Automatic method and apparatus for determining the area of ​​distraction, road vehicle, electronic equipment

Abstract

The present disclosure provides a method and device for automatically locating an inattentive area, a road vehicle, and an electronic device, and relates to the information processing field. The automatic location method includes the steps of capturing a plurality of face images of the driver currently in the vehicle within a predetermined time period, determining a normal driving angle of the driver in the current vehicle by combining the plurality of face images, and locating the non-inattentive region and the inattentive region of the driver in the current vehicle according to the normal driving angle and the predetermined critical inattentive deviation angle of the current vehicle. The present disclosure solves the technical problem in the related art that the inattentive region is changed due to the change of driving posture or person, and the inattentive detection still uses the fixed inattentive region, which leads to erroneous detection.

Description

Cross-reference to Related Applications ,

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[0001] This application claims the priority of a Chinese patent application filed with the China National Intellectual Property Administration on December 07, 2021, with the application number 202111489217.X and the application title "Method and Device for Automatically Calibrating Glancing Area, Road Vehicle, and Electronic Device", and all its contents are incorporated herein by reference.

Technical Field

[0002] This disclosure relates to the technical field of information processing, and specifically, to a method and device for automatically calibrating a glancing area, a road vehicle, and an electronic device.

Background Art

[0003] In related technologies, during the driving process of a driver, due to fatigue, external factors, etc., the driver often enters a glancing state, and such a glancing state may lead to traffic accidents. Therefore, it is necessary to monitor whether the driver is in a glancing state. Before monitoring whether the driver is in a glancing state, it is necessary to quickly locate the glancing area of each vehicle. Currently, when calibrating the glancing area, it is affected by the differences of the driver himself (differences due to driving postures or driver replacement), driving habits, and the vehicle types of various vehicles, resulting in a large calibration error. In addition, the current glancing area calibration method generally uses a fixed area threshold to calibrate the glancing area, so errors are likely to occur in the calibrated glancing area due to driving postures or driver replacement.

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, in the adopted calibration method, by acquiring the eye movement behavior information of the driver, the real-time detection of the driver's glancing state is realized, and an early warning is given to the glancing behavior that occurs during the driving process of the driver, effectively improving road traffic safety. However, such a method uses traditional machine learning methods, and due to the influence of light and individual differences of the driver, the accuracy is poor. In addition, this method does not handle special scenarios of real vehicles, and there are many situations of false detection.

[0005] We have yet to offer an effective solution to the above problem.

[0006] In at least the related technologies, the present disclosure provides an automatic distraction detection method and apparatus, a road vehicle, and electronic equipment to solve the technical problem in which the distraction detection continues to use a fixed distraction area when the distraction area is changed by the change in driving posture or personnel. [Means for solving the problem]

[0007] According to one aspect of the present disclosure, a method for automatically determining a distracted driving area is provided, comprising the steps of: capturing multiple facial images of a driver currently inside a vehicle within a predetermined time period; combining the multiple facial images to determine the driver's normal driving angle within the vehicle; and determining the driver's non-distracted driving area and distracted driving area within the vehicle based on the normal driving angle and a predetermined distracted driving deflection angle of the vehicle's critical point.

[0008] Preferably, the step of combining the plurality of face images to determine the driver's normal driving angle in the vehicle at the moment includes the steps of combining abnormal driving information to determine the driver's abnormal driving state, removing images corresponding to the abnormal driving state from all face images to obtain a set of normal driving images, and statistically updating the normal driving angle based on the set of normal driving images.

[0009] Preferably, before combining the plurality of facial images to determine the driver's normal driving angle in the vehicle at present, the method further includes a step of initializing the normal driving angle, which includes a step of initializing the normal driving angle using a default value, or a step of initializing the normal driving angle using a first line of sight angle when the driver is fixated on a first mark point.

[0010] Preferably, the abnormal driving information includes at least one of low vehicle speed, steering signal trigger, distracted driving, and grip strength.

[0011] Preferably, the step of determining the driver's abnormal driving state by combining low vehicle speeds includes the steps of acquiring the current vehicle speed and determining that the driver is in an abnormal driving state if the vehicle speed is lower than a predetermined speed threshold.

[0012] Preferably, the step of coupling a steering signal trigger to determine the driver's abnormal driving state includes the steps of: capturing the current vehicle's steering signal trigger state; determining that the driver is in a normal driving state if the signal trigger state indicates that the steering signal has not been triggered; and determining that the vehicle is currently cornering and the driver is in an abnormal driving state if the signal trigger state indicates that the steering signal has been triggered.

[0013] Preferably, the step of determining the driver's abnormal driving state by combining distracted driving bias includes the steps of capturing the driver's face and gaze angle, statistically calculating the period during which the driver's face and gaze angle are in a predetermined abnormal driving region, and determining that the driver is in an abnormal driving state if the period in the abnormal driving region reaches a first period threshold.

[0014] Preferably, the step of determining the driver's abnormal driving state by combining grip strength includes the steps of capturing the driver's grip strength on the steering wheel and determining that the driver is in an abnormal driving state if the grip strength on the steering wheel is lower than a predetermined grip strength threshold.

[0015] Preferably, the step of statistically updating the normal operating angle based on the normal operating image set includes the steps of outputting a normal face and gaze angle value corresponding to each image in the normal operating image set using a face angle and gaze angle model, and updating the normal operating angle by statistically compiling all of the normal face and gaze angle values.

[0016] Preferably, the step of predetermining the current vehicle critical distraction deflection angle includes the steps of: determining a non-distraction marking area and a distraction marking area inside the current vehicle based on a predetermined region of interest, wherein the non-distraction marking area includes normal gaze markers and the distraction area includes at least boundary markers; acquiring normal gaze images and distraction gaze images by capturing images of multiple drivers facing the normal gaze markers and boundary markers, respectively; and analyzing the normal gaze images and distraction gaze images to determine the current vehicle distraction deflection angle critical.

[0017] Preferably, the step of determining the non-distraction marking area and the distraction marking area inside the vehicle based on a predetermined region of interest includes the steps of: representing the predetermined region of interest as the non-distraction marking area inside the vehicle; determining the center point of the non-distraction marking area and obtaining the normal gaze marking point; and determining a plurality of boundaries of the non-distraction marking area, with each boundary as an edge, to determine the distraction marking area outside the non-distraction marking area, wherein any point on the boundary is represented as the boundary marking point.

