A method for detecting and processing red light violations based on an electric bicycle helmet and an electric bicycle

Abstract

The application provides a method for detecting and processing red light running based on an electric bicycle helmet and an electric bicycle. Real-time state information of the electric bicycle is obtained. When the electric bicycle is in a riding state, a camera loaded on the front of the helmet of the electric bicycle is controlled to enter a snapshot mode, and riding environment image information obtained by the camera is obtained at a first preset time interval. When two conditions are met in the riding environment image information, i.e., a preset red light image and an included angle between a shooting direction of the camera and a current driving direction of the electric bicycle on a horizontal plane is less than a preset angle, a first preset alarm instruction is executed. The camera on the helmet can detect the red light image, and timely early warning and reminding are performed to avoid red light running of the user as much as possible during riding, improve travel safety, and have the advantages of high timeliness and high detection efficiency.

Description

Technical Field

[0001] This invention relates to the field of electric bicycle technology, and in particular to a method for detecting and processing red light violations based on electric bicycle helmet detection, and an electric bicycle. Background Technology

[0002] With the diversification of travel options, shared electric bicycles, with their convenience, comfort, and low-carbon advantages, are increasingly favored by citizens, effectively solving the "last mile" problem from parking spots to destinations. However, in daily travel, it is common to see users running red lights while riding shared electric bicycles, which can easily cause traffic accidents, disrupt public transportation order, and seriously threaten the lives and property of themselves and others.

[0003] Currently, the existing technology for detecting and controlling shared electric bicycles that may run red lights without prior warning relies solely on road monitoring equipment (cameras) installed on the road to capture images. This method is difficult to accurately match the user of the electric bicycle. Even if a specific rider is captured, that rider has already run a red light, making it difficult to effectively alleviate the problem of shared electric bicycles running red lights.

[0004] In view of this, it is necessary to propose a method and a motorcycle based on helmet detection for red light running to solve or at least alleviate the above-mentioned defects. Summary of the Invention

[0005] The main objective of this invention is to provide a method and a motorcycle based on helmet-based detection for red-light running, in order to solve the problem that the detection / control method for shared motorcycles that may run red lights does not provide advance warnings, which can easily lead to safety hazards during riding.

[0006] To achieve the above objectives, the present invention provides a method for detecting and processing red-light violations based on electric bicycle helmets, comprising the following steps:

[0007] S1, Obtain the real-time status information of the electric bicycle, and determine whether the electric bicycle is in a riding state based on the real-time status information;

[0008] S2, when the electric bicycle is in riding mode, control the camera mounted on the front of the electric bicycle helmet to enter the capture mode, and acquire the riding environment image information captured by the camera according to the first preset time interval;

[0009] S3, determine whether each of the riding environment image information contains a preset red light image, and determine whether the angle between the shooting direction of the camera corresponding to each of the riding environment image information and the current driving direction of the electric bicycle on the horizontal plane is less than a preset angle;

[0010] S4, when two of the following conditions are met simultaneously in the cycling environment image information: the preset red light image, the shooting direction of the camera, and the angle between the current driving direction of the electric bicycle and the horizontal plane is less than a preset angle, the first preset alarm command is executed.

[0011] Preferably, step S4 is followed by the following step:

[0012] S51, determine the theoretical calculated distance between the red light image in the cycling environment image information and the camera;

[0013] S52, when the theoretically calculated distance is less than the first preset distance, execute the second preset alarm command;

[0014] S53, when the theoretical calculated distance is between the first preset distance and the second preset distance, the current running speed of the electric bicycle is obtained, and the theoretical calculated time required for the electric bicycle to move to the position of the red light image in the riding environment image information is determined according to the theoretical calculated distance and the current running speed.

[0015] S54, determine whether the theoretical calculation time is less than the time set value; wherein, the second preset distance is greater than the first preset distance;

[0016] S55, when the theoretical calculation time is less than the time set value, execute the third preset alarm command;

[0017] S56, when the theoretical calculation time is greater than or equal to the time setting value or the theoretical calculation distance is greater than the second preset distance, maintain the current state of the electric bicycle.

[0018] Preferably, step S3 is followed by the step:

[0019] If any two of the following conditions are not met in the riding environment image information: the preset red light image, the shooting direction of the camera, and the angle between the current driving direction of the electric bicycle and the horizontal plane is less than a preset angle, the current state of the electric bicycle shall be maintained.

