An automatic steering turn signal control system and method

Abstract

This invention relates to the field of voice recognition technology. More specifically, it provides an automatic turn signal control system and method. The automatic turn signal control system significantly improves driving safety and efficiency through precise advance prediction and timely response mechanisms. Utilizing advanced sensor technology and artificial intelligence algorithms, the system can identify the driver's intentions in advance. Whether turning, changing lanes, overtaking, or parking, it can predict the driver's behavior and automatically activate the corresponding turn signal in advance, improving the notification effect to surrounding road users and effectively preventing traffic accidents caused by improper use of turn signals. Simultaneously, the system can respond in real time to changes in traffic conditions and road environment, optimizing the use of turn signals to ensure timely and accurate communication of the driver's intentions in various driving situations, thereby improving overall traffic flow and road safety.

Description

Technical Field

[0001] This invention relates to the field of voice recognition technology, and more specifically, to a turn signal control system and method with automatic steering capability. Background Technology

[0002] Currently, drivers primarily rely on using turn signals while driving. However, in emergency situations, drivers may not be able to use their turn signals in time before turning. Furthermore, some experienced drivers often fail to use their turn signals when turning or changing lanes, lacking awareness of the importance of using them. This can lead to drivers behind lacking relevant turning information about the vehicle ahead and being unable to anticipate its movements, potentially resulting in traffic accidents.

[0003] Article 57 of the "Regulations for the Implementation of the Road Traffic Safety Law" clearly stipulates the following procedures for using motor vehicle turn signals: 1) When turning left, changing lanes to the left, preparing to overtake, leaving a parking spot, or making a U-turn, the left turn signal should be activated in advance; 2) When turning right, changing lanes to the right, returning to the original lane after overtaking, or parking on the side of the road, the right turn signal should be activated in advance. It is important to note that all turn signal activation methods specified in the "Regulations for the Implementation of the Road Traffic Safety Law" require activation in advance. In other words, many drivers currently use turn signals illegally. To address this, an automatic turn signal control solution has emerged, such as the turn signal control method, device, terminal equipment, and medium described in patent publication number CN202210059379.8. This method acquires the vehicle's steering wheel angle and current vehicle speed; calculates the predicted trajectory direction of the vehicle based on the steering wheel angle and current vehicle speed; determines the rear position of the adjacent vehicle; determines the intersection points between the predicted trajectory direction and the rear position of the preceding vehicle and the lane edge position of the vehicle currently traveling, obtaining corresponding first and second intersection points; when the distance between the first and second intersection points exceeds a preset driving distance, the target turn signal corresponding to the steering wheel angle is activated based on the steering wheel angle. This invention solves the technical problems in the prior art where drivers not using turn signals lead to unpredictable situations for following vehicles, resulting in traffic accidents.

[0004] However, the final implementation of this method still relies on the driver's operation to provide feedback and control the method, ultimately controlling the turn signal. This approach is lagging and does not comply with road traffic laws. Therefore, we disclose a turn signal control system and method that can automatically predict the driver's driving intentions and automatically steer. Summary of the Invention

[0005] The purpose of this invention is to provide an automatic turn signal control system and method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an automatic steering turn signal control system, the control system comprising a user interaction module, a navigation and route planning module, a global intent prediction module, a fixed strategy module, a turn signal control module, a sensor integration and data processing module, a real-time intent prediction module, a real-time strategy module, a situation definition module, a feedback module, and a learning optimization module;

[0007] The user interaction module is used to communicate with the driver, obtain destination information, and feed the interaction information back to the navigation and route planning module.

[0008] The navigation and route planning module is used to integrate real-time traffic data based on the destination information provided by the driver in order to optimize route planning, obtain and plan the optimal route, and select the navigation mode.

[0009] The global intent prediction module is used to predict the driver's fixed intent based on the optimal route and feed the fixed intent back to the fixed strategy module.

[0010] The fixed strategy module is used to select a fixed control strategy according to the fixed intention and feed it back to the turn signal control module;

[0011] The sensor integration and data processing module is used to acquire vehicle and road information on the optimal route in real time according to the optimal route, and to feed back the vehicle and road information to the real-time intent prediction module.

