CN114516340B - Driver disability judging method based on user driving habit - Google Patents

Driver disability judging method based on user driving habit Download PDF

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Publication number
CN114516340B
CN114516340B CN202210182700.1A CN202210182700A CN114516340B CN 114516340 B CN114516340 B CN 114516340B CN 202210182700 A CN202210182700 A CN 202210182700A CN 114516340 B CN114516340 B CN 114516340B
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driver
driving
user
variance
judging
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CN114516340A (en
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袁宁
肖雄
卢斌
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Chongqing Changan Automobile Co Ltd
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Chongqing Changan Automobile Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Human Computer Interaction (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a driver disability judging method based on user driving habits, which comprises the following steps: s1, judging the driving habit of a user, wherein the judging flow is as follows: calculating driving risk according to the current road environment classification and the distraction time of a driver, continuously calculating the driving habit variance of the user according to the driving risk value in the driving process of the user after obtaining the driving risk value, and storing a plurality of groups of calculated driving habit variance values of the user; s2, judging that the driver is disabled, wherein the judging flow is as follows: calculating according to the calculation formula in the S1 to obtain the current driving habit variance of the user; and then, monitoring the current driving behavior of the user, simultaneously comparing the current driving habit variance of the user with the stored driving habit variance of the user, and judging whether the driver is disabled after fusing the comparison result of the driving behavior and the variance value.

