CN112208541A

CN112208541A - Intelligent passenger compartment parameterization determination method and device and computer equipment

Info

Publication number: CN112208541A
Application number: CN202011088017.9A
Authority: CN
Inventors: 聂冰冰; 王情帆; 甘顺; 陶晔澜
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2020-10-13
Filing date: 2020-10-13
Publication date: 2021-01-12

Abstract

The application relates to a parameterization determination method and device for an intelligent passenger compartment, a computer device and a storage medium. The method comprises the steps that configuration parameters of an intelligent passenger compartment are obtained, wherein the configuration parameters comprise seat parameters, passenger physiological parameters and vehicle running parameters, the seat parameters comprise seat inclination angle data, seat front and rear position data, seat corner data and safety belt load limiter force limiting data, the passenger physiological parameters comprise height data, weight data, age data and gender data of passengers, and the vehicle running parameters comprise speed data, transverse acceleration data and longitudinal acceleration data; inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information; inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information; the configuration parameters of the intelligent passenger cabin are adjusted according to the damage detection information and the comfort detection information of the passengers, and the safety and the comfort of the passengers are ensured.

Description

Intelligent passenger compartment parameterization determination method and device and computer equipment

Technical Field

The present application relates to the field of automatic driving technologies, and in particular, to a method and an apparatus for parameterizing an intelligent passenger compartment, a computer device, and a storage medium.

Background

Under the traditional traffic mode, the occupation ratio of traffic accidents caused directly or indirectly by the error of a driver is up to 90 percent, and with the vigorous development of an automatic driving technology, the situation is expected to be improved, and the occurrence rate of the traffic accidents is reduced. Under the traffic condition of the automatic driving vehicle, more flexibly selected spaces are needed for the sitting environment and the posture of the passenger in the vehicle, new requirements are provided for parameter design of an intelligent passenger compartment of the automatic driving vehicle, and meanwhile, new sitting environment in the intelligent passenger compartment brings new challenges to safety and protection of the passenger.

Parameters in the existing parameter detection method of the passenger compartment are set based on the traditional vehicle, but the intelligent passenger compartment relates to a brand-new parameter space, and safety and comfort parameters of passengers in the traditional parameter detection method are not applicable any more.

Disclosure of Invention

In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for parameterizing an intelligent passenger compartment, which can detect safety and comfort of passengers.

A method of parameterising a smart passenger compartment, the method comprising:

acquiring configuration parameters of an intelligent passenger compartment, wherein the configuration parameters comprise seat parameters, passenger physiological parameters and vehicle driving parameters, the seat parameters comprise seat inclination angle data, seat front and rear position data, seat corner data and safety belt load limiter force limiting data, the passenger physiological parameters comprise height data, weight data, age data and gender data of passengers, and the vehicle driving parameters comprise speed data, transverse acceleration data and longitudinal acceleration data;

inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information;

inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information;

and adjusting configuration parameters of the intelligent passenger compartment according to the damage detection information and the comfort detection information of the passenger.

In one embodiment, the damage detection function comprises a passenger body part damage detection function and a neck ligament damage detection information determination table;

inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information, wherein the method comprises the following steps:

inputting the speed data in the seat parameters, the passenger physiological parameters and the vehicle running parameters into a body part damage detection function to obtain damage detection information of the corresponding passenger body part;

and determining passenger neck ligament damage detection information according to the seat parameters and the neck ligament damage detection information determination table.

In one embodiment, the body part damage detection function comprises a head damage detection function, a brain damage detection function, a neck damage benchmark detection function, a chest damage detection function, and a femoral damage detection function;

inputting the speed data in the seat parameters, the passenger physiological parameters and the vehicle running parameters into a body part damage detection function to obtain damage detection information of the corresponding passenger body part, wherein the damage detection information comprises the following steps:

the speed data in the seat parameters, the passenger physiological parameters and the vehicle driving parameters are input into a head injury detection function, a brain injury detection function, a neck injury benchmark detection function, a chest injury detection function and a femur injury detection function, and corresponding passenger head injury detection information, brain injury detection information, neck injury benchmark detection information, chest injury detection information and femur injury detection information are obtained.

In one embodiment, inputting the speed data of the seat parameters, the passenger physiological parameters and the vehicle driving parameters into a head injury detection function, a brain injury detection function, a neck injury benchmark detection function, a chest injury detection function and a femur injury detection function to obtain corresponding passenger head injury detection information, brain injury detection information, neck injury benchmark detection information, chest injury detection information and femur injury detection information, includes:

calculating according to the height data and the weight data of the passenger to obtain body mass index data of the passenger;

inputting the height data, the gender data, the age data, the speed data, the front and rear seat position data, the seat inclination angle data and the force limiting data of the safety belt load limiter of the passenger into a head injury detection function to obtain corresponding head injury detection information of the passenger;

inputting the height data, the sex data, the age data, the body mass index data, the speed data and the position data before and after the seat of the passenger into a brain injury detection function to obtain corresponding brain injury detection information of the passenger;

inputting the height data, the gender data, the age data, the body mass index data, the speed data, the front and rear seat position data, the seat inclination angle data and the seat belt load limiter force limiting data of the passenger into a neck injury reference detection function to obtain corresponding neck injury reference detection information of the passenger;

inputting the height data, the sex data, the age data, the body mass index data, the front and back position data of the seat and the force limiting data of the safety belt load limiter of the passenger into a chest injury detection function to obtain corresponding chest injury detection information of the passenger;

and inputting the height data, the gender data, the age data, the speed data, the front and rear seat position data, the seat inclination angle data and the limiting force data of the safety belt load limiter of the passenger into a femur damage detection function to obtain corresponding femur damage detection information of the passenger.