[0018] Preferably, the step of analyzing the normal gaze image and the distracted gaze image to obtain the distracted drift angle of the current vehicle distracted area critical includes: analyzing the normal gaze image to determine the line of sight angle for gazing at the normal gaze markers, and obtaining a first normal driving angle based on the distribution of line of sight angles for gazing at the normal gaze markers; analyzing all the distracted gaze images to obtain the distribution of critical distracted driving angles for the distracted area, and calculating the average value of the critical distracted driving angles for the distracted area based on the distribution of critical distracted driving angles for the distracted area; and calculating the difference between the first normal driving angle and the average value of the critical distracted driving angles to obtain all the distracted drift angles of the current vehicle critical.

[0019] Preferably, the step of determining the driver's non-distraction area and distraction area within the current vehicle based on the normal driving angle and the predetermined distraction deflection angle of the current vehicle critical includes the steps of obtaining the critical position of the distraction area boundary by adding the distraction deflection angle of the current vehicle critical based on the normal driving angle, and determining the area included in the critical position of the distraction area boundary as the non-distraction area, and the area excluding the area included in the critical position of the distraction area boundary as the distraction area.

[0020] Another aspect of the present disclosure provides an automatic distraction area location device comprising: an acquisition unit arranged to acquire a plurality of facial images of a driver currently inside a vehicle within a predetermined time period; a determination unit arranged to combine the plurality of facial images to determine the driver's normal driving angle within the vehicle; and a location unit arranged to locate the driver's non-distraction area and distraction area within the vehicle based on the normal driving angle and a predetermined distraction deflection angle of the vehicle's current critical.

[0021] Preferably, the determination unit includes a first determination module configured to combine abnormal driving information to determine the driver's abnormal driving state, remove images corresponding to the abnormal driving state from all face images, and obtain a set of normal driving images; and an update module configured to statistically update the normal driving angle based on the set of normal driving images.

[0022] Preferably, the automatic positioning device further includes an initialization unit configured to initialize the normal driving angle before combining the plurality of facial images to determine the driver's normal driving angle in the current vehicle, the initialization unit comprising a first initialization module configured to initialize the normal driving angle using default values, or a second initialization module configured to initialize the normal driving angle using a first line of sight angle when the driver is fixated on a first mark point.

[0023] Preferably, the abnormal driving information includes at least one of low vehicle speed, steering signal trigger, distracted driving, and grip strength.

[0024] Preferably, the first determination module includes a first acquisition submodule configured to acquire the current vehicle speed of the vehicle, and a first determination submodule configured to determine that the driver is in an abnormal driving state if the vehicle speed is below a predetermined speed threshold.

[0025] Preferably, the first decision module includes a second acquisition submodule arranged to acquire the signal trigger state of the steering signal of the current vehicle; a second decision submodule which determines that the driver is in a normal driving state if the signal trigger state indicates that the steering signal has not been triggered; and a third decision submodule which determines that the vehicle is currently in a cornering state and the driver is in an abnormal driving state if the signal trigger state indicates that the steering signal has been triggered.

[0026] Preferably, the first determination module includes a third capture sub-module arranged to capture the face and line-of-sight angle of the driver, a first statistical sub-module arranged to count the period during which the face and line-of-sight angle of the driver are in a predetermined abnormal driving area, and a fourth determination sub-module arranged to determine that the driver is in an abnormal driving state when the period in the abnormal driving area reaches a first period threshold.

[0027] Preferably, the first determination module includes a fourth capture sub-module arranged to capture the grip force of the driver's steering wheel, and a fifth determination sub-module arranged to determine that the driver is in an abnormal driving state when the grip force of the steering wheel is lower than a predetermined grip force threshold.

[0028] Preferably, the update module includes an output sub-module arranged to output normal face and line-of-sight angle values corresponding to each image in the normal driving image set by a face angle and line-of-sight angle model, and a second statistical sub-module arranged to count all the normal face and line-of-sight angle values and update the normal driving angle.

[0029] Preferably, the automatic calibration device for the side glance area is an area calibration module arranged to calibrate the non-side glance marking area and the side glance marking area inside the current vehicle based on a predetermined area of interest, where the non-side glance marking area includes a normal fixation marking point, and the side glance area includes at least a boundary marking point, an image capture module arranged to capture images of a plurality of drivers facing the normal fixation marking point and the boundary marking point respectively to obtain normal fixation images and side glance fixation images, and an image analysis module arranged to analyze the normal fixation images and the side glance fixation images to obtain the side glance deviation angle at the critical point of the side glance area of the current vehicle.

[0030] Preferably, the area determination module includes a sixth determination submodule configured to represent a predetermined area of ​​interest as a non-distraction marking area inside the vehicle; a seventh determination submodule configured to determine the center point of the non-distraction marking area and obtain the normal gaze marking point; and an eighth determination submodule configured to determine a plurality of boundaries of the non-distraction marking area and to determine a distraction marking area outside the non-distraction marking area, with each boundary as an edge, wherein any point on the boundary is represented as the boundary marking point.

[0031] Preferably, the image analysis module includes: an analysis submodule configured to analyze the normal gaze images to determine the line of sight angles for gazing at the normal gaze markers and to obtain a first normal driving angle based on the distribution of line of sight angles for gazing at the normal gaze markers; a first calculation submodule configured to analyze all the distracted gaze images to obtain the distribution of critical distracted driving angles in the distracted driving region and to calculate the average value of the critical distracted driving angles in the distracted driving region based on the distribution of critical distracted driving angles in the distracted driving region; and a second calculation submodule configured to calculate the difference between the first normal driving angle and the average value of the critical distracted driving angles to obtain all the current vehicle critical distracted driving angles.

[0032] Preferably, the positioning unit includes a first positioning module arranged to obtain a critical position of the distraction area boundary by adding the distraction deflection angle of the current vehicle critical based on the normal driving angle, and a second positioning module arranged to position the area included in the critical position of the distraction area boundary into the non-distraction area, and position the area excluding the area included in the critical position of the distraction area boundary into the distraction area.

[0033] Another aspect of the present disclosure provides a road vehicle further comprising: an on-board camera mounted on the windshield in front of the vehicle and positioned to capture road images of the road ahead; and an on-board control unit connected to the on-board camera and performing the automatic distraction area positioning method described in any one of the above paragraphs.

