CN114516340A - Driver incapability judgment method based on user driving habits - Google Patents
Driver incapability judgment method based on user driving habits Download PDFInfo
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- CN114516340A CN114516340A CN202210182700.1A CN202210182700A CN114516340A CN 114516340 A CN114516340 A CN 114516340A CN 202210182700 A CN202210182700 A CN 202210182700A CN 114516340 A CN114516340 A CN 114516340A
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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
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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
- 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
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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
- 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
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- 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
Abstract
The invention discloses a driver incapability judgment method based on driving habits of a user, which comprises the following steps: and S1, judging the driving habits of the user, wherein the judging flow is as follows: calculating driving risks according to the current road environment classification and the driver distraction time, 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, determining the incapability of the driver, wherein the determining process is as follows: calculating and obtaining the current driving habit variance of the user according to a calculation formula in S1; 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, fusing the driving behavior and the variance value comparison result, and judging whether the driver is disabled or not.
Description
Technical Field
The invention belongs to the field of intelligent driving safety, and particularly relates to a driver incapability judgment method based on driving habits of users.
Background
When a vehicle runs on a road, safety risks are caused to a driver by other vehicles on the road, and driving safety risks are caused by improper operation or abnormal driving states of the driver. At present, for reducing driver's driving risk, all can monitor, analyze through the condition such as driving data and driver face, rhythm of the heart to many intelligent automobile to according to analysis result, in driver's driving process, when the risk driving condition appears, send the early warning through early warning system. Such as the patent: a driving risk early warning method and system adaptive to user behaviors and a vehicle.
However, the above-mentioned situation warning system is far from sufficient to accurately monitor the driving ability of the driver, determine whether the driver loses the driving ability, detect the driver only by the fatigue and the facial features of the driver, and warn the driving risk.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problems to be solved by the invention are as follows: provided is a driver incapability determination method based on user driving habits, which can improve the determination of the driving capacity of a driver in the driving process and can accurately determine the result.
In order to solve the technical problems, the invention adopts the following technical scheme:
a driver disability determination method based on driving habits of a user is characterized by comprising the following steps: s1, judging the driving habits of the user, wherein the judging flow is as follows: calculating driving risks according to the current road environment classification and the driver distraction time, 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, determining the incapability of the driver, wherein the determining process is as follows: calculating and obtaining the current driving habit variance of the user according to a calculation formula in S1; 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, fusing the driving behavior and the variance value comparison result, and judging whether the driver is disabled or not.
After the method is adopted, when judging whether the driver is incapacitated, the historical driving habit variance value of the driver is obtained by calculating according to three conditions of different road environments, different driving risk degrees and driving distraction states 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 and judged together, more judgment bases are provided, whether the driver is in an incapacitating state can be accurately obtained according to the driving habits of the driver, the judgment accuracy is higher, and the method for individualized comparison judgment of different drivers is more accurate and provides powerful support for road safety and driving early warning.
Further, the driver incapability determination logic is as follows: monitoring whether the vehicle runs with the pressed line, and if the vehicle does not run with the pressed line, judging that the driver is disabled; if the vehicle has the pressed line running, comparing the current driving habit variance with the stored user driving habit variance, if the two variance value pairs are small and the pressed line running time exceeds a first threshold value, simultaneously extracting the current hand moment of the steering wheel, determining whether a driver hand is on the steering wheel according to the hand moment of the steering wheel, if the moment of the driver hand on the steering wheel is smaller than a second threshold value and lasts for a certain time, and monitoring that the driver is in a distracted state, judging that the driver is disabled.
Further, the current road environment classification is calculated according to the following formula: l ═ a + B, where a is the road type and B is the curvature rating; the road types are divided into an expressway and an urban area road, wherein the expressway is 1, and the urban area road is 2.
Further, the driver distraction time is accumulated time from distraction to non-distraction of the driver, when the driver distraction time is judged, the torque applied to the driver is firstly obtained, whether the driver does not operate the steering wheel for a long time or not is judged, if not, the driver is not distracted, if yes, the use frequency of a brake accelerator pedal of the driver is continuously obtained, whether the driver does not operate the pedal for a long time or not is judged, and if yes, the driver distraction is judged.
Further, the calculation formula of the driving risk is as follows: and R is a multiplied by L multiplied by N, wherein R is a driving risk value, a is a safety factor, a is constantly less than 0.1, L is the current road environment grade, and N is the driver distraction time in seconds.
Further, the driving habit variance of the user is calculated by the following formula:
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 average value of the driving risk data of the nearest n groups; when the current driving habit variance of the user is calculated by adopting the formula, 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 driving habit determination for a user in an embodiment;
FIG. 2 is a flowchart of the driver distraction determination in the embodiment;
FIG. 3 is a flowchart of the driver incapability determination in the embodiment;
FIG. 4 is a logic diagram of the driver incapability determination in the embodiment.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
Example (b):
as shown in the figure, the method for determining driver disability based on driving habits of a user according to the present embodiment includes S1, where the driving habits of the user are determined by the following steps: calculating driving risks according to the current road environment classification and the driver distraction time, 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, determining the incapability of the driver, wherein the determining process is as follows: calculating and acquiring the current driving habit variance of the user according to a calculation formula in S1 (the current driving habit variance is the driving habit variance of a plurality of times in the current driving period of the driver); 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, fusing the driving behavior and the variance value comparison result, and judging whether the driver is disabled or not.
