CN205541298U - System for utilize driving action variability feature detection driver fatigue - Google Patents

System for utilize driving action variability feature detection driver fatigue Download PDF

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Publication number
CN205541298U
CN205541298U CN201620309070.XU CN201620309070U CN205541298U CN 205541298 U CN205541298 U CN 205541298U CN 201620309070 U CN201620309070 U CN 201620309070U CN 205541298 U CN205541298 U CN 205541298U
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data
fatigue
module
driving
driver
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尤超
张伟
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Suzhou Tsingtech Microvision Electronic Technology Co ltd
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Suzhou Tsingtech Microvision Electronic Technology Co ltd
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Abstract

The utility model discloses a system for utilize driving action variability feature detection driver fatigue, including data acquisition module, operation behaviors analysis module, tired identifying module and tired early warning module are driven to vehicle data classification module. The utility model provides a system for utilize driving action variability feature detection driver fatigue utilize to drive operation behaviors variability characteristic, through individual differences establish the fatigue state detection model from learning methods, make build pattern more scientific. Simultaneously, regarding as ancillary data to carry out the fatigue state data such as the vehicle dynamic of system, operation and control selection and detecting, it reduces the tired accuracy scheduling problem that detects with the influence of the irrelevant factor of fatigue to have got rid of vehicle characteristic, operative skill, road environment, individual custom etc. Has greatly improved the rate of accuracy of tired detection, and extensive applicability.

