Automobile safety control method based on front-row passenger off-position sensing

Abstract

The invention provides an automobile safety control method based on front-row passenger off-position sensing. The automobile safety control method comprises the steps that S11, a seat basic coordinate system is established; s12, forming a passenger key coordinate system; step S13, establishing a key geometric parameter model of the front-row personnel constraint system; step S14, constructing an active avoidance AEB / AES strategy model library; step S15, according to the key geometric parameter model of the front-row personnel restraint system and an active avoidance AEB / AES strategy model library, obtaining a comprehensive off-location parameter according to a first preset rule; step S16, generating a comprehensive dislocation index according to the comprehensive dislocation parameter and a second preset rule; and S17, forming an optimal intelligent constraint parameter according to the comprehensive off-location index so as to determine a safety control strategy of the vehicle. According to the method, the position information of the front-row passenger is accurately obtained, whether the passenger is in the off-position state or not is judged, the off-position influence caused by AEB braking is accurately evaluated, and the automobile safety control strategy is optimized.

Description

Technical Field

[0001] The present invention relates to the field of automobiles, and in particular to an automobile safety control method based on front-seat passenger out-of-position perception. Background Art

[0002] In recent years, China New Car Assessment Program (CNCAP) has introduced AEB (Automatic Emergency Braking) technology into the field of active safety of automobiles. Its main function is to reduce the collision speed or avoid collision in the scenario of rear-end collision or collision with pedestrians. In view of the frequent accidents and high accident ratio of two-wheeled electric vehicles in China, AEB active braking technology for two-wheeled electric vehicles has been introduced into the evaluation system. AEB technology has a wide range of application scenarios and has won wide acclaim from consumers. However, at the same time, during the AEB request braking process, the occupants in the car, especially the front passengers, leave their original sitting positions and lean forward excessively, resulting in inappropriate contact time and position with the airbag, causing the occupants' injuries to worsen. Therefore, how to calibrate the airbag ignition time and ignition method under the action of AEB has always been a hot research topic.

[0003] With the rapid development of new energy vehicles and driverless technology, smart cockpit technology has developed rapidly and has become an important new development direction. The smart cockpit system mainly includes driver monitoring (DMS) and occupant monitoring (OMS). The former mainly monitors the driver, using cameras arranged on the steering column or A-pillar; the latter mainly monitors the passengers in the car, and generally arranges cameras near the rearview mirror, and may also add millimeter-wave radar as a supplement. In addition to obtaining the occupant's seat status, whether the driver is distracted, fatigued driving, face recognition, etc., the camera can also obtain key parameters such as the distance between the driver and the steering wheel and the instrument according to the internal and external parameters of the camera, which is extremely important for evaluating the comprehensive departure parameters after AEB intervention.

[0004] In the existing technology, the main methods used are gaze tracking, facial features, eye feature classification, etc., which are applied to monitor whether the driver is distracted, fatigued driving, drowsy, or even comatose due to sudden illness, but there is almost no application of algorithms for monitoring the driver's departure from the position. At the same time, the existing technology does not take into account the situation that the front passenger may have been out of the position before the AEB requests braking. Summary of the invention

[0005] In view of the defects in the prior art, the purpose of the present invention is to provide a vehicle safety control method based on front-seat occupant out-of-position perception, aiming to accurately obtain the front-seat occupant position information, determine whether the occupant is in an out-of-position state, accurately evaluate the out-of-position impact caused by AEB braking, and optimize the vehicle safety control strategy.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solution:

[0007] The present invention provides a vehicle safety control method based on front-seat passenger out-of-position perception, comprising:

[0008] Step S11, using a seat pressure sensor to determine whether a seat is occupied, using a seat belt buckle sensor to determine whether the driver or passenger is wearing a seat belt, and using a seat horizontal displacement sensor, a seat vertical displacement sensor, and a seat back angle sensor to obtain position information; establishing a seat basic coordinate system based on the information obtained from the seat pressure sensor, the seat belt buckle sensor, the seat horizontal displacement sensor, the seat vertical displacement sensor, and the seat back angle sensor;