After the method is adopted, when the incapability of the driver is judged, the historical driving habit variance value of the driver is obtained by calculating according to three conditions of the driver in different road environments, different driving risk degrees and the driving distraction state, then when the judgment is carried out, the current driving habit variance value is compared with the historical driving habit variance value, or the current driving behavior is fused and judged together, the judgment basis is more, whether the driver is in the incapability state can be accurately obtained according to the driving habit of the driver, the judgment accuracy is higher, the method for individualized comparison judgment of different drivers is more accurate, and powerful support is provided for road safety and driving early warning.
Specifically, the "user driving habit determination" process is shown in fig. 1, and mainly includes four parts, namely "determining a current road environment", "determining that a driver is not present in a loop time", "calculating a driving risk", and "correcting a user driving habit". The current road environment classification is to perform classification processing on the current driving environment of the vehicle, and performs classification calculation through the following formula: l ═ a + B, where a is the road type and B is the curvature rating; the road type is divided into expressway and urban area road, and wherein, expressway is 1, and urban area road is 2, divides the curvature into 1 ~ 10 grades in this embodiment, and the curvature is bigger, and the grade is higher.
Further, the driver distraction time (i.e., the driver out-of-loop time) is the cumulative time the driver is distracted to not distracted. As shown in fig. 2, when the driver distraction time is determined, the torque applied to the driver is acquired 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 use frequency of the brake and accelerator pedal of the driver is continuously acquired, and whether the driver does not operate the pedal for a long time is determined, if yes, the driver distraction is determined. The steering wheel torque and the use frequency of the brake and accelerator pedal are obtained by sensors, and are not described in detail herein for the prior art.
The calculation formula of the driving risk is as follows: and R is the driving risk value, a is a safety factor, a is constantly less than 0.1, L is the current road environment grade, and N is the driver distraction time in seconds.
Further, the driving habit variance of the user is calculated by the following formula:
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 average value of the driving risk data of the nearest n groups; when the current driving habit variance of the user is calculated by adopting the formula, 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 incapability determination logic is as follows: monitoring whether the vehicle runs with the pressed line, and if the vehicle does not run with the pressed line, judging that the driver is disabled; if the vehicle has the pressure line running, comparing the current driving habit variance with the stored user driving habit variance, if the two variance values are small, and the pressure line running time exceeds a first threshold value, extracting the current hand moment of the steering wheel at the same time, confirming whether a driver hand is on the steering wheel according to the hand moment of the steering wheel, if the moment of the driver hand on the steering wheel is smaller than a second threshold value and lasts for a certain time, and monitoring that the driver is in a distraction state, judging that the driver is disabled.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the technical solutions, and although the present invention has been described in detail by referring to the preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention can be made without departing from the spirit and scope of the technical solutions, and all the modifications and equivalent substitutions should be covered by the claims of the present invention.
Claims (6)
1. A driver disability determination method based on driving habits of a user is characterized by comprising the following steps: and S1, judging the driving habits of the user, wherein the judging flow is as follows: calculating driving risks according to the current road environment classification and the driver distraction time, 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, determining the incapability of the driver, wherein the determining process is as follows: calculating and obtaining the current driving habit variance of the user according to a calculation formula in S1; 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, fusing the driving behavior and the variance value comparison result, and judging whether the driver is disabled or not.
2. The method for determining the incapability of the driver based on the driving habits of the user according to claim 1, wherein the driver incapability determination logic is as follows: monitoring whether the vehicle runs with the pressed line, and if the vehicle does not run with the pressed line, judging that the driver is disabled; if the vehicle has the pressure line running, comparing the current driving habit variance with the stored user driving habit variance, if the two variance values are small, and the pressure line running time exceeds a first threshold value, extracting the current hand moment of the steering wheel at the same time, confirming whether a driver hand is on the steering wheel according to the hand moment of the steering wheel, if the moment of the driver hand on the steering wheel is smaller than a second threshold value and lasts for a certain time, and monitoring that the driver is in a distraction state, judging that the driver is disabled.
3. The method for determining the incapability of the driver based on the driving habits of the user according to claim 1 or 2, wherein the current road environment is graded by calculating according to the following formula: l ═ a + B, where a is the road type and B is the curvature grade; the road types are divided into an expressway and an urban area road, wherein the expressway is 1, and the urban area road is 2.
4. The method as claimed in claim 3, 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 driver applied torque 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 usage 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, and if yes, the driver distraction is determined.
5. The method for determining the incapacity of the driver based on the driving habits of the user according to claim 3 or 4, wherein the calculation formula of the driving risk is as follows: and R is a multiplied by L multiplied by N, wherein R is a driving risk value, a is a safety factor, a is constantly less than 0.1, L is the current road environment grade, and N is the driver distraction time in seconds.
6. The method as claimed in claim 5, wherein the variance of the driving habits of the user is calculated by the following formula:
wherein QRThe driving habit variance of the nearest n groups of users is shown, r is the driving risk value of the nearest n groups, and E (r) is the average value of the driving risk data of the nearest n groups; when the current driving habit variance of the user is calculated by adopting the formula, 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.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210182700.1A CN114516340B (en) | 2022-02-27 | 2022-02-27 | Driver disability judging method based on user driving habit |
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| CN202210182700.1A CN114516340B (en) | 2022-02-27 | 2022-02-27 | Driver disability judging method based on user driving habit |
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| CN114516340A true CN114516340A (en) | 2022-05-20 |
| CN114516340B CN114516340B (en) | 2023-09-12 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
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| 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 |
| US20220032924A1 (en) * | 2020-07-30 | 2022-02-03 | Morgan State University | System and method for driver distraction detection and classification |
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Patent Citations (4)
| 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 |
| US20220032924A1 (en) * | 2020-07-30 | 2022-02-03 | Morgan State University | System and method for driver distraction detection and classification |
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| Publication number | Priority date | Publication date | Assignee | Title |
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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 |
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