CN117302124A - AEB system-based self-learning active and passive safety integrated system and method - Google Patents

Abstract

The invention discloses a self-learning active and passive safety integrated system and a method based on an AEB system, which belong to the technical field of automobile safety systems. The invention discloses a self-learning active and passive safety integrated system and a self-learning active and passive safety integrated method, which solve the problems that active and passive safety cannot be linked, differential safety protection cannot be provided for different drivers, the control system provided for collision working conditions is not intelligent, and the corresponding working condition logic judgment time is long.

Description

AEB system-based self-learning active and passive safety integrated system and method

Technical Field

The invention belongs to the technical field of automobile safety systems, and particularly relates to a self-learning active and passive safety integrated system and method based on an AEB system.

Background

In the current stage, with the increase of the storage quantity of automobiles, the number of traffic accidents caused by the automobiles is increased, and the research on the safety technology of the automobiles is also more and more important. The main automobile safety technologies at present are divided into active safety technologies and passive safety technologies. Active safety techniques are mainly directed to collision avoidance such as AEB emergency automatic braking, LDW lane departure warning, FCW forward collision warning, BSM blind zone monitoring, RCTA rear traffic warning. The passive safety technology mainly protects a driver and passengers in a vehicle when or after collision occurs and mainly comprises the following steps: PTSB initiative pretension type safety belt, SRS air bag, CS car energy-absorbing device and AHLS engine bonnet spring-up device. However, at present, the active safety system and the passive safety system do not have any interaction, so that the safety of members cannot be better guaranteed by cooperative work in the event of collision, and meanwhile, the passive safety is not added into seat adjustment and steering wheel position adjustment and collapse.

Reference can be made to the prior art for an active/passive safety integrated control system and a control method thereof, patent numbers: CN115571122a, which describes a control system based on steering wheel rotation collision avoidance after active safety detection of a target, active pretensioned seat belt tightening, early airbag ejection, early activation of a pedestrian protection device, and the like. The prior art has the defects that an active safety system and a passive safety system are basically independent, the active safety can only avoid the collision as far as possible when the collision is about to happen, or the collision energy is reduced, so that the safety condition of the inside of the vehicle after the collision cannot be controlled. Passive safety can only carry out passive safety defense after collision occurs, and can not carry out linkage with active safety to carry out the protection of the vehicle interior for unavoidable collision, such as: the ignition airbag is ignited in advance, the seat moves backwards according to the collision position, the headrest of the seat moves down to the corresponding position in advance to protect the head, the steering wheel is automatically regulated and is retracted inwards, so that secondary injury to a human body is avoided. The active and passive safety integrated protection of self-learning of different drivers cannot be performed due to the difference of the body and the driving behavior of the drivers.

Disclosure of Invention

The invention aims to provide a self-learning active and passive safety integrated system and method based on an AEB system, which are used for solving the problems that the existing active and passive safety provided in the background art cannot be linked, different safety protection cannot be provided for different drivers, the control system provided for collision working conditions is not intelligent, and the corresponding working condition logic judgment time is long.

In order to achieve the above purpose, the present invention provides the following technical solutions: a self-learning active and passive safety integrated system and method based on an AEB system comprises an AEB active safety unit, a data analysis unit, an execution control unit, a passive safety unit and a self-learning unit.

By adopting the scheme, through the setting data input of the input driver to the device in the car, establish corresponding driver model storehouse through vehicle rearview mirror position data, seat position setting data and steering wheel position data, detect near target object information around the vehicle through vehicle sensor equipment simultaneously and contain: the method comprises the steps of processing and classifying target data by using Kalman filtering and median filtering, comparing the obtained dangerous data with data in a self-learning database, generating corresponding position data which needs to be reached by a passive safety device in the automobile, judging whether the corresponding data is queried, and controlling by using an actuator module.

As a preferred embodiment, the AEB active safety unit provides all information of the proximity target around the car, including e.g. target position, target speed, target acceleration, target size, target category.