In one embodiment, the occupant cervical ligament injury comprises anterior longitudinal ligament injury, posterior longitudinal ligament injury, joint capsule ligament injury, ligamentum flavum injury, and interspinous ligament injury;

determining passenger neck ligament damage detection information according to the seat parameters and the neck ligament damage detection information determination table, wherein the method comprises the following steps:

determining the front longitudinal ligament damage detection information, the rear longitudinal ligament damage detection information, the joint capsule ligament damage detection information, the ligamentum flavum damage detection information and the interspinous ligament damage detection information of the passenger according to the seat corner data and the neck ligament damage detection information determination table.

In one embodiment, the comfort detection function includes an occupant ride comfort detection function and an occupant ride comfort detection function;

inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information, wherein the method comprises the following steps:

inputting the transverse acceleration data and the longitudinal acceleration data into a passenger driving comfort detection function to obtain corresponding passenger driving comfort detection information;

the occupant weight data and the seat tilt angle data are input to an occupant riding comfort detection function, and corresponding occupant riding comfort detection information is obtained.

In one embodiment, adjusting the configuration parameters of the smart passenger compartment based on the occupant impairment detection information and comfort detection information comprises:

obtaining a mapping result of the damage detection information and the comfort detection information of the corresponding passenger according to the damage detection information and the comfort detection information of the passenger;

and adjusting the configuration parameters of the intelligent passenger compartment according to the mapping result.

An apparatus for parameterising a passenger compartment, the apparatus comprising:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring configuration parameters of an intelligent passenger compartment, the configuration parameters comprise seat parameters, passenger physiological parameters and vehicle driving parameters, the seat parameters comprise seat inclination angle data, seat front and rear position data, seat corner data and safety belt load limiter force limiting data, the passenger physiological parameters comprise height data, weight data, age data and gender data of passengers, and the vehicle driving parameters comprise speed data, transverse acceleration data and longitudinal acceleration data;

the damage detection module is used for inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information;

and the comfort detection module is used for inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information.

And the adjusting module is used for adjusting the configuration parameters of the intelligent passenger compartment according to the damage detection information and the comfort detection information of the passenger.

A computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program:

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

The intelligent passenger compartment parameterization determination method, the intelligent passenger compartment parameterization determination device, the computer equipment and the storage medium are characterized in that configuration parameters of the intelligent passenger compartment are obtained, wherein the configuration parameters comprise seat parameters, passenger physiological parameters and vehicle running parameters, the seat parameters comprise seat inclination angle data, seat front and rear position data, seat corner data and safety belt load limiter limiting force data, the passenger physiological parameters comprise height data, weight data, age data and gender data of passengers, and the vehicle running parameters comprise speed data, transverse acceleration data and longitudinal acceleration data; then inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information; inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information; and finally, adjusting configuration parameters of the intelligent passenger compartment according to the damage detection information and the comfort detection information of the passenger, thus comprehensively considering flexible seat parameters, passenger physiological parameters and vehicle running parameters in the automatic driving vehicle to detect the safety and the comfort of the passenger, and adjusting the configuration parameters according to the detection result to ensure the safety and the comfort of the passenger.

Drawings

FIG. 1 is a diagram of an exemplary implementation of a method for parameterizing a smart passenger compartment;

FIG. 2 is a schematic flow chart illustrating a method for parameterizing a smart passenger compartment according to one embodiment;

FIG. 3 is a schematic diagram of an interface for a smart passenger compartment according to one embodiment;

FIG. 4 is a schematic interface diagram illustrating a method for parameterizing a smart passenger compartment according to one embodiment;

FIG. 5 is a schematic interface diagram of a method for parameterizing a smart passenger compartment according to another embodiment;

FIG. 6 is a schematic flow chart illustrating a method for parameterizing a smart passenger compartment according to another embodiment;

FIG. 7 is a block diagram of an exemplary intelligent passenger compartment parameterization determination device;

FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The intelligent passenger compartment parameterization determination method provided by the application can be applied to the application environment shown in the figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.