[0034] Another aspect of the present disclosure provides an in-vehicle electronic device comprising a processor and a memory arranged to store executable instructions for the processor, wherein the processor is arranged to execute the automatic distraction area location method described in any one of the above paragraphs by executing the executable instructions.

[0035] Another aspect of the present disclosure further provides a computer-readable storage medium which includes a computer program to be stored, and which, when the computer program is running, controls the equipment on which the computer-readable storage medium is located to perform the automatic location method for the distraction area described in any one of the above paragraphs. [Effects of the Invention]

[0036] In this disclosure, automatic targeting is used to perform tracking processing for different individual drivers and driving conditions, thereby improving the accuracy of targeting the distraction area and effectively reducing false detections compared to fixed threshold methods.

[0037] In this disclosure, a multi-information integration solution is used to filter out abnormal driving conditions during operation and avoid false detections.

[0038] In this disclosure, multiple facial images of the driver currently inside the vehicle are used within a predetermined time period to combine the multiple facial images and determine the driver's normal driving angle within the vehicle. Based on the normal driving angle and a predetermined critical distraction deflection angle for the vehicle, the driver's non-distraction area and distraction area within the vehicle are determined. In this disclosure, multiple facial images of the driver currently inside the vehicle are analyzed to determine the non-distraction area and distraction area within the vehicle for the automated driver. Follow-up processing is performed for different individual drivers and driving states to improve the accuracy of determining the distraction area, and further improve the accuracy of detecting the distraction state. This solves a technical problem in related technologies where the distraction area is changed by changing the driving posture or personnel, and the distraction detection still uses a fixed distraction area, leading to false detections. [Brief explanation of the drawing]

[0039] Herein, the drawings constitute part of this application and provide a further understanding of the present disclosure, and the schematic embodiments and descriptions thereof are for interpreting the present disclosure and not an unreasonable limitation of it. [Figure 1] This is a flowchart of a preferred automatic location method for the distraction area according to an embodiment of the present disclosure. [Figure 2] This is a schematic diagram of a preferred predetermined abnormal operating region according to an embodiment of the present disclosure. [Figure 3] This is a schematic diagram of preferred side-view area location according to an embodiment of the present disclosure. [Figure 4] This is a schematic diagram of a preferred automatic location device for the distraction area according to an embodiment of the present disclosure. [Modes for carrying out the invention]

[0040] To enable those skilled in the art to better understand the solutions of this disclosure, the drawings of the embodiments of this disclosure are combined below to clearly and completely describe the technical solutions of the embodiments of this disclosure. Clearly, the embodiments described are not all embodiments, but only a selection of embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure, provided that they do not perform work worthy of inventive step, are all within the scope of this disclosure.

[0041] Herein, terms such as “first,” “second,” etc., in the specification, claims, and drawings of this disclosure do not indicate a specific order or sequence, but are for distinguishing similar subjects. Herein, where appropriate, the data used in this manner are interchangeable so that the embodiments of this disclosure described herein may be carried out in an order other than that illustrated or described herein. Furthermore, the terms “includes,” “equipped with,” and any variations thereof are intended to include non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to these steps or units that are explicitly listed, and includes elements that are not further explicitly enumerated or inherent to such a process, method, product, or apparatus.

[0042] This disclosure is applicable to various types of transportation (cars, buses, motorcycles, airplanes, trains, etc.), and in the schematic description of this embodiment using a vehicle as an example, it realizes the detection of the vehicle's distraction area and the driver's distracted state. The vehicle types include, but are not limited to, cars, trucks, sports cars, SUVs, and MINI cars. This disclosure automatically determines the distraction area in various parking spaces and areas, analyzes whether the driver is in a distracted state based on the determined distraction area, and performs tracking processing for different individual drivers and driving states, thereby improving the accuracy of distraction detection and effectively reducing false detections compared to fixed threshold methods. This disclosure adaptively adjusts the distraction area to avoid false detections of distraction due to changes in driving posture or personnel. The disclosure will be described in detail below by combining each embodiment.

[0043] Example 1 Embodiments of the present disclosure provide embodiments of an automatic method for determining a distraction area, wherein the steps shown in the flowchart of the drawings are performed, for example, on a computer system of a set of computer-executable instructions, and the flowchart shows a logical sequence, but in some cases the steps shown or described may be performed in a different order.

[0044] Figure 1 is a flowchart of a preferred automatic localization method for a distraction area according to an embodiment of the present disclosure, and as shown in Figure 1, the method includes the following steps: Step S102: Capture multiple facial images of drivers currently inside the vehicle within a predetermined time period; Step S104: Combine multiple facial images to determine the driver's current normal driving angle within the vehicle; Step S106: Based on the normal driving angle and the pre-determined distraction deflection angle of the current vehicle critical, the driver's non-distraction area and distraction area within the vehicle are determined.

[0045] The above steps capture multiple facial images of the driver currently inside the vehicle within a predetermined time period, combine the multiple facial images to determine the driver's normal driving angle within the vehicle, and determine the driver's non-distracted and distracted areas within the vehicle based on the normal driving angle and the pre-determined critical distracted deflection angle of the vehicle. In this embodiment, multiple facial images of the driver currently inside the vehicle are analyzed to determine the non-distracted and distracted areas within the vehicle of the automated driver, and tracking processing is performed for different individual drivers and driving states to improve the accuracy of determining the distracted area, further improving the accuracy of detecting the distracted state, and solving the technical problem in related technologies where the distracted area is changed by changing the driving posture or personnel, but the distracted detection still uses a fixed distracted area, leading to false detections.

[0046] The embodiments of this disclosure will be described in detail below by combining the steps of each of the above implementations.

[0047] Step S102: Capture multiple facial images of drivers currently inside the vehicle within a predetermined time period.

[0048] To accurately determine the distracted driving area, the system combines the vehicle's predetermined deflection areas (area shape and size) and takes into account the driver's driving attributes (driver's build and driving angle) in real time. For example, if the driver's build or height differs, the determined distracted driving area is modified. A camera module installed inside the vehicle captures multiple facial images of the driver within a predetermined time period, and real-time information accurately determining the distracted driving area is recovered from these multiple facial images.

[0049] In this embodiment, the predetermined time period is set in advance, and specifically, the duration of the predetermined time period is related to the actual detection accuracy requirement, for example, within 1 minute or within 30 seconds. The shooting module also captures the video stream and, in this application, performs slide processing on the cover frames for the predetermined period, processes the current frame, inputs the next frame, removes the first frame, updates the image set, and then processes the image set again to perform real-time and automatic positioning.

