CN113997939A - Road rage detection method and device for driver - Google Patents
Road rage detection method and device for driver Download PDFInfo
- Publication number
- CN113997939A CN113997939A CN202111314586.5A CN202111314586A CN113997939A CN 113997939 A CN113997939 A CN 113997939A CN 202111314586 A CN202111314586 A CN 202111314586A CN 113997939 A CN113997939 A CN 113997939A
- Authority
- CN
- China
- Prior art keywords
- road rage
- driver
- road
- rage
- degree
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Mathematical Physics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Traffic Control Systems (AREA)
Abstract
The application relates to the technical field of vehicle auxiliary driving, in particular to a road rage detection method and device for a driver. The road rage symptom detection method for the driver comprises the following steps: acquiring a road rage characteristic signal of a driver; determining the road rage degree of a driver according to the road rage characteristic signal based on a pre-trained mathematical model; the vehicle is controlled based on the degree of road rage of the driver. By adopting the scheme, the driving safety can be improved and the driving experience can be improved while the information safety of the user is protected.
Description
Technical Field
The application relates to the technical field of vehicle auxiliary driving, in particular to a road rage detection method and device for a driver.
Background
The automobile is an indispensable vehicle in people's daily life, and along with the development of science and technology, the quantity of automobile is more and more, and traffic accident that leads to because driver's road anger action also takes place frequently. Once the road rage behavior occurs, the emotion of the driver can fluctuate greatly, the judgment capability of the driver on the road condition and the stability of vehicle control are greatly reduced, traffic accidents are easily caused to cause unnecessary loss, even the driver is caused to actively make an initiative and deficient, and the traffic safety is seriously influenced. Therefore, a technical scheme for effectively preventing the road rage is needed to improve the driving safety and the driving experience.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present application is to provide a road rage disorder detection method for a driver, so as to solve the technical problem that the existing road rage disorder detection method cannot improve driving safety and improve driving experience while protecting user information safety.
A second object of the present application is to provide a road rage condition detection apparatus for a driver.
In order to achieve the above object, an embodiment of the present application provides a road rage detection method for a driver, including:
acquiring a road rage characteristic signal of a driver;
determining the road rage degree of the driver according to the road rage characteristic signal based on a pre-trained mathematical model;
and controlling the vehicle based on the road rage degree of the driver.
Optionally, in an embodiment of the present application, an illumination sensor is disposed on the vehicle head, a distance sensor is disposed on the vehicle body, and a noise sensor is disposed inside the vehicle;
the acquiring of the road rage characteristic signal of the driver comprises the following steps:
acquiring a road rage characteristic signal of a driver based on the illumination sensor, the distance sensor and the noise sensor;
the road rage characteristic signal comprises at least one of the following: illumination intensity, distance, sound intensity.
Optionally, in an embodiment of the present application, the determining, based on a pre-trained mathematical model, a road rage degree of the driver according to the road rage characteristic signal further includes:
preprocessing the road rage characteristic signal to obtain a preprocessed road rage characteristic signal;
calculating a road rage value of the driver according to the preprocessed road rage characteristic signal based on a pre-trained mathematical model;
and determining the road rage degree of the driver according to the road rage value.
Optionally, in an embodiment of the present application, the preprocessing the road rage characteristic signal includes:
and refining and/or analyzing the road rage characteristic signal to obtain a preprocessed road rage characteristic signal.
Optionally, in an embodiment of the present application, the refining and/or analyzing the road rage signature to obtain a preprocessed road rage signature includes:
filtering multimedia sound inside the vehicle, which is acquired by a noise sensor;
and filtering the illumination intensity of the illumination sensor in the daytime.
Optionally, in an embodiment of the present application, the pre-trained mathematical model includes:
based on a machine learning method, the mathematical model is trained according to scenes in the mathematical model, so that a road rage boundary value is determined.