As a preferred embodiment, the data analysis unit classifies and screens the target data, removes dangerous targets, tracks dangerous targets, and outputs dangerous target information.

As a preferred embodiment, the execution control unit compares the dangerous target data to select a proper safety value for output.

As a preferred embodiment, the passive safety unit outputs existing seat position state information, headrest position state information, steering wheel position state information, and rear view mirror state information, and adjusts the positions of the corresponding seat, headrest, and steering wheel with respect to the safety value of the execution control unit.

As a preferred implementation mode, the self-learning unit stores, classifies and counts dangerous data, analyzes the dangerous data to generate and stores an execution strategy, and is convenient for the execution unit to call.

As a preferred embodiment, the dangerous target TTC collision time Deltat.ltoreq.1.7 s is very dangerous, deltat.ltoreq.2.2 s is generally dangerous.

As a preferred embodiment, the hazard data may be classified into four aspects including right front data, right rear data, left side data, and right side data.

A self-learning active and passive safety integrated system and method based on an AEB system comprises the following steps:

step S1, inputting setting data of a driver on in-vehicle devices, and establishing a corresponding driver model library through vehicle rearview mirror position data, seat position setting data and steering wheel position data;

step S2, detecting, by the vehicle sensor device, information of approaching objects around the vehicle includes: target distance, target approaching speed, target size and shape, etc.;

s3, processing and classifying target data by using Kalman filtering and median filtering;

step S4, judging whether the data in the step S3 meet the requirement that the collision time is less than or equal to 2.2S, if the collision time is less than or equal to 2.2S, performing the step S6, and if the collision time is more than 2.2S, performing the step S5;

step S5, tracking the current target data, and re-judging the tracked target data to return to the step S4;

step S6, judging whether the data in the step S3 meet the collision time of less than or equal to 1.7S, if the collision time is less than or equal to 1.7S, performing the step S7, and if the collision time is more than 1.7S, performing the step S5 and the step S9;

step S7, comparing the obtained dangerous data with data in a self-learning database and generating corresponding position data which needs to be reached by the passive safety device in the automobile, wherein the steps comprise:

step S100, inputting new target data, and performing step S101;

step S101, judging whether the self-learning library has data, wherein the data is stored in the self-learning library to carry out step S102, and the data is not stored in the self-learning library to carry out step S103;

step S102, outputting the position data of the existing steering wheel, seat and headrest;

step S103, judging whether the target comes from the front, if yes, proceeding to step S108, otherwise proceeding to step S104;

step S104, judging whether the target is from the rear, if so, performing step S111, and if not, performing step S105;

step S105, judging whether the target is from the left side, if it is the left side target, proceeding to step S113, otherwise proceeding to step S106;

step S106, judging whether the target comes from the right side, if so, proceeding to step S115, otherwise proceeding to step S107;

step S107, deleting the target data, which may be a false target;

step S108, generating a steering wheel inward adjustment data value to increase the whole space collapse amount according to the target in the step S103 as a front target;

step S109, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the safety airbag when the steering wheel moving position collides;

step S110, calculating data information of backward adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S111, according to the target in the step S104 as a rear target, according to the seat position set by the driver and the adjustment angle of the rearview mirror, the head of the driver can be calculated to avoid other injuries in the center of the headrest when the moving position of the headrest collides;

step S112, calculating data information of forward adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S113, according to the left side target as the target in the step S105, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the side air curtain when the seat moves up and down to collide;

step S114, calculating data information of right adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S115, according to the left side target as the target in the step S106, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the side air curtain when the seat moves up and down to collide;

step S116, calculating data information of left adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S8, judging whether corresponding data is queried according to the step S7, if so, performing a step S10, and if not, performing a step S9;

step S9, adding the data which are not queried into a self-learning database;

step S10, outputting parameters in a self-learning database;

step S11, controlling by using an actuator module according to the data in the step S10;

step S12, judging whether the steering wheel reaches the corresponding position, carrying out step S13 when the steering wheel reaches the corresponding position, carrying out step S11 when the steering wheel does not reach the corresponding position, and moving to the specific position again by using the actuator control module and repeating the step S12;

step S13, judging whether the seat reaches the corresponding position, carrying out step S14 when the seat reaches the corresponding position, carrying out step S11 when the seat does not reach the corresponding position, and repeating steps S12-S13 when the seat moves to the specific position again by using the actuator control module;

and S14, judging whether the headrest reaches the corresponding position, completing the active and passive safety integrated system when the headrest reaches the corresponding position, and performing step S11 to move to the specific position again by using the actuator control module when the headrest does not reach the corresponding position, and repeating the steps S12-S14.