[0050] In this embodiment, at least one imaging module is provided inside the vehicle so as to be close to the vehicle's driving area. The specific mounting position of the imaging module is not limited, and it is preferable to capture an image that includes the driver's face. The type of imaging module includes, but is not limited to, cameras, depth cameras, infrared cameras, etc. The image type of the captured face image includes, but is not limited to, typical RGB images, depth images, thermal imaging, etc. In this embodiment, a typical RGB image will be used as an example for a general explanation.

[0051] Step S104: Multiple facial images are combined to determine the driver's current normal driving angle within the vehicle.

[0052] Preferably, the step of combining multiple facial images to determine the driver's current normal driving angle in the vehicle includes the steps of combining abnormal driving information to determine the driver's abnormal driving state, removing images corresponding to the abnormal driving state from all facial images to obtain a set of normal driving images, and statistically updating the normal driving angle based on the set of normal driving images.

[0053] Specifically, the normal driving angle refers to the gaze angle when the driver is currently focused on driving within the vehicle. Focusing on driving means that the driver's gaze is maintained directly in front of them, without situations such as making a phone call, turning around, or looking in the rearview mirror. Furthermore, the normal driving angle changes with the scenario. In clear weather driving, the normal driving angle is maintained directly in front of the driver, resulting in a wide field of view. When entering a tunnel or driving in fog, the driver unconsciously lowers their head to focus on the environment in front of them, reducing the field of view, and the normal driving angle shifts downward from the direct-in-sunlight position. In this application, a video stream is captured in real time, and slide processing is performed on the captured video stream in real time to ensure real-time updates of the normal driving angle information. In practice, the image sets captured within a predetermined time period do not all correspond to the state of a focused driver. In this application, images corresponding to abnormal driving states are deleted from all face images to obtain a normal driving image set, and the normal driving angle is statistically updated based on this normal driving image set over a long period of time.

[0054] Furthermore, abnormal driving information includes at least one of the following: low vehicle speed, steering signal trigger, distraction, and grip strength. The following describes a method for determining the driver's abnormal driving state by combining each of these abnormal driving information pieces.

[0055] Preferably, the step of determining the driver's abnormal driving state by combining low vehicle speeds includes the step of acquiring the current vehicle speed and the step of determining that the driver is in an abnormal driving state if the vehicle speed is lower than a predetermined speed threshold.

[0056] In this embodiment, generally, in scenarios such as starting stairs and roadside parking, the vehicle drives at a low speed, surveys the environment, and makes an accurate determination that the stairs do not belong to the normal driving stairs. In this application, whether the vehicle is in a low-speed driving state is determined by comparing the vehicle speed with a predetermined speed threshold. The predetermined speed threshold is pre-set, and the specific setting of the predetermined speed threshold can be determined based on actual user needs. For example, if the predetermined speed threshold is set to 30 km / h, and the current vehicle speed is > 30 km / h, it is considered to be in a normal speed driving state. If the vehicle speed is < 30 km / h, the vehicle speed is considered to be in a low-speed driving state and the driver is considered to be in an abnormal driving state. Furthermore, frames corresponding to abnormal driving stairs are removed from the image set.

[0057] Preferably, the step of coupling steering signal triggers to determine the driver's abnormal driving state includes: capturing the current steering signal trigger state of the vehicle; determining that the driver is in a normal driving state if the signal trigger state indicates that the steering signal is not being triggered; and determining that the vehicle is currently cornering and the driver is in an abnormal driving state if the signal trigger state indicates that the steering signal has been triggered.

[0058] When steering or changing direction, a steering signal is activated, and the driver looks around at the surrounding environment to make an accurate determination. In this application, the driving state is determined by detecting the trigger state of the steering signal. If the steering signal is not triggered, the driver is in a normal driving state; if the steering signal is triggered, the vehicle is in a cornering state; and furthermore, frames corresponding to abnormal driving stages are removed from the image set.

[0059] Preferably, the step of determining the driver's abnormal driving state by combining distracted driving bias includes the steps of capturing the driver's face and gaze angle, statistically calculating the period during which the driver's face and gaze angle are in a predetermined abnormal driving region, and determining that the driver is in an abnormal driving state if the period in the abnormal driving region reaches a first period threshold.

[0060] In this embodiment, the area of ​​the distracted driving zone is greater than or equal to a predetermined abnormal driving zone, which is set by the user and indicates that the driver is clearly in the area of ​​distracted driving.

[0061] Figure 2 is a schematic diagram of a preferred predetermined abnormal driving area according to an embodiment of the present disclosure. As shown in Figure 2, the area excluding the distracted driving area is the non-distracted driving area, and the area excluding the predetermined normal driving area is the predetermined abnormal driving area. Compared to the distracted driving area, which requires precise automatic positioning, the predetermined abnormal driving area is a roughly positioned area. When the line of sight angle falls within this area, it indicates that the driver is clearly in the area of ​​distracted driving. For example, if the driver's gaze is drawn to a roadside sign for an extended period while driving, and remains in the predetermined abnormal driving area for an extended period, it is determined that the driver is in an abnormal driving state, and the frame corresponding to the abnormal driving state is removed from the image set.

[0062] Furthermore, the step of determining the driver's abnormal driving state by combining grip strength includes the steps of capturing the driver's grip strength on the steering wheel and determining that the driver is in an abnormal driving state if the grip strength on the steering wheel is lower than a predetermined grip strength threshold.

[0063] In practice, when a driver is fatigued or in other distracted driving conditions, their grip strength on the steering wheel decreases. Based on the above, embodiments of the present disclosure acquire grip strength values ​​using sensors, combine the grip strengths to determine the driver's abnormal driving state, and the grip strength threshold of these embodiments is pre-set to self-adapt to the vehicle type of each vehicle, and if the grip strength on the steering wheel is lower than a predetermined grip strength threshold, it is determined that the driver is in an abnormal driving state, and furthermore, frames corresponding to the abnormal driving state are removed from the image set.

[0064] In the embodiments of this application, the types included in the abnormal operation information described above are not limited to any combination, nor is the priority order of the types included in the abnormal operation information when selecting image sets limited.