Optionally, in an embodiment of the present application, the training the mathematical model according to a scenario in the mathematical model includes:
at least one of the following scenarios is included within the mathematical model: the noise in the vehicle is too high when the vehicle is crossed by a high beam lamp at night, and the vehicle is inserted into a queue by forced lane change.
Optionally, in an embodiment of the present application, the determining the road rage degree of the driver according to the road rage value includes:
the road rage boundary value comprises a first threshold value and a second threshold value;
when the numerical value corresponding to the road rage value is lower than a first threshold value, the road rage degree of the driver is judged to be: no road rage;
when the numerical value corresponding to the road rage value is higher than a first threshold value and lower than a second threshold value, judging that the road rage degree of the driver is as follows: pre-triggering road rage;
when the value corresponding to the road rage value is higher than a second threshold value, judging that the road rage degree of the driver is as follows: road rage triggers.
Optionally, in an embodiment of the present application, the controlling the vehicle based on the road rage degree of the driver includes:
when the road rage degree of the driver is as follows: when no road is angry, no operation is performed;
when the road rage degree of the driver is as follows: when the road rage is pre-triggered, the control operation performed on the vehicle comprises at least one of the following operations: the voice prompts 'driving cautiously' and the vehicle body atmosphere lamp flickers;
when the road rage degree of the driver is as follows: when the road rage is triggered, the control operation performed on the vehicle comprises at least one of the following operations: and the ADAS assists in the intervention of a driving system, the detection precision of a vehicle body sensor is improved to the highest, the posture of the vehicle body is stabilized, and the automatic driving degree is L3 or above to take over the driving.
In summary, in the road rage detection method provided in the embodiment of the first aspect of the present application, the road rage characteristic signal of the driver is obtained; determining the road rage degree of the driver according to the road rage characteristic signal based on a pre-trained mathematical model; and controlling the vehicle based on the road rage degree of the driver. The driving safety can be improved and the driving experience can be improved while the information safety of the user is protected.
In order to achieve the above object, a road rage detection device for a driver according to an embodiment of a second aspect of the present application includes:
the acquisition module is used for acquiring a road rage characteristic signal of a driver;
the decision-making module is used for determining the road rage degree of the driver according to the road rage characteristic signal based on a pre-trained mathematical model;
and the control module is used for controlling the vehicle based on the road rage degree of the driver.
In summary, the technical scheme provided by the embodiment of the application at least brings the following beneficial effects:
1) the method comprises the following steps that the illumination intensity of a high beam of a coming vehicle is measured by arranging an illumination sensor on a vehicle head, the distance between the two vehicles is measured by arranging a distance sensor on a vehicle body, and the sound intensity of the volume in the vehicle is collected by arranging a noise sensor in the vehicle; the analysis of the data collected by the sensors does not involve image recognition and voice recognition, so that lower time delay can be realized;
2) a voice sensor and a camera are not used, and private data of a user are not collected, so that the information security of the user is protected;
3) the road rage degree of triggering and pre-triggering is provided, the road rage degree of opening can be judged according to the road rage value self-adaptability, the intervention of a vehicle auxiliary system is softer by the pre-triggering setting, the driving safety can be improved, and the driving experience is improved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a method for detecting a road rage of a driver according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a road rage condition detection device for a driver according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
The existing road rage condition detection method needs to establish a recognition characteristic model based on the technologies of voice recognition and facial recognition (expression recognition or emotion recognition) by means of a voice sensor or an optical sensor, and the effect of judging whether a driver is in a road rage state is achieved. The road rage detection method needs to collect voice or photos of a user, and the privacy of the user can be invaded; in addition, the accuracy and recognition speed of the algorithm training model need to be improved, and the algorithm training method cannot be generally applied to all scenes.
Example 1
Fig. 1 is a flowchart of a road rage detection method for a driver according to an embodiment of the present application.
As shown in fig. 1, a method for detecting a road rage of a driver according to an embodiment of the present application includes the following steps:
and step 130, controlling the vehicle based on the road rage degree of the driver.