[0009] Step S12, identifying key parameters through the OMS camera, wherein the key parameters include the occupant's seat status, the seat belt wearing status, the occupant's body shape characteristics, and key position information, to form a key coordinate system for the occupant;

[0010] Step S13, establishing a key geometric parameter model of the front occupant restraint system based on the seat basic coordinate system and the occupant key coordinate system;

[0011] Step S14, constructing an active AEB / AES avoidance strategy model library through the following process:

[0012] In step S141, a occupant forward leaning basic model library is established;

[0013] In step S142, the vehicle ABS speed information is read from the vehicle chassis CAN bus;

[0014] In step S143, the vehicle pitch angle information is read from the vehicle chassis CAN bus;

[0015] In step S144, an active avoidance AEB / AES strategy model library is established according to the occupant forward leaning basic model library, the vehicle ABS speed information, and the vehicle pitch angle information;

[0016] Step S15, obtaining a comprehensive departure parameter according to a first preset rule based on the key geometric parameter model of the front occupant restraint system and the active avoidance AEB / AES strategy model library;

[0017] Step S16, generating a comprehensive out-of-position index according to the comprehensive out-of-position parameter and a second preset rule;

[0018] Step S17, forming optimal intelligent constraint parameters according to the comprehensive out-of-position index to determine the safety control strategy of the vehicle.

[0019] Preferably, in step S12:

[0020] Step S121, determining the seat occupant status by combining the OMS with the seat pressure sensor information;

[0021] Determine the wearing status of seat belts by judging the seat belt position;

[0022] Identify the body shape of the occupants by judging their body shape;

[0023] Identify key position information, including the distance between the front of the occupant's nose and the steering wheel and instrument panel, as well as information on irregular driving behavior;

[0024] Through the above judgment results, the occupant's key coordinate system is formed.

[0025] Preferably, in step S141:

[0026] The occupant forward tilt basic model library is established based on the 5% percentile occupant forward tilt model library, the 50% percentile occupant forward tilt model library, and the 95% percentile occupant forward tilt model library.

[0027] Preferably, the first preset rule is: obtaining the initial position of the front occupant according to the key geometric parameter model of the front occupant restraint system, obtaining the AEB braking departure increment according to the active avoidance AEB / AES strategy model library, and generating a comprehensive departure parameter by superimposing the AEB braking departure increment on the initial position of the front occupant.

[0028] Preferably, the second preset rule is: the comprehensive dislocation parameter obtained according to the first preset rule is formed into a comprehensive dislocation index based at least on the damage risk and characteristic zoning algorithm principle.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] By introducing OMS parameters, the present invention can more accurately determine the initial key geometric position parameters of the front-seat driver and passengers in a collision accident scenario. At the same time, by performing occupant departure assessment after AEB braking, it can accurately assess the front-seat driver and passenger departure parameters to determine the vehicle safety control strategy after system optimization to maximize the reduction of occupant injury values ​​in accidents and protect the safety of front-seat passengers. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0032] Figure 1 Schematic diagram of the process described in the embodiment;

[0033] Figure 2Schematic diagram of the process of steps S11-S13 of the method described in the embodiment;

[0034] Figure 3 Detailed flow chart of step S14 of the method described in the embodiment DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0036] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0037] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, all directional indications in this application (such as up, down, left, right, front, back, bottom...) are only used to explain the relative position relationship, movement, etc. between the components in a certain specific posture (as shown in the drawings). If the specific posture changes, the directional indication will also change accordingly.

[0038] Example

[0039] This embodiment provides a vehicle safety control method based on front-seat occupant out-of-position perception, which fully exploits the OMS to capture the front-seat occupant position information in real time, and updates, stores, and feedbacks key geometric parameters in real time to determine whether the occupant is in an out-of-position state; the OMS monitors the position parameters of the driver and passengers, evaluates the out-of-position increment caused by AEB braking, and comprehensively optimizes the vehicle safety control strategy (such as airbag detonation strategy).