[0065] Preferably, the step of statistically updating the normal driving angle based on a set of normal driving images includes the steps of outputting normal face and gaze angle values ​​corresponding to each image in the set of normal driving images using a face angle and gaze angle model, and updating the normal driving angle by statistically analyzing all normal face and gaze angle values.

[0066] After capturing a set of images of normal driving inside the vehicle, the normal face and gaze angle values ​​corresponding to each image are determined by analyzing the images, and the normal driving angle is updated using all the face and gaze angle values ​​during normal driving. In this embodiment, the output face and gaze angle values ​​are obtained by inputting the images into a face angle and gaze angle model. This disclosure does not limit the form and type of the face angle and gaze angle model, and a traditional geometric model or a neural network model may be adopted. In this embodiment, the normal driving angle is updated by statistically analyzing the long-term normal driving angle, for example, by statistically analyzing the face and gaze angles of the driver during normal driving within the first 60 seconds, taking the average value, and updating it.

[0067] In a preferred embodiment of this model, before combining a plurality of facial images to determine the driver's current normal driving angle in the vehicle, the method further includes a step of initializing the normal driving angle, which includes a step of initializing the normal driving angle using a default value, or a step of initializing the normal driving angle using a first line of sight angle when the driver is fixated on a first mark point.

[0068] The first mark point in this embodiment may be a single point or the average value of multiple points. For example, the point directly in front of the vehicle's windshield may be the first mark point, or the average value of any multiple points included in a specific area of ​​the windshield may be the first mark point. By initializing the normal driving angle, the automatic distraction area quickly and smoothly enters the driving mode with the updated normal driving angle, and because there are extreme deviations in the initial data, the error in the initial calculation of the normal driving angle is not too large, preventing the system from having to return to normal calculations and consuming a large amount of time and resources.

[0069] Step S106: Based on the normal driving angle and the pre-determined distraction deflection angle of the current vehicle critical, the driver's non-distraction area and distraction area within the vehicle are determined.

[0070] Furthermore, the step of predetermining the current vehicle critical distraction deflection angle includes the steps of: determining a non-distraction marking area and a distraction marking area inside the vehicle based on a predetermined region of interest, wherein the non-distraction marking area includes normal gaze markers and the distraction area includes at least boundary markers; acquiring images of multiple drivers facing the normal gaze markers and boundary markers respectively to obtain normal gaze images and distraction gaze images; and analyzing the normal gaze images and distraction gaze images to obtain the current vehicle distraction deflection angle critical.

[0071] In this embodiment, the step of determining the non-distraction marking area and the distraction marking area inside the vehicle based on a predetermined region of interest includes the step of representing the predetermined region of interest as the non-distraction marking area inside the vehicle; the step of determining the center point of the non-distraction marking area and obtaining a normal gaze marking point; and the step of determining a plurality of boundaries of the non-distraction marking area and determining the distraction marking area outside the non-distraction marking area using each boundary as an edge, wherein any point on the boundary is represented as a boundary marking point.

[0072] Figure 3 is a schematic diagram of a preferred distraction area determination according to an embodiment of the present disclosure. As shown in Figure 3, a region of interest (for example, a marking frame located above the steering wheel in Figure 3) is divided from an image of the driving area inside the vehicle, and this region is the non-distraction marking region. The present application does not limit the shape of the region of interest, and it may be circular, triangular, or rectangular, and is generally set according to the vehicle's attributes at the time of factory shipment, with a rectangular shape being used as an example in Figure 3. Marking position 1 is placed within the non-distraction marking region, and marking position 1 is the center point within the region, i.e., the normal attention marking point. Multiple boundaries of the non-distraction marking region are determined, and any point on the boundary is represented as a boundary marking point. In Figure 3, marking positions 2, 3, 4, and 5 are placed at the four boundaries of the non-distraction marking region (up, down, left, and right), respectively, and marking positions 2, 3, 4, and 5 are the boundary positions of the upper, right, down, and left distraction areas, respectively, and the area excluding the boundary positions is the distraction marking region. Specifically, position 1 is the middle of the windshield, position 2 is the upper edge of the windshield, position 3 is the right edge of the rearview mirror, position 4 is the middle of the steering wheel, and position 5 is the left rearview mirror.

[0073] Preferably, the step of analyzing a normal gaze image and each distracted gaze image to obtain the current vehicle distraction critical distraction deflection angle includes: analyzing a normal gaze image to determine the line of sight angle for gazing at a normal gaze marker, and obtaining a first normal driving angle based on the distribution of line of sight angles for gazing at a normal gaze marker; analyzing all distracted gaze images to obtain the distribution of critical distraction driving angles for each distraction region, and calculating the average value of the critical distraction driving angles for each distraction region based on the distribution of critical distraction driving angles for each distraction region; and calculating the difference between the first normal driving angle and the average value of the critical distraction driving angles to obtain all current vehicle critical distraction deflection angles.

[0074] Using Figure 3 as an example, in this embodiment, multiple images of the driver facing sign positions 1 to 5 are captured, and these images are input into a face angle and gaze angle model obtained by pre-training, and face and gaze angle output values ​​are output. Specifically, based on the image set captured by gaze sign position 1, the distribution of gaze angles when looking at a normal gaze sign point is determined, and the average value is calculated to obtain Mean_A1, i.e., the first normal angle. Similarly, for the image sets captured by gaze sign positions 2 to 5, the average values ​​of critical distracted driving angles, Mean_A2, Mean_A3...Mean_A5, are determined, and the difference values ​​are calculated based on the yaw and pitch directions between the first normal driving angle and the average values ​​of critical distracted driving angles, and all current vehicle critical distracted driving angles are obtained. For example, the critical distracted driving angle at sign position 2 is calculated using the following formula, Yaw_A2_1=Yaw(Mean_A2)― Yaw(Mean_A1); Pitch_A2_1=Pitch(Mean_A2)―Pitch(Mean_A1).

[0075] By setting the distraction deflection angle for acquiring face and gaze based on different vehicles or distraction areas, the need to re-introduce the data training model is avoided, resulting in good applicability.

[0076] In this embodiment, the average angle value for each driver toward the normal gaze marker point and boundary marker point is calculated, and further, the difference between the first normal driving angle and the average value of the critical distracted driving angle is calculated to obtain all the current vehicle critical distracted deviation angles, and the driver's non-distracted area and distracted area within the vehicle are determined by the said distracted deviation angle and the driver's normal driving angle.

