Automatic steering lamp control system of bicycle

Abstract

The invention discloses an automatic steering lamp control system for a bicycle. The automatic steering lamp control system comprises a sensor module; the data collection module is responsible for collecting exercise data and environment data of a rider in real time; the control algorithm and decision module is responsible for intelligently controlling the steering lamp; the user interaction and setting module is responsible for allowing a rider to set the system according to personal habits or road condition requirements; the execution module is responsible for actually operating the state of the steering lamp; the data recording and intelligent learning module is responsible for recording use habits and behavior data of the rider and continuously optimizing a control strategy through a reinforcement learning algorithm; according to the invention, the road condition and environment adaptation module can monitor road condition and environment information in real time and dynamically adjust the control strategy of the steering lamp according to the information. The system can automatically adapt to different road conditions and riding environments, it is ensured that the steering lamp can play the best role under various conditions, and the steering intention of a rider is effectively conveyed.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle turn signal lamps, and in particular to an automatic turn signal lamp control system for a bicycle. Background Art

[0002] With the popularity of bicycles and the rise of cycling culture, cycling safety has become a focus of attention. Traditional bicycle turn signals require manual operation by riders, which increases the complexity and inconvenience of riding to a certain extent. At the same time, in an emergency, riders may forget to turn on the turn signal due to panic, thus increasing the risk of traffic accidents.

[0003] After searching, the invention patent with Chinese patent number CN114537266A discloses a control method and device for a turn signal, and a computer-readable storage medium. The control method includes the following steps: obtaining the driving data of the vehicle, wherein the driving data includes the direction data of the vehicle and the trajectory information of the road on which it is located; determining the road type of the road according to the trajectory information; judging the turning intention of the vehicle according to the direction data and / or the road type; and activating the turn signal in response to the judgment result that the vehicle has a turning intention. By executing these steps, the control method can judge the turning intention of the vehicle in combination with the direction data of the vehicle and the trajectory information of the road on which it is located, thereby automatically turning on the turn signal in the driving scenario of the vehicle changing lanes, turning and / or turning around, so as to meet the provisions of traffic regulations, and promptly remind other vehicles and pedestrians on the side and rear to avoid, so as to reduce the risk of traffic accidents. Compared with the existing technology, the invention patent with Chinese patent number CN114537266A solves the problem of automatically turning on the turn signal in driving scenarios where the vehicle changes lanes, turns and / or makes a U-turn, thereby meeting the requirements of traffic regulations and promptly reminding other vehicles and pedestrians on the side and rear to avoid and reduce the risk of traffic accidents.

[0004] However, in actual use of the above invention, it is difficult to set the sensitivity and reaction time of the turn signal control according to road conditions. It may be difficult to adapt to the requirements of turn signals for different road conditions and riding environments, causing some riders to feel inconvenienced when using it or difficulty in accurately conveying their turning intentions. It is difficult to ensure that the turn signal can play the best role under different road conditions. Therefore, a bicycle automatic turn signal control system is proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art that it is difficult to set the sensitivity and reaction time of the turn signal control according to road conditions, which may be difficult to adapt to the requirements of turn signals in different road conditions and riding environments, causing some riders to feel inconvenienced when using it or difficulty in accurately conveying turning intentions, and difficulty in ensuring that the turn signals can play the best role under different road conditions. A bicycle automatic turn signal control system is proposed to solve the problems.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] Sensor module; responsible for collecting the rider’s motion data and environmental data in real time;

[0008] Control algorithm and decision-making module: responsible for intelligent control of turn signals;

[0009] User interaction and settings module: responsible for allowing riders to set the system according to personal habits or road conditions;

[0010] Execution module: responsible for actually operating the state of the turn signal;

[0011] Data recording and intelligent learning module: responsible for recording the rider's usage habits and behavior data, and continuously optimizing the control strategy through reinforcement learning algorithms;

[0012] Road condition and environment adaptation module: responsible for real-time monitoring of road conditions and environmental information and adjusting the control strategy of turn signals;