[0040] like Figure 1 As shown, the main steps of this embodiment include:

[0041] S11: Obtain the position parameters of the electric seat such as pressure, horizontal, vertical, backrest angle, and seat belt buckle, and establish the basic coordinate system of the seat. Specifically, Figure 2 As shown:

[0042] The seat pressure sensor is used to determine whether there is someone sitting in the seat, the seat belt buckle sensor is used to determine whether the driver and passenger are wearing the seat belt, and the seat horizontal displacement sensor, seat vertical displacement sensor, and seat back angle sensor are used to obtain position information;

[0043] The basic seat coordinate system is established based on the information obtained from the seat pressure sensor, seat belt buckle sensor, seat horizontal displacement sensor, seat vertical displacement sensor, and seat back angle sensor. The coordinate system established in this way can more accurately identify the position status of the driver and passengers.

[0044] S12: Identify key parameters through the OMS camera, including the occupant's seat status, seat belt wearing status, occupant's body characteristics, and key position information, to form an occupant's key coordinate system to evaluate the out-of-position parameters under different AEB braking strategies. Specifically, Figure 2 As shown:

[0045] The OMS combines the seat pressure sensor information to determine the occupant's seat occupancy status, for example, whether the seat is occupied by a person, a pet, or a general heavy object.

[0046] The seat belt position can be used to determine the seat belt wearing status.

[0047] By judging the body shape, the body shape characteristics of the occupants can be identified, and the occupants' body shapes can be further judged to be close to the 5% percentile, 50% percentile, and 95% percentile population, fully considering the forward leaning state of occupants of different body shapes, improving the evaluation accuracy of the method of this embodiment, and at the same time improving the evaluation efficiency.

[0048] Identify key position information, including the distance between the front end of the occupant's nose and the steering wheel, instrument panel, and information on irregular driving behaviors, such as the seat being in a zero-gravity state, feet placed above the instrument panel, etc. By identifying relevant information, the driver and occupant status can be considered to the greatest extent, making the subsequent model establishment more realistic.

[0049] Through the above judgment results, the occupant's key coordinate system is formed.

[0050] S13: Based on the seat basic coordinate system established in S11 and the occupant key coordinate system formed in S12, a key geometric parameter model of the front occupant restraint system is established, such as Figure 2 shown.

[0051] S14: Build an active avoidance AEB / AES strategy model library, which is implemented through the following process: Figure 3 As shown:

[0052] In step S141, a basic model library of occupant forward tilt is established. Further, a 5% percentile occupant forward tilt model library, a 50% percentile occupant forward tilt model library, and a 95% percentile occupant forward tilt model library may be established respectively, and then the basic model library of occupant forward tilt is established according to the established occupant forward tilt model libraries of different percentiles, thereby fully considering the forward tilt states of occupants of different body shapes and improving the evaluation accuracy of the method of this embodiment;

[0053] In step S142, the vehicle ABS speed information is read from the vehicle chassis CAN bus;

[0054] In step S143, the vehicle pitch angle information is read from the vehicle chassis CAN bus;

[0055] In step S144, an active avoidance AEB / AES strategy model library is established according to the occupant forward leaning basic model library, the vehicle ABS speed information, and the vehicle pitch angle information.

[0056] S15: According to the key geometric parameter model of the front occupant restraint system and the active avoidance AEB / AES strategy model library, a comprehensive out-of-position parameter is obtained according to a first preset rule, for accurately evaluating the out-of-position impact caused by AEB braking. Specifically, the first preset rule is to obtain the initial position of the front occupant according to the key geometric parameter model of the front occupant restraint system, obtain the AEB braking out-of-position increment according to the active avoidance AEB / AES strategy model library, and generate a comprehensive out-of-position parameter by superimposing the AEB braking out-of-position increment on the initial position of the front occupant, so as to achieve accurate and practical evaluation of occupant out-of-position after AEB braking.

[0057] S16: Generate a comprehensive out-of-position index according to the comprehensive out-of-position parameter and the second preset rule. Further, the second preset rule is: the comprehensive out-of-position parameter obtained according to the first preset rule is used to form a comprehensive out-of-position index based at least on the damage risk and characteristic zoning algorithm principle. It should be noted that the comprehensive out-of-position index can also be formed according to other algorithm principles, which is not limited in this embodiment.