[0077] Preferably, the step of determining the driver's non-distraction area and distraction area within the vehicle based on the normal driving angle and a predetermined distraction deflection angle of the current vehicle critical includes the steps of: adding the distraction deflection angle of the current vehicle critical based on the normal driving angle to obtain the critical position of the distraction area boundary; and determining the area included in the critical position of the distraction area boundary as the non-distraction area, and determining the area excluding the area included in the critical position of the distraction area boundary as the distraction area.

[0078] After determining the driver's current non-distracted and distracted driving areas within the vehicle, the system outputs the driver's prolonged distracted state. For example, if the driver remains in the distracted driving area for 5 seconds continuously, it is considered a distracted driving state; otherwise, it is considered a normal driving state. When the vehicle speed reaches a threshold or when the steering signal is initiated, the driver is considered to be in a non-distracted state. By detecting whether the driver is distracted, if it is confirmed that the driver is distracted, the system promptly transmits information (e.g., voice prompts, warning sounds) to reduce the probability of accidents and improve vehicle driving safety.

[0079] In the embodiments of this disclosure, a multi-information blending solution is used to filter out abnormal driving conditions during driving to avoid false detections. Furthermore, the embodiments of this disclosure employ automatic targeting, which follows different individual drivers and driving conditions, thereby improving the accuracy of distraction targeting and effectively reducing false detections compared to fixed threshold targeting methods.

[0080] The present disclosure will be described below in conjunction with other preferred embodiments.

[0081] Example 2 This embodiment provides an automatic localization device for the distraction area, and each unit included in the automatic localization device corresponds to each implementation step in the above embodiment 1.

[0082] Figure 4 is a schematic diagram of a preferred automatic localization device for a distraction area according to an embodiment of the present disclosure, and as shown in Figure 4, the automatic localization device is An acquisition unit 41 is positioned to capture multiple facial images of drivers currently inside the vehicle within a predetermined time period. A determination unit 43 is positioned to combine multiple facial images to determine the driver's current normal driving angle within the vehicle, The system includes a positioning unit 45 positioned to locate the driver's non-distraction area and distraction area within the vehicle, based on the normal driving angle and a predetermined distraction deflection angle of the current vehicle critical.

[0083] In the above-described automatic distraction area determination device, the acquisition unit 41 acquires multiple facial images of the driver currently inside the vehicle within a predetermined time period, the determination unit 43 combines the multiple facial images to determine the driver's normal driving angle within the vehicle, and the determination unit 45 determines the driver's non-distraction area and distraction area within the vehicle based on the normal driving angle and the pre-determined critical distraction deflection angle of the vehicle. In this embodiment, multiple facial images of the driver currently inside the vehicle are analyzed to determine the non-distraction area and distraction area of ​​the automated driver within the vehicle, and tracking processing is performed for different individual drivers and driving states to improve the accuracy of distraction area determination, further improving the accuracy of distraction state detection, and solving the technical problem in related technologies where the distraction area is changed by changing driving posture or personnel, but distraction detection still uses a fixed distraction area, leading to false detection.

[0084] Preferably, the determination unit includes a first determination module configured to combine abnormal driving information to determine the driver's abnormal driving state, remove images corresponding to the abnormal driving state from all face images, and obtain a set of normal driving images; and an update module configured to statistically update the normal driving angle based on the set of normal driving images.

[0085] Preferably, the system further includes an initialization unit configured to initialize the normal driving angle before combining multiple facial images to determine the driver's current normal driving angle in the vehicle, the initialization unit further includes a first initialization module configured to initialize the normal driving angle using default values, or a second initialization module configured to initialize the normal driving angle using a first line of sight angle when the driver is fixated on a first mark point.

[0086] Preferably, the abnormal driving information includes at least one of low vehicle speed, steering signal trigger, distracted driving, and grip strength.

[0087] Preferably, the first determination module includes a first acquisition submodule configured to acquire the current vehicle speed of the vehicle, and a first determination submodule configured to determine that the driver is in an abnormal driving state if the vehicle speed is below a predetermined speed threshold.

[0088] Preferably, the first decision module includes a second acquisition submodule configured to acquire the current steering signal trigger state of the vehicle; a second decision submodule configured to determine that the driver is in a normal driving state if the signal trigger state indicates that the steering signal is not being triggered; and a third decision submodule configured to determine that the vehicle is currently cornering and the driver is in an abnormal driving state if the signal trigger state indicates that the steering signal has been triggered.

[0089] Preferably, the first determination module includes a third capture submodule configured to capture the driver's face and gaze angle, a first statistics submodule configured to statistically determine the duration for which the driver's face and gaze angle are in a predetermined abnormal driving range, and a fourth determination submodule configured to determine that the driver is in an abnormal driving state if the duration in the abnormal driving range reaches a first duration threshold.

[0090] Preferably, the first determination module includes a fourth intake submodule configured to capture the driver's grip strength on the steering wheel, and a fifth determination submodule configured to determine that the driver is in an abnormal driving state if the grip strength on the steering wheel is below a predetermined grip strength threshold.

[0091] Preferably, the update module includes an output submodule configured to output normal face and gaze angle values ​​corresponding to each image in the normal driving image set by a face angle and gaze angle model, and a second statistical submodule configured to update the normal driving angles by statistically compiling all normal face and gaze angle values.

[0092] Preferably, the automatic distraction area location device includes an area location module, which is arranged to locate a non-distraction marking area and a distraction marking area inside the vehicle based on a predetermined area of ​​interest, wherein the area location module includes normal gaze markers for the non-distraction marking area and at least boundary markers for the distraction area; an image acquisition module, which is arranged to acquire images of multiple drivers facing the normal gaze markers and boundary markers respectively, to obtain normal gaze images and distraction gaze images; and an image analysis module, which is arranged to analyze the normal gaze images and distraction gaze images to obtain the distraction deflection angle at the current vehicle distraction area critical.

[0093] Preferably, the area determination module includes a sixth determination submodule positioned to represent a predetermined area of ​​interest as a non-distraction marking area inside the vehicle; a seventh determination submodule positioned to determine the center point of the non-distraction marking area and obtain a normal gaze marking point; and an eighth determination submodule positioned to determine a plurality of boundaries of the non-distraction marking area and determine a distraction marking area outside the non-distraction marking area, with each boundary as an edge, wherein any point on the boundary is represented as a boundary marking point.