[0013] The sensor module is responsible for collecting the rider's motion data and environmental data through various sensors to provide data support for the system. The sensor module passes the collected data to the control algorithm and decision-making module and the road condition and environment adaptation module. The road condition and environment adaptation module is responsible for real-time monitoring of road conditions and environmental information, such as road type, traffic flow, weather conditions, etc., and adjusts the control strategy of the turn signal according to this information. The road condition and environment adaptation module feeds back the adjustment strategy to the control algorithm and decision-making module. The user interaction and setting module is responsible for providing a friendly user interface, allowing the rider to set the sensitivity and reaction time parameters of the turn signal control according to personal habits or road conditions. It can also display the current settings, status information, etc. The user interaction and setting module passes the rider's setting parameters to the control algorithm and decision-making module. The control algorithm and decision-making module is responsible for receiving The data from the sensor module and the adjustment strategy fed back by the road condition and environment adaptation module are used to intelligently control the turn signal according to the preset algorithm and the rider's settings, judge the turning intention, calculate the time when the turn signal should light up in advance, determine the flashing frequency and brightness of the turn signal, etc. The control algorithm and decision module sends the control instructions to the execution module and the data recording and intelligent learning module. The execution module is responsible for receiving the instructions from the control algorithm and decision module, and actually operates the switch, brightness, reaction time and flashing frequency of the turn signal. The execution module feeds back the current state of the turn signal to the user interaction and setting module. The data recording and intelligent learning module receives the data received in the decision-making process by the control algorithm and decision module, so as to record the riding behavior data, and optimize the decision strategy according to the rider's behavior pattern feedback, and gradually adjust the response time and sensitivity of the turn signal.

[0014] The above technical solution further includes:

[0015] Preferably, the sensor module includes an accelerometer, a gyroscope, a speed sensor, an ambient light sensor and a climate sensor. The accelerometer is responsible for measuring the acceleration and deceleration of the rider, helping to determine whether there is a turn or lane change, and further determining the timing of turning on the turn signal. The gyroscope is responsible for sensing the change in the steering angle of the bicycle, determining the amplitude and direction of the steering action, and providing a basis for enabling the turn signal. The speed sensor is responsible for monitoring the speed of the bicycle. Different speeds may correspond to different turn signal response strategies. The turn signal should respond quickly at high speeds and can be delayed appropriately at low speeds. The ambient light sensor is responsible for monitoring the lighting conditions around the rider, so as to automatically adjust the brightness of the turn signal according to day or night to ensure visibility under different lighting conditions. The climate sensor is responsible for monitoring weather conditions, such as rainy days, wind speed, etc., to help the system adjust the response time and the brightness of the light.

[0016] Preferably, the user interaction and setting module includes a display unit, an input unit, an audio feedback unit, a voice recognition unit, a personalized setting management unit and a real-time status feedback and adjustment unit. The display unit is responsible for providing a graphical interface or digital display to show the setting options, status information, and parameter adjustment results to the rider in real time. The input unit is responsible for allowing the rider to input settings or adjust parameters by touching, pressing keys, or turning knobs. The audio feedback unit is responsible for providing feedback of the current status or operation results of the system to the rider through audio prompts. The voice recognition unit is responsible for allowing the rider to adjust settings or inquire about status information through voice commands. The personalized setting management unit is responsible for saving and managing different setting files or configurations according to the rider's personal habits. The real-time status feedback and adjustment unit is responsible for providing real-time system feedback to enable the rider to understand the current operating status of the turn signal and adjust the settings based on the feedback.

[0017] Preferably, the specific steps of the control algorithm and decision module for intelligently controlling the turn signal include:

[0018] Data collection: The sensor module collects the rider’s motion status and environmental information in real time, including the rider’s steering action (such as tilt angle or direction change), current speed and acceleration, surrounding traffic conditions (for example, other vehicles, pedestrians, etc.), etc.

[0019] Turning intention judgment: The control algorithm judges whether the rider has the intention to turn based on sensor data, such as the rider's arm position, handlebar tilt angle, wheel steering, etc.

[0020] Turn signal activation conditions: Based on the intensity of the turning intention, the turning angle, and the riding speed, the algorithm determines when the turn signal comes on;

[0021] Flashing frequency and brightness adjustment: The control system adjusts the flashing frequency and brightness of the turn signal according to the speed, road conditions and ambient brightness;

[0022] User Personalization: Allows riders to adjust the behavior of turn signals based on their personal preferences;

[0023] Real-time decision making and execution: The control algorithm makes decisions based on real-time data, traffic information, user-defined preferences, and adaptive strategies.

[0024] Preferably, the specific steps of the data recording and intelligent learning module using the reinforcement learning algorithm to optimize the strategy include:

[0025] Environmental modeling: Define the state space (including the rider’s current riding status, road conditions, and weather) and action space (including the adjustment parameters of the turn signal) based on the rider’s behavior and road conditions.