[0058] S17: Based on the comprehensive out-of-position index, optimal intelligent constraint parameters are formed to determine the vehicle's safety control strategy, such as airbag deployment strategy.

[0059] Based on the actual collision accident scene, this embodiment introduces OMS parameters, which can more accurately determine the initial key geometric position parameters of the front-seat driver and passenger in the collision accident scene. This embodiment further performs the occupant departure assessment after AEB braking based on the initial key parameters, which will be more accurate and close to reality; based on the accurate assessment of the departure parameters of the front-seat driver and passenger, the vehicle safety control strategy (such as the airbag detonation strategy) after system optimization can maximize the reduction of the occupant injury value in the accident and protect the safety of the front-seat occupants.

[0060] The above describes the specific embodiments of the present invention. Based on the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention.

Claims

1. A vehicle safety control method based on front-seat passenger departure perception, characterized in that: include: Step S11, using a seat pressure sensor to determine whether a seat is occupied, using a seat belt buckle sensor to determine whether the driver or passenger is wearing a seat belt, and using a seat horizontal displacement sensor, a seat vertical displacement sensor, and a seat back angle sensor to obtain position information; establishing a seat basic coordinate system based on the information obtained from the seat pressure sensor, the seat belt buckle sensor, the seat horizontal displacement sensor, the seat vertical displacement sensor, and the seat back angle sensor; Step S12, identifying key parameters through the OMS camera, wherein the key parameters include the occupant's seat status, the seat belt wearing status, the occupant's body shape characteristics, and key position information, to form a key coordinate system for the occupant; Step S13, establishing a key geometric parameter model of the front occupant restraint system based on the seat basic coordinate system and the occupant key coordinate system; Step S14, constructing an active AEB / AES avoidance strategy model library through the following process: In step S141, a occupant forward leaning basic model library is established; In step S142, the vehicle ABS speed information is read from the vehicle chassis CAN bus; In step S143, the vehicle pitch angle information is read from the vehicle chassis CAN bus; In step S144, an active avoidance AEB / AES strategy model library is established according to the occupant forward leaning basic model library, the vehicle ABS speed information, and the vehicle pitch angle information; Step S15, obtaining a comprehensive departure parameter according to a first preset rule based on the key geometric parameter model of the front occupant restraint system and the active avoidance AEB / AES strategy model library; Step S16, generating a comprehensive out-of-position index according to the comprehensive out-of-position parameter and a second preset rule; Step S17, forming optimal intelligent constraint parameters according to the comprehensive out-of-position index to determine the safety control strategy of the vehicle.

2. The automobile safety control method based on front-seat passenger departure perception according to claim 1 is characterized in that: In step S12: Step S121, determining the seat occupant status by combining the OMS with the seat pressure sensor information; Determine the wearing status of seat belts by judging the seat belt position; Identify the body shape of the occupants by judging their body shape; Identify key position information, including the distance between the front of the occupant's nose and the steering wheel and instrument panel, as well as information on irregular driving behavior; Through the above judgment results, the occupant's key coordinate system is formed.

3. The automobile safety control method based on front-seat passenger departure perception according to claim 1 is characterized in that: In step S141: The occupant forward tilt basic model library is established based on the 5% percentile occupant forward tilt model library, the 50% percentile occupant forward tilt model library, and the 95% percentile occupant forward tilt model library.

4. The automobile safety control method based on front-seat passenger departure perception according to claim 1 is characterized in that: The first preset rule is: obtain the initial position of the front occupant according to the key geometric parameter model of the front occupant restraint system, obtain the AEB brake departure increment according to the active avoidance AEB / AES strategy model library, and generate a comprehensive departure parameter by superimposing the AEB brake departure increment on the initial position of the front occupant.

5. The automobile safety control method based on front-seat passenger departure perception according to claim 1 is characterized in that: The second preset rule is: the comprehensive dislocation parameters obtained according to the first preset rule are used to form a comprehensive dislocation index based at least on the damage risk and characteristic zoning algorithm principles.

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