Mobile phone illegal use snapshot method for distinguishing manual driving and automatic driving

Abstract

The invention discloses a mobile phone illegal use snapshot method for distinguishing manual driving and automatic driving, which is characterized in that a roadside camera with the height of 6 meters is erected on one side of a straight road section to capture and record continuous video pictures of vehicle driving in a fixed straight road section with the length of 100 meters, so that license plate information of a vehicle can be acquired, and the license plate information can be acquired; and comparing the vehicle model registered by the license plate with the database of the automatic driving vehicle model, judging whether the vehicle has an automatic driving mode, and judging whether the vehicle is in the automatic driving mode and whether the driver has the behavior of illegal use of the mobile phone by analyzing the watching mode of the driver. Whether the vehicle has the automatic driving mode or not is judged according to the automatic driving vehicle model database, whether the automatic driving vehicle is in the manual driving mode or the automatic driving mode is accurately recognized by analyzing the watching behavior of the driver, and whether the driver has the behavior of illegally using a mobile phone or not is judged. And the accuracy of penalizing the behavior of illegal use of the mobile phone by the driver is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic safety, and in particular relates to a method for capturing illegal use of a mobile phone for distinguishing between manual driving and automatic driving. Background Art

[0002] In recent years, autonomous driving technology has made significant progress and is about to usher in more extensive technological applications. At the same time, with the increasing popularity of smartphones, traffic accidents caused by drivers using mobile phones have become more frequent. According to statistics, traffic accidents caused by drivers distracted by using mobile phones have become one of the main causes of casualties. At present, traffic regulatory authorities can capture drivers' illegal use of mobile phones through law enforcement cameras. The camera can monitor the driver's eye movements, head posture, and hand movements to determine whether the driver has violated the rules by using the mobile phone. However, existing technologies have certain limitations. It is usually impossible to distinguish whether the vehicle is in manual driving mode or autonomous driving mode. In addition, in autonomous driving mode, the driver can use the mobile phone more freely, so traditional driver behavior monitoring systems may misjudge it as a violation.

[0003] Based on this, the present invention is proposed. Summary of the invention

[0004] In order to solve the problems in the prior art, the present invention proposes a novel method for capturing illegal mobile phone use, aiming to address the limitations of the prior art. The method determines whether the vehicle has an automatic driving mode based on the automatic driving vehicle model database, and accurately identifies whether the automatic driving vehicle is in manual driving or automatic driving mode by analyzing the driver's gaze behavior, and determines whether the driver has illegal use of the mobile phone. This improves the accuracy of the penalty for the driver's illegal use of the mobile phone and provides a basis for penalty for traffic supervision.

[0005] The present invention provides a method for capturing illegal use of a mobile phone to distinguish between manual driving and automatic driving, comprising the steps of:

[0006] S1. A roadside camera is set up on one side of a straight road section to capture and record continuous video images of vehicles traveling on a fixed straight road section to obtain the vehicle license plate information;

[0007] S2. Determine whether the vehicle has an automatic driving mode by comparing the obtained vehicle license plate information with the vehicle registration information;

[0008] S3, first locate the vehicle in the image, obtain the time taken by the vehicle to pass through a fixed straight section, then locate the driver's head image using the Adaboost algorithm, obtain the driver's head video information, and obtain the driver's gaze pattern information based on the driver's head posture information and eye feature information;

[0009] S4. Analyze the driver's gaze pattern information to determine whether the vehicle is in the automatic driving mode and whether the driver has violated the regulations in using the mobile phone.

[0010] Furthermore, in step S1, the height of the roadside camera is 6 meters, and the length of the fixed straight section is 100 meters.

[0011] Furthermore, in step S2, the obtained vehicle license plate information is compared with the vehicle registration information by: identifying the vehicle license plate number through the license plate recognition system, querying the vehicle registration information according to the license plate number, obtaining the vehicle model data, and comparing it with the autonomous driving vehicle database.

[0012] Furthermore, the driver's gaze pattern information obtained in step S3 includes: the frequency of the driver's gaze direction deviating from the vehicle's travel direction in a fixed straight section of the recorded video, the ratio of the duration of the gaze deviation from the travel direction to the total duration of the vehicle's passage, the gaze deviation direction, and the blinking frequency.