[0012] The real-time intent prediction module is used to predict the driver's real-time intent based on the vehicle and road information, and then feed it back to the real-time strategy module.

[0013] The instant strategy module is used to select an instant control strategy based on the predicted instant intention and feed it back to the turn signal control module.

[0014] A further technical solution of this application: the navigation mode is divided into a pilot mode and a route-finding mode, wherein the pilot mode is used to provide navigation for the driver and obtain the optimal route;

[0015] The pathfinding mode is used to obtain the optimal route.

[0016] A further technical solution of this application: the scenario definition module is used to generate driving scenarios and allocate the driving scenarios to the global intent prediction module and the real-time intent prediction module respectively;

[0017] The driving scenarios include turning, changing lanes, overtaking, returning to the original lane after overtaking, leaving the parking area, making a U-turn, and parking on the side of the road.

[0018] A further technical solution of this application: the fixed control strategy includes turning on the turn signal when turning left or right, turning on the left turn signal when leaving the parking location, turning on the left turn signal when making a U-turn, and turning on the right turn signal when parking on the side of the road;

[0019] The real-time control strategy includes turning on the turn signal when changing lanes to the left or right, turning on the left turn signal when preparing to overtake, and turning on the right turn signal when returning to the original lane after overtaking.

[0020] A further technical solution of this application: the vehicle and road information includes vehicle speed, vehicle position, vehicle direction and heading, vehicle acceleration and deceleration, vehicle steering angle, lane lines, traffic signs and signals, traffic flow and vehicle density, pedestrians and non-motorized vehicles, and weather conditions.

[0021] This application also provides a control method for an automatic steering turn signal control system, the control method comprising the following steps:

[0022] S1. Start the vehicle to activate the automatic steering turn signal control system and activate the user interaction module to communicate with the driver and obtain destination information;

[0023] S2. Based on the destination information provided by the driver, integrate real-time traffic data, optimize route planning, obtain and plan the optimal route, select navigation mode or route finding mode, and feed back the optimal route information to the global intent prediction module and sensor integration and data processing module.

[0024] S3. Global Intent Prediction: Predicts the driver's fixed intent based on the optimal route and feeds the fixed intent back to the fixed strategy module.

[0025] S4. Select the appropriate fixed control strategy according to the fixed intention, and feed the fixed control strategy back to the turn signal control module;

[0026] S5. While the vehicle is in motion, the sensor integration and data processing module is activated to acquire vehicle and road information in real time and feed the vehicle and road information back to the instant intent prediction module.

[0027] S6. Predict the driver's real-time intention based on vehicle and road information, and feed the real-time intention back to the real-time strategy module.

[0028] S7. Select the appropriate real-time control strategy based on the predicted real-time intention, and feed the real-time control strategy back to the turn signal control module.

[0029] S8. Combining fixed control strategy and real-time control strategy, control the opening and closing of turn signals, and automatically turn on the corresponding turn signals when needed.

[0030] S9. The usage scenario definition module generates driving scenarios and assigns them to the global intent prediction module and the real-time intent prediction module respectively.

[0031] S10. Collect system performance data and driver feedback through the feedback module, analyze the data, optimize the prediction algorithm and control strategy, and update system parameters; continuously monitor vehicle and road information, as well as system performance, and dynamically adjust the control strategy based on real-time data and feedback to ensure that the system always complies with traffic regulations and safety standards.

[0032] A further technical solution of this application: Step S3 also includes the following step:

[0033] S3.1 Receive route information. The global intent prediction module waits for the navigation and route planning module to provide the optimal route information and receives route information including route details, estimated travel time, and traffic conditions.

[0034] S3.2 Analyze route information by comparing the received route information with the built-in map data to identify key nodes on the route and analyze the key nodes on the route.

[0035] S3.3 Identify fixed intent points. Based on traffic rules and driving habits, identify fixed intent points on the route that require the use of turn signals, and determine the specific type of each fixed intent point.

[0036] S3.4 Determine fixed intent. For each identified fixed intent point, determine the driver's possible intent. When approaching a turning point, predict whether the driver will turn left or right.

