Vehicle cabin intra-domain monitoring method and system based on WiFi module

Abstract

The invention provides a vehicle cabin intra-domain monitoring method and system based on a WiFi module, and belongs to the technical field of vehicle safe driving. The vehicle cabin intra-domain monitoring method based on the WiFi module comprises the following steps: acquiring driver driving data, passenger taking data and driving interaction data; calculating first driving risk degree data according to the driver driving data and the driving interaction data; determining second driving risk degree data according to the passenger taking data; outputting comprehensive driving risk degree data according to the first driving risk degree data and the second driving risk degree data; by the adoption of the scheme, safe driving of a driver in the vehicle driving process is guaranteed, and the vehicle taking safety of passengers is improved.

Description

Technical Field

[0001] This application relates to the technical field of automotive safe driving, and particularly to a method and system for monitoring within a vehicle cockpit domain based on a WiFi module. Background Art

[0002] Currently, with the development of technology, as one of the essential means of transportation for people, cars have increasingly penetrated into people's lives, and there are not a few families that own cars as a means of transportation; consequently, the population of people with the identity of drivers is becoming larger; however, during actual driving, factors such as fatigue driving, drunk driving, arguments between the driver and passengers, and the behavior of the driver or passengers can all affect driving safety; therefore, in order to ensure the safe driving of the driver and improve the safety of passengers, it is necessary to monitor within the vehicle cockpit domain. Summary of the Invention

[0003] This application provides a method and system for monitoring within a vehicle cockpit domain based on a WiFi module, which can ensure the safe driving of the driver.

[0004] In a first aspect, this application provides a method for monitoring within a vehicle cockpit domain based on a WiFi module. The method includes: Obtain driver driving data, passenger riding data, and driving interaction data; the driver driving data includes the total driver driving duration, emergency braking frequency, and driver two-handed driving time; the passenger riding data includes passenger type data, the number of passengers, and the passenger seat belt wearing rate; the driving interaction data includes the driver-passenger interaction time and the number of times the in-domain sound decibel exceeds the threshold; Calculate first driving risk data based on the driver driving data and the driving interaction data; Determine second driving risk data based on the passenger riding data; Output comprehensive driving risk data based on the first driving risk data and the second driving risk data; and output a dangerous driving warning based on the comprehensive driving risk data.

[0005] Further, the calculating the first driving risk data based on the driver driving data and the driving interaction data includes: Calculate first sub-risk data based on the driver driving data; the first sub-risk data is associated with the total driver driving duration, emergency braking frequency, and driver two-handed driving time; Calculate second sub-risk data based on the driving interaction data; the second sub-risk data is associated with the driver-passenger interaction time and the number of times the in-domain sound decibel exceeds the threshold; Calculate the first driving risk data based on the first sub-risk data and the second sub-risk data.

[0006] Further, the calculation method of the first driving risk degree data includes: ; In the formula, is the first driving risk degree data; is the total driving duration of the driver, is the emergency braking frequency, is the time when the driver drives with both hands; there are n batches of passengers, then is the driver-passenger interaction time during the ride of the i-th batch of passengers, is the ride time of the i-th batch of passengers; is the number of times the sound decibel in the domain exceeds the threshold, is the natural constant; are the preset first calculation coefficient and the preset second calculation coefficient respectively, and, .

[0007] Further, the calculation method of the second driving risk degree data includes: ; ; In the formula, is the second driving risk degree data; when there are elderly or children among the passengers, ; when there are no elderly or children among the passengers, ; is the number of passengers, is the passenger seat belt wearing rate.

[0008] Further, the calculation method of the comprehensive driving risk degree data includes: ; In the formula, is the comprehensive driving risk degree data, is the first driving risk degree data, is the second driving risk degree data; are the first preset risk degree weight and the second preset risk degree weight respectively, and, .

[0009] In the second aspect, the present application provides a vehicle cockpit in-domain monitoring system based on a WiFi module. The system includes: An acquisition module for acquiring driver driving data, passenger riding data, and driving interaction data; the driver driving data includes the total driver driving duration, emergency braking frequency, and driver two-handed driving time; the passenger riding data includes passenger type data, the number of passengers, and the passenger seat belt wearing rate; the driving interaction data includes the driver-passenger interaction time and the number of times the in-vehicle sound decibel exceeds the threshold value. A calculation module for calculating first driving risk data based on the driver driving data and the driving interaction data; A determination module for determining second driving risk data based on the passenger riding data; An output module for outputting comprehensive driving risk data based on the first driving risk data and the second driving risk data; and outputting a dangerous driving warning based on the comprehensive driving risk data.