[0020] Preferably, step S51 specifically includes the following steps:

[0021] S511, determine the area of ​​the red light image region in the cycling environment image information;

[0022] S512, according to formula The theoretical distance between the red light image in the cycling environment image information and the camera is determined; where S1 is the area of ​​the red light image region when the distance between the camera and the red light is d1, and S2 is the area of ​​the red light image region when the distance between the camera and the red light image is d2.

[0023] Preferably, step S4 is followed by the following step:

[0024] S41, determine whether the cycling environment image information contains a preset image of a vehicle running a red light;

[0025] S42, when the cycling environment image information contains a preset image of a vehicle running a red light, the cycling environment image information is sent to the target object;

[0026] S43, if the cycling environment image information does not contain a preset image of a vehicle running a red light, return to step S1.

[0027] Preferably, step S3 is followed by the step:

[0028] S31, when two of the following conditions are met simultaneously: the riding environment image information does not contain a preset red light image and the angle between the camera's shooting direction and the current driving direction of the electric bicycle in the horizontal plane is less than a preset angle, it is determined whether there is a target obstacle in the riding environment image information.

[0029] S32, when the target obstacle is present in the cycling environment image information, the first preset time interval is adjusted to a second preset time interval; wherein the second preset time interval is less than the first preset time interval.

[0030] Preferably, step S32 is followed by the step:

[0031] S33, from the first time point after the second preset time period after the first detection of the target obstacle, determine whether the corresponding cycling environment image information has the target obstacle;

[0032] S34, when the cycling environment image information does not contain the target obstacle, the second preset time interval is adjusted to the first time interval;

[0033] S35, when the cycling environment image information contains the target obstacle, the camera continues to capture images according to the second preset time interval.

[0034] Preferably, step S4 is followed by the following step:

[0035] S401, Obtain the historical location data of the electric bicycle within a third preset time period before the current moment and determine the vector motion path of the electric bicycle based on the historical location dataset;

[0036] S402, based on the current position of the electric bicycle and the vector motion path, determine whether there is a target shared vehicle in an area behind the electric bicycle and within a preset range from the current position; wherein, the target shared vehicle is communicatively connected to the electric bicycle;

[0037] S403, when there is a target shared vehicle behind the electric bicycle and within a preset range from the current location, a preset reminder instruction is sent to the target shared vehicle.

[0038] The present invention also provides an electric bicycle, including a vehicle body, a helmet, and a control system disposed within the vehicle body; wherein, the helmet is provided with a control module communicatively connected to the control system, a camera for acquiring images of the riding environment is mounted on the front of the helmet, the camera is connected to the control module, the control system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the steps of the above-described method for detecting and processing red-light violations based on an electric bicycle helmet.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This invention provides a method and a motorcycle for detecting and handling red-light violations using a motorcycle helmet. By acquiring real-time status information of the motorcycle, when the motorcycle is being ridden, a camera mounted on the front of the helmet is activated to capture images. The camera captures images of the riding environment at preset time intervals. If two of the following conditions are met simultaneously: a preset red light image is present, and the angle between the camera's shooting direction and the motorcycle's current direction of travel in the horizontal plane is less than a preset angle, a first preset alarm command is executed. This invention can detect red light images using a helmet-mounted camera, providing timely early warnings and minimizing the risk of users running red lights while riding, thus improving travel safety. It has the advantages of high timeliness and high detection efficiency. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of a process in one embodiment of the present invention;

[0043] Figure 2This is a flowchart illustrating steps following step S4 in one embodiment of the present invention.

[0044] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0045] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0047] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0048] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, such a combination should be considered non-existent and not within the scope of protection claimed by this invention.

[0049] Please see the appendix Figures 1-2 An embodiment of the present invention provides a method for detecting and processing red-light violations based on electric bicycle helmet detection, comprising the following steps:

[0050] S1. Obtain the real-time status information of the electric bicycle and determine whether the electric bicycle is in a riding state based on the real-time status information. For example, it can be determined by detecting whether the output current of the motor is greater than a preset threshold, or by detecting the power-on status of the entire electric bicycle, or by determining whether the user has unlocked the bicycle and generated timing information after riding. Those skilled in the art can set the specific settings according to actual needs. It is understood that the riding state includes the electric bicycle being in motion and the electric bicycle being stationary after starting (e.g., the state when waiting for a traffic light).