Compared with the prior art, the invention has the beneficial effects that:

the linkage of the active safety unit and the passive safety unit can be carried out, differential safety protection is provided for different drivers, meanwhile, a more intelligent control system is provided for collision working conditions, and the logic judgment time of the corresponding working conditions is shortened.

Drawings

FIG. 1 is a flow chart of a self-learning active-passive safety integrated system and method based on an AEB system of the present invention;

FIG. 2 is a flow chart of the dangerous object data and self-learning database information comparison of the present invention.

Description of the embodiments

The invention is further described below with reference to examples.

The following examples are illustrative of the present invention but are not intended to limit the scope of the invention. The conditions in the examples can be further adjusted according to specific conditions, and simple modifications of the method of the invention under the premise of the conception of the invention are all within the scope of the invention as claimed.

Referring to fig. 1-2, the present invention provides a self-learning active and passive safety integrated system and method based on an AEB system, which includes an AEB active safety unit, a data analysis unit, an execution control unit, a passive safety unit, and a self-learning unit.

The AEB active safety unit provides all information of approaching targets around the automobile, including target positions, target speeds, target accelerations, target sizes and target categories, the data analysis unit classifies and screens target data, removes dangerous targets, tracks dangerous targets and outputs dangerous target information, the execution control unit compares and selects proper safety values for output, the passive safety unit outputs the existing seat position state information, headrest position state information, steering wheel position state information and rearview mirror state information, the safety values of the execution control unit adjust the positions of corresponding seats, headrests and steering wheels, the self-learning unit stores and classifies and counts dangerous data, analyzes and generates an execution strategy and stores the dangerous data, the execution unit is convenient to call, the TTC collision time Deltat is not more than 1.7s and not more than 2.2s is general risk, and the dangerous data can be classified into four aspects including right front data, right rear data, left data and right data.

Specifically, as shown in fig. 1, a self-learning active-passive safety integrated system and method based on an AEB system includes the steps of:

step S1, inputting setting data of a driver on in-vehicle devices, and establishing a corresponding driver model library through vehicle rearview mirror position data, seat position setting data and steering wheel position data;

step S2, detecting, by the vehicle sensor device, information of approaching objects around the vehicle includes: target distance, target approaching speed, target size and shape, etc.;

s3, processing and classifying target data by using Kalman filtering and median filtering;

step S4, judging whether the data in the step S3 meet the requirement that the collision time is less than or equal to 2.2S, if the collision time is less than or equal to 2.2S, performing the step S6, and if the collision time is more than 2.2S, performing the step S5;

step S5, tracking the current target data, and re-judging the tracked target data to return to the step S4;

step S6, judging whether the data in the step S3 meet the collision time of less than or equal to 1.7S, if the collision time is less than or equal to 1.7S, performing the step S7, and if the collision time is more than 1.7S, performing the step S5 and the step S9;

s7, comparing the obtained dangerous data with data in a self-learning database and generating corresponding position data which needs to be reached by the passive safety device in the automobile;

step S8, judging whether corresponding data is queried according to the step S7, if so, performing a step S10, and if not, performing a step S9;

step S9, adding the data which are not queried into a self-learning database;

step S10, outputting parameters in a self-learning database;

step S11, controlling by using an actuator module according to the data in the step S10;

step S12, judging whether the steering wheel reaches the corresponding position, carrying out step S13 when the steering wheel reaches the corresponding position, carrying out step S11 when the steering wheel does not reach the corresponding position, and moving to the specific position again by using the actuator control module and repeating the step S12;

step S13, judging whether the seat reaches the corresponding position, carrying out step S14 when the seat reaches the corresponding position, carrying out step S11 when the seat does not reach the corresponding position, and repeating steps S12-S13 when the seat moves to the specific position again by using the actuator control module;

and S14, judging whether the headrest reaches the corresponding position, completing the active and passive safety integrated system when the headrest reaches the corresponding position, and performing step S11 to move to the specific position again by using the actuator control module when the headrest does not reach the corresponding position, and repeating the steps S12-S14.