[0026] Select reinforcement algorithm: Select Q-learning algorithm to find the optimal control strategy by assigning a Q value to each state-action pair;

[0027] Reward function design: Design a reward function to give positive or negative rewards based on the rider's behavior and the performance of the turn signal. For example, when the response time and sensitivity of the turn signal are in line with the rider's habits, a positive reward (+1) can be given; if the response is too slow or too fast, a negative reward (-1) can be given; if the system can adapt and adjust correctly when the road conditions change, a higher positive reward can be given;

[0028] Policy learning and updating: The update of the Q-value function follows the following Bellman equation: Among them, Q(S t ,A t ) indicates that in state S t Next take action A t Q value, α is the learning rate, γ is the discount factor, R t is the reward received at time t;

[0029] Data recording and model optimization: Real-time recording of rider behavior data, such as turning frequency, turn signal reaction time, speed, riding environment, etc., as well as feedback, and regular updates of the model.

[0030] Preferably, the road condition and environment adaptation module includes a data receiving unit, a road condition monitoring unit, a strategy adjustment unit and a feedback unit. The data receiving unit is responsible for receiving the rider's motion data and environmental data collected from the sensor module. The road condition monitoring unit is responsible for monitoring traffic information, including monitoring road types (such as urban roads, rural roads, mountain roads, etc.), traffic flow, vehicle speed, pedestrian density and other traffic-related information. The strategy adjustment unit is responsible for dynamically adjusting the control strategy of the turn signal. The feedback unit is responsible for feeding back the adjusted control strategy to the control algorithm and decision module so as to adjust the working state of the turn signal in real time.

[0031] Preferably, the strategy adjustment unit adjusts the control strategy of the turn signal including sensitivity adjustment and reaction time adjustment, and the formula of the sensitivity adjustment is expressed as: Where S(t) is the current sensitivity, S 0 is the default sensitivity, k 1 , k 2 is the sensitivity adjustment coefficient, Δv is the speed change, v max is the maximum speed, Δθ is the angle change, θ max is the maximum turning angle, and the reaction time adjustment formula is expressed as: T 1 (t) = T 0 +k3 traffic(t)+k 4 road(t), where T 1 (t) is the current reaction time, T 0 is the default reaction time, k 3 , k 4 is the reaction time adjustment coefficient, traffic(t) is the current traffic density, and road(t) is the current road condition score.

[0032] Preferably, the speed change is obtained by using GPS to obtain the speed of the rider at consecutive time points t and t-1, and the speed formula is: Wherein, d(t) and d(t-1) are the displacements at t and the previous moment t-1, respectively, Δt is the time interval, the speed change formula is: Δv=v(t)-v(t-1), and the angle change is measured by using a gyroscope in real time to measure the rider's steering angle θ(t), and then calculated by the following formula: Δθ=|θ(t)-θ(t-1)|, where θ(t) and θ(t-1) are the steering angles at the current moment and the previous moment, respectively.

[0033] The present invention has the following beneficial effects:

[0034] 1. In the present invention, the road condition and environment adaptation module can monitor the road condition and environment information in real time, and dynamically adjust the control strategy of the turn signal according to this information. The system can automatically adapt to different road conditions and riding environments, ensuring that the turn signal can play the best role under various conditions and effectively convey the rider's turning intention.

[0035] 2. In the present invention, through the user interaction and setting module, the rider can set the sensitivity and reaction time of the turn signal control according to personal habits or specific road conditions, ensuring that the system can adapt to the preferences of different riders and improving the applicability of the system and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a system architecture diagram of a bicycle automatic steering light control system proposed by the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] like Figure 1As shown, a bicycle automatic steering light control system comprises:

[0039] Sensor module; responsible for collecting the rider’s motion data and environmental data in real time;

[0040] Control algorithm and decision-making module: responsible for intelligent control of turn signals;

[0041] User interaction and settings module: responsible for allowing riders to set the system according to personal habits or road conditions;

[0042] Execution module: responsible for actually operating the state of the turn signal;

[0043] Data recording and intelligent learning module: responsible for recording the rider's usage habits and behavior data, and continuously optimizing the control strategy through reinforcement learning algorithms;

