A vehicle cockpit domain monitoring method and system based on a WiFi module

Abstract

The present application provides a vehicle cabin domain monitoring method and system based on a WiFi module, belonging to the field of automobile safe driving technology; wherein, a vehicle cabin domain monitoring method based on a WiFi module includes obtaining driver driving data, passenger riding data and driving interaction data; calculating first driving risk data based on the driver driving data and the driving interaction data; determining second driving risk data based on the passenger riding data; and outputting comprehensive driving risk data based on the first driving risk data and the second driving risk data; adopting this solution, the driver's safe driving is ensured during vehicle driving and the safety of passengers riding in the vehicle is improved.

Description

Technical Field

[0001] The present application relates to the field of automobile safe driving technology, and in particular to a vehicle cabin monitoring method and system based on a WiFi module. Background Art

[0002] At present, with the development of science and technology, cars have become one of the indispensable means of transportation for people and are becoming more and more deeply integrated into people's lives. There are many families that own cars as a means of transportation. As a result, the number of people who are drivers is becoming larger and larger. However, in the actual driving process, such as fatigue driving, drunk driving, quarrels between drivers and passengers, and the behavior of drivers or passengers, they will affect driving safety. Therefore, in order to ensure the safety of drivers and improve the safety of passengers, it is necessary to monitor the vehicle cabin. Summary of the Invention

[0003] The present application provides a vehicle cabin monitoring method and system based on a WiFi module, which can ensure the driver's safe driving.

[0004] In a first aspect, the present application provides a vehicle cabin monitoring method based on a WiFi module. The method comprises:

[0005] Acquire driver driving data, passenger riding data, and driving interaction data; the driver driving data includes the driver's total driving time, emergency braking frequency, and the driver's two-handed driving time; the passenger riding data includes passenger type data, number of passengers, and passenger seatbelt wearing rate; the driving interaction data includes the driver-passenger interaction time and the number of times the decibel level exceeds the threshold within the domain;

[0006] Calculating first driving risk data based on the driver driving data and the driving interaction data;

[0007] determining second driving risk data based on the passenger riding data;

[0008] Comprehensive driving risk data is output according to the first driving risk data and the second driving risk data; and a dangerous driving warning is output based on the comprehensive driving risk data.

[0009] Furthermore, the calculating of the first driving risk data based on the driver driving data and the driving interaction data includes:

[0010] Calculating first sub-risk data based on the driver's driving data; the first sub-risk data is associated with the driver's total driving time, emergency braking frequency, and driver's two-handed driving time;

[0011] 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 decibel level of the sound in the domain exceeds the threshold;

[0012] First driving risk level data is calculated based on the first sub-risk level data and the second sub-risk level data.

[0013] Furthermore, the calculation method of the first driving risk data includes:

[0014] ;

[0015] Where, is the first driving risk data; The total driving time for the driver, is the emergency braking frequency, is the driver's two-handed driving time; if 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 travel time of the i-th batch of passengers; is the number of times the sound decibel exceeds the threshold in the domain, is a natural constant; are respectively a preset first calculation coefficient and a preset second calculation coefficient, and, .

[0016] Furthermore, the calculation method of the second driving risk data includes:

[0017] ;

[0018] ;

[0019] Where, is the second driving risk data; when there are elderly people and children among the passengers, ; When there are no elderly or children among the passengers, ; is the number of passengers, Passenger seat belt wearing rate.

[0020] Furthermore, the calculation method of the comprehensive driving risk data includes:

[0021] ;

[0022] Where, For 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, .

[0023] In a second aspect, the present application provides a vehicle cabin monitoring system based on a WiFi module. The system includes:

[0024] An acquisition module is configured to acquire driver driving data, passenger riding data, and driving interaction data; the driver driving data includes the driver's total driving time, emergency braking frequency, and the driver's two-handed driving time; the passenger riding data includes passenger type data, number of passengers, and passenger seatbelt wearing rate; and the driving interaction data includes the driver-passenger interaction time and the number of times the decibel level exceeds the threshold within the domain;

[0025] a calculation module, configured to calculate first driving risk data based on the driver's driving data and the driving interaction data;

[0026] a determination module, configured to determine second driving risk data based on the passenger riding data;

[0027] An output module is configured to output comprehensive driving risk data according to the first driving risk data and the second driving risk data; and output a dangerous driving warning based on the comprehensive driving risk data.

[0028] Furthermore, the calculation module is further configured such that the calculation of the first driving risk data based on the driver driving data and the driving interaction data includes:

[0029] Calculating first sub-risk data based on the driver's driving data; the first sub-risk data is associated with the driver's total driving time, emergency braking frequency, and driver's two-handed driving time;

[0030] 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 decibel level of the sound in the domain exceeds the threshold;

[0031] First driving risk level data is calculated based on the first sub-risk level data and the second sub-risk level data.