In the embodiment of the application, the head of the vehicle is provided with an illumination sensor, the body of the vehicle is provided with a distance sensor, and the interior of the vehicle is provided with a noise sensor;
the method for acquiring the road rage characteristic signal of the driver comprises the following steps:
acquiring a road rage characteristic signal of a driver based on the illumination sensor, the distance sensor and the noise sensor;
the road rage characteristic signal comprises at least one of the following: illumination intensity, distance, sound intensity.
Specifically, the analysis of the data collected by the illumination sensor, the distance sensor and the noise sensor does not involve image recognition and voice recognition, and lower time delay can be realized; meanwhile, a voice sensor and a camera are not used, and private data of a user do not need to be collected.
In this application embodiment, based on the mathematical model that trains in advance, confirm driver's road anger degree according to road anger characteristic signal, still include:
preprocessing the road rage characteristic signal to obtain a preprocessed road rage characteristic signal;
calculating a road rage value of a driver according to the preprocessed road rage characteristic signal based on a pre-trained mathematical model;
and determining the road rage degree of the driver according to the road rage value.
In this embodiment of the present application, the preprocessing of the road rage characteristic signal includes:
and refining and/or analyzing the road rage characteristic signal to obtain a preprocessed road rage characteristic signal.
Specifically, 85% of the factors inducing the road rage are external factors, and whether the driver is in the road rage state can be effectively judged by extracting and analyzing the external interference factors.
In this application embodiment, refine and/or analyze the road anger characteristic signal and obtain the road anger characteristic signal after the preliminary treatment, include:
filtering multimedia sound inside the vehicle, which is acquired by a noise sensor;
and filtering the illumination intensity of the illumination sensor in the daytime.
In the embodiment of the present application, the pre-trained mathematical model includes:
and training the mathematical model according to scenes in the mathematical model based on a machine learning method so as to determine the road rage boundary value.
In an embodiment of the present application, training a mathematical model according to a scenario within the mathematical model includes:
at least one of the following scenarios is included within the mathematical model: the noise in the vehicle is too high when the vehicle is crossed by a high beam lamp at night, and the vehicle is inserted into a queue by forced lane change.
Particularly, when the driver drives at night, the visual visibility of the meeting of the high beam is reduced, the probability of traffic accidents is improved, and meanwhile, the driver is easy to be angry;
forced lane change and queue insertion can increase the possibility of hard contact between vehicles, so that the probability of traffic accidents is increased, and meanwhile, the driver is easy to be angry;
excessive noise in the vehicle can interfere with decision-making ability and operation ability of a driver, so that the probability of traffic accidents is increased, and meanwhile, the driver is easy to be angry.
Specifically, the mathematical model is determined by:
p＝F(x,y,z)
wherein p is the road rage value, x is the illumination intensity, x is in Lux (Lux or lx), y is the distance, y is in meters (m), z is the sound intensity, and z is in decibels (db); p and y are inversely related, and x and z are positively related.
Further, when the average illumination intensity is greater than 5 lux, which indicates that the time is daytime, x is set to be 0;
when the average illumination intensity is less than 5 lux, the night is illustrated, and at this time, the standard light intensity of the dipped headlight illuminating the opposite vehicle when the vehicle crossing distance is 100 meters is defined as x0, for example, x1> x0, x is equal to x1-x0, otherwise, x is equal to 0.
Further, a safe distance y0 is determined if y1> y0, y1-y 0; otherwise, y is 0;
further, the safety sound intensity is determined to be z0 decibels, when multimedia is not played in the vehicle, z1-z0(z > -0 db) is obtained, and when multimedia is played in the vehicle, the multimedia sound is filtered, and then z1-z0(z > -0 db) is obtained;
further, the safety distance y0 is preferably 0.5 m; the security sound intensity is preferably 60 db.