[0044] Road condition and environment adaptation module: responsible for real-time monitoring of road conditions and environmental information and adjusting the control strategy of turn signals;

[0045] The sensor module is responsible for collecting the rider's motion data and environmental data through various sensors. The sensor module passes the collected data to the control algorithm and decision-making module and the road condition and environment adaptation module. The road condition and environment adaptation module is responsible for real-time monitoring of road conditions and environmental information, and adjusts the control strategy of the turn signal according to this information. The road condition and environment adaptation module feeds back the adjustment strategy to the control algorithm and decision-making module. The user interaction and setting module is responsible for providing a friendly user interface, allowing the rider to set the sensitivity and reaction time parameters of the turn signal control according to personal habits or road conditions. The user interaction and setting module passes the rider's setting parameters to the control algorithm and decision-making module. The control algorithm and decision-making module is responsible for receiving The module receives data from the sensor module and the adjustment strategy fed back by the road condition and environment adaptation module, and intelligently controls the turn signal according to the preset algorithm and the rider's settings. The control algorithm and decision-making module sends the control instructions to the execution module and the data recording and intelligent learning module. The execution module is responsible for receiving instructions from the control algorithm and decision-making module, and actually operates the switch, brightness, reaction time and flashing frequency of the turn signal. The execution module feeds back the current status of the turn signal to the user interaction and setting module. The data recording and intelligent learning module receives the data received by the control algorithm and decision-making module during the decision-making process, and optimizes the decision-making strategy based on the rider's behavior pattern feedback, and gradually adjusts the response time and sensitivity of the turn signal.

[0046] In one embodiment, the sensor module includes an accelerometer, a gyroscope, a speed sensor, an ambient light sensor, and a climate sensor. The accelerometer is responsible for measuring the acceleration and deceleration of the rider, the gyroscope is responsible for sensing the change in the steering angle of the bicycle, the speed sensor is responsible for monitoring the speed of the bicycle, the ambient light sensor is responsible for monitoring the lighting conditions around the rider, and the climate sensor is responsible for monitoring the weather conditions.

[0047] In the embodiment of the present invention, the acceleration sensor provides acceleration a in three directions: x (t), a y (t), a z (t), the total acceleration is obtained by combining the acceleration values ​​in the three directions

[0048]

[0049] Where a(t) represents the acceleration per unit time. The gyroscope is used to sense the steering angle change of the bicycle and help understand the steering situation of the rider. The gyroscope provides angular velocity. By integrating the angular velocity data, the angle change can be obtained.

[0050] θ(t)=θ(t-1)+∫ω(t)dt,

[0051] Among them, ω(t) is the angular velocity at the current moment, θ(t) is the angle at the current moment, and θ(t-1) is the angle at the previous moment.

[0052] In one embodiment, the user interaction and setting module includes a display unit, an input unit, an audio feedback unit, a voice recognition unit, a personalized setting management unit, and a real-time status feedback and adjustment unit. The display unit is responsible for providing a graphical interface or digital display to show the setting options, status information, and parameter adjustment results to the rider in real time. The input unit is responsible for allowing the rider to input settings or adjust parameters by touching, pressing keys, or turning knobs. The audio feedback unit is responsible for providing feedback on the current status or operation results of the system to the rider through audio prompts. The voice recognition unit is responsible for allowing the rider to adjust settings or inquire about status information through voice commands. The personalized setting management unit is responsible for saving and managing different setting files or configurations according to the rider's personal habits. The real-time status feedback and adjustment unit is responsible for providing real-time system feedback so that the rider can understand the current operating status of the turn signal and adjust the settings based on the feedback.