Description

A kind of system utilizing driving behavior variability feature detection fatigue driving
Technical field
This utility model relates to safe driving field, particularly to a kind of system utilizing driving behavior variability feature detection fatigue driving.
Background technology
Along with developing rapidly of global economy and automobile industry, vehicles number gets more and more, and road traffic problem is the most increasingly serious, and vehicle accident takes place frequently.Analyzing China road traffic accident reason in recent years to find, the vehicle accident of about 90% causes owing to driving human factors, and wherein fatigue driving is one of major reason of causing road traffic accident to occur.Driver, when fatigue, to the perception of surrounding, travels judgement and is greatly lowered the manipulation ability of vehicle, it is easy to vehicle accident, the life of serious threat people and property safety occur.Even if but when driver is in fatigue state, still may proceed to drive.Accordingly, it would be desirable to the driving condition of driver is detected in real time, when fatigue state occurs, give effective early warning, it is to avoid the generation of road traffic accident.
At present, the detection of driver fatigue state has more research method, can be roughly divided into detection based on driver's physiological signal, detection based on driver's physiological reaction feature, detection three major types based on driver behavior behavior by the classification of detection.Wherein, driving fatigue detection method based on driver behavior behavior is non-contact detection, the normal driving behavior of driver will not be interfered by measurement process, and not by such environmental effects such as illumination, and characteristic parameter (wheel steering speed, steering wheel angle etc.) easily extracts, the method has become as the focus of research both at home and abroad.But it is currently based on the methods and applications of driver behavior behavioral value fatigue driving still without remarkable effect, mainly show themselves in that in driver behavior behavioral analysis technology, vehicle data is the most comprehensive, driver behavior Analysis model of network behaviors sets up not science, warning without safe driving, driving behavior is easily affected with tired irrelevant factor by vehicle feature, operant skill, road environment, personal habits etc. and is reduced fatigue detecting degree of accuracy.
Utility model content
This utility model purpose is: provide a kind of system utilizing driving behavior variability feature detection fatigue driving, driver behavior behavior variability feature is utilized to set up individual difference driver fatigue state-detection model, utilize this model and driving behavior is detected and carry out safe driving early warning, the advance of raising driver behavior behavior analysis and the safety of driving by driver behavior behavioral data, auxiliary vehicle data in real time in real time.
The technical solution of the utility model is:
A kind of system utilizing driving behavior variability feature detection fatigue driving, including:
Data acquisition module, is used for gathering driver behavior behavioral data, vehicle status data and operation and controls to select data;
Vehicle data sort module, carries out classification according to the different purposes of the data of data collecting module collected and integrates, and is divided into modeling reference data, driving fatigue state detection assistance data and real-time parameter analytical data;
Driver behavior behavior analysis module, the modeling reference data utilizing the classification of vehicle data sort module to integrate sets up fatigue state detection model;
Tired discrimination module, differentiates fatigue relative to the variability feature of data model under waking state according to the data of Real-time Collection;
Giving fatigue pre-warning module, when tired discrimination module sends fatigue driving signal, it is achieved warn or report to the police.
Preferably, described data collecting module collected:
Driver behavior behavioral data, including wheel steering angle, wheel steering speed, wheel steering torque;
Vehicle status data, including level, normal acceleration, speed;
Operation with control to select data, including control key operation on the operation of joystick, steering wheel, the control key operation of driver side car door, for recording the most different operation of driver and controlling to select.
Preferably, the foundation of described fatigue state detection model uses individual difference self-learning method, driver is utilized to start one section of clear-headed data after driving as reference data, individual operating characteristic is carried out self study, in reference data, the average of the tired discriminant criterion of extraction is as reference index, then obtain individual character index with the ratio of tired discriminant criterion and reference index, utilize individual character index to build the feature space of driver fatigue pattern classification.
Preferably, when described tired discrimination module carries out tired differentiation, also judge the fatigue state of driver by driving fatigue state detection assistance data, when judging to send fatigue driving signal to giving fatigue pre-warning module when fatigue state occurs in driver.
Preferably, described data acquisition module obtains vehicle level, normal acceleration from the built-in integrated 3-axis acceleration sensor of system;Obtain driver behavior behavioral data from CAN, operate and control to select data and speed.
Prior art is had to compare, the beneficial effects of the utility model:
The system utilizing driving behavior variability feature detection fatigue driving the most provided by the utility model, utilizes driver behavior behavior variability feature, sets up fatigue state detection model by the self-learning method of individual difference, make modeling more science.Simultaneously, using vehicle dynamically, operate and control the data such as selection and carry out fatigue state detection as assistance data, eliminate the impact of vehicle feature, operant skill, road environment, personal habits etc. and tired irrelevant factor and reduce the problems such as fatigue detecting degree of accuracy, greatly improve the accuracy rate of fatigue detecting.
The system utilizing driving behavior variability feature detection fatigue driving the most provided by the utility model, it is adaptable to all drivers, may be mounted on home-use car, is particularly suited for the professional driver of long-distance passenger transportation, long haul and carriage of special cargo industry.The popularization and application of this system, to ensureing driver, occupant and the safety of vehicle-mounted cargo, is greatly reduced the incidence rate of China's vehicle accident, particularly serious accident, is of great immediate significance, meanwhile will have a tremendous social and economic benefits.
Accompanying drawing explanation
Below in conjunction with the accompanying drawings and this utility model is further described by embodiment:
Fig. 1 is the system architecture diagram of the system utilizing driving behavior variability feature detection fatigue driving described in the utility model;
Fig. 2 is the workflow diagram of the system utilizing driving behavior variability feature detection fatigue driving described in the utility model.
Detailed description of the invention
As it is shown in figure 1, the system utilizing driving behavior variability feature detection fatigue driving disclosed in this utility model, including data acquisition module, vehicle data sort module, driver behavior behavior analysis module, tired discrimination module and giving fatigue pre-warning module.
Data acquisition module, is used for gathering driver behavior behavioral data, vehicle status data and operation and controls to select data;Wherein:
(1) driver behavior behavioral data, including wheel steering angle, wheel steering speed, wheel steering torque;
(2) vehicle status data, including level, normal acceleration, speed;
(3) operate and control to select data, including control key operation on the operation of joystick, steering wheel, the control key operation of driver side car door, for recording the most different operation of driver and controlling to select.
Concrete, data acquisition module obtains vehicle level, normal acceleration from the built-in integrated 3-axis acceleration sensor of system;Obtain driver behavior behavioral data from CAN, operate and control to select data and speed.
Vehicle data sort module, carries out classification according to the different purposes of the data of data collecting module collected and integrates, and is divided into modeling reference data, driving fatigue state detection assistance data and real-time parameter analytical data.
Driver behavior behavior analysis module, the modeling reference data utilizing the classification of vehicle data sort module to integrate sets up fatigue state detection model.
Tired discrimination module, differentiates fatigue relative to the variability feature of data model under waking state according to the data of Real-time Collection;And judge the fatigue state of driver by other assistance data, when judging to send signal to giving fatigue pre-warning module when fatigue state occurs in driver.
Giving fatigue pre-warning module, when tired discrimination module sends fatigue driving signal, it is achieved warn or report to the police.
As in figure 2 it is shown, be the workflow diagram of the system utilizing driving behavior variability feature detection fatigue driving described in the utility model.Concrete includes:
1. data acquisition module obtains vehicle level, normal acceleration from the built-in integrated 3-axis acceleration sensor of system;Obtain driver behavior behavioral data from CAN, operate and control to select data and speed.
2. the data collected are carried out real-time grading by data categorization module, driver start clear-headed driving behavior in the 15 minutes operation data after driving as modeling reference data.Driving behavior operation data after this are as real-time parameter analytical data.Select data and vehicle status data as assistance data with control operation.And by categorical data in real time to driver behavior behavior analysis module transfer.
3. driver behavior behavior analysis module will be modeled according to the modeling reference data with variability feature got.The foundation of described fatigue state detection model uses individual difference self-learning method, driver is utilized to start 15 minutes clear-headed period data after driving as reference data, individual operating characteristic is carried out self study, in reference data, the average of the tired discriminant criterion of extraction is as reference index, then obtain individual character index with the ratio of tired discriminant criterion and reference index, utilize individual character index to build the feature space of driver fatigue pattern classification.
4. tired discrimination module utilizes whether real-time parameter analytical data is in fatigue state relative to the variability feature detection driver of data model under waking state.Utilize the assistance datas such as the operation of vehicle acceleration, control key, speed to get rid of the erroneous judgement of the fatigue state because of factor generations such as road environment, vile weather, turn inside diameters simultaneously.Giving fatigue pre-warning signal is sent to giving fatigue pre-warning module when fatigue state occurs.
5. when giving fatigue pre-warning module receives giving fatigue pre-warning signal, show alarm signal in time or reported to the police by auditory tone cues, reminding driver to be the most in fatigue driving state.
Above-described embodiment only for technology of the present utility model design and feature are described, its object is to allow person skilled in the art will appreciate that content of the present utility model and to implement according to this, can not limit protection domain of the present utility model with this.All modifications done according to the spirit of this utility model main technical schemes, all should contain within protection domain of the present utility model.