Description

Driver disability judging method based on user driving habit
Technical Field
The invention belongs to the field of intelligent driving safety, and particularly relates to a driver disability judging method based on user driving habits.
Background
The vehicle runs on the road, and other vehicles on the road can cause safety risks to the driver, and improper operation or abnormal driving state of the driver can also cause driving safety risks. At present, in order to reduce driving risk of a driver, a plurality of intelligent automobiles can monitor and analyze driving data of the driver, the face, heart rate and other conditions of the driver, and according to analysis results, when the driver drives in a driving process, risk driving conditions occur, early warning is sent out through an early warning system. Such as the patent: driving risk early warning method and system for self-adaptive user behaviors and vehicle.
However, the above-mentioned situation early warning system cannot accurately monitor the driving ability of the driver, cannot determine whether the driver loses the driving ability, detects the driver only by fatigue and facial features of the driver, and early warns the driving risk, which is far from sufficient.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to solve the technical problems that: how to provide a driver disability judging method based on user driving habits, which can improve the judgment of the driving capability of the driver in the driving process and has accurate judging results.
In order to solve the technical problems, the invention adopts the following technical scheme:
a driver disability determination method based on user driving habits, characterized by comprising: s1, judging the driving habit of a user, wherein the judging flow is as follows: calculating driving risk according to the current road environment classification and the distraction time of a driver, continuously calculating the driving habit variance of the user according to the driving risk value in the driving process of the user after obtaining the driving risk value, and storing a plurality of groups of calculated driving habit variance values of the user; s2, judging that the driver is disabled, wherein the judging flow is as follows: calculating according to the calculation formula in the S1 to obtain the current driving habit variance of the user; and then, monitoring the current driving behavior of the user, simultaneously comparing the current driving habit variance of the user with the stored driving habit variance of the user, and judging whether the driver is disabled after fusing the comparison result of the driving behavior and the variance value.
After the method is adopted, when judging whether a driver is disabled, firstly, calculation is carried out according to three conditions of different road environments, different driving risk degrees and driving distraction states of the driver to obtain the historical driving habit variance value of the driver, then, when judging, the current driving habit variance value is compared with the historical driving habit variance value, or the current driving behaviors are fused together for judgment, more judgment bases are provided, whether the driver is in a disabled state or not can be accurately obtained according to the driving habits of the driver, the judgment accuracy is higher, and the method for individually comparing and judging different drivers is more accurate, so that powerful support is provided for road safety and driving early warning.
Further, the driver disability determination logic is as follows: monitoring whether the vehicle runs with the line pressing, if not, judging that the driver is not disabled; if the line pressing driving exists, comparing the current driving habit variance with the stored user driving habit variance, if the two variance value pairs are smaller, and the line pressing driving time exceeds a first threshold value, extracting the current steering wheel hand moment at the same time, confirming whether the driver is on the steering wheel according to the steering wheel hand moment, and if the moment of the driver on the steering wheel is smaller than a second threshold value and lasts for a certain time, and if the driver is in a distraction state, judging that the driver is disabled.
Further, the current road environment is classified, and classified calculation is performed through the following formula: l=a+b, where a is the road type and B is the curvature level; road types are classified into expressways and urban roads, wherein the expressway is 1 and the urban road is 2.
Further, the driver distraction time is the accumulated time from distraction to non-distraction of the driver, when the driver distraction time is judged, the moment borne by the driver is firstly obtained, whether the driver does not operate the steering wheel for a long time is judged, if not, the driver is not distracted, if yes, the use frequency of the brake accelerator pedal of the driver is continuously obtained, whether the driver does not operate the pedal for a long time is judged, and if yes, the driver distraction is judged.
Further, the calculation formula of the driving risk is as follows: r=a×l×n, where R is a driving risk value, a is a safety coefficient, a is constantly less than 0.1, L is a current road environment level, and N is a driver distraction time in seconds.
Further, the driving habit variance of the user is calculated by the following formula:wherein QR is the driving habit variance of the nearest n groups of users, r is the driving risk value of the nearest n groups, and E (r) is the driving risk data average value of the nearest n groups; when the formula is adopted to calculate the current driving habit variance of the user, n is 3-5, and when the driving habit variance of the user is calculated in S1, n is more than or equal to 15.
Drawings
FIG. 1 is a flow chart of a user driving habit determination in an embodiment;
FIG. 2 is a flow chart of driver distraction determination in an embodiment;
FIG. 3 is a flow chart of a driver disablement determination process in an embodiment;
fig. 4 is a logic diagram of driver disablement determination in an embodiment.
Detailed Description
The invention will be further described with reference to the drawings and examples.
Examples:
as shown in the figure, the driver disability determination method based on the driving habit of the user provided by the embodiment comprises S1, the driving habit of the user is determined, and the determination flow is as follows: calculating driving risk according to the current road environment classification and the distraction time of a driver, continuously calculating the driving habit variance of the user according to the driving risk value in the driving process of the user after obtaining the driving risk value, and storing a plurality of groups of calculated driving habit variance values of the user; s2, judging that the driver is disabled, wherein the judging flow is as follows: calculating and obtaining the current driving habit variance of the user according to the calculation formula in the S1 (the current driving habit variance is the multiple driving habit variance of a driver in the current driving period); and then, monitoring the current driving behavior of the user, simultaneously comparing the current driving habit variance of the user with the stored driving habit variance of the user, and judging whether the driver is disabled after fusing the comparison result of the driving behavior and the variance value.
After the method is adopted, when judging whether a driver is disabled, firstly, calculation is carried out according to three conditions of different road environments, different driving risk degrees and driving distraction states of the driver to obtain the historical driving habit variance value of the driver, then, when judging, the current driving habit variance value is compared with the historical driving habit variance value, or the current driving behaviors are fused together for judgment, more judgment bases are provided, whether the driver is in a disabled state or not can be accurately obtained according to the driving habits of the driver, the judgment accuracy is higher, and the method for individually comparing and judging different drivers is more accurate, so that powerful support is provided for road safety and driving early warning.
Specifically, as shown in fig. 1, the "user driving habit determination" flow mainly comprises four parts, namely "determining the current road environment", "determining the time when the driver is not in the loop", "calculating the driving risk" and "correcting the user driving habit". The current road environment grading is to grade the current running environment of the vehicle, and the grading calculation is carried out through the following formula: l=a+b, where a is the road type and B is the curvature level; the road types are classified into expressways and urban roads, wherein the expressway is 1, the urban road is 2, and the curvature is classified into 1 to 10 levels in this embodiment, the greater the curvature, the higher the level.
Further, the driver distraction time (i.e., the driver is not in the loop time) is the cumulative time from driver distraction to non-distraction. As shown in fig. 2, when the distraction time of the driver is determined, the moment applied to the driver is firstly obtained, and whether the driver does not operate the steering wheel for a long time is determined, if not, the driver is not distracted, if yes, the use frequency of the brake and accelerator pedal of the driver is continuously obtained, and whether the driver does not operate the pedal for a long time is determined, if yes, the distraction of the driver is determined. The steering wheel moment and the frequency of use of the brake and accelerator pedal are obtained through sensors, and are not described in detail herein.
The calculation formula of the driving risk is as follows: r=a×l×n, where R is a driving risk value, a is a safety coefficient, a is constantly less than 0.1, L is a current road environment level, and N is a driver distraction time in seconds.
Further, the driving habit variance of the user is calculated by the following formula:wherein QR is the driving habit variance of the nearest n groups of users, r is the driving risk value of the nearest n groups, and E (r) is the driving risk data average value of the nearest n groups; when the formula is adopted to calculate the current driving habit variance of the user, n is 3-5, and when the driving habit variance of the user is calculated in S1, n is more than or equal to 15.
As shown in fig. 4, the driver disability determination logic is as follows: monitoring whether the vehicle runs with the line pressing, if not, judging that the driver is not disabled; if the line pressing driving exists, comparing the current driving habit variance with the stored user driving habit variance, if the two variance value pairs are smaller, and the line pressing driving time exceeds a first threshold value, extracting the current steering wheel hand moment at the same time, confirming whether the driver is on the steering wheel according to the steering wheel hand moment, and if the moment of the driver on the steering wheel is smaller than a second threshold value and lasts for a certain time, and if the driver is in a distraction state, judging that the driver is disabled.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the technical solution, and although the applicant has described the present invention in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents of the technical solution of the present invention can be made without departing from the spirit and scope of the technical solution, and all such modifications and equivalents are intended to be encompassed in the scope of the claims of the present invention.