[0051] S2, when the electric bicycle is being ridden, the camera mounted on the front of the helmet is controlled to enter a capture mode, and the camera captures images of the riding environment at a first preset time interval. It should be noted that the camera in this application is positioned in front of the helmet to capture images of the riding environment during the riding process. The first preset time interval can be set as needed, for example, it can be set to 2 seconds, meaning that one image of the riding environment is captured every 2 seconds. To eliminate irrelevant information in the images, obtain useful and accurate information, enhance the detectability of relevant information, and simplify the data to the maximum extent, effective riding environment image information is extracted from the riding environment image information.

[0052] S3, determine whether each of the cycling environment image information contains a preset red light image, and determine whether the angle between the shooting direction of the camera corresponding to each of the cycling environment image information and the current driving direction of the electric bicycle on the horizontal plane is less than a preset angle. It is worth noting that there are multiple ways to determine whether the valid cycling environment image information contains a preset red light image. For example, it can be done through an image template matching algorithm. By setting a preset red light image in advance, for example, setting the detected red light image feature as a preset red light image, and then using an image matching algorithm to match the current cycling environment image information with the template, the matching degree between the current cycling environment image information and the template can be determined. When the matching degree is greater than a preset value, the current cycling environment image information can be considered to contain the preset red light image. In another preferred example, the presence of a preset red light image in the cycling environment image information can be detected by deep learning. For example, by pre-collecting a large number / sufficient image samples, dividing all image samples into training and testing sets, and manually labeling each image sample (i.e., whether it corresponds to a preset red light image), a high-precision neural network model can be trained, and then the trained neural network model can be used for prediction / detection. This method of detection using neural networks is a mature existing technology, and will not be elaborated on further here.

[0053] As a preferred example, the "current driving direction" in step S3 is obtained through the following steps: S31, acquiring the historical location data of the electric bicycle within a first preset time period before the current moment and determining the vector movement path of the electric bicycle based on the historical location dataset; the historical location data can be obtained by acquiring the real-time GPS data of the electric bicycle, and the vector movement path of the electric bicycle can be obtained by connecting each historical location in chronological order. S32, determining the current driving direction based on the vector movement path. For example, the first preset time period can be set to 10 seconds, and the vector direction formed by the last 2 seconds of historical location data can be taken as the current driving direction. The vector direction determined in this way can more accurately reflect the current driving direction of the electric bicycle.

[0054] Furthermore, it is worth noting that the shooting direction of this application is aligned with the axis of the camera's own length direction. Specifically, a gyroscope can be installed on the helmet to detect the shooting direction of the helmet camera. The angle between the gyroscope-detected direction and the current driving direction can be calculated. In another embodiment, a gyroscope can be installed on the helmet, with a preset reference direction for gyroscope detection. Simultaneously, the shooting direction directly in front of the camera is detected in real time (the camera is fixed on the helmet, and the rotation angle of the helmet is the rotation angle of the camera). As is known, the angular relationship between the shooting direction and the reference direction can be determined in real time, and the vector path direction and the reference direction can also be determined, thereby determining the angle between the shooting direction and the current driving direction.

[0055] Understandably, a larger angle indicates that the user is looking around more while wearing a helmet, and the camera's shooting direction deviates significantly from the motorcycle's current direction of travel. In this case, the captured image is essentially invalid or irrelevant, making it difficult to determine whether the captured image represents the area in front of the motorcycle and thus discarding it. Therefore, when the angle is within a preset range, for example, when the shooting direction is consistent with the current direction of travel, the angle is set to 0°, and the preset angle range is set to [-45°, 45°].

[0056] S4. When two of the following conditions are met simultaneously in the cycling environment image information: the preset red light image is present, and the angle between the camera's shooting direction and the current driving direction of the electric bicycle on the horizontal plane is less than a preset angle, the first preset alarm command is executed. That is, when the preset red light image is present in the cycling environment image information, and the angle between the camera's shooting direction and the current driving direction of the electric bicycle on the horizontal plane is less than a preset angle, the captured image is a valid image. Preferably, the first preset alarm command includes one or more of the following: issuing a prompt voice command, issuing an alarm command, sending a message to the server or user account, and disconnecting the electric bicycle's power supply. For example, the first preset alarm command can be set to issue a prompt voice command or a credit warning to promptly remind the user not to run a red light.