[0010] Further, the calculation module is further configured such that calculating the first driving risk data based on the driver driving data and the driving interaction data includes: Calculating first sub-risk data based on the driver driving data; the first sub-risk data is associated with the total driver driving duration, emergency braking frequency, and driver two-handed driving time; Calculating second sub-risk data based on the driving interaction data; the second sub-risk data is associated with the driver-passenger interaction time and the number of times the in-vehicle sound decibel exceeds the threshold value; Calculating the first driving risk data based on the first sub-risk data and the second sub-risk data.

[0011] Further, the calculation module is further configured such that the calculation method of the first driving risk data includes: ; In the formula, is the first driving risk data; is the total driver driving duration, is the emergency braking frequency, is the driver two-handed driving time; assuming there are n batches of passengers, then is the driver-passenger interaction time during the ride of the i-th batch of passengers, is the riding time of the i-th batch of passengers; is the number of times the in-vehicle sound decibel exceeds the threshold value, is the natural constant; are the preset first calculation coefficient and the preset second calculation coefficient respectively, and, 。

[0012] Further, the determination module is further configured such that the calculation method of the second driving risk data includes: ; ; In the formula, is the second driving risk degree data; when there are elderly people or children among the passengers, ; when there are no elderly people or children among the passengers, ; is the number of passengers, is the seat belt wearing rate of the passengers.

[0013] Furthermore, the output module is further configured that the calculation method of the comprehensive driving risk degree data includes: ; In the formula, is the comprehensive driving risk degree data, is the first driving risk degree data, is the second driving risk degree data; are the first preset risk degree weight and the second preset risk degree weight respectively, and, .

[0014] It should be understood that the content described in the invention content part is not intended to limit the key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Combined with the drawings and referring to the following detailed description, the above and other features, advantages and aspects of the embodiments of the present application will become more obvious. In the drawings, the same or similar reference numerals represent the same or similar elements, where: Figure 1 shows a flowchart of a vehicle cockpit domain monitoring method based on a WiFi module in an embodiment of the present application; Figure 2 shows a block diagram of a vehicle cockpit domain monitoring system based on a WiFi module in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0017] In addition, the term "and / or" in this text is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this text generally indicates that the associated objects before and after are in an "or" relationship.

[0018] The present application provides a method and system for in-vehicle cockpit domain monitoring based on a WiFi module, which can ensure the safe driving of the vehicle by the driver.

[0019] In a first aspect, the present application provides a method for in-vehicle cockpit domain monitoring based on a WiFi module. As Figure 1 shown, the specific steps included in the system are as follows.

[0020] Step S110: Obtain driver driving data, passenger riding data, and driving interaction data; the driver driving data includes the total driver driving duration, emergency braking frequency, and driver two-handed driving time; the passenger riding data includes passenger type data, the number of passengers, and the passenger seat belt wearing rate; the driving interaction data includes the driver-passenger interaction time and the number of times the in-domain sound decibel exceeds the threshold.

[0021] In an embodiment of the present application, by analyzing the states of the driver and passengers, a method for calculating the driving risk degree that can reflect the driver's driving risk is obtained; in this way, when it is analyzed that the driver is driving dangerously, a corresponding warning can be given to ensure the safe driving of the driver and the safe riding of the passengers; it can be understood that the data acquisition method for analyzing the states of the driver and passengers is the cooperation of the WiFi module, processing module, and monitoring module in the in-vehicle cockpit domain, and the information collection and analysis of the driver and passengers are completed through a processing system; it can be understood that data can also be obtained and analyzed through other methods, and this is only an exemplary illustration here.

[0022] Step S120: Calculate first driving risk degree data according to the driver driving data and the driving interaction data.

[0023] In an embodiment of the present application, calculating the first driving risk degree data according to the driver driving data and the driving interaction data specifically includes calculating first sub-risk degree data according to the driver driving data; the first sub-risk degree data is associated with the total driver driving duration, emergency braking frequency, and driver two-handed driving time; calculating second sub-risk degree data according to the driving interaction data; the second sub-risk degree data is associated with the driver-passenger interaction time and the number of times the in-domain sound decibel exceeds the threshold; calculating the first driving risk degree data according to the first sub-risk degree data and the second sub-risk degree data.

[0024] Further, the calculation method of the first driving risk degree data includes: ; Wherein, is the first driving risk degree data; is the total driving duration of the driver, is the emergency braking frequency, is the time of the driver's two - hand driving; Suppose there are n batches of passengers, then is the driver - passenger interaction time during the ride of the i - th batch of passengers, is the ride time of the i - th batch of passengers; is the number of times the sound decibel in the domain exceeds the threshold, is the natural constant; are the preset first calculation coefficient and the preset second calculation coefficient respectively, and, .

[0025] Step S130: Determine the second driving risk degree data according to the passenger ride data.

[0026] In the embodiment of the present application, determining the second driving risk degree data according to the passenger ride data specifically includes that the calculation method of the second driving risk degree data includes: ; ; Wherein, is the second driving risk degree data; When there are elderly people or children among the passengers, ; When there are no elderly people or children among the passengers, ; is the number of passengers, is the passenger seat - belt wearing rate.

[0027] Step S140: Output the comprehensive driving risk degree data according to the first driving risk degree data and the second driving risk degree data; and output a dangerous driving warning based on the comprehensive driving risk degree data.

[0028] In the embodiment of the present application, outputting the comprehensive driving risk degree data according to the first driving risk degree data and the second driving risk degree data specifically includes that the calculation method of the comprehensive driving risk degree data includes: ; Wherein, is the comprehensive driving risk degree data, is the first driving risk degree data, is the second driving risk degree data; are the first preset risk degree weight and the second preset risk degree weight respectively, and, .

[0029] After obtaining the comprehensive driving risk data, compare the comprehensive driving risk data with a preset risk threshold. If the comprehensive driving risk data is not less than the preset risk threshold, issue a dangerous driving warning to remind the driver to drive safely; in this way, the safety of the driver's driving is improved, and the riding safety of the passengers is ensured.

[0030] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to the embodiments of this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0031] In a second aspect, this application provides a vehicle cockpit domain monitoring system based on a WiFi module. Refer to Figure 2 , the system includes an acquisition module 210 for acquiring driver driving data, passenger riding data, and driving interaction data; the driver driving data includes the total driver driving duration, emergency braking frequency, and driver's two-handed driving time; the passenger riding data includes passenger type data, the number of passengers, and the passenger seat belt wearing rate; the driving interaction data includes the driver-passenger interaction time and the number of times the in-domain sound decibel exceeds the threshold; a calculation module 220 for calculating first driving risk data according to the driver driving data and the driving interaction data; a determination module 230 for determining second driving risk data according to the passenger riding data; an output module 240 for outputting comprehensive driving risk data according to the first driving risk data and the second driving risk data; and outputting a dangerous driving warning based on the comprehensive driving risk data.

[0032] Further, the calculation module 220 is further configured such that calculating the first driving risk data according to the driver driving data and the driving interaction data includes: Calculating first sub-risk data according to the driver driving data; the first sub-risk data is associated with the total driver driving duration, emergency braking frequency, and driver's two-handed driving time; Calculating second sub-risk data according to the driving interaction data; the second sub-risk data is associated with the driver-passenger interaction time and the number of times the in-domain sound decibel exceeds the threshold; Calculating the first driving risk data according to the first sub-risk data and the second sub-risk data.

[0033] Further, the calculation module 220 is further configured such that the calculation method of the first driving risk data includes: ; Wherein, is the first driving risk data; is the total driving duration of the driver, is the emergency braking frequency, is the time when the driver drives with both hands; there are n batches of passengers, then is the driver-passenger interaction time during the ride of the i-th batch of passengers, is the ride time of the i-th batch of passengers; is the number of times the sound decibel in the domain exceeds the threshold, is the natural constant; are the preset first calculation coefficient and the preset second calculation coefficient respectively, and, .

[0034] Furthermore, the determining module 230 is further configured that the calculation method of the second driving risk data includes: ; ; Wherein, is the second driving risk data; when there are elderly people and children among the passengers, ; when there are no elderly people and children among the passengers, ; is the number of passengers, is the seat belt wearing rate of the passengers.

[0035] Furthermore, the output module 240 is further configured that the calculation method of the comprehensive driving risk data includes: ; Wherein, is the comprehensive driving risk data, is the first driving risk data, is the second driving risk data; are the first preset risk weight and the second preset risk weight respectively, and, .

[0036] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the described device can refer to the corresponding process in the foregoing method embodiment, which will not be elaborated herein.

[0037] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the present application that have similar functions.