Claims (5)

1. A driver disability determination method based on user driving habits, characterized by comprising: s1, judging the driving habit of a user, wherein the judging flow is as follows: calculating driving risk according to the current road environment classification and the distraction time of a driver, continuously calculating the driving habit variance of the user according to the driving risk value in the driving process of the user after obtaining the driving risk value, and storing a plurality of groups of calculated driving habit variance values of the user; s2, judging that the driver is disabled, wherein the judging flow is as follows: calculating according to the calculation formula in the S1 to obtain the current driving habit variance of the user; then, monitoring the current driving behavior of the user, simultaneously comparing the current driving habit variance of the user with the stored driving habit variance of the user, and judging whether the driver is disabled after fusing the comparison result of the driving behavior and the variance value; the driver disability determination logic is as follows: monitoring whether the vehicle runs with the line pressing, if not, judging that the driver is not disabled; if the line pressing driving exists, comparing the current driving habit variance with the stored user driving habit variance, if the two variance value pairs are smaller, and the line pressing driving time exceeds a first threshold value, extracting the current steering wheel hand moment at the same time, confirming whether the driver is on the steering wheel according to the steering wheel hand moment, and if the moment of the driver on the steering wheel is smaller than a second threshold value and lasts for a certain time, and if the driver is in a distraction state, judging that the driver is disabled.
2. The driver disability determination method based on the driving habits of the user according to claim 1, wherein the current road environment is classified, and the classification calculation is performed by the following formula: l=a+b, where a is the road type and B is the curvature level; road types are classified into expressways and urban roads, wherein the expressway is 1 and the urban road is 2.
3. The method for determining the disability of a driver based on the driving habits of a user according to claim 1 or 2, wherein the driver distraction time is an accumulated time from distraction to non-distraction of the driver, and when the driver distraction time is determined, the moment applied to the driver is obtained first and whether the driver does not operate the steering wheel for a long time is determined, if not, the driver is not distracted, if yes, the frequency of use of the brake accelerator pedal of the driver is continuously obtained, and if the driver does not operate the pedal for a long time is determined, and if yes, the driver distraction is determined.
4. The driver disability determination method based on the driving habits of the user according to claim 3, wherein the driving risk is calculated as follows: r=a×l×n, where R is a driving risk value, a is a safety coefficient, a is constantly less than 0.1, L is a current road environment level, and N is a driver distraction time in seconds.
5. The method for determining the disability of a driver based on the driving habits of a user according to claim 4, wherein the variance of the driving habits of the user is calculated by the following formula:wherein Q is R For the driving habit variance of the nearest n groups of users, r is the driving risk value of the nearest n groups, and E (r) is the driving risk data average value of the nearest n groups; when the formula is adopted to calculate the current driving habit variance of the user, n is 3-5, and when the driving habit variance of the user is calculated in S1, n is more than or equal to 15.
CN202210182700.1A 2022-02-27 2022-02-27 Driver disability judging method based on user driving habit Active CN114516340B (en)

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WO2023225811A1 (en) * 2022-05-23 2023-11-30 华为技术有限公司 Method and apparatus for assisting with driving, and vehicle

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012157192A1 (en) * 2011-05-18 2012-11-22 日産自動車株式会社 Driving instablity determination device
CN107310553A (en) * 2017-06-27 2017-11-03 安徽江淮汽车集团股份有限公司 It is a kind of to prevent the unilateral deviation alarm method and system for deviateing repetition of alarms
CN111137284A (en) * 2020-01-04 2020-05-12 长安大学 Early warning method and early warning device based on driving distraction state

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* Cited by examiner, † Cited by third party
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US20220032924A1 (en) * 2020-07-30 2022-02-03 Morgan State University System and method for driver distraction detection and classification

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012157192A1 (en) * 2011-05-18 2012-11-22 日産自動車株式会社 Driving instablity determination device
CN107310553A (en) * 2017-06-27 2017-11-03 安徽江淮汽车集团股份有限公司 It is a kind of to prevent the unilateral deviation alarm method and system for deviateing repetition of alarms
CN111137284A (en) * 2020-01-04 2020-05-12 长安大学 Early warning method and early warning device based on driving distraction state

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