[0037] S3.5 Calculate the intended time. Based on the vehicle's current speed, road conditions, and distance from the fixed intended point, estimate the estimated time to reach each fixed intended point and determine when the turn signal needs to be activated to comply with traffic rules.

[0038] S3.6 Generate fixed intent instructions, generating corresponding control instructions for each predicted fixed intent;

[0039] S3.7 Send fixed intent information: Send the predicted fixed intent information and corresponding control instructions to the fixed policy module, ensuring that the information includes intent type, expected time and control instructions.

[0040] S3.8 Monitoring, updating, and anomaly handling: Continuously monitor vehicle location and route information to update fixed intent predictions when routes change. If the route changes, re-identify and predict fixed intent points. If the system detects an anomaly, trigger the anomaly handling process, which includes requesting driver confirmation, replanning the route, or temporarily disabling the automatic turn signal function.

[0041] A further technical solution of this application: Step S5 also includes the following step:

[0042] S5.1 Start the sensor integration and data processing module, activate and test all relevant sensors to collect vehicle speed, position, direction information and road information in real time;

[0043] S5.2 Data Preprocessing and Environmental Perception: The collected raw data is preprocessed by denoising, filtering and format conversion, and lane lines, traffic signs, traffic lights and other road users are identified through image recognition and sensor data analysis technology.

[0044] S5.3 Traffic flow and weather condition analysis: Analyze traffic flow and vehicle density, predict possible traffic changes, and monitor weather conditions;

[0045] S5.4 Data Fusion and Information Report Generation: This feature integrates data from different sensors to generate comprehensive vehicle and road environment information reports, providing accurate data support for real-time intent prediction.

[0046] S5.5 Send information to the real-time intent prediction module and monitor the sensor status. Send information reports to the real-time intent prediction module and continuously monitor the sensor's working status to ensure data continuity and accuracy, and perform data updates and anomaly handling.

[0047] A further technical solution of this application: Step S6 also includes the following step:

[0048] S6.1 Real-time data analysis and pattern recognition, the instant intent prediction module first receives and analyzes real-time vehicle information and real-time road information from the sensor integration and data processing module, and uses pattern recognition technology to identify the current traffic conditions and driving behavior patterns.

[0049] S6.2 Behavior prediction and probability assessment: Combine traffic rules, historical data and real-time data analysis to predict the driver's possible immediate behavior, taking into account driver habits, traffic conditions and road conditions.

[0050] S6.3 Generate instant intention predictions. Based on behavior predictions and probability assessments, determine the instant intentions that the driver is most likely to take.

[0051] S6.4 Real-time Intent Communication and Strategy Formulation: The predicted real-time intents are sent to the real-time strategy module, and specific control strategies are formulated based on these intents.

[0052] S6.5 Monitoring, updating predictions and system optimization: Continuously monitor changes in vehicle and road information, update real-time intent predictions, adjust the prediction model and re-predict if the predicted behavior does not match the actual behavior or anomalies are detected, and record the prediction results and actual behavior for subsequent system optimization and learning.

[0053] Compared with the prior art, the technical solution provided by this invention has the following advantages:

[0054] The automatic turn signal control system of this invention significantly improves driving safety and efficiency through a precise advance prediction and timely response mechanism. Utilizing advanced sensor technology and artificial intelligence algorithms, the system can identify the driver's intentions in advance. Whether turning, changing lanes, overtaking, or parking, it can predict the driver's behavior and automatically activate the corresponding turn signal in advance. This predictive mechanism not only reduces the driver's workload but also improves the alerting effect to surrounding road users, effectively preventing traffic accidents caused by improper use of turn signals. Simultaneously, the system can respond in real time to changes in traffic conditions and road environment, optimizing the use of turn signals to ensure timely and accurate communication of the driver's intentions in various driving scenarios, thereby improving overall traffic flow and road safety. Furthermore, the system's intelligent learning and optimization functions enable it to continuously adapt to driver habits and improve prediction accuracy, providing users with a more intelligent, safe, and convenient driving experience. Attached Figure Description

[0055] Figure 1 This is a system block diagram of the present invention;

[0056] Figure 2 This is a flowchart of the process of the present invention. Detailed Implementation

[0057] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention will be further described below with reference to the embodiments.