[0094] Preferably, the image analysis module includes: an analysis submodule configured to analyze normal gaze images, determine the line of sight angles for gazing at normal gaze markers, and obtain a first normal driving angle based on the distribution of line of sight angles for gazing at normal gaze markers; a first calculation submodule configured to analyze all distracted gaze images to obtain the distribution of critical distracted driving angles in the distracted driving region, and calculate the average value of the critical distracted driving angles in the distracted driving region based on the distribution of critical distracted driving angles in the distracted driving region; and a second calculation submodule configured to calculate the difference between the first normal driving angle and the average value of the critical distracted driving angle to obtain all current vehicle critical distracted deflection angles.

[0095] Preferably, the positioning unit includes a first positioning module positioned to obtain a critical position of the distraction area boundary by adding the current vehicle critical distraction deflection angle based on the normal driving angle, and a second positioning module positioned to position the area included in the critical position of the distraction area boundary as a non-distraction area, and position the area excluding the area included in the critical position of the distraction area boundary as a distraction area.

[0096] The above-mentioned automatic targeting device for the distracted viewing area further includes a processor and memory. The acquisition unit 41, determination unit 43, targeting unit 45, etc., are all stored in memory as program units, and the processor executes the above-mentioned program units stored in memory to realize the corresponding functions.

[0097] The above processor includes a kernel, which calls the corresponding program unit from memory. One or more kernels are deployed, and by adjusting the kernel parameters, the driver's non-distraction zone and distraction zone within the vehicle are determined based on the normal driving angle and the pre-determined distraction deflection angle of the current vehicle critical.

[0098] The above-mentioned memory includes non-persistent memory in a computer-readable medium, such as random access memory (RAM) and / or non-volatile memory, for example, read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one storage chip.

[0099] Another aspect of the present disclosure provides a road vehicle further comprising: an on-board camera mounted on the windshield in front of the vehicle and positioned to capture road images of the road ahead; and an on-board control unit connected to the on-board camera and performing the automatic distraction area positioning method of any one of the above.

[0100] According to another aspect of the present disclosure, an in-vehicle electronic device is provided, comprising a processor and a memory arranged to store executable instructions for the processor, wherein the processor is arranged to execute the automatic distraction area location method of any one of the above paragraphs by executing the executable instructions.

[0101] Another aspect of the present disclosure further provides a computer-readable storage medium containing a stored computer program, and when the computer program is running, the device on which the computer-readable storage medium is located controls to perform the automatic location method for the distraction area described in any one of the above paragraphs.

[0102] This application further provides a computer program product, which, when executed on a data processing device, is suitable for initialization, namely, a program comprising the following method steps: capturing multiple facial images of a driver currently inside a vehicle within a predetermined time period; combining the multiple facial images to determine the driver's normal driving angle within the vehicle; and determining the driver's non-distraction area and distraction area within the vehicle based on the normal driving angle and a predetermined distraction deflection angle of the current vehicle critical.

[0103] The numbering of the embodiments described above in this disclosure does not indicate any ranking of the embodiments, but is solely for descriptive purposes.

[0104] In the embodiments described above in this disclosure, each embodiment is described in detail, and for parts not described in detail in one embodiment, one should refer to the relevant descriptions in other embodiments.

[0105] In some embodiments provided in this application, the disclosed technical content may be implemented in other ways. The apparatus embodiments described above are merely schematic, and for example, the division of the units is a division of logical functions, and in actual implementation, there may be different division methods, for example, multiple units or components may be coupled or integrated into another system, or certain features may be ignored or not performed. Also, the coupling, direct coupling, or communication connection between each other shown or discussed may be an indirect coupling or communication connection by several interfaces, units, or modules, and may be in electrical or other forms.

[0106] The units described as separating members may or may not be physically separated, and the members referred to as units may or may not be physical units; that is, they may be located in one place or distributed among multiple units. Depending on the actual needs, some or all of these units may be selected to achieve the objectives of the solution of this embodiment.

[0107] Furthermore, each functional unit in each embodiment of this disclosure may be integrated into a single processing unit, each unit may exist physically independently, or two or more units may be integrated into a single unit. The integrated unit may be implemented in hardware form or in the form of a software functional unit.

[0108] The integrated units may be implemented in the form of software function units and, if sold or used as independent products, may be stored on computer-readable storage media. Based on this understanding, the essence of the proposed technology of the present disclosure, or any part of it that contributes to the prior art, or all or part of the proposed technology, may be embodied in the form of a software product, which is stored on a single storage medium and includes several instructions that cause a single computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present disclosure. The storage medium may include various media capable of storing program code, such as U disks, read-only memory (ROM), random access memory (RAM), portable hard disks, magnetic disks, or optical disks.

[0109] The foregoing describes only preferred embodiments of the present disclosure, and thereafter, a person skilled in the art may make some improvements and modifications, provided that they do not depart from the principles of the present disclosure, and such improvements and modifications should also be considered within the scope of the present disclosure.

Claims

1. An automatic method for determining the area of ​​the side view, A step of capturing multiple facial images of drivers currently inside the vehicle within a predetermined time period, The steps include combining the multiple facial images to determine the driver's normal driving angle within the vehicle at the time of operation, The step of determining the driver's non-distraction area and distraction area within the vehicle at the current vehicle, based on the normal driving angle and the predetermined distraction deflection angle of the current vehicle critical, The step of combining the aforementioned multiple facial images to determine the driver's normal driving angle within the vehicle at the time is: A step of combining abnormal driving information to determine the driver's abnormal driving state, removing images corresponding to the abnormal driving state from all face images, and obtaining a set of normal driving images, wherein the abnormal driving information includes at least one of low vehicle speed, steering signal trigger, distracted gaze, and grip strength, the low vehicle speed is a vehicle speed lower than a predetermined speed threshold, the steering signal trigger indicates that the steering signal trigger state indicates that the steering signal has been triggered, the distracted gaze indicates that the period during which the driver's face and gaze angle are in a predetermined abnormal driving region has reached a first period threshold, and the grip strength indicates that the driver's grip strength on the steering wheel is lower than a predetermined grip strength threshold. A method comprising the step of statistically updating the normal operating angle based on the set of normal operating images.

2. The method further includes the step of initializing the normal driving angle before combining the multiple facial images to determine the normal driving angle of the driver in the current vehicle, A step of initializing the normal operating angle using a default value, or, The method according to claim 1, comprising the step of initializing the normal driving angle using a first line of sight angle when the driver is fixating on a first mark point.