In one embodiment, as shown in fig. 2, a method for determining a parameterization of an intelligent passenger compartment is provided, which is illustrated by way of example in the application of the method to the terminal of fig. 1, and comprises the following steps:

step 202, obtaining configuration parameters of an intelligent passenger compartment, wherein the configuration parameters comprise seat parameters, passenger physiological parameters and vehicle running parameters, the seat parameters comprise seat inclination angle data, seat front and rear position data, seat corner data and safety belt load limiter force limiting data, the passenger physiological parameters comprise height data, weight data, age data and gender data of passengers, the vehicle running parameters comprise speed data, transverse acceleration data and longitudinal acceleration data, and the vehicle running parameters are used for controlling the running speed of an automatic driving automobile.

The vehicle is an automatic driving vehicle, a novel intelligent vehicle which realizes unmanned driving through a computer computing system, and the sensing of traffic conditions, the decision of driving behaviors and the cooperative control between other vehicles are realized by carrying a sensor, a control and execution device on the traditional vehicle, so that the driving of a driver is finally replaced, and the unmanned technology is realized. The intelligent passenger compartment is designed for an automatic driving vehicle, and as shown in fig. 3, the following differences are mainly found in the structure of the passenger compartment from the conventional passenger compartment: the foldable car door has the characteristics of no pedal, no center console or instrument panel, no steering wheel or retractable steering wheel, rotatable seat, opposite seat, larger degree of freedom of the pitching angle of the seat, various opening modes of the car door, such as a split door, a butterfly door, a scissor door and the like, and no B column. In terms of functions, real-time evaluation on safety and comfort of passengers under the current vehicle running state can be provided, so that relevant parameter configuration in a passenger compartment is optimized, and safety of the passengers is guaranteed.

Specifically, the terminal obtains configuration parameters of the smart passenger compartment under different working conditions, where the configuration parameters are input by the passenger on a display interface of the terminal, as shown in fig. 4 and 5, the passenger may input relevant configuration parameters on the display interface of the terminal, and the different working conditions mainly include two aspects: the vehicle layer refers to the motion state, speed, longitudinal acceleration and transverse acceleration of the vehicle at the current moment; in the aspect of the passenger, the physiological parameters of the passenger, such as height, weight, age and sex, are defined, and the parameters of different passenger compartments mainly comprise seat inclination angle, front and rear positions of the seat, seat belt force limit and seat orientation. The configuration parameters include seat parameters, occupant physiological parameters, and vehicle travel parameters. The seat parameters comprise seat inclination angle data, seat front and rear position data, seat corner data and safety belt load limiter force limiting data, the adjustment range of the seat inclination angle is 0-90 degrees, the 0 degree is the vertical condition of a seat back, and the-90 degree is the flat condition of the seat back; the adjustment range of the front and rear positions of the seat is 0mm to-100 mm, the seat position is kept at the original position by 0mm, and the seat moves backwards by 100mm by-100 mm; the seat angle is adjusted in the range of 0 ° to 180 ° and rotated clockwise at 45 ° intervals, 0 ° being the direction in which the occupant faces the vehicle, and 180 ° being the direction in which the occupant faces the vehicle. The belt load limiter has a force limit in the range of 0kN to 4 kN. The passenger physiological parameters comprise height data, weight data, age data and gender data of passengers, and because the physiological parameters of different passengers are different, the safety and the comfort of the passengers in the passenger compartment are different, and the physiological parameters of different passengers are required to be acquired to detect the safety and the comfort of the passengers in the passenger compartment, wherein the passengers refer to the passengers riding the automatic driving automobile. The vehicle running parameters comprise speed data, transverse acceleration data and longitudinal acceleration data, wherein the speed data refer to the running speed of the vehicle; the acceleration is the speed change rate, the lateral acceleration and the longitudinal acceleration refer to the acceleration, the lateral direction is the direction vertical to the driving direction of the automobile, the lateral acceleration refers to the acceleration caused by the centrifugal force generated when the automobile turns and drives, namely the tendency that the automobile is thrown away, and the larger the lateral acceleration is, the more the automobile is theoretically, the automobile is easy to be thrown away from the driving path; the longitudinal acceleration is an acceleration in the axial direction of the vehicle, and the longitudinal acceleration and the lateral acceleration are used to keep the vehicle stable.

Step 204, inputting the configuration parameters into the damage detection function to obtain corresponding passenger damage detection information.

Specifically, parameters required for damage detection in the configuration parameters of the intelligent passenger compartment acquired by the terminal are input into the damage detection function, and corresponding damage detection information of the passenger is obtained. The damage detection comprises passenger body part damage detection and neck ligament damage detection, configuration parameters needed by the passenger body part damage detection and the neck ligament damage detection are input into corresponding damage detection functions, and corresponding passenger body part damage detection information and neck ligament damage detection information are obtained.

And step 206, inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information.

Specifically, parameters required for comfort detection in configuration parameters of the intelligent passenger compartment acquired by the terminal are input into a comfort detection function, and comfort detection information of corresponding passengers is obtained. The comfort detection comprises passenger driving comfort detection and passenger riding comfort detection, configuration parameters needed by the passenger driving comfort detection and the passenger riding comfort detection are input into corresponding comfort detection functions, and corresponding passenger driving comfort detection and passenger riding comfort detection information are obtained.