[0058] Please see Figure 1 and Figure 2In the embodiments of this application, an automatic steering turn signal control system is provided. The control system includes a user interaction module, a navigation and route planning module, a global intent prediction module, a fixed strategy module, a turn signal control module, a sensor integration and data processing module, an instant intent prediction module, an instant strategy module, a situation definition module, a feedback module, and a learning optimization module.

[0059] The user interaction module is used to communicate with the driver, obtain destination information, and feed the interaction information back to the navigation and route planning module.

[0060] The navigation and route planning module is used to integrate real-time traffic data based on the destination information provided by the driver in order to optimize route planning, obtain and plan the optimal route, and select the navigation mode.

[0061] The global intent prediction module is used to predict the driver's fixed intent based on the optimal route and feed the fixed intent back to the fixed strategy module.

[0062] The fixed strategy module is used to select a fixed control strategy according to the fixed intention and feed it back to the turn signal control module;

[0063] The sensor integration and data processing module is used to acquire vehicle and road information on the optimal route in real time according to the optimal route, and to feed back the vehicle and road information to the real-time intent prediction module.

[0064] The real-time intent prediction module is used to predict the driver's real-time intent based on the vehicle and road information, and then feed it back to the real-time strategy module.

[0065] The instant strategy module is used to select an instant control strategy based on the predicted instant intention and feed it back to the turn signal control module.

[0066] Specifically, the present invention relates to a series of highly integrated modules that work together to achieve automatic control of turn signals. After the system is started, the user interaction module communicates with the driver via voice or interface to obtain destination information and transmits it to the navigation and route planning module. This module uses real-time traffic data to optimize the route and determine the optimal path, while selecting the corresponding navigation mode. Then, the global intent prediction module analyzes the route, predicts fixed intents such as turning or U-turns, and notifies the fixed strategy module. The fixed strategy module selects a preset control strategy based on these intents and guides the turn signal control module to execute it.

[0067] Meanwhile, the sensor integration and data processing module collects vehicle and road information in real time, such as speed, position and lane lines, and provides this data to the real-time intention prediction module. This module analyzes the data, predicts the driver's real-time intention, such as changing lanes or overtaking, and transmits the information to the real-time strategy module. The real-time strategy module then selects the corresponding real-time control strategy and sends it to the turn signal control module to automatically adjust the turn signals.

[0068] The scenario definition module is responsible for generating specific driving scenarios and assigning these scenarios to the prediction module so that the system can respond to specific situations. Finally, the feedback module collects system performance data and driver feedback, while the learning optimization module uses this data to optimize the prediction algorithm and control strategy, ensuring that the system continuously improves and adapts to different driving habits and traffic conditions. In this way, the system achieves automated control of turn signals, improving driving safety and convenience.

[0069] Furthermore, the navigation mode is divided into a pilot mode and a route-finding mode. The pilot mode is used to provide navigation for the driver and obtain the optimal route.

[0070] The pathfinding mode is used to obtain the optimal route.

[0071] Furthermore, the scenario definition module is used to generate driving scenarios and assign the driving scenarios to the global intent prediction module and the real-time intent prediction module respectively;

[0072] The driving scenarios include turning, changing lanes, overtaking, returning to the original lane after overtaking, leaving the parking area, making a U-turn, and parking on the side of the road.

[0073] Furthermore, the fixed control strategy includes turning on the turn signal when turning left or right, turning on the left turn signal when leaving the parking location, turning on the left turn signal when making a U-turn, and turning on the right turn signal when parking on the side of the road.

[0074] The real-time control strategy includes turning on the turn signal when changing lanes to the left or right, turning on the left turn signal when preparing to overtake, and turning on the right turn signal when returning to the original lane after overtaking.

[0075] Furthermore, the vehicle and road information includes vehicle speed, vehicle position, vehicle direction and heading, vehicle acceleration and deceleration, vehicle steering angle, lane markings, traffic signs and signals, traffic flow and vehicle density, pedestrians and non-motorized vehicles, and weather conditions.