[0032] Furthermore, the calculation module is further configured to calculate the first driving risk data in a manner including:

[0033] ;

[0034] Where, is the first driving risk data; The total driving time for the driver, is the emergency braking frequency, is the driver's two-handed driving time; if 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 travel time of the i-th batch of passengers; is the number of times the sound decibel exceeds the threshold in the domain, is a natural constant; are respectively a preset first calculation coefficient and a preset second calculation coefficient, and, .

[0035] Furthermore, the determination module is further configured to calculate the second driving risk data in a manner including:

[0036] ;

[0037] ;

[0038] Where, is the second driving risk data; when there are elderly people and children among the passengers, ; When there are no elderly or children among the passengers, ; is the number of passengers, Passenger seat belt wearing rate.

[0039] Furthermore, the output module is further configured to calculate the comprehensive driving risk data in the following manner:

[0040] ;

[0041] Where, For 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, .

[0042] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0044] Figure 1 A flowchart of a vehicle cabin monitoring method based on a WiFi module in an embodiment of the present application is shown;

[0045] Figure 2 A block diagram of a vehicle cabin monitoring system based on a WiFi module in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0046] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0048] The present application provides a vehicle cabin domain monitoring method and system based on a WiFi module, which can ensure that the driver drives the vehicle safely.

[0049] In the first aspect, the present application provides a vehicle cabin monitoring method based on a WiFi module. Figure 1 As shown, the specific steps included in the system are as follows.

[0050] Step S110: Acquire driver driving data, passenger riding data and driving interaction data; the driver driving data includes the driver's total driving time, emergency braking frequency and the 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 decibel level of sound in the domain exceeds the threshold.

[0051] In an embodiment of the present application, by analyzing the status of the driver and the passenger, a method that can reflect the driver's driving risk is calculated; in this way, a corresponding warning can be given when the driver's dangerous driving is analyzed to ensure the driver's safe driving and the passengers' safe riding; it can be understood that the data acquisition method for the analysis of the driver and passenger status is the cooperation of the WiFi module, processing module and monitoring module in the vehicle cabin domain, and the information collection and analysis of the driver and passengers is completed by a processing system; it can be understood that data can also be acquired and analyzed by other methods, which is only used as an example here.

[0052] Step S120: Calculating first driving risk data based on the driver's driving data and the driving interaction data.

[0053] In an embodiment of the present application, calculating the first driving risk data based on the driver's driving data and the driving interaction data specifically includes: calculating first sub-risk data based on the driver's driving data; the first sub-risk data is associated with the driver's total driving time, emergency braking frequency, and driver's 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 decibel level of sound in the domain exceeds the threshold; calculating the first driving risk data based on the first sub-risk data and the second sub-risk data.

[0054] Furthermore, the calculation method of the first driving risk data includes:

[0055] ;

[0056] Where, is the first driving risk data; The total driving time for the driver, is the emergency braking frequency, is the driver's two-handed driving time; if 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 travel time of the i-th batch of passengers; is the number of times the sound decibel exceeds the threshold in the domain, is a natural constant; are respectively a preset first calculation coefficient and a preset second calculation coefficient, and, .

[0057] Step S130: Determine second driving risk data based on the passenger riding data.

[0058] In the embodiment of the present application, determining the second driving risk data according to the passenger riding data specifically includes: calculating the second driving risk data in a manner including:

[0059] ;

[0060] ;

[0061] Where, is the second driving risk data; when there are elderly people and children among the passengers, ; When there are no elderly or children among the passengers, ; is the number of passengers, Passenger seat belt wearing rate.

[0062] Step S140: 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.

[0063] In the embodiment of the present application, outputting comprehensive driving risk data according to the first driving risk data and the second driving risk data specifically includes: calculating the comprehensive driving risk data in the following manner:

[0064] ;

[0065] Where, For 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, .

[0066] After obtaining the comprehensive driving risk data, the comprehensive driving risk data is compared with the preset risk threshold. If the comprehensive driving risk data is not less than the preset risk threshold, a dangerous driving warning is issued to remind the driver to drive safely. In this way, the driver's driving safety is improved and the safety of passengers is guaranteed.

[0067] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to the embodiments of this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for this application.