In step 208, the configuration parameters of the smart passenger compartment are adjusted according to the occupant impairment detection information and the occupant comfort detection information.

Specifically, the damage detection information and the comfort detection information of the passenger are obtained according to the acquired configuration parameters of the intelligent passenger compartment, the damage detection level and the comfort detection level of the passenger are determined according to the damage detection information and the comfort detection information of the passenger and the corresponding mapping function, and if the damage detection level and the comfort detection level of the passenger are not optimal, the configuration parameters are adjusted to enable the safety and the comfort of the passenger to be optimal.

In the above-mentioned parameterized determination method for the intelligent passenger compartment, as shown in fig. 6, the configuration parameters of the intelligent passenger compartment are obtained, and include seat parameters, passenger physiological parameters and vehicle driving parameters, wherein the seat parameters include seat inclination angle data, seat front and rear position data, seat rotation angle data and seat belt load limiter force limit data, the passenger physiological parameters include height data, weight data, age data and gender data of the passenger, and the vehicle driving parameters include speed data, lateral acceleration data and longitudinal acceleration data; then inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information; inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information; and finally, adjusting configuration parameters of the intelligent passenger compartment according to the damage detection information and the comfort detection information of the passenger, thus comprehensively considering flexible seat parameters, passenger physiological parameters and vehicle running parameters in the automatic driving vehicle to detect the safety and the comfort of the passenger, and adjusting the configuration parameters according to the detection result to ensure the safety and the comfort of the passenger.

In one embodiment, the injury detection function includes an occupant body part injury detection function and a cervical ligament injury detection information determination table;

Specifically, parameters needed by damage detection in configuration parameters of the intelligent passenger compartment acquired by a terminal are input into a damage detection function, the damage detection function comprises a passenger body part damage detection function and a neck ligament damage detection information determination table, the damage detection function is obtained by performing test analysis in a collision model according to test data of each part of a corpse, correlation between the configuration parameters and damage values is obtained according to test results, and the damage detection function is further obtained by analyzing. And inputting the speed data in the seat parameters, the passenger physiological parameters and the vehicle running parameters required by the damage detection into the body part damage detection function to obtain the damage detection information of the corresponding passenger body part.

In one embodiment, the body part injury detection function comprises a head injury detection function, a brain injury detection function, a neck injury baseline detection function, a chest injury detection function, and a femoral injury detection function;

Specifically, the body part damage detection function includes a head damage detection function, a brain damage detection function, a neck damage reference detection function, a chest damage detection function, and a femur damage detection function, and configuration parameters required for head damage detection, brain damage detection, neck damage reference detection, chest damage detection, and femur damage detection are input to the corresponding damage detection function, so as to obtain corresponding occupant head damage detection information, brain damage detection information, neck damage reference detection information, chest damage detection information, and femur damage detection information.

In one embodiment, inputting the speed data of the seat parameter, the passenger physiological parameter and the vehicle driving parameter into a head injury detection function, a brain injury detection function, a neck injury benchmark detection function, a chest injury detection function and a femur injury detection function to obtain corresponding passenger head injury detection information, brain injury detection information, neck injury benchmark detection information, chest injury detection information and femur injury detection information, comprises:

Specifically, determining the occupant's damage detection information also requires using the occupant's body mass index data, which is obtained by dividing the weight of the occupant by the height squared, where the weight is in kilograms and the height is in meters.

Based on the national crash data of 1998 to 2008, in the event of a similar accident, the probability of a female driver, also restrained by a seat belt, suffering a severe injury is 47% higher than that of a male driver restrained by a seat belt, and therefore, to take into account the influence of the occupant's gender factor, the gender influence factor P is taken into account in the injury prediction function in table 1_sex

Male occupant P

_sex1, P of female passenger_sex＝1.47。

Based on the two NASS-CDS and FARS U.S. databases, compared with the aged passengers, the aged passengers refer to passengers older than 65 years old, and when the passengers in the age range of 16 to 33 years old are seriously damaged 0.32 times of the aged passengers, P of the age range is taken_age0.32; when the age of the passenger is 34 to 64 years old, the frequency of serious injury of the passenger is 0.56 of that of the old passengerTwice, so take P of this age group_age0.56; when the age group of the passenger is more than 65 years old, taking P of the age group_age＝1。

Based on the occupant injury risk results of the four occupant orientations at 0 °, 90 °, 135 ° and 180 ° under frontal collision, the occupant injury prediction functions, three of which are the head injury detection function, the neck injury criterion detection function, and the femur injury detection function shown in table 2, are obtained as the seat orientation correction coefficients P_seat-x。

In Table 1, HIC (head Injury criterion) indicates a head Injury index, BrIC (brain Injury criterion) indicates a brain Injury index, Nij indicates a neck Injury criterion, and D_maxThe compression depth of the finger chest is expressed in unit percent, FN means the force of the femur is expressed in unit kN, v means the instantaneous speed during collision, km/h is expressed in unit, h represents the height of the passenger, and the unit is mm; m represents the occupant BMI, l represents the seat fore and aft position in mm, α represents the seat back angle in rad, and f represents the seatbelt load limiter in kN.

