CN111301428A - Motor vehicle driver distraction detection warning method and system and motor vehicle - Google Patents
Motor vehicle driver distraction detection warning method and system and motor vehicle Download PDFInfo
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- CN111301428A CN111301428A CN201811509787.9A CN201811509787A CN111301428A CN 111301428 A CN111301428 A CN 111301428A CN 201811509787 A CN201811509787 A CN 201811509787A CN 111301428 A CN111301428 A CN 111301428A
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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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- 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
- 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
- B60W2050/143—Alarm means
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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
- B60W2540/00—Input parameters relating to occupants
- B60W2540/30—Driving style
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Abstract
The invention relates to a driver distraction detection warning method based on artificial intelligence and a system for implementing the method. The driver distraction detection warning method comprises the steps of collecting behavior and action information of a driver; inputting the collected behavior and action information into a driver distraction detection program; the driver distraction detection program judges and classifies the distraction of the driver using the deep learning model, and warns the driver when the distraction class of the driver is successfully judged. Simultaneously, driver distraction detection warning system includes driver distraction detection sensor subassembly, computer processor and driver distraction warning subassembly in the car, wherein, driver distraction detection sensor subassembly detects the action information of gathering the driver and sends it for computer processor in the car, and the storage has driver distraction detection procedure and sends the instruction to driver distraction warning subassembly simultaneously in the computer processor in the car, and driver distraction warning subassembly is used for sending the warning for the driver.
Description
Technical Field
The invention relates to an automobile active safety early warning control system, in particular to a driver distraction detection warning method and system based on artificial intelligence.
Background
The driver is often influenced by the surrounding environment and cannot concentrate on the driving behavior all the time while driving the vehicle. And driver distraction during driving is one of the important causes of traffic accidents. The driving distraction can be basically summarized into visual distraction, behavioral distraction and attention distraction according to the behavior mode. Visual distraction refers to the act of the driver looking away from the driving direction at other targets; behavioral distraction refers to the behavior of the driver leaving both hands from the steering wheel for other activities; distraction refers to the act of the driver not being able to concentrate on the current driving event. These different kinds of distraction can basically be detected by observing the driver's behavioural actions as a whole.
Some existing solutions for driver distraction systems include installing sensors in the cockpit to collect the eye or head movements of the driver or installing fingerprint sensors on the steering wheel to monitor whether the driver's hands are away from the steering wheel, which are individual driver behaviors that determine whether the driver is distracted, and draw the driver's attention through appropriate warning means such as seat vibration, steering wheel vibration, in-vehicle alarm, steering wheel light flashing, etc. These distraction warning systems can guide the driver to a certain extent to regain attention to driving behavior, but the detection accuracy may be low in some scenarios, and there is also a possibility of excessive warning, causing excessive startle to the driver.
Disclosure of Invention
The object of the invention is therefore to provide a driver distraction detection warning method and a system for carrying out the method, which have a higher accuracy in detecting driver distraction behavior and which have less adverse interference on the driver.
In order to solve the above technical problems, the present invention provides the following technical solutions.
According to a first aspect of the present disclosure, there is provided a driver distraction detection warning method, including:
acquiring behavior and action information of a driver;
inputting the collected behavior and action information into a driver distraction detection program;
the driver distraction detection program judges and classifies the distraction of the driver by using the deep learning model, and warns the driver under the condition of successfully judging the distraction type of the driver, thereby preventing the driver from continuing distraction.
According to the driver distraction detection warning method provided by the embodiment of the disclosure, the behavior and action information of the driver is an image.
According to another embodiment of the disclosure or any one of the above embodiments, the method for detecting driver distraction comprises the following steps:
labels are respectively assigned to each image to indicate different types of distraction of drivers;
inputting the image attached with the label into an unlearned deep learning model;
training is carried out through the input images to obtain the optimal layer in the model, the node network weight and the bias in the model are adjusted, the training process is finally completed, and the deep learning model after learning is obtained.
According to another embodiment of the disclosure or any one of the above embodiments, the image contains different types of distracted driving behaviors, including left-handed texting, right-handed texting, left-handed mobile phone dialing, right-handed mobile phone dialing, vehicle entertainment equipment operation, drinking, combing or making up, passenger speaking, and normal driving behaviors.
The method for driver distraction detection warning according to another embodiment of the present disclosure or any one of the above embodiments, wherein each image is preprocessed before being input into the deep learning model to generate a larger number of image samples.
The method for detecting driver distraction according to another embodiment of the present disclosure or any one of the above embodiments, wherein the preprocessing includes rotating the image by a certain angle or adjusting the gray scale of the image.
According to a second aspect of the present disclosure, a driver distraction detection warning system for implementing the driver distraction detection warning method according to any one of the above technical solutions is provided, which includes a driver distraction detection sensor component, an in-vehicle computer processor, and a driver distraction warning component, wherein the driver distraction detection sensor component detects and collects behavior action information of a driver and sends the behavior action information to the in-vehicle computer processor, the in-vehicle computer processor stores therein a driver distraction detection program for classifying distraction of the driver, and simultaneously the in-vehicle computer processor sends an instruction to the driver distraction warning component, and the driver distraction warning component is configured to send a warning to the driver to prevent the driver from continuing distraction.