Claims (4)

1. the system utilizing driving behavior variability feature detection fatigue driving, it is characterised in that including:
Data acquisition module, is used for gathering driver behavior behavioral data, vehicle status data and operation and controls to select data;
Vehicle data sort module, carries out classification according to the different purposes of the data of data collecting module collected and integrates, and is divided into modeling reference data, driving fatigue state detection assistance data and real-time parameter analytical data;
Driver behavior behavior analysis module, the modeling reference data utilizing the classification of vehicle data sort module to integrate sets up fatigue state detection model;
Tired discrimination module, differentiates fatigue relative to the variability feature of data model under waking state according to the data of Real-time Collection;
Giving fatigue pre-warning module, when tired discrimination module sends fatigue driving signal, it is achieved warn or report to the police.
The system utilizing driving behavior variability feature detection fatigue driving the most according to claim 1, it is characterised in that described data collecting module collected:
Driver behavior behavioral data, including wheel steering angle, wheel steering speed, wheel steering torque;
Vehicle status data, including level, normal acceleration, speed;
Operation with control to select data, including control key operation on the operation of joystick, steering wheel, the control key operation of driver side car door, for recording the most different operation of driver and controlling to select.
The system utilizing driving behavior variability feature detection fatigue driving the most according to claim 2, it is characterized in that, when described tired discrimination module carries out tired differentiation, also judge the fatigue state of driver by driving fatigue state detection assistance data, when judging to send fatigue driving signal to giving fatigue pre-warning module when fatigue state occurs in driver.
The system utilizing driving behavior variability feature detection fatigue driving the most according to claim 2, it is characterised in that described data acquisition module obtains vehicle level, normal acceleration from the built-in integrated 3-axis acceleration sensor of system;Obtain driver behavior behavioral data from CAN, operate and control to select data and speed.
CN201620309070.XU 2016-04-14 2016-04-14 System for utilize driving action variability feature detection driver fatigue Active CN205541298U (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105701972A (en) * 2016-04-14 2016-06-22 苏州清研微视电子科技有限公司 System for detecting fatigue driving by using driving behavior variability characteristics
CN108922186A (en) * 2018-07-20 2018-11-30 大连理工大学 It is a kind of that analyzing and alarming system and method are monitored based on the streaming for vehicle that mist calculates
CN109591825A (en) * 2018-11-29 2019-04-09 北京新能源汽车股份有限公司 Driving fatigue detection method and device and vehicle

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105701972A (en) * 2016-04-14 2016-06-22 苏州清研微视电子科技有限公司 System for detecting fatigue driving by using driving behavior variability characteristics
CN108922186A (en) * 2018-07-20 2018-11-30 大连理工大学 It is a kind of that analyzing and alarming system and method are monitored based on the streaming for vehicle that mist calculates
CN108922186B (en) * 2018-07-20 2019-10-11 大连理工大学 It is a kind of that analyzing and alarming system and method are monitored based on the streaming for vehicle that mist calculates
CN109591825A (en) * 2018-11-29 2019-04-09 北京新能源汽车股份有限公司 Driving fatigue detection method and device and vehicle

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