[0076] Specifically, in this embodiment, after the system starts, the user interaction module first communicates with the driver through the in-vehicle interface or voice recognition system to obtain destination information. This process may employ natural language processing technology to understand and process the driver's voice commands. After obtaining the destination information, the navigation and route planning module immediately begins operation, utilizing GPS technology and real-time traffic data to calculate and plan the optimal route using algorithms such as Dijkstra's algorithm or A* algorithm. This module also determines whether to use a pilot mode or a route-finding mode; the pilot mode provides detailed navigation instructions, while the route-finding mode only provides route guidance.

[0077] Next, the global intent prediction module analyzes the optimal route, identifies key nodes along the route such as turning points and intersections, and predicts the driver's fixed intents, such as turning or making a U-turn. This module may employ machine learning algorithms, such as decision trees or random forests, to identify patterns and predict intents based on historical driving data.

[0078] The fixed strategy module selects a preset control strategy based on the predicted fixed intent, such as activating the corresponding turn signal in advance when turning, and sends a command to the turn signal control module for execution. These control strategies may be based on the knowledge base of an expert system, which includes traffic rules and best practices.

[0079] The sensor integration and data processing module collects real-time information such as vehicle speed, position, and direction, as well as road information such as lane markings, traffic signs, and traffic flow. This module may integrate multiple sensors, such as radar, LiDAR, cameras, and accelerometers, and uses data fusion technology to combine the data from these sensors to provide comprehensive environmental perception.

[0080] The real-time intent prediction module analyzes real-time vehicle and road information to predict the driver's real-time intent, such as lane changing or overtaking, and feeds it back to the real-time strategy module. This module may employ deep learning techniques, particularly convolutional neural networks (CNNs) to process visual data and recurrent neural networks (RNNs) to process time-series data.

[0081] The real-time policy module selects the appropriate control strategy based on the predicted real-time intent, such as activating the corresponding turn signal when changing lanes, and sends a command to the turn signal control module for execution. These control strategies may be based on reinforcement learning algorithms, learning the optimal behavior strategy through interaction with the environment.

[0082] The scenario definition module generates specific driving scenarios, such as turning, lane changing, and overtaking, and assigns these scenarios to the global intent prediction module and the real-time intent prediction module so that the system can respond to specific situations. This module may use a rule engine to define and process different driving scenarios.

[0083] The turn signal control module combines fixed and real-time control strategies to automatically control the activation and deactivation of the turn signals, thus notifying other road users of the driver's intentions in advance. This module may employ electronic control unit (ECU) technology to directly control the vehicle's hardware.

[0084] Finally, the system collects performance data and driver feedback through the feedback module, and the learning and optimization module analyzes this data to optimize the prediction algorithm and control strategy, thereby improving system performance. This process may involve big data analytics and machine learning techniques to continuously improve the system's accuracy and responsiveness.

[0085] In this implementation method, the system can predict the driver's intentions in advance and automatically control the turn signals, reducing the driver's workload, improving driving safety, and ensuring that the use of turn signals complies with traffic rules, thus enhancing the driving experience.

[0086] This application provides a control method for an automatic steering turn signal control system, the control method comprising the following steps:

[0087] S1. Start the vehicle to activate the automatic steering turn signal control system and activate the user interaction module to communicate with the driver and obtain destination information;

[0088] S2. Based on the destination information provided by the driver, integrate real-time traffic data, optimize route planning, obtain and plan the optimal route, select navigation mode or route finding mode, and feed back the optimal route information to the global intent prediction module and sensor integration and data processing module.

[0089] S3. Global Intent Prediction: Predicts the driver's fixed intent based on the optimal route and feeds the fixed intent back to the fixed strategy module.

[0090] S4. Select the appropriate fixed control strategy according to the fixed intention, and feed the fixed control strategy back to the turn signal control module;

[0091] S5. While the vehicle is in motion, the sensor integration and data processing module is activated to acquire vehicle and road information in real time and feed the vehicle and road information back to the instant intent prediction module.

[0092] S6. Predict the driver's real-time intention based on vehicle and road information, and feed the real-time intention back to the real-time strategy module.