The driver distraction detection warning system according to another embodiment of the present disclosure or any of the above embodiments, wherein the distraction detection sensor assembly is configured as a camera sensor.
The driver distraction detection warning system according to another embodiment of the present disclosure or any of the above embodiments, wherein the driver distraction warning component comprises a steering wheel or a rear view mirror.
According to a third aspect of the present disclosure, a motor vehicle is provided with a driver distraction detection warning system according to the second aspect of the present invention.
The driver distraction detection warning method and the driver distraction detection warning system are different from the traditional method of directly judging whether the driver is distracted or not by only observing the behavior of the driver by a sensor, but classify the driver behavior information detected by the sensor by using a driver distraction detection program, thereby greatly improving the accuracy of classifying the driver distraction, avoiding the bad interference of the driver distraction detection warning system on the driver and improving the driving experience of the driver.
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The above and other objects and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like or similar elements are designated by like reference numerals.
FIG. 1 illustrates a flow of a driver distraction detection alerting method in accordance with the present disclosure;
FIG. 2 illustrates a training process of a deep learning model in a driver distraction detection procedure in accordance with the present disclosure; and
fig. 3 shows the structure of the driver distraction detection warning system according to the present disclosure.
Detailed Description
For the purposes of brevity and explanation, the principles of the present invention are described herein with reference primarily to exemplary embodiments thereof. However, those skilled in the art will readily recognize that the same principles are equally applicable to all types of driver distraction detection warning methods and/or driver distraction detection warning systems, and that these same or similar principles may be implemented therein, with any such variations not departing from the true spirit and scope of the present patent application. Moreover, in the following description, reference is made to the accompanying drawings that illustrate certain exemplary embodiments. Changes may be made in these embodiments without departing from the spirit and scope of the invention. In addition, while a feature of the invention may have been disclosed with respect to only one of several implementations/embodiments, such feature may be combined with one or more other features of the other implementations/embodiments as may be desired and/or advantageous for any given or identified function. The following description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
FIG. 1 illustrates a flow chart of a driver distraction detection alerting method in accordance with the present disclosure. As can be seen from the figure, in order to detect the distraction behavior of the driver in the driving behavior process, the behavior and action information of the driver is collected in a certain form; then the collected behavior and action information is input into a driver distraction detection program utilizing a deep learning model for analysis; the driver distraction detection program judges and classifies the input driver behavior and action information, and under the condition that the driver distraction is not judged, the driver distraction detection warning method is ended so as not to generate any adverse interference on the driver, and under the condition that the distraction type of the driver is successfully judged, the driver is warned, so that the driver is prevented from being distracted continuously.
On the basis of the flow of the driver distraction detection warning method shown in fig. 1, according to a preferred embodiment of the invention, the behavior and action information of the driver is collected and detected and judged in the form of an image.
On the basis of the flow of the driver distraction detection warning method shown in fig. 1, according to another preferred embodiment of the invention, the deep learning model of the driver distraction detection program for judging the category of driver distraction can be obtained by using a large amount of image training.
On the basis of the flow of the driver distraction detection warning method shown in fig. 1, according to a preferred embodiment of the present invention, the image contains different types of distraction driving behaviors, including left-handed texting, right-handed texting, left-handed mobile phone calling, right-handed mobile phone calling, vehicle-mounted entertainment device operating, water drinking, head combing or makeup, and passenger speaking and normal driving behaviors (as a comparison). That is to say, in the training process, the deep learning model learns the category corresponding to the distracted driving behavior of the driver, so as to obtain the capability of performing distraction judgment on the driving behavior of the driver in the future.
On the basis of the flow of the driver distraction detection warning method shown in fig. 1, according to a preferred embodiment of the present invention, in order to obtain as many images as possible as training samples, each image is preprocessed before being input into the deep learning model, for example, the image is turned by a certain angle or the gray scale of the image is adjusted.
FIG. 2 illustrates a training process for a deep learning model utilized in the driver distraction detection procedure disclosed in accordance with the present invention. It can be seen from the figure how the deep learning model is trained to classify the driver's distraction behavior. Wherein the deep learning model learns using image information as a sample. For each type of driver distraction, the deep learning model requires a large number of image samples to learn. In order to acquire image samples as many as possible, before the driving behavior image information of the driver is input into the unlearned deep learning model, certain preprocessing is performed on the image, for example, the image is rotated by a certain angle or the gray scale is adjusted, so that the number of the image samples is increased more conveniently and more efficiently. Each image sample then needs to be tagged to indicate the type of driver distraction it is associated with. After the above work is completed, inputting the image samples attached with the labels into the depth learning model which is not learned, training a large number of image samples to obtain the optimal layer in the model and adjusting the node network weight and the bias in the model, and finally completing the training process to obtain the depth learning model which is learned and used for classifying the driver distraction by the driver distraction detection program.