Claims

1. A vehicle cockpit domain monitoring method based on a WiFi module, characterized in that, Including: Obtain driver driving data, passenger riding data, and driving interaction data; The driver driving data includes the total driver driving duration, emergency braking frequency, and driver two-handed driving time; The passenger riding data includes passenger type data, the number of passengers, and the passenger seat belt wearing rate; the driving interaction data includes the driver-passenger interaction time and the number of times the in-vehicle sound decibel exceeds the threshold; Calculate first driving risk data based on the driver driving data and the driving interaction data; Determine second driving risk data based on the passenger riding data; Output comprehensive driving risk data based on the first driving risk data and the second driving risk data; And output a dangerous driving warning based on the comprehensive driving risk data; The calculating the first driving risk data based on the driver driving data and the driving interaction data includes calculating first sub-risk data according to the driver driving data; the first sub-risk data is associated with the total driver driving duration, emergency braking frequency, and driver two-handed driving time; Calculate second sub-risk data according to the driving interaction data; the second sub-risk data is associated with the driver-passenger interaction time and the number of times the in-vehicle sound decibel exceeds the threshold; Calculate first driving risk data according to the first sub-risk data and the second sub-risk data; The calculation method of the first driving risk data includes: ； Wherein, is the first driving risk degree data; is the total driving duration of the driver, is the emergency braking frequency, is the time when the driver drives with both hands; There are n batches of passengers, then is the driver-passenger interaction time during the ride of the i-th batch of passengers, is the passenger ride time of the i-th batch of passengers; is the number of times the sound decibel in the domain exceeds the threshold, is the natural constant; are the preset first calculation coefficient and the preset second calculation coefficient respectively, and, .

2. The method according to claim 1, wherein The calculation method of the second driving risk data includes: ； ； In the formula, is the second driving risk degree data; when there are elderly people or children among the passengers, ; when there are no elderly people or children among the passengers, ; is the number of passengers, is the seat belt wearing rate of the passengers.

3. The method according to claim 2, characterized in that, The calculation method of the comprehensive driving risk data includes: ； Wherein, is the comprehensive driving risk degree data, is the first driving risk degree data, is the second driving risk degree data; are the first preset risk degree weight and the second preset risk degree weight respectively, and, .

4. A vehicle cockpit in - domain monitoring system based on a WiFi module, characterized in that, Including: An acquisition module (210) for obtaining driver driving data, passenger riding data, and driving interaction data; The driver driving data includes the total driver driving duration, emergency braking frequency, and driver two-handed driving time; The passenger riding data includes passenger type data, the number of passengers, and the passenger seat belt wearing rate; the driving interaction data includes the driver-passenger interaction time and the number of times the in-vehicle sound decibel exceeds the threshold; A calculation module (220) for calculating first driving risk data according to the driver driving data and the driving interaction data; A determination module (230) for determining second driving risk data according to the passenger riding data; An output module (240) for outputting comprehensive driving risk data according to the first driving risk data and the second driving risk data; and outputting a dangerous driving warning based on the comprehensive driving risk data; The calculation module (220) is further configured that the calculating the first driving risk data according to the driver driving data and the driving interaction data includes calculating first sub-risk data according to the driver driving data; the first sub-risk data is associated with the total driver driving duration, emergency braking frequency, and driver two-handed driving time; Calculate second sub-risk data according to the driving interaction data; the second sub-risk data is associated with the driver-passenger interaction time and the number of times the in-vehicle sound decibel exceeds the threshold; Calculate first driving risk data according to the first sub-risk data and the second sub-risk data; The calculation method of the first driving risk data includes: ； In the formula, is the first driving risk degree data; is the total driving duration of the driver, is the emergency braking frequency, is the time when the driver drives with both hands; There are n batches of passengers, then is the driver-passenger interaction time during the ride of the i-th batch of passengers, is the passenger ride time of the i-th batch of passengers; is the number of times the sound decibel in the domain exceeds the threshold, is the natural constant; are the preset first calculation coefficient and the preset second calculation coefficient respectively, and, .

5. The system according to claim 4, wherein The determining module (230) is further configured such that the calculation method of the second driving risk degree data includes: ； ； Wherein, is the second driving risk degree data; when there are elderly people or children among the passengers, ; when there are no elderly people or children among the passengers, ; is the number of passengers, is the seat belt wearing rate of the passengers.

6. The system according to claim 5, characterized in that, The output module (240) is further configured such that the calculation method of the comprehensive driving risk degree data includes: ； In the formula, is the comprehensive driving risk degree data, is the first driving risk degree data, is the second driving risk degree data; are the first preset risk degree weight and the second preset risk degree weight respectively, and, .

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