In this application embodiment, determining the road rage degree of the driver according to the road rage value includes:
the road rage boundary value comprises a first threshold value p1 and a second threshold value p 2;
when the value corresponding to the road rage value is lower than a first threshold value, namely p < p1, the road rage degree of the driver is judged as follows: no road rage;
when the value corresponding to the road rage value is higher than the first threshold value and lower than the second threshold value, namely p1< p < p2, the road rage degree of the driver is judged as follows: pre-triggering road rage;
when the value corresponding to the road rage value is higher than the second threshold value, namely p > p2, the road rage degree of the driver is judged as follows: road rage triggers.
In the embodiment of the application, the control of the vehicle based on the road rage degree of the driver comprises the following steps:
when the road rage degree of the driver is as follows: when no road is angry, no operation is performed;
when the road rage degree of the driver is as follows: when the road rage is pre-triggered, the control operation performed on the vehicle comprises at least one of the following operations: the voice prompts 'driving cautiously' and the vehicle body atmosphere lamp flickers;
when the road rage degree of the driver is as follows: when the road rage is triggered, the control operation performed on the vehicle comprises at least one of the following operations: and the ADAS assists in the intervention of a driving system, the detection precision of a vehicle body sensor is improved to the highest, the posture of the vehicle body is stabilized, and the automatic driving degree is L3 or above to take over the driving.
Specifically, the road rage degree is judged according to the road rage value self-adaptability, and the intervention of the vehicle auxiliary system is softer due to the road rage pre-triggering setting.
In summary, in the road rage detection method provided in the embodiment of the first aspect of the present application, the road rage characteristic signal of the driver is obtained; determining the road rage degree of a driver according to the road rage characteristic signal based on a pre-trained mathematical model; the vehicle is controlled based on the degree of road rage of the driver. The driving safety can be improved and the driving experience can be improved while the information safety of the user is protected.
In order to implement the above embodiment, the present application also provides a road rage condition detection device for a driver.
Fig. 2 is a schematic structural diagram of a road rage condition detection device for a driver according to an embodiment of the present application.
As shown in fig. 2, a road rage condition detection apparatus for a driver includes:
the obtaining module 210 is configured to obtain a road rage characteristic signal of a driver;
the decision-making module 220 is used for determining the road rage degree of the driver according to the road rage characteristic signal based on a pre-trained mathematical model;
and a control module 230 for controlling the vehicle based on the driver's road rage level.
To sum up, the road anger symptom detection device provided in the embodiment of the first aspect of the present application obtains the road anger characteristic signal of the driver through the obtaining module; the decision-making module determines the road rage degree of a driver according to the road rage characteristic signal based on a pre-trained mathematical model; the control module controls the vehicle based on the degree of road rage of the driver. The driving safety can be improved and the driving experience can be improved while the information safety of the user is protected.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present application, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A road rage disorder detection method for a driver, the road rage disorder detection method comprising:
acquiring a road rage characteristic signal of a driver;
determining the road rage degree of the driver according to the road rage characteristic signal based on a pre-trained mathematical model;
and controlling the vehicle based on the road rage degree of the driver.
2. The method for detecting the road rage disorder according to claim 1, wherein an illumination sensor is provided on the head, a distance sensor is provided on the body, and a noise sensor is provided inside the vehicle;
the acquiring of the road rage characteristic signal of the driver comprises the following steps:
acquiring a road rage characteristic signal of a driver based on the illumination sensor, the distance sensor and the noise sensor;
the road rage characteristic signal comprises at least one of the following: illumination intensity, distance, sound intensity.
3. The road rage condition detecting method according to claim 2, wherein the determining the road rage degree of the driver according to the road rage characteristic signal based on a pre-trained mathematical model further comprises:
preprocessing the road rage characteristic signal to obtain a preprocessed road rage characteristic signal;
calculating a road rage value of the driver according to the preprocessed road rage characteristic signal based on a pre-trained mathematical model;
and determining the road rage degree of the driver according to the road rage value.
4. The road rage condition detecting method according to claim 3, wherein the preprocessing the road rage characteristic signal comprises:
and refining and/or analyzing the road rage characteristic signal to obtain a preprocessed road rage characteristic signal.