Fig. 3 shows a driver distraction detection warning system for implementing the driver distraction detection warning method shown in fig. 1. It can be seen from the figure, driver distraction detection warning system includes driver distraction detection sensor subassembly 100, computer processor 200 and driver distraction warning subassembly 300 in the car, wherein, driver distraction detection sensor subassembly 100 detects the action information of gathering the driver and sends it for computer processor 200 in the car, the storage has driver distraction detection procedure in the computer processor in the car to be used for classifying driver's distraction, simultaneously computer processor 200 in the car to driver distraction warning subassembly 300 sends the instruction, driver distraction warning subassembly is used for sending the warning for the driver, prevents that the driver from continuing distraction.
On the basis of the driver distraction detection warning system shown in fig. 3, according to a preferred embodiment of the present invention, the driver distraction detection sensor assembly 100 detects and collects the driving behavior of the driver by using a camera and judges whether the driver has a distracted behavior during driving by using an image of the driver during the driving behavior collected by the camera.
On the basis of the driver distraction detection warning system shown in fig. 3, according to a preferred embodiment of the present invention, the driver distraction warning component includes a steering wheel or a rear-view mirror, and the driver distraction warning component uses certain actions of the steering wheel or the rear-view mirror to warn a distracted driver to return the distracted driver to the driving behavior.
The above examples mainly illustrate the driver distraction detection warning method and/or the driver distraction detection warning system of the present disclosure. Although only a few embodiments of the present invention have been described, those skilled in the art will appreciate that the present invention may be embodied in many other forms without departing from the spirit or scope thereof. Accordingly, the present examples and embodiments are to be considered as illustrative and not restrictive, and various modifications and substitutions may be made therein without departing from the spirit and scope of the present invention as defined by the appended claims.
Claims (10)
1. A driver distraction detection warning method includes:
acquiring behavior and action information of a driver;
inputting the collected behavior and action information into a driver distraction detection program;
the driver distraction detection program judges and classifies the distraction of the driver by using the deep learning model, and warns the driver under the condition of successfully judging the distraction type of the driver, thereby preventing the driver from continuing distraction.
2. The driver distraction detection warning method according to claim 1, wherein said driver's behavioral action information is an image.
3. The driver distraction detection alerting method of claim 2 wherein said deep learning model is trained using a plurality of images, wherein the training process comprises:
labels are respectively assigned to each image to indicate different types of distraction of drivers;
inputting the image attached with the label into an unlearned deep learning model;
training is carried out through the input images to obtain the optimal layer in the model, the node network weight and the bias in the model are adjusted, the training process is finally completed, and the deep learning model after learning is obtained.
4. The driver distraction detection warning method of claim 3 wherein said image contains different types of distraction driving behavior including left handed texting, right handed texting, left handed mobile phone, right handed mobile phone, operating vehicle entertainment equipment, drinking, combing or making up, speaking to passengers, and normal driving behavior.
5. The driver distraction detection warning method according to claim 3 or 4, wherein each image is preprocessed before being input into said deep learning model to generate a larger number of image samples.
6. The driver distraction detection warning method of claim 5 wherein said preprocessing comprises angling or adjusting the intensity of the image.
7. A driver distraction detection warning system for implementing the driver distraction detection warning method according to any one of claims 1 to 6, comprising a driver distraction detection sensor component, an in-vehicle computer processor and a driver distraction warning component, wherein the driver distraction detection sensor component detects and collects behavior action information of a driver and sends the behavior action information to the in-vehicle computer processor, a driver distraction detection program is stored in the in-vehicle computer processor for classifying distraction of the driver, and simultaneously the in-vehicle computer processor sends an instruction to the driver distraction warning component, and the driver distraction warning component is used for giving a warning to the driver to prevent the driver from continuing distraction.
8. The driver distraction detection warning system according to claim 7 wherein said distraction detection sensor assembly is configured as a camera sensor.
9. The driver distraction detection warning system of claim 7 wherein said driver distraction warning assembly comprises a steering wheel or a rear view mirror.
10. A motor vehicle, characterized in that it has a driver distraction detection warning system according to any of claims 7 to 9.
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| CN201811509787.9A CN111301428A (en) | 2018-12-11 | 2018-12-11 | Motor vehicle driver distraction detection warning method and system and motor vehicle |
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| CN201811509787.9A CN111301428A (en) | 2018-12-11 | 2018-12-11 | Motor vehicle driver distraction detection warning method and system and motor vehicle |
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| CN113335296A (en) * | 2021-06-24 | 2021-09-03 | 东风汽车集团股份有限公司 | Self-adaptive detection system and method for decentralized driving |
| CN113428162A (en) * | 2021-07-21 | 2021-09-24 | 铜陵学院 | All-round car anticollision early warning system |
| CN114511798A (en) * | 2021-12-10 | 2022-05-17 | 安徽大学 | Transformer-based driver distraction detection method and device |
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