[0013] Furthermore, analyzing the driver's gaze pattern information in step S4 includes: determining a critical scoring threshold and a weight corresponding to the gaze pattern information,

[0014] When the frequency of the driver's sight deviating from the driving direction is 7 seconds / time, the frequency score of the driver's sight deviating from the driving direction is determined to be 3, and the critical scoring threshold corresponding to this gaze pattern information is 3, and the weight is 0.35;

[0015] When the ratio of the duration of the driver's sight deviation from the driving direction to the total time of the vehicle passing is 1 / 3, the ratio of the duration of the driver's sight deviation from the driving direction to the total time of the vehicle passing is determined to be 5, and the critical scoring threshold corresponding to this gaze pattern information is 5, and the weight is 0.4;

[0016] When the driver's gaze deviation area is the area below the vehicle center console, the driver's gaze deviation area score is determined to be 8. The gaze area of ​​this gaze pattern information is divided by the platform on the vehicle center console, and the corresponding scoring critical threshold is 8, with a weight of 0.1;

[0017] When the driver's blinking frequency is 4 seconds / time, it is determined that the driver's blinking frequency score is 5, and the scoring critical threshold corresponding to this gaze pattern information is 5, and the weight is 0.15.

[0018] Furthermore, each type of gaze pattern information appearing in the acquired image is scored on a 10-point scale according to the scoring critical threshold corresponding to each type of gaze pattern information, and a scoring model is established according to the weights.

[0019] Specifically, the weight of each type of gaze pattern information is w i , the score of each type of gaze pattern information is A i , where i = 1, 2, 3, 4, the final score

[0020] Furthermore, by bringing the weights of various gaze pattern information and the critical scoring threshold into the scoring model for drivers using mobile phones, the threshold score for drivers using mobile phones is calculated to be 4.2.

[0021] Furthermore, whether the driver uses a mobile phone in the currently acquired image is determined based on a score threshold of the driver using a mobile phone while driving a vehicle. Specifically,

[0022] For vehicles with automatic driving mode, if P ≥ 4.2 (A1 ≠ 0, A2 ≠ 10), it is determined that the driver is using a mobile phone while driving the vehicle, that is, the vehicle is in manual driving mode and the driver is using a mobile phone;

[0023] If P<4.2 (A1≠0, A2≠10), it is determined that the vehicle is in manual driving mode and the driver is not using a mobile phone or the vehicle is in automatic driving mode;

[0024] If A1=0, A2=10, it is determined that the vehicle is in automatic driving mode and the driver is using a mobile phone;

[0025] For vehicles without autonomous driving mode, if P ≥ 4.2, the driver is judged to be using a mobile phone while driving the vehicle;

[0026] If P<4.2 (A1≠0, A2≠10), it is determined that the driver is not using a mobile phone;

[0027] If A1=0, A2=10, it is determined that the driver is using a mobile phone.

[0028] Furthermore, if the current vehicle model is determined not to have an autonomous driving mode by comparing it with the database of autonomous driving models, and if it is determined that the driver is using a mobile phone, the driver is considered to have violated the regulations by using the mobile phone, and a photo of the driver is taken for violation; if it is determined that the driver is not using the mobile phone, the driver is considered not to have violated the regulations by using the mobile phone, and no photo of the driver is taken for violation;

[0029] If it is determined through comparison with the automatic driving vehicle model database that the current vehicle model has an automatic driving mode, and if it is determined that the vehicle is in manual driving mode and the driver is not using a mobile phone, the driver is deemed not to have used the mobile phone in violation of the regulations, and will not be photographed for violations; if it is determined that the vehicle is in manual driving mode and the driver is using a mobile phone, the driver is deemed to have used the mobile phone in violation of the regulations, and will be photographed for violations; if it is determined that the vehicle is in automatic driving mode, the driver is deemed not to have used the mobile phone in violation of the regulations, and will not be photographed for violations.

[0030] Compared with the prior art, the present invention determines whether the vehicle has an autonomous driving mode based on the autonomous driving vehicle model database, and formulates vehicle driving mode and driver mobile phone usage assessment rules. By accurately analyzing the driver's various gaze pattern information, it can accurately identify whether the autonomous driving vehicle is in manual driving or autonomous driving mode, and determine whether the driver has violated the regulations in using the mobile phone. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of the method for capturing illegal use of mobile phones for distinguishing manual driving and automatic driving according to the present invention. DETAILED DESCRIPTION

[0032] The present invention is further explained below in conjunction with the drawings and specific embodiments of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0033] Combined with Figure 1 The present invention provides a method for capturing illegal use of a mobile phone to distinguish between manual driving and automatic driving, which specifically includes the following steps:

[0034] First, a roadside camera with a height of 6 meters is set up on one side of the straight section to capture and record continuous video footage of vehicles driving in a fixed straight section of 100 meters, so as to obtain the vehicle's license plate information. Secondly, the vehicle model registered with the license plate is compared with the database of autonomous driving models to determine whether the vehicle has an autonomous driving mode, and the driver's gaze pattern information is analyzed to determine whether the vehicle is in autonomous driving mode and whether the driver is using a mobile phone. Finally, if the comparison with the database of autonomous driving models determines that the vehicle model does not have an autonomous driving mode, and the driver is determined to be using a mobile phone, the driver is considered to have violated the regulations by using the mobile phone, and he will be photographed for violation; if the driver is determined not to be using a mobile phone, the driver is considered not to have violated the regulations by using the mobile phone, and he will not be photographed for violation. If the comparison with the automatic driving vehicle model database determines that the vehicle model has automatic driving mode, and the vehicle is determined to be in manual driving mode and the driver is not using a mobile phone, the driver is deemed not to have used the mobile phone in violation of the regulations, and will not be photographed for violations; if the vehicle is determined to be in manual driving mode and the driver is using a mobile phone, the driver is deemed to have used the mobile phone in violation of the regulations, and will be photographed for violations; if the vehicle is determined to be in automatic driving mode, the driver is deemed not to have used the mobile phone in violation of the regulations, and will not be photographed for violations.

[0035] Formulate the assessment rules for vehicle driving patterns and drivers’ use of mobile phones, as follows:

[0036] First, a roadside camera with a height of 6 meters was set up on one side of the straight road section to capture and record continuous video images of vehicles driving in a fixed straight road section of 100 meters. The driver's facial image was located by the Adaboost algorithm to obtain the driver's head video information. The driver's gaze behavior can be accurately obtained based on the driver's head posture information and eye feature information. By analyzing the driver's gaze behavior, the frequency (seconds / time) of the driver's gaze direction deviating from the vehicle's driving direction during the video shooting period, the ratio of the driver's line of sight deviating from the driving direction to the total vehicle travel time, the driver's gaze deviation direction, and the driver's blinking frequency (seconds / time) were obtained. By analyzing the driver's gaze method when using a mobile phone in actual situations, the scoring threshold and corresponding weight value of each of the above gaze information were determined.

[0037] When the frequency of the driver's sight deviating from the driving direction is greater than 7 seconds / time, the driver has a 60% probability of using a mobile phone while driving the vehicle. When the frequency of the driver's sight deviating from the driving direction is equal to 7 seconds / time, the score for determining the frequency of the driver's sight deviating from the driving direction is 3. This item is bounded by 7 seconds / time, the critical scoring threshold is 3, and the weight is 0.35.

[0038] When the ratio of the duration of the driver's line of sight deviating from the direction of travel to the total time the vehicle passes is greater than 1 / 3, there is an 80% probability that the driver uses a mobile phone while driving. When the ratio of the duration of the driver's line of sight deviating from the direction of travel to the total time the vehicle passes is equal to 1 / 3, the score of the ratio of the duration of the driver's line of sight deviating from the direction of travel to the total time the vehicle passes is determined to be 4. This item is bounded by 1 / 3, the critical scoring threshold is 4, and the weight is 0.4.

[0039] When the driver's gaze deviates to the area below the vehicle's center console, there is a 50% probability that the driver is using a mobile phone while driving the vehicle. When the driver's gaze deviates to the area below the vehicle's center console, the driver's gaze deviation area score is determined to be 8. This gaze area is divided by the platform on the vehicle's center console, the critical threshold for scoring is 8, and the weight is 0.1.

[0040] When the driver's blinking frequency is less than 4 seconds / time, the driver has a 60% chance of using a mobile phone while driving. When the driver's blinking frequency is equal to 4 seconds / time, the driver's blinking frequency score is determined to be 5. This item is based on 4 seconds / time, the critical scoring threshold is 5, and the weight is 0.15.

[0041] Then, the above four items are scored on a 10-point scale based on the driver's actual gaze behavior. Where i = 1, 2, 3, 4, the final score P is obtained, where w i , A i The weight of each gaze information and the actual score on a 10-point scale are shown respectively.

[0042] Finally, the score threshold for determining whether the driver uses a mobile phone while driving the vehicle is 4.2. For vehicles with an automatic driving mode, if P≥4.2 (A1≠0, A2≠10), it is determined that the driver uses a mobile phone while driving the vehicle, that is, the vehicle is in manual driving mode and the driver uses a mobile phone; if P<4.2 (A1≠0, A2≠10), it is determined that the vehicle is in manual driving mode and the driver does not use a mobile phone or the vehicle is in automatic driving mode. In particular, if A1=0, A2=10, it is determined that the vehicle is in automatic driving mode and the driver is using a mobile phone. For vehicles without automatic driving mode, if P≥4.2, it is determined that the driver uses a mobile phone while driving the vehicle; if P<4.2 (A1≠0, A2≠10), it is determined that the driver does not use a mobile phone. In particular, if A1=0, A2=10, it is determined that the driver is using a mobile phone.

[0043] Example

[0044] According to an embodiment of the present invention, a method for capturing illegal use of a mobile phone for distinguishing manual driving and automatic driving includes the following steps:

[0045] Step 1: A roadside camera with a height of 6 meters is set up on one side of the straight road section to capture and record continuous video images of vehicles traveling in a fixed straight road section with a length of 100 meters to obtain the vehicle's license plate information.

[0046] Step 2: Identify the vehicle's license plate number through the license plate recognition system, query the vehicle registration information based on the license plate number, obtain the vehicle model data, and compare it with the autonomous driving vehicle database to determine whether the vehicle has an autonomous driving mode.

[0047] Step 3: Locate the vehicle position in the image by the license plate position, obtain the time it takes the vehicle to pass through the 100m fixed straight section based on the video shooting time, and then locate the driver's head image through the Adaboost algorithm to obtain the driver's head video information, and obtain the driver's gaze pattern information based on the driver's head posture information and eye feature information.

[0048] The gaze pattern information includes four categories: the frequency (seconds / time) of the driver's gaze deviation from the vehicle's travel direction in a fixed straight section of the recorded video, the ratio of the duration of gaze deviation from the travel direction to the total duration of the vehicle's passage, the gaze deviation direction, and the blinking frequency (seconds / time).

[0049] Step 4: Analyze the driver's gaze pattern information to determine whether the vehicle is in autonomous driving mode and whether the driver has violated the regulations in using the mobile phone.

[0050] First, analyzing the gaze pattern information of the driver includes: determining a critical scoring threshold and a weight corresponding to the gaze pattern information.

[0051] When the frequency of the driver's sight deviating from the driving direction is 7 seconds / time, it is determined that the frequency score of the driver's sight deviating from the driving direction is 3, and the critical scoring threshold corresponding to this gaze pattern information is 3, and the weight is 0.35.

[0052] When the ratio of the duration of the driver's line of sight deviating from the direction of travel to the total time the vehicle passes is 1 / 3, the ratio of the duration of the driver's line of sight deviating from the direction of travel to the total time the vehicle passes is determined to be scored as 5, and the critical scoring threshold corresponding to this gaze pattern information is 5, and the weight is 0.4.

[0053] When the driver's gaze deviation area is the area below the vehicle's center console, the driver's gaze deviation area score is determined to be 8. The gaze area of ​​this gaze pattern information is divided by the platform on the vehicle's center console, and the corresponding scoring critical threshold is 8, with a weight of 0.1.

[0054] When the driver's blinking frequency is 4 seconds / time, it is determined that the driver's blinking frequency score is 5, and the scoring critical threshold corresponding to this gaze pattern information is 5, and the weight is 0.15.

[0055] Next, the various types of gaze pattern information appearing in the acquired image are scored on a 10-point scale according to the scoring critical threshold corresponding to each type of gaze pattern information, and a scoring model is established based on the weights.

[0056] Specifically, the weight of each type of gaze pattern information is w i , the score of each type of gaze pattern information is A i , where i = 1, 2, 3, 4, the final score

[0057] By bringing the weights of various gaze pattern information and the critical scoring threshold into the scoring model for drivers using mobile phones, the threshold score for drivers using mobile phones is calculated to be 4.2.

[0058] Finally, the score threshold of whether the driver uses a mobile phone while driving a vehicle is used to determine whether the driver uses a mobile phone in the current image. Specifically,

[0059] For vehicles with automatic driving mode, if P≥4.2 (A1≠0, A2≠10), it is determined that the driver is using a mobile phone while driving the vehicle, that is, the vehicle is in manual driving mode and the driver is using a mobile phone; if P<4.2 (A1≠0, A2≠10), it is determined that the vehicle is in manual driving mode and the driver is not using a mobile phone or the vehicle is in automatic driving mode; if A1=0, A2=10, it is determined that the vehicle is in automatic driving mode and the driver is using a mobile phone.

[0060] For vehicles without automatic driving mode, if P≥4.2, it is determined that the driver is using a mobile phone while driving the vehicle; if P<4.2 (A1≠0, A2≠10), it is determined that the driver is not using a mobile phone; if A1=0, A2=10, it is determined that the driver is using a mobile phone.

[0061] If it is determined through comparison with the autonomous driving vehicle model database that the current vehicle model does not have the autonomous driving mode, and if it is determined that the driver is using a mobile phone, the driver is considered to have violated the regulations by using the mobile phone, and a photo of the driver is taken for the violation; if it is determined that the driver is not using a mobile phone, the driver is considered to have not violated the regulations by using the mobile phone, and no photo is taken for the violation.

[0062] Specifically, if it is determined through comparison with the automatic driving vehicle model database that the current vehicle model has an automatic driving mode, and if it is determined that the vehicle is in manual driving mode and the driver is not using a mobile phone, then the driver is deemed not to have used the mobile phone in violation of the regulations, and will not be photographed for violations; if it is determined that the vehicle is in manual driving mode and the driver is using a mobile phone, then the driver is deemed to have used the mobile phone in violation of the regulations, and will be photographed for violations; if it is determined that the vehicle is in automatic driving mode, then the driver is deemed not to have used the mobile phone in violation of the regulations, and will not be photographed for violations.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for capturing illegal use of mobile phones to distinguish between manual driving and automatic driving, characterized in that: Includes steps: S1. A roadside camera is set up on one side of a straight road section to capture and record continuous video images of vehicles traveling on a fixed straight road section to obtain the vehicle license plate information; S2, comparing the obtained vehicle license plate information with the vehicle registration information to determine whether the vehicle has an automatic driving mode; S3, first locate the vehicle in the image, obtain the time taken by the vehicle to pass through a fixed straight section, then locate the driver's head image using the Adaboost algorithm, obtain the driver's head video information, and obtain the driver's gaze pattern information based on the driver's head posture information and eye feature information; S4. Analyze the driver's gaze pattern information to determine whether the vehicle is in the automatic driving mode and whether the driver has violated the regulations in using the mobile phone.

2. The method for capturing illegal use of mobile phones for distinguishing between manual driving and automatic driving according to claim 1 is characterized in that: In step S1, the height of the roadside camera is 6 meters, and the length of the fixed straight section is 100 meters.

3. The method for capturing illegal use of mobile phones for distinguishing between manual driving and automatic driving according to claim 1 is characterized in that: In step S2, the obtained vehicle license plate information is compared with the vehicle registration information by: identifying the vehicle license plate number through the license plate recognition system, querying the vehicle registration information according to the license plate number, obtaining the vehicle model data, and comparing it with the autonomous driving vehicle database.

4. The method for capturing illegal use of mobile phones for distinguishing between manual driving and automatic driving according to claim 1 is characterized in that: The driver's gaze pattern information obtained in step S3 includes: the frequency of the driver's gaze direction deviating from the vehicle's travel direction in the fixed straight section of the recorded video, the ratio of the duration of the driver's gaze deviation from the travel direction to the total duration of the vehicle's travel, the gaze deviation direction, and the blinking frequency.

5. The method for capturing illegal use of mobile phones for distinguishing between manual driving and automatic driving according to claim 1 is characterized in that: Analyzing the driver's gaze pattern information in step S4 includes: determining a critical scoring threshold and a weight corresponding to the gaze pattern information, When the frequency of the driver's sight deviating from the driving direction is 7 seconds / time, the frequency score of the driver's sight deviating from the driving direction is determined to be 3, and the critical scoring threshold corresponding to this gaze pattern information is 3, and the weight is 0.35; When the ratio of the duration of the driver's sight deviation from the driving direction to the total time of the vehicle passing is 1 / 3, the ratio of the duration of the driver's sight deviation from the driving direction to the total time of the vehicle passing is determined to be 5, and the critical scoring threshold corresponding to this gaze pattern information is 5, and the weight is 0.4; When the driver's gaze deviation area is the area below the vehicle center console, the driver's gaze deviation area score is determined to be 8. The gaze area of ​​this gaze pattern information is divided by the platform on the vehicle center console, and the corresponding scoring critical threshold is 8, with a weight of 0.1; When the driver's blinking frequency is 4 seconds / time, it is determined that the driver's blinking frequency score is 5, and the scoring critical threshold corresponding to this gaze pattern information is 5, and the weight is 0.

15.

6. The method for capturing illegal use of mobile phones for distinguishing between manual driving and automatic driving according to claim 5 is characterized in that: According to the scoring threshold corresponding to each type of gaze pattern information, each type of gaze pattern information appearing in the acquired image is scored out of 10, and a scoring model is established based on the weights. Specifically, the weight of each type of gaze pattern information is w i , the score of each type of gaze pattern information is A i , where i = 1, 2, 3, 4, the final score 7. The method for capturing illegal use of mobile phones for distinguishing between manual driving and automatic driving according to claim 6 is characterized in that: By bringing the weights of various gaze pattern information and the critical scoring threshold into the scoring model for drivers using mobile phones, the threshold score for drivers using mobile phones is calculated to be 4.

2.

8. The method for capturing illegal use of mobile phones for distinguishing manual driving and automatic driving according to any one of claims 1 to 7, characterized in that: According to the score threshold of the driver using the mobile phone while driving the vehicle, it is determined whether the driver in the currently acquired image is using the mobile phone. Specifically, For vehicles with automatic driving mode, if P ≥ 4.2 (A1 ≠ 0, A2 ≠ 10), it is determined that the driver is using a mobile phone while driving the vehicle, that is, the vehicle is in manual driving mode and the driver is using a mobile phone; If P < 4.2 (A1≠0, A2≠10), it is determined that the vehicle is in manual driving mode and the driver is not using a mobile phone or the vehicle is in automatic driving mode; If A1=0, A2=10, it is determined that the vehicle is in automatic driving mode and the driver is using a mobile phone; For vehicles without autonomous driving mode, if P ≥ 4.2, the driver is judged to be using a mobile phone while driving the vehicle; If P < 4.2 (A1≠0, A2≠10), it is determined that the driver is not using a mobile phone; If A1=0, A2=10, it is determined that the driver is using a mobile phone.

9. The method for capturing illegal use of mobile phones for distinguishing between manual driving and automatic driving according to claim 8, characterized in that: If the current vehicle model is determined not to have an autonomous driving mode by comparing it with the database of autonomous driving models, and if the driver is determined to be using a mobile phone, the driver is considered to have violated the regulations by using the mobile phone, and a photo of the driver is taken for violation; if the driver is determined not to be using a mobile phone, the driver is considered not to have violated the regulations by using the mobile phone, and no photo is taken for violation; If the current vehicle model is determined to have an autonomous driving mode by comparing it with the autonomous driving model database, and if it is determined that the vehicle is in manual driving mode and the driver is not using a mobile phone, then it is considered that the driver has not violated the regulations on mobile phone use and will not be captured for violation; If it is determined that the vehicle is in manual driving mode and the driver is using a mobile phone, the driver is considered to have used the mobile phone in violation of the regulations and will be photographed for the violation; if it is determined that the vehicle is in automatic driving mode, the driver is considered to have not used the mobile phone in violation of the regulations and will not be photographed for the violation.

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