5. The road rage disorder detecting method of claim 4, wherein the refining and/or analyzing the road rage signature to obtain the pre-processed road rage signature comprises:
filtering multimedia sound inside the vehicle, which is acquired by a noise sensor;
and filtering the illumination intensity of the illumination sensor in the daytime.
6. The method of detecting road rage symptoms as claimed in claim 3, wherein the pre-trained mathematical model comprises:
based on a machine learning method, the mathematical model is trained according to scenes in the mathematical model, so that a road rage boundary value is determined.
7. The method of detecting road rage symptoms as claimed in claim 6, wherein the training of the mathematical model according to the scenarios within the mathematical model comprises:
at least one of the following scenarios is included within the mathematical model: the noise in the vehicle is too high when the vehicle is crossed by a high beam lamp at night, and the vehicle is inserted into a queue by forced lane change.
8. The road rage condition detection method of claim 6, wherein the determining the degree of road rage of the driver based on the road rage value comprises:
the road rage boundary value comprises a first threshold value and a second threshold value;
when the numerical value corresponding to the road rage value is lower than a first threshold value, the road rage degree of the driver is judged to be: no road rage;
when the numerical value corresponding to the road rage value is higher than a first threshold value and lower than a second threshold value, judging that the road rage degree of the driver is as follows: pre-triggering road rage;
when the value corresponding to the road rage value is higher than a second threshold value, judging that the road rage degree of the driver is as follows: road rage triggers.
9. The road rage condition detecting method according to any one of claims 8, wherein the controlling a vehicle based on the degree of road rage of the driver includes:
when the road rage degree of the driver is as follows: when no road is angry, no operation is performed;
when the road rage degree of the driver is as follows: when the road rage is pre-triggered, the control operation performed on the vehicle comprises at least one of the following operations: the voice prompts 'driving cautiously' and the vehicle body atmosphere lamp flickers;
when the road rage degree of the driver is as follows: when the road rage is triggered, the control operation performed on the vehicle comprises at least one of the following operations: and the ADAS assists in the intervention of a driving system, the detection precision of a vehicle body sensor is improved to the highest, the posture of the vehicle body is stabilized, and the automatic driving degree is L3 or above to take over the driving.
10. A road rage detection apparatus for a driver, the apparatus comprising:
the acquisition module is used for acquiring a road rage characteristic signal of a driver;
the decision-making module is used for determining the road rage degree of the driver according to the road rage characteristic signal based on a pre-trained mathematical model;
and the control module is used for controlling the vehicle based on the road rage degree of the driver.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111314586.5A CN113997939A (en) | 2021-11-08 | 2021-11-08 | Road rage detection method and device for driver |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111314586.5A CN113997939A (en) | 2021-11-08 | 2021-11-08 | Road rage detection method and device for driver |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113997939A true CN113997939A (en) | 2022-02-01 |
Family
ID=79928067
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111314586.5A Pending CN113997939A (en) | 2021-11-08 | 2021-11-08 | Road rage detection method and device for driver |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113997939A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105450761A (en) * | 2015-12-04 | 2016-03-30 | 谭兴奎 | Vehicular high beam irradiation induction prompting system |
| CN105551303A (en) * | 2015-12-14 | 2016-05-04 | 谭兴奎 | Vehicle-borne following vehicle distance sensing and prompting system |
| DE102015004748A1 (en) * | 2015-04-11 | 2016-10-13 | Audi Ag | Method for predicting a dangerous driving situation |
| CN107662611A (en) * | 2017-11-06 | 2018-02-06 | 吉林大学 | A kind of automatic driving mode switching system based on driver's Emotion identification |