[0057] In this application, by acquiring the real-time status information of the electric bicycle, when the electric bicycle is being ridden, the camera mounted on the front of the electric bicycle helmet is controlled to enter a capture mode. The camera captures images of the riding environment at first preset time intervals. When two of the following conditions are simultaneously met: a preset red light image is present, and the angle between the camera's shooting direction and the electric bicycle's current direction of travel on the horizontal plane is less than a preset angle, a first preset alarm command is executed. This application can detect red light images through the helmet-mounted camera, providing timely early warnings and minimizing the risk of users running red lights while riding, thus improving travel safety. It has the advantages of high timeliness and high detection efficiency.

[0058] In a preferred embodiment of the present invention, the step S4 is followed by the following step:

[0059] S51, determine the theoretical distance between the red light image in the cycling environment image information and the camera; as a preferred example, step S51 specifically includes the following steps:

[0060] S511, determine the area of ​​the red light image region in the cycling environment image information;

[0061] S512, according to formula The theoretical distance between the red light image in the cycling environment image information and the camera is determined; where S1 is the area of ​​the red light image region when the distance between the camera and the red light is d1, and S2 is the area of ​​the red light image region when the distance between the camera and the red light image is d2.

[0062] Specifically, according to the theorem of the ratio of the areas of similar triangles to the ratio of their side lengths: the ratio of the areas of similar triangles is equal to the square of the similarity ratio. Similarly, we can first determine the area S1 of the red light image region when the distance between the camera and the red light is d1. For example, we can determine the red light area as S1 at a distance of 10m. Since the corresponding red light area area can be determined in the captured image, the theoretical distance between the red light image in the cycling environment image information and the camera can be calculated according to the formula, i.e., the reference is:

[0063]

[0064] S52, when the theoretically calculated distance is less than the first preset distance, execute the second preset alarm command; this indicates that the electric bicycle is relatively close to the red light position (for example, the first preset distance is set to 10m, or the first preset distance is set according to the lane type), then execute the second preset alarm command, such as issuing an alarm command.

[0065] S53, when the theoretically calculated distance is between the first preset distance and the second preset distance, the current running speed of the electric bicycle is obtained, and the theoretical calculation time required for the electric bicycle to move to the red light image position in the riding environment image information is determined according to the theoretically calculated distance and the current running speed. At this time, it means that the electric bicycle is still a certain distance away from the red light position. Considering the riding speed of the electric bicycle, the time required for the user to reach the red light position is determined. By obtaining the current running speed of the electric bicycle and determining the theoretical calculation time required for the electric bicycle to move to the red light image position in the riding environment image information according to the theoretically calculated distance and the current running speed, if the theoretical calculation time is sufficient (the required time is relatively long), the current state of the electric bicycle can be maintained. If the theoretical calculation time is relatively short, a reminder and warning need to be issued.

[0066] S54, determine whether the theoretical calculation time is less than the time set value; wherein, the second preset distance is greater than the first preset distance;

[0067] S55, when the theoretical calculation time is less than the time set value, execute the third preset alarm command;

[0068] S56, when the theoretical calculation time is greater than or equal to the time setting value or the theoretical calculation distance is greater than the second preset distance, the current state of the electric bicycle is maintained. In this case, the electric bicycle is far from the red light or it will take a long time for the user to reach the red light, so the current state of the electric bicycle can be maintained.

[0069] Furthermore, step S3 is followed by the following step:

[0070] If any two of the following conditions are not met in the riding environment image information: the preset red light image, the shooting direction of the camera, and the angle between the current driving direction of the electric bicycle and the horizontal plane is less than a preset angle, the current state of the electric bicycle shall be maintained.

[0071] In another preferred embodiment, step S4 is followed by the following step:

[0072] S41, determine whether the cycling environment image information contains a preset image of a vehicle running a red light;

[0073] S42, when the cycling environment image information contains a preset image of a vehicle running a red light, the cycling environment image information is sent to the target object. It is understood that the cycling environment image information captured by the camera on the helmet can not only be used to detect whether the electric bicycle itself is approaching a red light, but also to determine whether there are other vehicles running red lights. When the preset image of a vehicle running a red light is detected in the cycling environment image information (for example, a vehicle at an intersection with a red light), the cycling environment image information can be sent to the target object (such as a back-end management platform, traffic management department system, etc.) to record the violation scene in a timely manner.

[0074] S43, if the cycling environment image information does not contain a preset image of a vehicle running a red light, return to step S1.