Inputting the height data, the gender data, the age data, the speed data, the front and rear seat position data, the seat inclination angle data and the limiting force data of the safety belt load limiter of the passenger into the head injury detection function in the table 1 to obtain corresponding head injury detection information of the passenger;

inputting the height data, the sex data, the age data, the body mass index data, the speed data and the position data of the front and the back of the seat of the passenger into the brain injury detection function in the table 1 to obtain the corresponding brain injury detection information of the passenger;

inputting the height data, the gender data, the age data, the body mass index data, the speed data, the seat front and rear position data, the seat inclination angle data and the safety belt load limiter limiting force data of the passenger into the neck injury reference detection function in the table 1 to obtain the corresponding neck injury reference detection information of the passenger;

inputting the height data, the sex data, the age data, the body mass index data, the front and back seat position data and the force limiting data of the safety belt load limiter of the passenger into the chest injury detection function in the table 1 to obtain corresponding passenger chest injury detection information;

the height data, sex data, age data, speed data, seat front and rear position data, seat inclination angle data, and seat belt load limiter force limit data of the occupant are input to the femoral bone injury detection function in table 1, and corresponding occupant femoral bone injury detection information is obtained.

TABLE 1 prediction function of the existing occupant injury index

TABLE 2 seat orientation correction factor P for three occupant injury prediction functions_seat-x

In particular, the cervical ligaments of the passenger relate to the Anterior Longitudinal Ligament (ALL), the Posterior Longitudinal Ligament (PLL), the Capsule Ligament (CL), the Ligamenta Flava (LF), and the Interspinous Ligament (ISL). The passenger neck ligament injury comprises an anterior longitudinal ligament injury, a posterior longitudinal ligament injury, an articular capsule ligament injury, a ligamentum flavum injury and an interspinous ligament injury, seat corner data is needed for the anterior longitudinal ligament injury detection, the posterior longitudinal ligament injury detection, the articular capsule ligament injury detection, the ligamentum flavum injury detection and the interspinous ligament injury detection, the seat corner data is determined according to the seat corner data and the cervical ligament injury detection, the anterior longitudinal ligament injury detection information, the posterior longitudinal ligament injury detection information, the articular capsule ligament injury detection information, the ligamentum flavum injury detection information and the interspinous ligament injury detection information of the passenger are determined, the neck ligament injury detection information determination table is obtained by utilizing the corpse experiment data to carry out experiment analysis under a collision model, as shown in table 3, wherein the seat corner data comprises 0 degree, 45 degrees, 90 degrees, 135 degrees and 180 degrees, and the seat corner data can also be other angles between 0 degree and 180 degrees, the neck ligament damage detection information determination table comprises seat corner data and damage strain thresholds of corresponding anterior longitudinal ligament damage detection, posterior longitudinal ligament damage detection, joint capsule ligament damage detection, ligamentum flavum damage detection and interspinous ligament damage detection, and when the damage information of corresponding parts exceeds the corresponding thresholds, the risk of corresponding ligament parts facing downwards of the current seat is considered to be greater.

TABLE 3 five neck injury values with different seats facing downwards

Specifically, the comfort detection includes occupant traveling comfort detection and occupant riding comfort detection. Riding deviceThe detection information of the driving comfort of the driver is based on the longitudinal acceleration a of the vehicle during driving_yWith transverse acceleration a_xIs calculated as a function of the passenger's driving comfort detection

Based on experiments, the pressure of the seat back and the seat chassis under different seat back angle conditions is obtained, the result is shown in table 4, the reaction force values under only three seat inclination angles in table 4 are obtained based on quadratic fitting to adapt to continuous angle values, a riding comfort detection function, namely a function of the seat reaction force is obtained, F1 represents the reaction force of the seat back to the passenger, F2 represents the reaction force of the seat plate to the passenger, and the riding comfort detection function is shown as follows:

F₁＝k₁w(a₁θ²+b₁θ+c₁)

F₂＝k₁w(a₂θ²+b₂θ+c₂)

where w is the occupant weight data in kg, θ is the seat back tilt angle in ° and k₁、k₂The average value of the body weight in the experimental data, namely 74.9, is taken. A in the formula₁、b₁、c₁、a₂、b₂、c₂For the parameters to be solved, the corresponding optimal parameter values can be obtained based on least square solution.

The occupant weight data and the seat inclination angle data are input to an occupant riding comfort detection function, and corresponding occupant riding comfort detection information is obtained.