| US20180162391A1 (en) * | 2016-12-08 | 2018-06-14 | Infobank Corp. | Vehicle control method and vehicle control apparatus for preventing retaliatory driving |
| CN108216254A (en) * | 2018-01-10 | 2018-06-29 | 山东大学 | The road anger Emotion identification method merged based on face-image with pulse information |
| CN110525447A (en) * | 2019-10-09 | 2019-12-03 | 吉林大学 | A kind of the man-machine of anti-commercial vehicle driver road anger drives system altogether |
| CN113191212A (en) * | 2021-04-12 | 2021-07-30 | 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) | Driver road rage risk early warning method and system |
-
2021
- 2021-11-08 CN CN202111314586.5A patent/CN113997939A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102015004748A1 (en) * | 2015-04-11 | 2016-10-13 | Audi Ag | Method for predicting a dangerous driving situation |
| CN105450761A (en) * | 2015-12-04 | 2016-03-30 | 谭兴奎 | Vehicular high beam irradiation induction prompting system |
| CN105551303A (en) * | 2015-12-14 | 2016-05-04 | 谭兴奎 | Vehicle-borne following vehicle distance sensing and prompting system |
| US20180162391A1 (en) * | 2016-12-08 | 2018-06-14 | Infobank Corp. | Vehicle control method and vehicle control apparatus for preventing retaliatory driving |
| CN107662611A (en) * | 2017-11-06 | 2018-02-06 | 吉林大学 | A kind of automatic driving mode switching system based on driver's Emotion identification |
| CN108216254A (en) * | 2018-01-10 | 2018-06-29 | 山东大学 | The road anger Emotion identification method merged based on face-image with pulse information |
| CN110525447A (en) * | 2019-10-09 | 2019-12-03 | 吉林大学 | A kind of the man-machine of anti-commercial vehicle driver road anger drives system altogether |
| CN113191212A (en) * | 2021-04-12 | 2021-07-30 | 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) | Driver road rage risk early warning method and system |
Non-Patent Citations (1)
| Title |
|---|
| 李向荣: "《实用礼仪训练》", 山东人民出版社, pages: 107 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7830266B2 (en) | Drowsy state determination device and method | |
| KR101361663B1 (en) | Image processing apparatus and metnod of vehicle | |
| CN108216251B (en) | Driver state monitoring method, system and non-transitory computer readable medium | |
| CN105539293B (en) | Lane departure warning method and device and car steering accessory system | |
| US20120206252A1 (en) | Lane departure warning system | |
| JP5100159B2 (en) | Automatic switching control method for in-vehicle projector | |
| CN110472556B (en) | Monocular vision-based driver attention state analysis system and analysis method | |
| CN103507809A (en) | Method and device for controlling vehicle to run | |
| JP2005092285A (en) | Vehicle driving status estimating device and driver's vehicle driving characteristic estimating device | |
| CN102941851B (en) | Lane Mark identification fiduciary level improves system and method thereof | |
| CN107640160B (en) | Vehicle safe driving auxiliary system and control method thereof | |
| CN113468678B (en) | Method and device for calculating accuracy of automatic driving algorithm | |
| JP4952545B2 (en) | Vehicle environment information notification system and vehicle environment information notification method | |
| CN113997939A (en) | Road rage detection method and device for driver | |
| CN115366909B (en) | Dynamic early warning method and device for driver accidents in long and large longitudinal slope section and electronic equipment | |
| KR100811499B1 (en) | Method and device for a lane departure warming system of automobile | |
| CN111986488B (en) | Fatigue driving detection method and device, electronic equipment and storage medium | |
| CN113903017A (en) | Fatigue driving detection method and system | |
| CN111382631B (en) | Identification method, identification device, terminal, server and storage medium | |
| CN111142720B (en) | Vehicle-mounted machine screen rotation display method and device | |
| JP2007328455A (en) | Device and method for evaluating driving quality | |
| CN116022158B (en) | Driving safety control method and device for cooperation of multi-domain controller | |
| KR100853444B1 (en) | Method of sensing car accidents | |
| CN111152653A (en) | Fatigue driving detection method based on multi-information fusion | |
| CN116620296B (en) | Danger avoiding method and system for intelligent automobile |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |