阅读说明：本技术 滑板车骑行辅助系统控制方法与辅助系统 (Scooter riding auxiliary system control method and auxiliary system ) 是由 唐伟 侯健 于 2020-06-23 设计创作，主要内容包括：本申请涉及一种滑板车骑行辅助系统控制方法与辅助设备。其中,所述滑板车骑行辅助系统控制方法,通过实时监控骑行者是否出现违规操作,并在骑行者出现违规操作时进行报警,使得骑行者在驾驶电动滑板车过程中,一旦出现违规操作,可以及时给骑行者预警,使得骑行者及时知晓自身不合理或不合法的违规操作。此外,当骑行者出现多个类型的违规操作时,可以依据不同类型违规操作的权重值,优先对权重值大的违规操作进行报警,使得报警可以有序进行,从而使得危险系数高的违规操作优先被报警。(The application relates to a scooter riding auxiliary system control method and auxiliary equipment. The scooter riding auxiliary system control method is characterized in that whether illegal operation occurs to a rider is monitored in real time, and an alarm is given when the illegal operation occurs to the rider, so that the rider can give early warning to the rider in time in case of the illegal operation in the process of driving the electric scooter, and the rider can know the unreasonable or illegal operation in time. In addition, when the rider has multiple types of illegal operations, the illegal operations with large weight values can be preferentially alarmed according to the weight values of the different types of illegal operations, so that the alarming can be orderly carried out, and the illegal operations with high danger coefficients are preferentially alarmed.)

1. A scooter riding auxiliary system control method is applied to an electric scooter and is characterized by comprising the following steps:

s100, acquiring the running state of the electric scooter;

s200, judging whether the rider is in a riding state or not according to the running state of the electric scooter;

s300, if the rider is in a riding state, monitoring whether the rider has illegal operation in real time;

s400, when the single type of illegal operation occurs to the rider, alarming the illegal operation; when the rider has multiple types of illegal operations, the illegal operation with a large weight value is preferentially alarmed according to the weight values of the different types of illegal operations.

2. The scooter ride assist system control method of claim 1, wherein prior to step S100, the method further comprises:

s010, acquiring a head image of the rider;

s020, judging whether the rider wears a helmet or not according to the head image;

s030, if the rider wears the helmet, the following step S100 is performed.

3. The scooter riding assist system control method as claimed in claim 2, wherein the step S300 includes:

s311, acquiring millimeter wave data sent by a millimeter wave radar in real time, and judging whether obstacles appear around the electric scooter according to the millimeter wave data;

s312, if obstacles appear around the electric scooter, calculating the linear distance between the obstacles and the electric scooter further according to the millimeter wave data;

s313, acquiring running data of the electric scooter, and judging whether the electric scooter collides with an obstacle or not according to the millimeter wave data, the linear distance between the obstacle and the electric scooter and the running data; the driving data comprises one or more of speed, acceleration and driving track of the electric scooter;

and S314, if the electric scooter collides with the obstacle, judging that the rider has illegal operation, and defining the illegal operation as impact illegal operation.

4. The scooter riding assist system control method as claimed in claim 3, wherein the step S300 comprises:

s321, acquiring a road condition image in front of the electric scooter;

s323, identifying the road condition image, and judging whether traffic lights appear in the road condition image;

s325, if a traffic light appears in the road condition image, further judging whether the traffic light is displayed as a red light or a green light currently according to the road condition image;

s327, if the traffic light is displayed as a red light currently, further acquiring the acceleration of the electric scooter, and judging whether the acceleration of the electric scooter is less than 0;

s329, if the acceleration of the electric scooter is greater than 0 or equal to 0, determining that the illegal operation of the rider occurs, and defining the illegal operation as the illegal operation of running a red light.

5. The scooter ride assist system control method of claim 4, wherein the step S325 comprises:

s325a, inputting the road condition image into the trained deep learning model;

s325b, extracting traffic light characteristic information in the road condition image, and comparing the traffic light characteristic information with the traffic light sample characteristic information in the trained deep learning model one by one to judge whether traffic lights appear in the road condition image.

6. The scooter riding assist system control method as claimed in claim 5, wherein the step S300 comprises:

s331, acquiring a road condition image in front of the electric scooter;

s332, recognizing the road condition image, and judging whether motor vehicle lane recognition information appears in the road condition image; the motor vehicle lane identification information includes one or more of a curb marker, a pavement marker, and a marking line;

s333, if the road condition image has the motor vehicle lane identification information, judging whether the electric scooter runs on a non-motor vehicle lane currently according to the motor vehicle lane identification information;

and S334, if the electric scooter is currently running on a non-motor vehicle lane, determining that the illegal operation occurs to the rider, and defining the illegal operation as the road illegal operation.