[0068] In the second aspect, the present application provides a vehicle cabin monitoring system based on a WiFi module. Figure 2The system includes an acquisition module 210 for acquiring driver driving data, passenger riding data and driving interaction data; the driver driving data includes the driver's total driving time, 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 decibel level of sound in the domain exceeds the threshold; a calculation module 220 for calculating first driving risk data based on the driver driving data and the driving interaction data; a determination module 230 for determining second driving risk data based on the passenger riding data; an output module 240 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.

[0069] Furthermore, the calculation module 220 is further configured to calculate the first driving risk data according to the driver driving data and the driving interaction data, including:

[0070] Calculating first sub-risk data based on the driver's driving data; the first sub-risk data is associated with the driver's total driving time, emergency braking frequency, and driver's two-handed driving time;

[0071] 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 decibel level of the sound in the domain exceeds the threshold;

[0072] First driving risk level data is calculated based on the first sub-risk level data and the second sub-risk level data.

[0073] Furthermore, the calculation module 220 is further configured to calculate the first driving risk data in a manner including:

[0074] ;

[0075] Where, is the first driving risk data; The total driving time for the driver, is the emergency braking frequency, is the driver's two-handed driving time; if 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 travel time of the i-th batch of passengers; is the number of times the sound decibel exceeds the threshold in the domain, is a natural constant; are respectively a preset first calculation coefficient and a preset second calculation coefficient, and, .

[0076] Furthermore, the determination module 230 is further configured to calculate the second driving risk data in a manner including:

[0077] ;

[0078] ;

[0079] Where, is the second driving risk data; when there are elderly people and children among the passengers, ; When there are no elderly or children among the passengers, ; is the number of passengers, Passenger seat belt wearing rate.

[0080] Furthermore, the output module 240 is further configured to calculate the comprehensive driving risk data in the following manner:

[0081] ;

[0082] Where, For 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, .

[0083] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0084] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A vehicle cabin monitoring method based on a WiFi module, characterized in that: include: Obtain driver driving data, passenger riding data and driving interaction data; The driver driving data includes the driver's total driving time, emergency braking frequency and the driver's two-handed driving time; The passenger riding data includes passenger type data, passenger number and passenger seat belt wearing rate; the driving interaction data includes driver-passenger interaction time and the number of times the sound decibel exceeds the threshold in the domain; Calculating first driving risk data based on the driver driving data and the driving interaction data; determining second driving risk data based on the passenger riding data; 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; Calculating the first driving risk data based on the driver's driving data and the driving interaction data includes calculating first sub-risk data based on the driver's driving data; the first sub-risk data is associated with the driver's total driving time, emergency braking frequency, and driver's 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 decibel level of the sound in the domain exceeds the threshold; calculating 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: ； Where, is the first driving risk data; The total driving time for the driver, is the emergency braking frequency, Driving time for drivers’ hands; 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 passenger travel time of the i-th batch of passengers; is the number of times the sound decibel exceeds the threshold in the domain, is a natural constant; are respectively a preset first calculation coefficient and a preset second calculation coefficient, and, .

2. The method according to claim 1, characterized in that The calculation method of the second driving risk data includes: ； ； Where, is the second driving risk data; when there are elderly people and children among the passengers, ; When there are no elderly or children among the passengers, ; is the number of passengers, Passenger seat belt wearing rate.

3. The method according to claim 2, characterized in that The calculation method of the comprehensive driving risk data includes: ； Where, For 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, .

4. A vehicle cabin monitoring system based on a WiFi module, characterized in that: include: An acquisition module (210) is used to acquire driver driving data, passenger riding data and driving interaction data; The driver driving data includes the driver's total driving time, emergency braking frequency and the driver's two-handed driving time; The passenger riding data includes passenger type data, passenger number and passenger seat belt wearing rate; the driving interaction data includes driver-passenger interaction time and the number of times the sound decibel exceeds the threshold in the domain; A calculation module (220), configured to calculate first driving risk data based on the driver's driving data and the driving interaction data; A determination module (230) is used to determine second driving risk data based on the passenger riding data; an output module (240), configured to 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 calculation module (220) is further configured such that the calculation of the first driving risk data based on the driver's driving data and the driving interaction data includes calculating first sub-risk data based on the driver's driving data; the first sub-risk data is associated with the driver's total driving time, emergency braking frequency, and driver's 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 decibel level of the sound in the domain exceeds the threshold; calculating 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: ； Where, is the first driving risk data; The total driving time for the driver, is the emergency braking frequency, Driving time for drivers’ hands; 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 passenger travel time of the i-th batch of passengers; is the number of times the sound decibel exceeds the threshold in the domain, is a natural constant; are respectively a preset first calculation coefficient and a preset second calculation coefficient, and, .

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

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 data includes: ； Where, For 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, .

Images