[0093] S7. Select the appropriate real-time control strategy based on the predicted real-time intention, and feed the real-time control strategy back to the turn signal control module.

[0094] S8. Combining fixed control strategy and real-time control strategy, control the opening and closing of turn signals, and automatically turn on the corresponding turn signals when needed.

[0095] S9. The usage scenario definition module generates driving scenarios and assigns them to the global intent prediction module and the real-time intent prediction module respectively.

[0096] S10. Collect system performance data and driver feedback through the feedback module, analyze the data, optimize the prediction algorithm and control strategy, and update system parameters; continuously monitor vehicle and road information, as well as system performance, and dynamically adjust the control strategy based on real-time data and feedback to ensure that the system always complies with traffic regulations and safety standards.

[0097] Specifically, after the system starts up, it first activates the user interaction module to interact with the driver through the in-vehicle interface or voice recognition technology to obtain destination information. This step utilizes natural language processing technology to understand and process the driver's instructions.

[0098] Subsequently, the navigation and route planning module optimizes route planning and determines the optimal route based on the destination information provided by the driver and real-time traffic data, using path search algorithms such as the A* algorithm. The system selects either a navigation mode or a route-finding mode based on the route's complexity and the driver's preferences, and feeds back the optimal route information to the global intent prediction module and the sensor integration and data processing module.

[0099] The global intent prediction module analyzes the optimal route, identifies key nodes along the route such as turning points and intersections, and predicts the driver's fixed intents, such as turning or making a U-turn. This module may employ machine learning algorithms, such as decision trees or random forests, to identify patterns and predict intents based on historical driving data.

[0100] The fixed strategy module selects a preset control strategy based on the predicted fixed intent, such as activating the corresponding turn signal in advance when turning, and sends a command to the turn signal control module for execution. These control strategies may be based on the knowledge base of an expert system, which includes traffic rules and best practices.

[0101] During vehicle operation, the sensor integration and data processing module collects real-time information such as vehicle speed, position, and direction, as well as road information such as lane markings, traffic signs, and traffic flow. This module may integrate multiple sensors, such as radar, LiDAR, cameras, and accelerometers, and uses data fusion technology to combine the data from these sensors to provide comprehensive environmental perception.

[0102] The real-time intent prediction module analyzes real-time vehicle and road information to predict the driver's real-time intent, such as lane changing or overtaking, and feeds it back to the real-time strategy module. This module may employ deep learning techniques, particularly convolutional neural networks (CNNs) to process visual data and recurrent neural networks (RNNs) to process time-series data.

[0103] The real-time policy module selects the appropriate control strategy based on the predicted real-time intent, such as activating the corresponding turn signal when changing lanes, and sends a command to the turn signal control module for execution. These control strategies may be based on reinforcement learning algorithms, learning the optimal behavior strategy through interaction with the environment.

[0104] The scenario definition module generates specific driving scenarios, such as turning, lane changing, and overtaking, and assigns these scenarios to the global intent prediction module and the real-time intent prediction module so that the system can respond to specific situations. This module may use a rule engine to define and process different driving scenarios.

[0105] The turn signal control module combines fixed and real-time control strategies to automatically control the activation and deactivation of the turn signals, thus notifying other road users of the driver's intentions in advance. This module may employ electronic control unit (ECU) technology to directly control the vehicle's hardware.

[0106] Finally, the system collects performance data and driver feedback through the feedback module, and the learning and optimization module analyzes this data to optimize the prediction algorithm and control strategy, thereby improving system performance. This process may involve big data analytics and machine learning techniques to continuously improve the system's accuracy and responsiveness. In this way, the system achieves automated control of turn signals, improving driving safety and convenience while ensuring that turn signal usage complies with traffic rules, thus enhancing the driving experience.

[0107] Furthermore, step S3 also includes the following steps:

[0108] S3.1 Receive route information. The global intent prediction module waits for the navigation and route planning module to provide the optimal route information and receives route information including route details, estimated travel time, and traffic conditions.

[0109] S3.2 Analyze route information by comparing the received route information with the built-in map data to identify key nodes on the route and analyze the key nodes on the route.