[0075] In a preferred embodiment, step S3 is followed by the following step:

[0076] S31, when two of the following conditions are met simultaneously: the riding environment image information does not contain a preset red light image and the angle between the camera's shooting direction and the current driving direction of the electric bicycle in the horizontal plane is less than a preset angle, it is determined whether there is a target obstacle in the riding environment image information.

[0077] S32, when the target obstacle is present in the cycling environment image information, the first preset time interval is adjusted to a second preset time interval; wherein the second preset time interval is less than the first preset time interval.

[0078] Furthermore, step S32 is followed by the following step:

[0079] S33, from the first time point after the second preset time period after the first detection of the target obstacle, determine whether the corresponding cycling environment image information has the target obstacle;

[0080] S34, when the cycling environment image information does not contain the target obstacle, the second preset time interval is adjusted to the first time interval;

[0081] S35, when the cycling environment image information contains the target obstacle, the camera continues to capture images according to the second preset time interval.

[0082] It is important to note that when the cycling environment image information does not contain a preset red light image, a helmet-wearing user may encounter a target obstacle between the vehicle and the red light location, such as a large truck or car. To detect red light violations in such cases, this embodiment further processes the cycling environment image information. The system determines whether the image contains a target obstacle, which can be pre-defined (e.g., a large truck or car). Image recognition / detection algorithms are well-established and will not be elaborated upon here. For example, an image matching algorithm can be used to determine if the current cycling environment image contains a target obstacle. Alternatively, in other embodiments, deep learning, such as the YOLO V5 model, can be employed. By comparing the current cycling environment image information with a pre-trained YOLO V5 model, the system can detect whether the label corresponding to the current cycling environment image contains a target obstacle.

[0083] In this embodiment, when the target obstacle is present in the cycling environment image information, the first preset time interval is adjusted to a second preset time interval. In other words, when there is a target obstacle, the camera will detect it at a higher frequency to maximize the detection of whether the user has run a red light during cycling. As a specific example, the first preset time interval can be set to 2 seconds, and the second preset time interval can be set to 0.6 seconds.

[0084] In a preferred embodiment, step S4 is followed by the following step:

[0085] S401, Obtain the historical location data of the electric bicycle within a third preset time period before the current moment and determine the vector motion path of the electric bicycle based on the historical location dataset;

[0086] S402, based on the current position of the electric bicycle and the vector motion path, determine whether there is a target shared vehicle in an area behind the electric bicycle and within a preset range from the current position; wherein, the target shared vehicle is communicatively connected to the electric bicycle;

[0087] S403, when there is a target shared vehicle behind the electric bicycle and within a preset range from the current location, a preset reminder instruction is sent to the target shared vehicle.

[0088] It is worth noting that, in this embodiment, after determining that the environmental image information contains a preset red light image, in order to provide reminders and warnings to vehicles behind, this embodiment obtains the historical location data of the electric bicycle within a third preset time period and determines the vector movement path of the electric bicycle based on the historical location dataset. For example, by obtaining the historical location dataset within 3 seconds before the current moment, the direction of travel of the electric bicycle at the current moment can be defined. Then, based on the direction of travel, vehicles behind the electric bicycle and within a preset range from the current position can be identified, such as electric bicycles within 50 meters behind the electric bicycle, and a warning reminder instruction is given. The target shared vehicle and the electric bicycle can communicate with each other; for example, they can be vehicles of the same model or different models that can establish a communication connection. The warning reminder instruction can be sending a reminder to slow down, a reminder that a red light is about to be run, etc., to the target shared vehicle. In this way, early warning reminders can be given to vehicles behind, improving traffic safety.

[0089] The present invention also provides an electric bicycle, including a vehicle body, a helmet, and a control system disposed within the vehicle body; wherein, the helmet is provided with a control module communicatively connected to the control system, a camera for acquiring images of the riding environment is mounted on the front of the helmet, the camera is connected to the control module, the control system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the steps of the above-described method for detecting and processing red-light violations based on an electric bicycle helmet.