TABLE 4 experimental data for seat back angle and seat reaction force

In one embodiment, adjusting the configuration parameters of the smart passenger compartment based on the occupant's impairment detection information and comfort detection information comprises:

Specifically, the safety evaluation index is calculated based on the frontal collision of the vehicle, and comprises the damage risk of the body part of the passenger and the damage risk of the neck ligament. The result of the injury risk of the body part of the passenger is obtained according to the injury detection information of the body part of the passenger and an injury mapping function, the injury risk of the body part of the passenger is represented by the probability that the AIS is greater than 2, five injuries of the head, the brain, the neck, the chest and the femur are involved, the AIS is greater than 2, the injury is moderate trauma, and the injury mapping function is shown in table 5. Comparing the detection information of the ligament injury at the neck of the passenger with the corresponding ligament injury strain threshold in the table 3, and when the injury information of the corresponding part exceeds the corresponding threshold, considering that the risk of the corresponding ligament part facing downwards of the current seat is larger.

The comfort evaluation index includes both riding comfort, which is measured by a reaction force F1 of the seat back to the occupant and a reaction force F2 of the seat panel to the occupant, and traveling comfort, which is higher the smaller the two reaction forces. When the traveling comfort evaluation result is obtained by comparing the traveling comfort detection information of the occupant with the occupant traveling comfort quantization table, as shown in table 6, the traveling comfort includes 6 levels, that is, Not noncompletable, a little noncompletable, fair noncompletable, unonfortable, Very noncompletable, and extreme noncompletable, and the comfort level is sequentially lowered.

The safety evaluation index and the comfort evaluation index are output, and as shown in fig. 4 and 5, a user can visually see the parameter configuration result of the intelligent passenger compartment.

TABLE 5 mapping function of damage probability

Table 6 passenger's travelling comfort quantization table

In the embodiment, by obtaining configuration parameters of the smart passenger compartment, the configuration parameters include seat parameters, passenger physiological parameters and vehicle driving parameters, wherein the seat parameters include seat inclination angle data, seat front and rear position data, seat corner data and seat belt load limiter force limiting data, the passenger physiological parameters include height data, weight data, age data and gender data of a passenger, and the vehicle driving parameters include speed data, lateral acceleration data and longitudinal acceleration data; then inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information; inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information; and finally, adjusting configuration parameters of the intelligent passenger compartment according to the damage detection information and the comfort detection information of the passenger, thus comprehensively considering flexible seat parameters, passenger physiological parameters and vehicle running parameters in the automatic driving vehicle to detect the safety and the comfort of the passenger, and adjusting the configuration parameters according to the detection result to ensure the safety and the comfort of the passenger.

It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

In one embodiment, as shown in fig. 7, there is provided an intelligent passenger compartment parameterization determining device comprising: an acquisition module 702, an impairment detection module 704, a comfort detection module 706, and an adjustment module 708, wherein:

an obtaining module 702 configured to obtain configuration parameters of an intelligent passenger compartment, where the configuration parameters include seat parameters, passenger physiological parameters, and vehicle driving parameters, where the seat parameters include seat tilt angle data, seat front and rear position data, seat corner data, and seat belt load limiter force limit data, the passenger physiological parameters include height data, weight data, age data, and gender data of a passenger, and the vehicle driving parameters include speed data, lateral acceleration data, and longitudinal acceleration data;

a damage detection module 704, configured to input the configuration parameter into a damage detection function to obtain corresponding passenger damage detection information;

and a comfort detection module 706, configured to input the configuration parameter into a comfort detection function to obtain corresponding passenger comfort detection information.

An adjustment module 708 configured to adjust a configuration parameter of the smart passenger compartment based on the occupant's impairment detection information and comfort detection information.

the damage detection module 704 inputs the configuration parameters into a damage detection function to obtain corresponding damage detection information of the occupant, including:

the body part damage detection submodule inputs speed data in the seat parameters, the passenger physiological parameters and the vehicle running parameters into a body part damage detection function to obtain damage detection information of the corresponding passenger body part;

and the neck ligament damage detection submodule determines the neck ligament damage detection information of the passenger according to the seat parameters and the neck ligament damage detection information determination table.

the body part damage detection submodule inputs speed data in the seat parameters, the passenger physiological parameters and the vehicle running parameters into a body part damage detection function to obtain damage detection information of corresponding passenger body parts, and the damage detection information comprises the following steps:

the body part damage detection submodule inputs the speed data in the seat parameters, the passenger physiological parameters and the vehicle driving parameters into a head damage detection function, a brain damage detection function, a neck damage benchmark detection function, a chest damage detection function and a femur damage detection function to obtain corresponding passenger head damage detection information, brain damage detection information, neck damage benchmark detection information, chest damage detection information and femur damage detection information.