[0110] S3.3 Identify fixed intent points. Based on traffic rules and driving habits, identify fixed intent points on the route that require the use of turn signals, and determine the specific type of each fixed intent point.

[0111] S3.4 Determine fixed intent. For each identified fixed intent point, determine the driver's possible intent. When approaching a turning point, predict whether the driver will turn left or right.

[0112] S3.5 Calculate the intended time. Based on the vehicle's current speed, road conditions, and distance from the fixed intended point, estimate the estimated time to reach each fixed intended point and determine when the turn signal needs to be activated to comply with traffic rules.

[0113] S3.6 Generate fixed intent instructions, generating corresponding control instructions for each predicted fixed intent;

[0114] S3.7 Send fixed intent information: Send the predicted fixed intent information and corresponding control instructions to the fixed policy module, ensuring that the information includes intent type, expected time and control instructions.

[0115] S3.8 Monitoring, updating, and anomaly handling: Continuously monitor vehicle location and route information to update fixed intent predictions when routes change. If the route changes, re-identify and predict fixed intent points. If the system detects an anomaly, trigger the anomaly handling process, which includes requesting driver confirmation, replanning the route, or temporarily disabling the automatic turn signal function.

[0116] Specifically, in this embodiment, the specific implementation method is as follows:

[0117] The global intent prediction module first waits for the navigation and route planning module to provide optimal route information, including route details, estimated travel time, and traffic conditions. This information is transmitted to the global intent prediction module in real time using the vehicle's communication system. Upon receiving the information, the module compares this data with built-in high-definition map data to accurately identify key nodes on the route, such as intersections, turning points, and U-turn areas. This step may involve Geographic Information System (GIS) technology and the vehicle's GPS positioning system to ensure accurate matching.

[0118] Next, the system uses its built-in traffic rules and driving habit databases to identify fixed intention points along the route that require the use of turn signals, and determines the specific type of each fixed intention point. For example, the system will identify whether an upcoming intersection requires a left or right turn and mark it as a fixed intention point. This step may be implemented using an expert system or a rule engine.

[0119] For each identified fixed intent point, the system predicts the driver's likely intent. For example, if the vehicle approaches an intersection marked as a right turn, the system predicts the driver will make a right turn. This prediction is based on historical data analysis and driving behavior pattern recognition, and may involve machine learning algorithms to improve prediction accuracy.

[0120] The system further calculates the intent time, estimating the expected time to reach each fixed intent point. This is based on the vehicle's current speed, real-time traffic information, and distance to the fixed intent point. The system determines when the turn signal needs to be activated to comply with traffic rules, such as requiring the turn signal to be activated a certain distance before turning. Based on these calculations, the system generates fixed intent instructions, developing specific control strategies for each predicted fixed intent. For example, the system might generate an instruction: "Activate the right turn signal 100 meters from the next intersection."

[0121] The system then sends the predicted fixed intent information and corresponding control commands to the fixed strategy module, ensuring that the information includes the intent type, expected time, and control commands. This communication process may be achieved through the vehicle's internal Controller Area Network (CAN) bus or other in-vehicle communication protocols.

[0122] Finally, the system continuously monitors vehicle location and route information to update fixed intent predictions when the route changes. If the route changes, the system will re-identify and predict fixed intent points. If the system detects anomalies, such as sensor malfunctions or unclear route information, it triggers an anomaly handling procedure, which may include requesting driver confirmation, replanning the route, or temporarily disabling the automatic turn signal function to ensure driving safety.

[0123] Through this series of detailed steps, the global intent prediction module can accurately predict the driver's fixed intent and generate control commands in a timely manner, thereby realizing automatic control of the turn signals and improving driving safety and efficiency.

[0124] Furthermore, step S5 also includes the following steps:

[0125] S5.1 Start the sensor integration and data processing module, activate and test all relevant sensors to collect vehicle speed, position, direction information and road information in real time;

[0126] S5.2 Data Preprocessing and Environmental Perception: The collected raw data is preprocessed by denoising, filtering and format conversion, and lane lines, traffic signs, traffic lights and other road users are identified through image recognition and sensor data analysis technology.

