The evaluation data is generated based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, and is recorded in a multidimensional table. After the evaluation data is mapped to the driver's number in the driver's number column, the following is also included: Every second preset time interval, all records with the same driver number in the multidimensional table are summarized, and when the mileage corresponding to the driver number reaches the first preset value, the average value of all evaluation data corresponding to the driver number is calculated to obtain the average evaluation data. The style learning module of the adaptive control module obtains new control adjustment parameter values ​​based on the mean of the evaluation data; The control adjustment parameter values ​​are obtained based on the evaluation data in the multidimensional table. The control adjustment parameter values ​​include velocity compensation value and acceleration compensation value. Determine whether the regulation control adjustment parameter value meets the adjustment conditions. If it does, send the regulation control adjustment parameter value to the chassis actuator so that the chassis actuator controls the target vehicle to make adjustments according to the regulation control adjustment parameter value.

2. The vehicle-based adaptive navigation intelligent driving assistance method according to claim 1, characterized in that, The step of determining whether the regulation control adjustment parameter value meets the adjustment conditions, and if so, sending the regulation control adjustment parameter value to the chassis actuator so that the chassis actuator controls the target vehicle to make adjustments according to the regulation control adjustment parameter value includes: Based on the aforementioned regulation adjustment parameter values, the regulation parameters of the target vehicle are pre-calculated to obtain the adjusted regulation parameters of the target vehicle. After the adjustment is completed, determine whether the control parameters of the target vehicle are within the upper and lower limits of the control parameters of the factory control module. If they are, the adjustment conditions are met, and the control parameters of the target vehicle are adjusted.

3. The vehicle-based adaptive navigation intelligent driving assistance method according to claim 1, characterized in that, The step of establishing a multidimensional table based on the driving data, evaluating the scene information based on the road information, and recording the evaluation data into the multidimensional table further includes: Obtain the facial recognition information from the facial recognition device and compare the facial recognition information with the driver number in the driver number column of the multidimensional table; If the comparison is successful, the regulation control adjustment parameter value corresponding to the driver number is sent to the chassis actuator, so that the chassis actuator can control the target vehicle to make adjustments according to the regulation control adjustment parameter value.

4. A system applied in the vehicle-based adaptive navigation intelligent driving assistance method as described in any one of claims 1-3, characterized in that, An adaptive control module for vehicles, the system comprising: Data acquisition module: used to actively acquire driving data every first preset time interval. The driving data includes facial recognition information, scene information and road information. The scene information includes the vehicle's driving position, braking status information and following distance to the vehicle in front. The road information includes the center line position. Table creation module: used to create a multidimensional table based on the driving data, evaluate the scene information based on the road information, and record the evaluation data into the multidimensional table; Adjustment value calculation module: used to obtain the control adjustment parameter values ​​based on the evaluation data in the multidimensional table, the control adjustment parameter values ​​including velocity compensation value and acceleration compensation value; Chassis execution module: used to determine whether the regulation control adjustment parameter value meets the adjustment conditions. If it does, the regulation control adjustment parameter value is sent to the chassis actuator so that the chassis actuator controls the target vehicle to make adjustments according to the regulation control adjustment parameter value.

5. The system according to claim 4, characterized in that, The table creation module is also used for: The facial recognition information in the driving data is numbered, and a driver number column is defined in the multidimensional table based on the numbering results; The driving position of the vehicle in the scene information is compared with the center line position in the road information. If the driving position of the vehicle in the scene information deviates from the center line position of the road, the specific deviation value is recorded. Obtain braking status information from the scene information. If the braking status is detected as depressed, record the specific acceleration value. Record the following distance between the target vehicle and the vehicle in front; Based on specific deviation values, specific acceleration values, and following distance to the vehicle ahead, evaluation data is generated and recorded in a multidimensional table. The evaluation data corresponds to the driver number in the driver number column.

6. The system according to claim 5, characterized in that, The table creation module is also used for: Every second preset time interval, all records with the same driver number in the multidimensional table are summarized, and when the mileage corresponding to the driver number reaches the first preset value, the average value of all evaluation data corresponding to the driver number is calculated to obtain the average value of evaluation data. The style learning module of the adaptive control module obtains new control adjustment parameter values ​​based on the mean of the evaluation data.

7. The system according to claim 4, characterized in that, The chassis execution module is also used for: Based on the aforementioned regulation adjustment parameter values, the regulation parameters of the target vehicle are pre-calculated to obtain the adjusted regulation parameters of the target vehicle. After the adjustment is completed, determine whether the control parameters of the target vehicle are within the upper and lower limits of the control parameters of the factory control module. If they are, the adjustment conditions are met, and the control parameters of the target vehicle are adjusted.

8. The system according to claim 4, characterized in that, The table creation module is also used for: Obtain the facial recognition information from the facial recognition device and compare the facial recognition information with the driver number in the driver number column of the multidimensional table; If the comparison is successful, the regulation control adjustment parameter value corresponding to the driver number is sent to the chassis actuator, so that the chassis actuator can control the target vehicle to make adjustments according to the regulation control adjustment parameter value.

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