In one embodiment, the body part damage detection sub-module inputs the speed data of the seat parameter, the passenger physiological parameter and the vehicle driving parameter into a head damage detection function, a brain damage detection function, a neck damage reference detection function, a chest damage detection function and a femur damage detection function to obtain corresponding passenger head damage detection information, brain damage detection information, neck damage reference detection information, chest damage detection information and femur damage detection information, and includes:

the head injury detection unit inputs height data, gender data, age data, speed data, seat front and back position data, seat inclination angle data and safety belt load limiter force limiting data of the passenger into a head injury detection function to obtain corresponding passenger head injury detection information;

the brain damage detection unit inputs height data, sex data, age data, body mass index data, speed data and position data before and after the seat of the passenger into a brain damage detection function to obtain corresponding passenger brain damage detection information;

the neck injury reference detection unit inputs height data, gender data, age data, body mass index data, speed data, seat front and rear position data, seat inclination angle data and safety belt load limiter limiting force data of the passenger into a neck injury reference detection function to obtain corresponding neck injury reference detection information of the passenger;

the chest injury detection unit inputs the height data, the sex data, the age data, the body mass index data, the front and back seat position data and the force limiting data of the safety belt load limiter of the passenger into a chest injury detection function to obtain corresponding passenger chest injury detection information;

the femur damage detection unit inputs height data, gender data, age data, speed data, seat front and back position data, seat inclination angle data and safety belt load limiter force limiting data of the passenger into a femur damage detection function to obtain corresponding passenger femur damage detection information.

the comfort detection module 706 determines passenger neck ligament damage detection information from the seat parameters and the neck ligament damage detection information determination table, including:

the comfort detection module 706 determines the injury detection information of the anterior longitudinal ligament, the injury detection information of the posterior longitudinal ligament, the injury detection information of the joint capsule ligament, the injury detection information of the ligamentum flavum and the injury detection information of the interspinous ligament of the passenger according to the seat corner data and the injury detection information of the cervical ligament.

the comfort detection module 706 inputs the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information, which includes:

the driving comfort detection submodule inputs the transverse acceleration data and the longitudinal acceleration data into a passenger driving comfort detection function to obtain corresponding passenger driving comfort detection information;

the comfort detection sub-module inputs the weight data and the seat inclination angle data of the passenger into a passenger riding comfort detection function to obtain corresponding passenger riding comfort detection information.

In one embodiment, the adjustment module 708 adjusts the configuration parameters of the smart passenger compartment based on the occupant's impairment detection information and comfort detection information, including:

For specific definition of the intelligent passenger compartment parameterization determining device, reference may be made to the above definition of the intelligent passenger compartment parameterization determining method, which is not described in detail here. The various modules in the above-described intelligent passenger compartment parameterization determination device may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of parameterising a determination of an intelligent passenger compartment. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:

In one embodiment, the processor, when executing the computer program, further performs the steps of: the damage detection function comprises a passenger body part damage detection function and a neck ligament damage detection information determination table; inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information, wherein the method comprises the following steps: inputting the speed data in the seat parameters, the passenger physiological parameters and the vehicle running parameters into a body part damage detection function to obtain damage detection information of the corresponding passenger body part; and determining passenger neck ligament damage detection information according to the seat parameters and the neck ligament damage detection information determination table.

In one embodiment, the processor, when executing the computer program, further performs the steps of: the body part damage detection function comprises a head damage detection function, a brain damage detection function, a neck damage benchmark detection function, a chest damage detection function and a femur damage detection function; inputting the speed data in the seat parameters, the passenger physiological parameters and the vehicle running parameters into a body part damage detection function to obtain damage detection information of the corresponding passenger body part, wherein the damage detection information comprises the following steps: the speed data in the seat parameters, the passenger physiological parameters and the vehicle driving parameters are input into a head injury detection function, a brain injury detection function, a neck injury benchmark detection function, a chest injury detection function and a femur injury detection function, and corresponding passenger head injury detection information, brain injury detection information, neck injury benchmark detection information, chest injury detection information and femur injury detection information are obtained.

In one embodiment, the processor, when executing the computer program, further performs the steps of: inputting the speed data of the seat parameters, the passenger physiological parameters and the vehicle driving parameters into a head injury detection function, a brain injury detection function, a neck injury benchmark detection function, a chest injury detection function and a femur injury detection function to obtain corresponding passenger head injury detection information, brain injury detection information, neck injury benchmark detection information, chest injury detection information and femur injury detection information, and the method comprises the following steps: calculating according to the height data and the weight data of the passenger to obtain body mass index data of the passenger; inputting the height data, the gender data, the age data, the speed data, the front and rear seat position data, the seat inclination angle data and the force limiting data of the safety belt load limiter of the passenger into a head injury detection function to obtain corresponding head injury detection information of the passenger; inputting the height data, the sex data, the age data, the body mass index data, the speed data and the position data before and after the seat of the passenger into a brain injury detection function to obtain corresponding brain injury detection information of the passenger; inputting the height data, the gender data, the age data, the body mass index data, the speed data, the front and rear seat position data, the seat inclination angle data and the seat belt load limiter force limiting data of the passenger into a neck injury reference detection function to obtain corresponding neck injury reference detection information of the passenger; inputting the height data, the sex data, the age data, the body mass index data, the front and back position data of the seat and the force limiting data of the safety belt load limiter of the passenger into a chest injury detection function to obtain corresponding chest injury detection information of the passenger; and inputting the height data, the gender data, the age data, the speed data, the front and rear seat position data, the seat inclination angle data and the limiting force data of the safety belt load limiter of the passenger into a femur damage detection function to obtain corresponding femur damage detection information of the passenger.