[0053] In the embodiment of the present invention, the display unit can clearly see the current state of the turn signal control, including the current sensitivity, reaction time setting, environmental mode, etc., through an intuitive screen display. In addition, the display unit can also display system fault information or remind the rider to adjust the settings. The input unit provides an intuitive interactive method, and the rider can easily select and adjust parameters, such as the sensitivity, reaction time, and lighting mode of the turn signal. The design of the input unit needs to be simple and easy to use, and take into account the safety of riding, so as to avoid the rider from performing complex operations during riding. The audio feedback of the audio feedback unit can remind the rider of the changes or current status of certain settings, such as "settings have been saved", "the current mode is night mode", or "sensitivity has been adjusted to medium". Audio prompts are very important for riders to quickly confirm settings during exercise, so as to avoid missing important information due to visual interference. The voice recognition unit provides a hands-free operation method, and the rider can adjust the sensitivity or response time of the turn signal through voice commands during riding. For example, the rider can say "increase the sensitivity of the turn signal" or "reduce the reaction time". This function is of great significance to improving safety and convenience, especially to avoid distraction during riding. The personalized settings management unit allows riders to have different turn signal settings for urban and mountain riding, and these settings can be saved and switched. When the rider changes to a different riding mode or scene, the appropriate settings can be automatically loaded according to the settings to enhance the riding experience. The feedback and adjustment unit allows riders to see the current reaction status of the turn signal through real-time display and feedback, such as whether it has been activated, whether it has reached the set sensitivity level, and even whether it has automatically adjusted due to environmental changes (such as light changes). The unit can provide instant feedback to help riders understand the effect of adjustments and ensure that their turn signals perform optimally in different environments.

[0054] In one embodiment, the specific steps of the control algorithm and decision module for intelligently controlling the turn signal include:

[0055] Data collection: Collect the rider’s motion status and environmental information in real time from the sensor module;

[0056] Turning intention judgment: The control algorithm determines whether the rider has a turning intention based on the sensor data;

[0057] Turn signal activation conditions: Based on the intensity of the turning intention, the turning angle, and the riding speed, the algorithm determines when the turn signal comes on;

[0058] Flashing frequency and brightness adjustment: The control system adjusts the flashing frequency and brightness of the turn signal according to the speed, road conditions and ambient brightness;

[0059] User Personalization: Allows riders to adjust the behavior of turn signals based on their personal preferences;

[0060] Real-time decision making and execution: The control algorithm makes decisions based on real-time data, traffic information, user-defined preferences, and adaptive strategies.

[0061] In the embodiment of the present invention, the start condition includes: Steering angle threshold: For example, when the rider turns the handlebars beyond a certain angle, the system considers it to be a clear steering action. Time delay: According to the requirements of road conditions and environment, the control module can set the advance lighting time of the turn signal. For example, when riding at high speed, it may be necessary to light up the turn signal longer in advance to warn other vehicles. In the adjustment of flashing frequency and brightness, for example, on busy streets in the city, a higher flashing frequency helps to increase the visibility of the turn signal; while riding at night or on relatively empty roads, the frequency may be slowed down accordingly. At night or in dim light, the turn signal will increase the brightness; in daytime or brighter environments, the brightness will be reduced to ensure clear display under different light conditions. In real-time decision-making and execution, if the rider is detected to make a sharp turn or slow down, the system will immediately adjust the flashing frequency and advance time of the turn signal. In complex environments (such as dense traffic, complex road conditions, etc.), the reaction of the turn signal may be faster and more obvious to ensure that other traffic participants can clearly see the rider's intention.

[0062] In one embodiment, the specific steps of the data recording and intelligent learning module using the reinforcement learning algorithm to optimize the strategy include:

[0063] Environment modeling: define the state space and action space based on the cyclist's behavior and road environment;

[0064] Select reinforcement algorithm: Select Q-learning algorithm to find the optimal control strategy by assigning a Q value to each state-action pair;

[0065] Reward function design: Design a reward function to give positive or negative rewards based on the rider’s behavior and turn signal performance;

[0066] Policy learning and updating: The update of the Q-value function follows the following Bellman equation: Among them, Q(S t ,A t ) indicates that in state S t Next take action A t Q value, α is the learning rate, γ is the discount factor, R t is the reward received at time t;

[0067] Data recording and model optimization: record riders’ behavior data and feedback in real time, and update the model regularly.

[0068] In the embodiment of the present invention, the reward function is designed, for example, when the response time and sensitivity of the turn signal are in line with the rider's habits, a positive reward (+1) can be given; if the response is too slow or too fast, a negative reward (-1) is given; if the system can adapt and adjust correctly when the road conditions change, a higher positive reward is given. Assume that the reward function is defined as:

[0069] R t =ω 1 ·(S time )+ω 2 ·(S speed )+ω 3 ·(S accuracy ),

[0070] Among them, S time is the error of the turn signal response, S speed is the matching degree between the turn signal sensitivity adjustment and the current riding speed, S accuracy The accuracy of the turn signal response ensures that the rider's intention is correctly communicated. 1 ,ω 2 ,ω 3 is the weight coefficient, reflecting the importance of each factor.