3. The step of determining the driver's abnormal driving state by combining low vehicle speed is: The steps include: acquiring the current vehicle speed of the vehicle, The method according to claim 1, comprising the step of determining that the driver is in an abnormal driving state if the vehicle speed is lower than the predetermined speed threshold.

4. The step of determining the driver's abnormal driving state by linking the steering signal trigger is: The steps include acquiring the signal trigger state of the steering signal of the vehicle, If the signal trigger state indicates that the steering signal is not being triggered, the driver is determined to be in a normal driving state. The method according to claim 1, comprising the step of determining that the vehicle is currently in a cornering state and the driver is in an abnormal driving state if the signal trigger state indicates that the steering signal has been triggered.

5. The step of determining the driver's abnormal driving state by combining distracted driving bias is: The steps include capturing the driver's face and gaze angle, The steps include: stating the period during which the driver's face and gaze angle are in the predetermined abnormal driving range; The method according to claim 1, comprising the step of determining that the driver is in an abnormal operating state when the period in the abnormal operating region reaches the first period threshold.

6. The step of determining the driver's abnormal driving state by combining grip strength is: The steps include capturing the driver's grip on the steering wheel, The method according to claim 1, comprising the step of determining that the driver is in an abnormal driving state if the grip strength of the steering wheel is lower than a predetermined grip strength threshold.

7. The step of statistically updating the normal operating angle based on the set of normal operating images is as follows: For each image in the set of normal operation images, the steps include outputting the normal face and gaze angle values ​​corresponding to each image using a face angle and gaze angle model, The method according to claim 1, comprising the step of statistically compiling all of the above-mentioned normal face and gaze angle values ​​to update the above-mentioned normal driving angle.

8. The step of predetermining the distraction deflection angle of the current vehicle critical is as follows: A step of determining the non-distraction marking area and the distraction marking area within the current vehicle interior based on a predetermined area of ​​interest, wherein the non-distraction marking area includes normal gaze marking points and the distraction area includes at least boundary marking points, The steps include capturing images of multiple drivers facing the normal gaze markers and boundary markers respectively, and obtaining normal gaze images and distracted gaze images, The method according to claim 1, comprising the step of analyzing the normal gaze image and the distracted gaze image to obtain the current vehicle distracted gaze critical angle.

9. The step of determining the non-distraction marking area and the distraction marking area inside the vehicle based on a predetermined area of ​​interest is: The steps include: representing the predetermined area of ​​interest as a non-distraction marking area inside the vehicle at present; The steps include determining the center point of the non-distraction marking area and obtaining the normal gaze marking point, The method according to claim 8, comprising the steps of determining a plurality of boundaries of the non-distraction marking area, and determining a distraction marking area outside the non-distraction marking area using each of the boundaries as an edge, wherein any point on the boundary is represented as the boundary marker point.

10. The step of analyzing the normal gaze image and the distracted gaze image to obtain the distracted gaze deflection angle of the current vehicle distracted gaze critical region is: The steps include: analyzing the normal gaze image to determine the gaze angle for fixating on the normal gaze marker, and obtaining a first normal driving angle based on the distribution of gaze angles for fixating on the normal gaze marker; The steps include: analyzing all of the aforementioned distracted gaze images to obtain the distribution of critical distracted driving angles in the distracted driving area, and calculating the average value of the critical distracted driving angles in the distracted driving area based on the distribution of critical distracted driving angles in the distracted driving area; The method according to claim 8, comprising the step of calculating the difference between the first normal driving angle and the average value of the critical distracted driving angles to obtain all of the current vehicle critical distracted deflection angles.

11. The step of determining the driver's non-distraction area and distraction area within the vehicle at the current vehicle, based on the normal driving angle and the predetermined distraction deflection angle of the current vehicle critical, is: The steps include adding the distraction deflection angle of the current vehicle critical based on the normal operating angle and obtaining the critical position of the distraction region boundary, The method according to claim 1, comprising the step of locating the region included in the critical position of the boundary of the distraction region in the non-distraction region, and locating the region excluding the region included in the critical position of the boundary of the distraction region in the distraction region.

12. An automatic location device for the distraction area, An acquisition unit is positioned to capture multiple facial images of drivers currently inside the vehicle within a predetermined time period, A determination unit is positioned to combine the multiple facial images to determine the driver's normal driving angle within the vehicle at the moment, Includes a positioning unit positioned to position the driver's non-distraction area and distraction area within the vehicle at the current vehicle, based on the normal driving angle and the predetermined distraction deflection angle of the current vehicle critical. The aforementioned decision unit, A first determination module is configured to combine abnormal driving information to determine the driver's abnormal driving state, remove images corresponding to the abnormal driving state from all face images, and obtain a set of normal driving images, wherein the abnormal driving information includes at least one of low vehicle speed, steering signal trigger, distraction, and grip strength, the low vehicle speed being a vehicle speed lower than a predetermined speed threshold, the steering signal trigger indicating that the steering signal trigger state indicates that the steering signal has been triggered, the distraction indicating that the period during which the driver's face and gaze angle are in a predetermined abnormal driving region has reached a first period threshold, and the grip strength indicating that the driver's grip strength on the steering wheel is lower than a predetermined grip strength threshold. An apparatus comprising: an update module arranged to statistically update the normal operating angle based on the set of normal operating images.

13. The system further includes an initialization unit which is configured to initialize the normal driving angle before combining the plurality of facial images to determine the normal driving angle of the driver in the current vehicle, wherein the initialization unit A first initialization module is configured to initialize the normal operating angle using default values, or The apparatus according to claim 12, further comprising a second initialization module arranged to initialize the normal driving angle using a first line of sight angle when the driver is fixating on a first mark point.

14. Road vehicles, An on-board camera mounted on the windshield at the front of the vehicle, positioned to capture road images of the road ahead, A road vehicle comprising an on-board camera connected to an on-board control unit that performs the automatic distraction area determination method according to any one of claims 1 to 11.

15. In-vehicle electronic equipment, Processor and Includes a memory arranged to store executable commands for the processor, The processor is an in-vehicle electronic device arranged to execute the automatic location method for the distracted area according to any one of claims 1 to 11 by executing the executable command.

16. A computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, and when the computer program is running, the computer-readable storage medium controls the device on which the computer-readable storage medium is located so as to execute the automatic location method for the distraction area described in any one of claims 1 to 11.

Images