Specifically, as shown in fig. 2, the obtained dangerous data is compared with the data in the self-learning database to generate corresponding position data which needs to be reached by the passive safety device in the automobile, and the steps include:

step S100, inputting new target data, and performing step S101;

step S101, judging whether the self-learning library has data, wherein the data is stored in the self-learning library to carry out step S102, and the data is not stored in the self-learning library to carry out step S103;

step S102, outputting the position data of the existing steering wheel, seat and headrest;

step S103, judging whether the target comes from the front, if yes, proceeding to step S108, otherwise proceeding to step S104;

step S104, judging whether the target is from the rear, if so, performing step S111, and if not, performing step S105;

step S105, judging whether the target is from the left side, if it is the left side target, proceeding to step S113, otherwise proceeding to step S106;

step S106, judging whether the target comes from the right side, if so, proceeding to step S115, otherwise proceeding to step S107;

step S107, deleting the target data, which may be a false target;

step S108, generating a steering wheel inward adjustment data value to increase the whole space collapse amount according to the target in the step S103 as a front target;

step S109, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the safety airbag when the steering wheel moving position collides;

step S110, calculating data information of backward adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S111, according to the target in the step S104 as a rear target, according to the seat position set by the driver and the adjustment angle of the rearview mirror, the head of the driver can be calculated to avoid other injuries in the center of the headrest when the moving position of the headrest collides;

step S112, calculating data information of forward adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S113, according to the left side target as the target in the step S105, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the side air curtain when the seat moves up and down to collide;

step S114, calculating data information of right adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S115, according to the left side target as the target in the step S106, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the side air curtain when the seat moves up and down to collide;

and step S116, calculating data information of left adjustment of the seat according to the target data and the position information of the control in the vehicle.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (9)

1. A self-learning active and passive safety integrated system and method based on an AEB system are characterized in that: the system comprises an AEB active safety unit, a data analysis unit, an execution control unit, a passive safety unit and a self-learning unit.

2. The AEB system-based self-learning active-passive safety integrated system and method of claim 1, wherein: the AEB active safety unit provides all information of the approaching target around the car including e.g. target position, target speed, target acceleration, target size, target category.

3. The AEB system-based self-learning active-passive safety integrated system and method of claim 1, wherein: the data analysis unit classifies and screens the target data, removes dangerous targets, tracks dangerous targets and outputs dangerous target information.

4. The AEB system-based self-learning active-passive safety integrated system and method of claim 1, wherein: and the execution control unit compares the dangerous target data and selects a proper safety value for output.

5. The AEB system-based self-learning active-passive safety integrated system and method of claim 1, wherein: the passive safety unit outputs the existing seat position state information, headrest position state information, steering wheel position state information and rearview mirror state information, and adjusts the positions of the corresponding seat, headrest and steering wheel according to the safety value of the execution control unit.

6. The AEB system-based self-learning active-passive safety integrated system and method of claim 1, wherein: the self-learning unit stores, classifies and counts the dangerous data, analyzes and generates an execution strategy and stores the dangerous data, and is convenient for the execution unit to call.

7. The AEB system-based self-learning active-passive safety integrated system and method as claimed in claim 3, wherein: the TTC collision time delta t is less than or equal to 1.7s of the dangerous target and is very dangerous, and delta t is less than or equal to 2.2s of the dangerous target is general danger.