[0071] In one embodiment, the road condition and environment adaptation module includes a data receiving unit, a road condition monitoring unit, a strategy adjustment unit and a feedback unit. The data receiving unit is responsible for receiving the rider's motion data and environmental data collected from the sensor module, the road condition monitoring unit is responsible for monitoring traffic information, the strategy adjustment unit is responsible for dynamically adjusting the control strategy of the turn signal, and the feedback unit is responsible for feeding back the adjusted control strategy to the control algorithm and decision module.

[0072] In one embodiment, the strategy adjustment unit adjusts the control strategy of the turn signal including sensitivity adjustment and reaction time adjustment. The sensitivity adjustment formula is expressed as: Where S(t) is the current sensitivity, S 0 is the default sensitivity, k 1 , k 2 is the sensitivity adjustment coefficient, Δv is the speed change, v max is the maximum speed, Δθ is the angle change, θ max is the maximum turning angle, and the reaction time adjustment formula is expressed as: T 1 (t) = T 0 +k 3 traffic(t)+k 4 road(t), where T 1 (t) is the current reaction time, T 0 is the default reaction time, k 3, k 4 is the reaction time adjustment coefficient, traffic(t) is the current traffic density, and road(t) is the current road condition score.

[0073] In one embodiment, the speed change is obtained by using GPS to obtain the speed of the rider at consecutive time points t and t-1. The speed formula is: Wherein, d(t) and d(t-1) are the displacements at t and the previous moment t-1, respectively, Δt is the time interval, the speed change formula is: Δv=v(t)-v(t-1), and the angle change is calculated by using a gyroscope to measure the rider's steering angle θ(t) in real time, and then using the following formula: Δθ=|θ(t)-θ(t-1)|, where θ(t) and θ(t-1) are the steering angles at the current moment and the previous moment, respectively.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A bicycle automatic turn signal control system, characterized in that: include: Sensor module; Responsible for collecting riders’ motion data and environmental data in real time; Control algorithm and decision-making module: responsible for intelligent control of turn signals; User interaction and settings module: responsible for allowing riders to set the system according to personal habits or road conditions; Execution module: responsible for actually operating the state of the turn signal; Data recording and intelligent learning module: responsible for recording the rider's usage habits and behavior data, and continuously optimizing the control strategy through reinforcement learning algorithms; Road condition and environment adaptation module: responsible for real-time monitoring of road conditions and environmental information and adjusting the control strategy of turn signals; The sensor module is responsible for collecting the rider's motion data and environmental data through various sensors. The sensor module transmits the collected data to the control algorithm and decision-making module and the road condition and environment adaptation module. The road condition and environment adaptation module is responsible for real-time monitoring of road conditions and environmental information, and adjusting the control strategy of the turn signal according to this information. The road condition and environment adaptation module feeds back the adjustment strategy to the control algorithm and decision-making module. The user interaction and setting module is responsible for providing a friendly user interface, allowing the rider to set the sensitivity and reaction time parameters of the turn signal control according to personal habits or road conditions. The user interaction and setting module transmits the rider's setting parameters to the control algorithm and decision-making module. It is responsible for receiving the data from the sensor module and the adjustment strategy fed back by the road condition and environment adaptation module, and intelligently controls the turn signal according to the preset algorithm and the rider's settings. The control algorithm and decision module sends the control instructions to the execution module and the data recording and intelligent learning module. The execution module is responsible for receiving the instructions from the control algorithm and decision module, and actually operates the switch, brightness, reaction time and flashing frequency of the turn signal. The execution module feeds back the current state of the turn signal to the user interaction and setting module. The data recording and intelligent learning module receives the data received in the decision-making process by the control algorithm and decision module, and optimizes the decision strategy according to the rider's behavior pattern feedback, and gradually adjusts the response time and sensitivity of the turn signal.

2. The automatic steering light control system for a bicycle according to claim 1, characterized in that: The sensor module includes an acceleration sensor, a gyroscope, a speed sensor, an ambient light sensor and a climate sensor. The acceleration sensor is responsible for measuring the acceleration and deceleration of the rider, the gyroscope is responsible for sensing the change in the steering angle of the bicycle, the speed sensor is responsible for monitoring the bicycle's speed, the ambient light sensor is responsible for monitoring the lighting conditions around the rider, and the climate sensor is responsible for monitoring weather conditions.