7. The scooter riding assist system control method as claimed in claim 6, wherein the step S400 comprises:

s410, judging whether the rider has multiple types of illegal operations;

s420, if the rider has a single type of illegal operation, alarming the illegal operation;

s430, if the rider has multiple types of illegal operations, respectively obtaining a weight value corresponding to each illegal operation to obtain multiple weight values;

s440, sequencing the weighted values, and giving an alarm for illegal operations of different types in sequence according to the sequence of the weighted values from large to small.

8. The scooter riding assistance system control method of claim 7, wherein the type of violation comprises an impact violation, a red light violation, and a road violation; the weight value of the impact violation operation is larger than that of the red light violation operation, and the weight value of the red light violation operation is larger than that of the road violation operation.

9. The scooter riding assisting system control method according to claim 8, wherein the warning of the illegal operation comprises sending a first warning instruction to a sound prompting device, and the first warning instruction is used for controlling the sound prompting device to send a warning sound.

The alarming for the illegal operation further comprises sending a second alarming instruction to the vibration device, wherein the second alarming instruction is used for driving the vibration device to vibrate.

10. The utility model provides a scooter auxiliary system that rides which characterized in that includes:

the electric scooter comprises a scooter body frame, a handlebar, a pedal mechanism, a motor and a motor controller;

the scooter riding auxiliary equipment comprises an auxiliary equipment controller and a monitoring device, wherein the auxiliary equipment controller is electrically connected with the motor controller and the monitoring device respectively; the auxiliary device controller is used for acquiring monitoring data sent by the monitoring device and executing the control method of the scooter riding auxiliary system as claimed in any one of claims 1-9.

Technical Field

The application relates to the technical field of scooters, in particular to a scooter riding auxiliary system control method and an auxiliary system.

Background

With the vigorous development of travel tools, the use frequency of the electric scooter is higher and higher in daily life of residents, and the user population is gradually expanded from young people to children and the old. However, many safety issues arise with it as well as issues relating to legal regulations. During the driving process of the electric scooter by more and more users, due to the lack of related safety awareness and the lack of understanding of legal regulations, the users do not know that the users have illegal operations during the driving process, for example, some road sections do not allow the electric scooter to go on the road.

Traditional electric scooter does not have device or system of reminding the user to operate in violation of rules and regulations, leads to producing the problem that the user can't foresee the operation in violation of regulations that self produced at the driving in-process, causes the potential safety hazard.

Disclosure of Invention

Therefore, it is necessary to provide a scooter riding auxiliary system control method and an auxiliary system for solving the problem that a user cannot predict illegal operation generated in the driving process because a conventional electric scooter is not provided with a device or system for reminding the user of illegal operation.

The application provides a scooter auxiliary system control method that rides is applied to electric scooter, include:

acquiring the running state of the electric scooter;

judging whether the rider is in a riding state or not according to the running state of the electric scooter;

if the rider is in a riding state, monitoring whether the rider has illegal operation in real time;

when the single type of illegal operation occurs to the rider, alarming the illegal operation; when the rider has multiple types of illegal operations, the illegal operation with a large weight value is preferentially alarmed according to the weight values of the different types of illegal operations.

The application still provides a scooter auxiliary system that rides, includes:

the electric scooter comprises a scooter body frame, a handlebar, a pedal mechanism, a motor and a motor controller;

the scooter riding auxiliary equipment comprises an auxiliary equipment controller and a monitoring device, wherein the auxiliary equipment controller is electrically connected with the motor controller and the monitoring device respectively; the auxiliary device controller is configured to obtain monitoring data sent by the monitoring device, and execute the scooter riding auxiliary system control method mentioned in the foregoing.

The application relates to a scooter riding auxiliary system control method and auxiliary equipment, the scooter riding auxiliary equipment is through real time monitoring ride passerby and whether appear the violation operation to report to the police when riding passerby and appearing the violation operation, make and ride passerby in the drive electric scooter in-process, in case appear the violation operation, can in time give and ride passerby's early warning, make and ride passerby in time know self unreasonable or illegal violation operation. In addition, when riding passerby and appearing a plurality of types of violation operation, the scooter ride auxiliary assembly can be according to the weight value of different grade type violation operation, preferentially reports to the police to the violation operation that the weight value is big for the warning can go on in order, thereby makes the violation operation that danger coefficient is high preferentially by the warning.

Drawings

Fig. 1 is a schematic flow chart illustrating a control method of a scooter riding assist system according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a scooter riding assistance system provided in an embodiment of the present application.

Reference numerals:

10-electric scooter; 110-body frame 110; 120-a handlebar; 130-a pedal mechanism; 140-a motor; 150-a motor controller; 20-scooter riding auxiliary equipment; 210-an auxiliary device controller; 220-monitoring device

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The application provides a scooter riding auxiliary system control method.

It should be noted that the scooter riding auxiliary system control method provided by the present application is applied to an electric scooter, but is not limited to the type of electric scooter applied thereto. The electric scooter 10 includes a body frame 110, a handlebar 120, a pedal mechanism 130, a motor 140, and a motor controller 150. The motor 140 drives the electric scooter 10 to run. The motor controller 150 is used for controlling the motor 140 to drive the electric scooter 10 to run. The scooter rides auxiliary equipment 20, including auxiliary equipment controller 210 and monitoring device 220. The auxiliary device controller 210 is electrically connected to the motor controller 150. The monitoring device 220 is disposed on the electric scooter 10, and the monitoring device 220 is electrically connected to the auxiliary device controller 210. The auxiliary device controller 210 is configured to obtain monitoring data sent by the monitoring device 220, so as to execute the scooter riding auxiliary system control method.

The main execution body of the scooter riding auxiliary system control method can be scooter riding auxiliary equipment 20. Specifically, the main executing body of the scooter riding auxiliary system control method may be an auxiliary device controller 210 in the scooter riding auxiliary device 20. The auxiliary device controller 210 may be one or more processors having data processing capabilities.

As shown in fig. 1, in an embodiment of the present application, the method for controlling a scooter riding assist system includes the following steps S100 to S400:

s100, acquiring the running state of the electric scooter 10.

Specifically, the auxiliary device controller 210 can obtain the running state of the electric scooter 10 from the motor 140 in the electric scooter 10 in real time.

And S200, judging whether the rider is in a riding state or not according to the running state of the electric scooter 10.

Specifically, the auxiliary device controller 210 may determine whether the rider is in the riding state according to the operating state of the motor 140.

And S300, if the rider is in a riding state, monitoring whether the rider has illegal operation in real time.

Specifically, if the rider is in the non-riding state, it indicates that the electric scooter 10 is in the non-riding state (e.g., parked state), and it is not necessary to monitor whether the rider has an illegal operation, and the step S100 is returned to.

S400, when the single type of illegal operation occurs to the rider, alarming the illegal operation. When the rider has multiple types of illegal operations, the illegal operation with a large weight value is preferentially alarmed according to the weight values of the different types of illegal operations.

Specifically, the scooter riding auxiliary system can classify illegal operations in advance, such as impact illegal operations, red light violation operations and road illegal operations. The scooter riding auxiliary system can also endow each type of violation operation with a weighted value in advance. In this step, when the rider has multiple types of illegal operations at the same time, the auxiliary device controller 210 may preferentially alarm the illegal operation with a large weight value according to the weight values of the different types of illegal operations.

In this embodiment, whether the passerby appears the violation operation through real time monitoring to report to the police when the passerby appears the violation operation, make the passerby in the driving electric scooter 10 in-process of riding, in case the violation operation appears, can in time give the passerby early warning, make the passerby in time know self unreasonable or illegal violation operation. In addition, when the rider has multiple types of illegal operations, the illegal operations with large weight values can be preferentially alarmed according to the weight values of the different types of illegal operations, so that the alarming can be orderly carried out, and the illegal operations with high danger coefficients are preferentially alarmed.

In an embodiment of the application, before the step S100, the scooter riding assist system control method further includes the following steps S010 to S030:

and S010, acquiring the head image of the rider.

In particular, the monitoring device 220 may include an instrument panel. The dashboard may be disposed on the handlebar 120. The dashboard may include one or more first cameras. Before the step of acquiring the running state of the electric scooter 10, the auxiliary device controller 210 may control the first camera to capture an image of the rider's head. The first camera sends the head image of the rider to the auxiliary device controller 210.

And S020, judging whether the rider wears the helmet or not according to the head image.

Specifically, the auxiliary device controller 210 may perform feature recognition on the head image. The auxiliary device controller 210 may be provided with a deep learning model. The deep learning model may be trained in advance using a plurality of helmet-worn pose images. When the auxiliary device controller 210 performs feature recognition on the head image, the auxiliary device controller 210 may input the head image to a trained deep learning model to realize judgment of whether the rider wears a helmet.

S030, if the rider wears the helmet, the following step of acquiring the operation state of the electric scooter 10, that is, step S100, is performed.

Specifically, if the rider has worn the helmet, which indicates that the rider passes the helmet detection, the subsequent step of acquiring the operating state of the electric scooter 10 may be performed.

Optionally, if the rider is not wearing a helmet, it is an indication that the rider has not passed helmet detection. Further, the driver can be judged to have the abnormal helmet wearing operation and give an alarm. The alarm mode can send out alarm sound through a sound prompt device arranged on the instrument panel, so that the alarm is realized.

In this embodiment, whether through whether appear the violation operation before the passerby is ridden in the control, carry out the preliminary detection to the helmet wearing state of riding passerby, can improve the factor of safety of riding passerby and riding.

In an embodiment of the present application, the step S300 includes the following steps S311 to S314:

and S311, acquiring millimeter wave data sent by the millimeter wave radar in real time. Further, it is determined whether an obstacle is present around the electric scooter 10 according to the millimeter wave data.

Specifically, the millimeter wave radar is a component in the monitoring apparatus 220. The millimeter wave radar can be a plurality of, lay around electric scooter 10's automobile body. The millimeter wave radar may preset a scanning range. The scanning range may be an area covered by a circle formed by taking 10 meters as a radius and taking the physical center of the electric scooter 10 as a center of the circle. The millimeter wave radar may transmit a radio wave (radar wave) to any obstacle within a preset scanning range, then receive an echo, measure position data of the obstacle according to a time difference between time nodes of receiving and transmitting the radio wave, generate millimeter wave data, and transmit the millimeter wave data to the auxiliary device controller 210.

The auxiliary device controller 210 may obtain millimeter wave data sent by the millimeter wave radar in real time. Further, the auxiliary device controller 210 may determine whether an obstacle is present around the electric scooter 10 according to the millimeter wave data.

S312, if an obstacle appears around the electric scooter 10, calculating a linear distance between the obstacle and the electric scooter 10 according to the millimeter wave data.

Of course, the auxiliary device controller 210 may also acquire a map of the current driving road section based on the GPS device, and locate the obstacle in the form of three-dimensional coordinates or two-dimensional coordinates based on the map and millimeter wave data.

S313, obtain the driving data of the electric scooter 10, and determine whether the electric scooter 10 collides with the obstacle according to the millimeter wave data, the linear distance between the obstacle and the electric scooter 10, and the driving data. The driving data includes one or more of a speed, an acceleration, and a driving trajectory of the electric scooter 10.

Specifically, the vehicle body frame 110 may further be provided with a second camera. The auxiliary device controller 210 may further acquire the shape of the obstacle photographed by the second camera, so as to comprehensively analyze and judge whether the electric scooter 10 collides with the obstacle.

S314, if the electric scooter 10 collides with an obstacle, determining that the rider has an illegal operation. Further, the violation is defined as an offending violation.

Specifically, if the electric scooter 10 does not collide with an obstacle, it is determined that the rider does not have an illegal operation, and the process returns to the step S311 to continue monitoring.

In this embodiment, through the millimeter wave data of acquireing in real time according to the millimeter wave radar, with electric scooter 10 self's the data of traveling, can realize the impact nature control to scooter and barrier to make when striking analysis appears, in time report to the police, the suggestion is ridden passerby and is slowed down or parkked, prevents electric scooter 10 takes place with the phenomenon of barrier striking.

In an embodiment of the present application, the step S300 includes the following steps S321 to S329:

s321, acquiring an image of the road condition in front of the electric scooter 10.

In particular, as already mentioned above, the body frame 110 may also be provided with a second camera. The second camera is different from the first camera in that the first camera shoots head images of the rider, and the second camera shoots road condition images in front of the rider. The second camera may send the road condition image shot in real time to the auxiliary device controller 210.

And S323, identifying the road condition image and judging whether traffic lights appear in the road condition image.

And S325, if a traffic light appears in the road condition image, further judging whether the traffic light is displayed as a red light or a green light currently according to the road condition image.

Specifically, the traffic light in this embodiment only includes the red light display state and the green light display state, which is not described herein again.

S327, if the traffic light is currently displayed as a red light, further acquiring an acceleration of the electric scooter 10, and determining whether the acceleration of the electric scooter 10 is less than 0.

Specifically, if the traffic light is currently displayed as a red light, it indicates that the electric scooter 10 is at a risk edge of running the red light, and the acceleration of the electric scooter 10 needs to be further determined according to the acceleration of the electric scooter 10 to determine whether the rider is aware of the red light.

If the traffic light is currently displayed as a green light, the acceleration of the electric scooter 10 can be further acquired, and whether the acceleration of the electric scooter 10 is equal to 0 is judged. If the acceleration of the electric scooter 10 is equal to 0, feedback can be given to the rider. Specifically, the specific feedback manner may be: the driver is prompted to turn green in front by driving the vibration device provided on the handlebar 120 to vibrate. The vibration device may be a linear motor. This is to prevent the electric scooter 10 from standing still when the red light appears in front of the rider, and prevent the rider from missing the green light.

S329, if the acceleration of the electric scooter 10 is greater than 0 or equal to 0, determining that the rider has an illegal operation, and defining the illegal operation as a red light violation operation.

Specifically, if the acceleration of the electric scooter 10 is greater than 0 or equal to 0, it is determined that the rider has an illegal operation. This indicates that the rider is not aware of the red light and does not take any deceleration action, which requires warning of the rider.

In this embodiment, through the road conditions image that acquires in real time according to the second camera, can realize discerning the traffic lights that appear in the road conditions of electric scooter 10 the place ahead, judge whether be the red light. Further, when the red light appears, whether the acceleration according to electric scooter 10 is greater than or equal to 0 can judge whether the operation of violating the regulations appears on the road, for when the red light appears in the front, in time report to the police, the suggestion is ridden the road and is slowed down or parkked, prevents electric scooter 10 makes a dash across the phenomenon of red light and takes place.

In an embodiment of the present application, the step S325 includes the following steps S325a and correction 325 b:

and S325a, inputting the road condition image into the trained deep learning model.

Specifically, the auxiliary device controller 210 may be provided therein with the deep learning model. The deep learning module can be trained by utilizing a plurality of traffic light sample images in advance. Each traffic light sample image has traffic light sample characteristic information. The traffic light sample characteristic information represents whether traffic lights appear in the traffic light sample image.

S325b, extracting traffic light characteristic information in the road condition image, and comparing the traffic light characteristic information with the traffic light sample characteristic information in the trained deep learning model one by one to judge whether traffic lights appear in the road condition image.

In the embodiment, the traffic lights in the road condition images are identified through the trained deep learning model, the accuracy is high, and the identification speed is high.

In an embodiment of the present application, the step S300 includes the following steps S331 to S334:

s331, acquiring an image of a road condition in front of the electric scooter 10.

Specifically, this step is similar to step S321 in the foregoing embodiment, and is not repeated here.

S332, recognizing the road condition image, and judging whether the motor vehicle lane recognition information appears in the road condition image. The vehicle lane identification information includes one or more of a curb marker, a pavement marker, and a marking line.

Specifically, of course, the vehicle lane identification information may include not only the above-described informational objects that can implement lane marking.

S333, if the traffic lane identification information appears in the traffic image, determining whether the electric scooter 10 is currently driving on a non-motor vehicle lane according to the traffic lane identification information.

Specifically, the scooter self-walking auxiliary system can establish a motor vehicle lane identification information base in advance. And matching the motor vehicle lane identification information with sample identification information in a motor vehicle lane identification information base, and judging whether the electric scooter 10 runs on a non-motor vehicle lane currently.

S334, if the electric scooter 10 is currently running on a non-motorized lane, it is determined that the rider has an illegal operation, and the illegal operation is defined as a road illegal operation.

Specifically, if the electric scooter 10 is currently running on a non-motor vehicle lane, it is determined that the rider has an illegal operation, so that a subsequent alarm is given to prompt the rider to change the lane to the motor vehicle lane.

In this embodiment, the judgment of whether the electric scooter 10 is running on the non-motor vehicle lane can be realized by automatically identifying the motor vehicle lane identification information in the road condition image, so that an alarm can be given in time when the electric scooter 10 is running on the non-motor vehicle lane.

In an embodiment of the present application, the step S400 includes the following steps S410 to S440:

and S410, judging whether the rider has multiple types of illegal operations.

Specifically, as can be seen from the above, the violation operations may occur individually or simultaneously.

And S420, if the single type of illegal operation occurs to the rider, alarming the illegal operation.

Specifically, if the rider has a single type of violation operation, for example, only a road violation operation, the rider may directly warn of the road violation operation.

And S430, if the rider has multiple types of illegal operations, respectively obtaining a weight value corresponding to each illegal operation to obtain multiple weight values.

Specifically, for example, when a red light violation operation and a road violation operation occur simultaneously, weight values corresponding to the two violation operations are acquired respectively. For example, the weight value of the violation operation of running red light is 8, and the weight value of the violation operation of road is 5.

S440, sequencing the weighted values, and giving an alarm for illegal operations of different types in sequence according to the sequence of the weighted values from large to small.

Specifically, in the example in the step S430, if the weight value of the red light violation operation is 8 and the weight value of the road violation operation is 5, the red light violation operation is firstly alarmed, and then the road violation operation is alarmed.

In this embodiment, when the passerby appears a plurality of types of violation operations, scooter ride auxiliary assembly 20 can be according to the weight value of different types of violation operations, preferentially report to the police to the violation operation that the weight value is big for the warning can go on in order, thereby makes the violation operation that danger coefficient is high preferentially alarmed.

In an embodiment of the present application, the types of violation operations include an impact violation operation, a red light violation operation, and a road violation operation. The weight value of the impact violation operation is greater than the weight value of the red light violation operation. The weight value of the violation operation of running the red light is larger than that of the violation operation of the road.

Specifically, the weight values may be assigned in order from high to low of the risk coefficient, that is, the high and low of the risk coefficient are positively correlated with the magnitude of the weight value. The higher the risk factor, the higher the weight value for the violation. In this embodiment, the weight value of the impact violation is greater than the weight value of the red light violation. The weight value of the violation operation of running the red light is larger than that of the violation operation of the road.

In this embodiment, the weight values of the three types of violations, namely, the impact violations, the red light violations and the road violations, are numerically defined, so that the scooter riding auxiliary equipment 20 can give an alarm for the violations with large weight values according to the weight values of the violations with different types, and the alarm can be performed in order, so that the violations with high risk coefficients are preferentially given an alarm.

In an embodiment of the application, the alarming for the illegal operation includes sending a first alarm instruction to a voice prompt device, where the first alarm instruction is used to control the voice prompt device to send an alarm sound.

Specifically, the sound prompt device may be a buzzer.

In this embodiment, the sound alarm can be realized by the sound prompt device.

In an embodiment of the application, the alarming for the illegal operation includes sending a second alarm instruction to a vibration device, where the second alarm instruction is used to drive the vibration device to vibrate.

In particular, the vibration device may be a linear motor.

In this embodiment, the somatosensory alarm can be realized through the vibrating device.

The application also provides a scooter auxiliary system that rides.

As shown in fig. 2, in an embodiment of the present application, the scooter ride assist system includes an electric scooter 10 and a scooter ride assist device 20. The electric scooter 10 includes a body frame 110, a handlebar 120, a pedal mechanism 130, a motor 140, and a motor controller 150. The scooter rides auxiliary equipment 20, including auxiliary equipment controller 210 and monitoring device 220. The auxiliary device controller 210 is electrically connected to the motor controller 150. The auxiliary device controller 210 is also electrically connected to the monitoring apparatus 220.

The auxiliary device controller 210 is configured to obtain the monitoring data sent by the monitoring device 220, and execute the scooter riding auxiliary system control method as mentioned above.

Specifically, the monitoring device 220 may include a dashboard, a second camera, and a millimeter wave radar. The instrument panel may be disposed on the handlebar 120. A first camera, a vibrating device and a sound prompting device can be arranged in the instrument panel. The second camera may be provided to the vehicle body frame 110. The millimeter wave radar may be disposed around the vehicle body (including the vehicle body frame 110, the handlebar 120, and the pedal mechanism 130). The respective uses and functions of these components have been set forth in the foregoing description.

The scooter riding auxiliary system has the same beneficial effects as the scooter riding auxiliary system control method mentioned in the foregoing, and the details are not repeated herein.

The technical features of the embodiments described above may be arbitrarily combined, the order of execution of the method steps is not limited, and for simplicity of description, all possible combinations of the technical features in the embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the combinations of the technical features should be considered as the scope of the present description.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.