8. The AEB system-based self-learning active-passive safety integrated system and method of claim 6, wherein: the hazard data may be categorized into four aspects including right front data, right rear data, left side data, and right side data.

9. The AEB system-based self-learning active and passive safety integrated system and method according to claim 1, characterized in that the steps thereof include:

step S1, inputting setting data of a driver on in-vehicle devices, and establishing a corresponding driver model library through vehicle rearview mirror position data, seat position setting data and steering wheel position data;

step S2, detecting, by the vehicle sensor device, information of approaching objects around the vehicle includes: target distance, target approaching speed, target size and shape, etc.;

s3, processing and classifying target data by using Kalman filtering and median filtering;

step S4, judging whether the data in the step S3 meet the requirement that the collision time is less than or equal to 2.2S, if the collision time is less than or equal to 2.2S, performing the step S6, and if the collision time is more than 2.2S, performing the step S5;

step S5, tracking the current target data, and re-judging the tracked target data to return to the step S4;

step S6, judging whether the data in the step S3 meet the collision time of less than or equal to 1.7S, if the collision time is less than or equal to 1.7S, performing the step S7, and if the collision time is more than 1.7S, performing the step S5 and the step S9;

step S7, comparing the obtained dangerous data with data in a self-learning database and generating corresponding position data which needs to be reached by the passive safety device in the automobile, wherein the steps comprise:

step S100, inputting new target data, and performing step S101;

step S101, judging whether the self-learning library has data, wherein the data is stored in the self-learning library to carry out step S102, and the data is not stored in the self-learning library to carry out step S103;

step S102, outputting the position data of the existing steering wheel, seat and headrest;

step S103, judging whether the target comes from the front, if yes, proceeding to step S108, otherwise proceeding to step S104;

step S104, judging whether the target is from the rear, if so, performing step S111, and if not, performing step S105;

step S105, judging whether the target is from the left side, if it is the left side target, proceeding to step S113, otherwise proceeding to step S106;

step S106, judging whether the target comes from the right side, if so, proceeding to step S115, otherwise proceeding to step S107;

step S107, deleting the target data, which may be a false target;

step S108, generating a steering wheel inward adjustment data value to increase the whole space collapse amount according to the target in the step S103 as a front target;

step S109, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the safety airbag when the steering wheel moving position collides;

step S110, calculating data information of backward adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S111, according to the target in the step S104 as a rear target, according to the seat position set by the driver and the adjustment angle of the rearview mirror, the head of the driver can be calculated to avoid other injuries in the center of the headrest when the moving position of the headrest collides;

step S112, calculating data information of forward adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S113, according to the left side target as the target in the step S105, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the side air curtain when the seat moves up and down to collide;

step S114, calculating data information of right adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S115, according to the left side target as the target in the step S106, according to the seat position set by the driver and the rearview mirror adjustment angle, the head of the driver can be calculated to avoid other injuries in the center of the side air curtain when the seat moves up and down to collide;

step S116, calculating data information of left adjustment of the seat according to the target data and the position information of the control in the vehicle;

step S8, judging whether corresponding data is queried according to the step S7, if so, performing a step S10, and if not, performing a step S9;

step S9, adding the data which are not queried into a self-learning database;

step S10, outputting parameters in a self-learning database;

step S11, controlling by using an actuator module according to the data in the step S10;

step S12, judging whether the steering wheel reaches the corresponding position, carrying out step S13 when the steering wheel reaches the corresponding position, carrying out step S11 when the steering wheel does not reach the corresponding position, and moving to the specific position again by using the actuator control module and repeating the step S12;

step S13, judging whether the seat reaches the corresponding position, carrying out step S14 when the seat reaches the corresponding position, carrying out step S11 when the seat does not reach the corresponding position, and repeating steps S12-S13 when the seat moves to the specific position again by using the actuator control module;

and S14, judging whether the headrest reaches the corresponding position, completing the active and passive safety integrated system when the headrest reaches the corresponding position, and performing step S11 to move to the specific position again by using the actuator control module when the headrest does not reach the corresponding position, and repeating the steps S12-S14.