3. The bicycle automatic steering light control system according to claim 1, characterized in that: The user interaction and setting module includes a display unit, an input unit, an audio feedback unit, a voice recognition unit, a personalized setting management unit and a real-time status feedback and adjustment unit. The display unit is responsible for providing a graphical interface or digital display to show the setting options, status information, and parameter adjustment results to the rider in real time. The input unit is responsible for allowing the rider to input settings or adjust parameters by touching, pressing keys, or turning knobs. The audio feedback unit is responsible for providing feedback of the current status or operation results of the system to the rider through audio prompts. The voice recognition unit is responsible for allowing the rider to adjust settings or inquire about status information through voice commands. The personalized setting management unit is responsible for saving and managing different setting files or configurations according to the rider's personal habits. The real-time status feedback and adjustment unit is responsible for providing real-time system feedback to enable the rider to understand the current operating status of the turn signal and adjust the settings based on the feedback.

4. The bicycle automatic steering light control system according to claim 1, characterized in that: The specific steps of the control algorithm and decision module for intelligently controlling the turn signal include: Data collection: Collect the rider’s motion status and environmental information in real time from the sensor module; Turning intention judgment: The control algorithm determines whether the rider has a turning intention based on the sensor data; Turn signal activation conditions: Based on the intensity of the turning intention, the turning angle, and the riding speed, the algorithm determines when the turn signal comes on; Flashing frequency and brightness adjustment: The control system adjusts the flashing frequency and brightness of the turn signal according to the speed, road conditions and ambient brightness; User Personalization: Allows riders to adjust the behavior of turn signals based on their personal preferences; Real-time decision making and execution: The control algorithm makes decisions based on real-time data, traffic information, user-defined preferences, and adaptive strategies.

5. The bicycle automatic steering light control system according to claim 1, characterized in that: The specific steps of the data recording and intelligent learning module using the reinforcement learning algorithm to optimize the strategy include: Environment modeling: define the state space and action space based on the cyclist's behavior and road environment; Select reinforcement algorithm: Select Q-learning algorithm to find the optimal control strategy by assigning a Q value to each state-action pair; Reward function design: Design a reward function to give positive or negative rewards based on the rider’s behavior and turn signal performance; Policy learning and updating: The update of the Q-value function follows the following Bellman equation: Among them, Q(S t ,A t ) indicates that in state S t Next take action A t Q value, α is the learning rate, γ is the discount factor, R t is the reward received at time t; Data recording and model optimization: record riders’ behavior data and feedback in real time, and update the model regularly.

6. The bicycle automatic steering light control system according to claim 1, characterized in that: The road condition and environment adaptation module includes a data receiving unit, a road condition monitoring unit, a strategy adjustment unit and a feedback unit. The data receiving unit is responsible for receiving the rider's motion data and environmental data collected by the sensor module, the road condition monitoring unit is responsible for monitoring traffic information, the strategy adjustment unit is responsible for dynamically adjusting the control strategy of the turn signal, and the feedback unit is responsible for feeding back the adjusted control strategy to the control algorithm and decision module.

7. The bicycle automatic steering light control system according to claim 6, characterized in that: The control strategy of the turn signal lamp adjusted by the strategy adjustment unit includes sensitivity adjustment and reaction time adjustment. The formula of the sensitivity adjustment is expressed as: Where S(t) is the current sensitivity, S0 is the default sensitivity, k1 and k2 are sensitivity adjustment coefficients, Δv is the speed change, and v max is the maximum speed, Δθ is the angle change, θ max is the maximum turning angle, and the reaction time adjustment formula is expressed as: T1(t)=T0+k3·traffic(t)+k4·road(t), wherein T1(t) is the current reaction time, T0 is the default reaction time, k3 and k4 are reaction time adjustment coefficients, traffic(t) is the current traffic density, and road(t) is the current road condition score.

8. The bicycle automatic steering light control system according to claim 7, characterized in that: The speed change is obtained by using GPS to obtain the speed of the rider at consecutive time points t and t-1. The speed formula is: Wherein, d(t) and d(t-1) are the displacements at t and the previous moment t-1, respectively, Δt is the time interval, the speed change formula is: Δv=v(t)-v(t-1), and the angle change is measured by using a gyroscope in real time to measure the rider's steering angle θ(t), and then calculated by the following formula: Δθ=|θ(t)-θ(t-1)|, where θ(t) and θ(t-1) are the steering angles at the current moment and the previous moment, respectively.

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