In one embodiment, the processor, when executing the computer program, further performs the steps of: the passenger neck ligament injury comprises anterior longitudinal ligament injury, posterior longitudinal ligament injury, joint capsule ligament injury, ligamentum flavum injury and interspinous ligament injury; determining passenger neck ligament damage detection information according to the seat parameters and the neck ligament damage detection information determination table, wherein the method comprises the following steps: determining the front longitudinal ligament damage detection information, the rear longitudinal ligament damage detection information, the joint capsule ligament damage detection information, the ligamentum flavum damage detection information and the interspinous ligament damage detection information of the passenger according to the seat corner data and the neck ligament damage detection information determination table.

In one embodiment, the processor, when executing the computer program, further performs the steps of: the comfort detection function comprises an occupant traveling comfort detection function and an occupant riding comfort detection function; inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information, wherein the method comprises the following steps: inputting the transverse acceleration data and the longitudinal acceleration data into a passenger driving comfort detection function to obtain corresponding passenger driving comfort detection information; the occupant weight data and the seat tilt angle data are input to an occupant riding comfort detection function, and corresponding occupant riding comfort detection information is obtained.

In one embodiment, the processor, when executing the computer program, further performs the steps of: adjusting configuration parameters of the smart passenger compartment according to the occupant's impairment detection information and comfort detection information, comprising: obtaining a mapping result of the damage detection information and the comfort detection information of the corresponding passenger according to the damage detection information and the comfort detection information of the passenger; and adjusting the configuration parameters of the intelligent passenger compartment according to the mapping result.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A method for parameterising a passenger compartment, the method comprising:

and adjusting configuration parameters of the intelligent passenger compartment according to the damage detection information and the comfort detection information of the passengers.

2. The method of claim 1, wherein the injury detection function comprises an occupant body part injury detection function and a cervical ligament injury detection information determination table;

the inputting the configuration parameters into a damage detection function to obtain corresponding passenger damage detection information includes:

determining passenger neck ligament damage detection information according to the seat parameters and the neck ligament damage detection information determination table.

3. The method of claim 2, wherein the body part injury detection function comprises a head injury detection function, a brain injury detection function, a neck injury baseline detection function, a chest injury detection function, and a femoral injury detection function;

the step of inputting the speed data in the seat parameters, the passenger physiological parameters and the vehicle running parameters into the body part damage detection function to obtain damage detection information of the corresponding passenger body part comprises the following steps:

and inputting the speed data in the seat parameters, the passenger physiological parameters and the vehicle driving parameters into a head injury detection function, a brain injury detection function, a neck injury benchmark detection function, a chest injury detection function and a femur injury detection function to obtain corresponding passenger head injury detection information, brain injury detection information, neck injury benchmark detection information, chest injury detection information and femur injury detection information.

4. The method of claim 3, wherein inputting the speed data in the seat parameters, the occupant physiological parameters and the vehicle driving parameters into a head injury detection function, a brain injury detection function, a neck injury baseline detection function, a chest injury detection function and a femur injury detection function to obtain corresponding occupant head injury detection information, brain injury detection information, neck injury baseline detection information, chest injury detection information and femur injury detection information comprises:

and inputting the height data, the gender data, the age data, the speed data, the front and rear seat position data, the seat inclination angle data and the force limiting data of the safety belt load limiter of the passenger into a femur damage detection function to obtain corresponding femur damage detection information of the passenger.

5. The method of claim 2, wherein the occupant cervical ligament injury comprises anterior longitudinal ligament injury, posterior longitudinal ligament injury, joint capsule ligament injury, ligamentum flavum injury, and interspinous ligament injury;

determining passenger neck ligament damage detection information according to the seat parameters and the neck ligament damage detection information determination table, wherein the determining comprises the following steps:

determining the front longitudinal ligament damage detection information, the rear longitudinal ligament damage detection information, the joint capsule ligament damage detection information, the ligamentum flavum damage detection information and the interspinous ligament damage detection information of the passenger according to the seat corner data and the neck ligament damage detection information.

6. The method of claim 1, wherein the comfort detection function comprises an occupant ride comfort detection function and an occupant ride comfort detection function;

the step of inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information comprises the following steps:

and inputting the weight data and the seat inclination angle data of the passengers into a passenger riding comfort detection function to obtain corresponding passenger riding comfort detection information.

7. The method of claim 1, wherein said adjusting configuration parameters of the smart passenger compartment based on the occupant's impairment detection information and comfort detection information comprises:

and adjusting configuration parameters of the intelligent passenger compartment according to the mapping result.

8. An apparatus for parameterising a passenger compartment, said apparatus comprising:

the comfort detection module is used for inputting the configuration parameters into a comfort detection function to obtain corresponding passenger comfort detection information;

and the adjusting module is used for adjusting the configuration parameters of the intelligent passenger compartment according to the damage detection information and the comfort detection information of the passengers.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.