[0090] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for detecting and processing red-light violations based on electric bicycle helmet detection, characterized in that, Including the following steps: S1, obtain the real-time status information of the electric bicycle, and determine whether the electric bicycle is in a riding state based on the real-time status information; S2, when the electric bicycle is in riding mode, control the camera mounted on the front of the electric bicycle helmet to enter the capture mode, and acquire the riding environment image information captured by the camera according to the first preset time interval; S3, determine whether each of the riding environment image information contains a preset red light image, and determine whether the angle between the shooting direction of the camera corresponding to each of the riding environment image information and the current driving direction of the electric bicycle on the horizontal plane is less than a preset angle; S4, when two of the following conditions are met simultaneously in the riding environment image information: the preset red light image, the shooting direction of the camera, and the current driving direction of the electric bicycle are at an angle less than a preset angle in the horizontal plane, the first preset alarm command is executed. The step S4 is followed by the following step: S51, determine the theoretical calculated distance between the red light image in the cycling environment image information and the camera; S52, when the theoretically calculated distance is less than the first preset distance, execute the second preset alarm command; S53, when the theoretical calculated distance is between the first preset distance and the second preset distance, the current running speed of the electric bicycle is obtained, and the theoretical calculated time required for the electric bicycle to move to the position of the red light image in the riding environment image information is determined according to the theoretical calculated distance and the current running speed. S54, determine whether the theoretical calculation time is less than the time set value; wherein, the second preset distance is greater than the first preset distance; S55, when the theoretical calculation time is less than the time set value, execute the third preset alarm command; S56, when the theoretical calculation time is greater than or equal to the time setting value or the theoretical calculation distance is greater than the second preset distance, maintain the current state of the electric bicycle; Step S51 specifically includes the following steps: S511, determine the area of ​​the red light image region in the cycling environment image information; S512, according to formula The theoretical distance between the red light image in the cycling environment image information and the camera is determined; where S1 is the area of ​​the red light image region when the distance between the camera and the red light is d1, and S2 is the area of ​​the red light image region when the distance between the camera and the red light image is d2.

2. The method for detecting and processing red-light violations based on electric bicycle helmets according to claim 1, characterized in that, The step S3 is followed by the following step: If any two of the following conditions are not met in the riding environment image information: the preset red light image, the shooting direction of the camera, and the angle between the current driving direction of the electric bicycle and the horizontal plane is less than a preset angle, the current state of the electric bicycle shall be maintained.

3. The method for detecting and processing red-light violations based on electric bicycle helmets according to claim 1, characterized in that, The step S4 is followed by the following step: S41, determine whether the cycling environment image information contains a preset image of a vehicle running a red light; S42, when the cycling environment image information contains a preset image of a vehicle running a red light, the cycling environment image information is sent to the target object; S43, if the cycling environment image information does not contain a preset image of a vehicle running a red light, return to step S1.

4. The method for detecting and processing red-light violations based on electric bicycle helmets according to claim 1, characterized in that, The step S3 is followed by the following step: S31, when two of the following conditions are met simultaneously: the riding environment image information does not contain a preset red light image and the angle between the camera's shooting direction and the current driving direction of the electric bicycle in the horizontal plane is less than a preset angle, it is determined whether there is a target obstacle in the riding environment image information. S32, when the target obstacle is present in the cycling environment image information, the first preset time interval is adjusted to a second preset time interval; wherein the second preset time interval is less than the first preset time interval.

5. The method for detecting and processing red-light violations based on electric bicycle helmets according to claim 4, characterized in that, The step S32 is followed by the following step: S33, from the first time point after the second preset time period after the first detection of the target obstacle ends, determine whether the corresponding cycling environment image information has the target obstacle; S34, when the cycling environment image information does not contain the target obstacle, the second preset time interval is adjusted to the first preset time interval; S35, when the cycling environment image information contains the target obstacle, the camera continues to capture images according to the second preset time interval.

6. The method for detecting and processing red-light violations based on electric bicycle helmets according to claim 1, characterized in that, The step S4 is followed by the following step: S401, Obtain the historical location data of the electric bicycle within a third preset time period before the current moment and determine the vector motion path of the electric bicycle based on the historical location dataset; S402, based on the current position of the electric bicycle and the vector motion path, determine whether there is a target shared vehicle in an area behind the electric bicycle and within a preset range from the current position; wherein, the target shared vehicle is communicatively connected to the electric bicycle; S403, when there is a target shared vehicle behind the electric bicycle and within a preset range from the current location, a preset reminder instruction is sent to the target shared vehicle.

7. An electric bicycle, characterized in that, The system includes a vehicle body, a helmet, and a control system installed within the vehicle body. The helmet is equipped with a control module communicatively connected to the control system. A camera for acquiring images of the riding environment is mounted on the front of the helmet and is connected to the control module. The control system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for detecting and processing red-light violations based on a motorcycle helmet as described in any one of claims 1 to 6.

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