[0127] S5.3 Traffic flow and weather condition analysis: Analyze traffic flow and vehicle density, predict possible traffic changes, and monitor weather conditions;

[0128] S5.4 Data Fusion and Information Report Generation: This feature integrates data from different sensors to generate comprehensive vehicle and road environment information reports, providing accurate data support for real-time intent prediction.

[0129] S5.5 Send information to the real-time intent prediction module and monitor the sensor status. Send information reports to the real-time intent prediction module and continuously monitor the sensor's working status to ensure data continuity and accuracy. Update data and handle anomalies when necessary.

[0130] Specifically, first, the system activates the sensor integration and data processing module, initiating all relevant sensors, including GPS, speed sensors, direction sensors, radar, LiDAR, and cameras, and performs a self-test to ensure the sensors are functioning correctly. This combination of sensors provides the vehicle with comprehensive perception capabilities, including but not limited to information such as speed, position, direction, and the distance and speed of surrounding objects.

[0131] Subsequently, the system performs data preprocessing and environmental perception. Raw data undergoes preprocessing operations such as noise reduction, filtering, and format conversion to improve accuracy and usability. Image recognition technologies, such as computer vision algorithms, are used to analyze images captured by cameras to identify lane lines, traffic signs, traffic lights, and other road users. Simultaneously, sensor data analysis technologies, such as pattern recognition and object tracking algorithms, are used to process radar and lidar data to identify and track surrounding vehicles and pedestrians.

[0132] Next, the system analyzes traffic flow and vehicle density to predict potential traffic changes. This may involve statistical analysis methods using historical and real-time traffic data, as well as machine learning models to predict traffic flow trends. Simultaneously, the system monitors weather conditions such as rain, snow, and fog; this information can be obtained through integrated weather sensors or external data services and has a significant impact on driving conditions and sensor performance.

[0133] In the data fusion and information report generation step, the system fuses data from different sensors to generate comprehensive vehicle and road environment information reports. Data fusion techniques, such as Kalman filtering or particle filtering, are used to integrate data from different sensors to obtain more accurate and reliable vehicle and road environment information. These information reports provide necessary data support for real-time intent prediction.

[0134] Finally, the system sends information reports to the real-time intent prediction module and continuously monitors the sensor's operational status. This includes real-time monitoring of parameters such as sensor signal strength, battery life, and error range to ensure data continuity and accuracy. When necessary, the system performs data updates and anomaly handling, such as sensor calibration, data re-acquisition, or switching to a backup sensor, to ensure stable system operation under various conditions. Through this series of detailed steps, the sensor integration and data processing module provides the automatic turn signal control system with real-time, accurate, and comprehensive vehicle and road information, providing a solid data foundation for real-time intent prediction and control decisions.

[0135] Furthermore, step S6 also includes the following steps:

[0136] S6.1 Real-time data analysis and pattern recognition, the instant intent prediction module first receives and analyzes real-time vehicle information and real-time road information from the sensor integration and data processing module, and uses pattern recognition technology to identify the current traffic conditions and driving behavior patterns.

[0137] S6.2 Behavior prediction and probability assessment: Combine traffic rules, historical data and real-time data analysis to predict the driver's possible immediate behavior, taking into account driver habits, traffic conditions and road conditions.

[0138] S6.3 Generate instant intention predictions. Based on behavior predictions and probability assessments, determine the instant intentions that the driver is most likely to take.

[0139] S6.4 Real-time Intent Communication and Strategy Formulation: The predicted real-time intents are sent to the real-time strategy module, and specific control strategies are formulated based on these intents.

[0140] S6.5 Monitoring, updating predictions and system optimization: Continuously monitor changes in vehicle and road information, update real-time intent predictions, adjust the prediction model and re-predict if the predicted behavior does not match the actual behavior or anomalies are detected, and record the prediction results and actual behavior for subsequent system optimization and learning.

[0141] Specifically, the real-time intent prediction module first receives real-time vehicle and road information from the sensor integration and data processing module. This information includes the vehicle's speed, acceleration, steering angle, lane position, and surrounding traffic conditions, such as the position and speed of other vehicles, pedestrian activity, and traffic signal status. Utilizing advanced pattern recognition technologies, such as machine learning and deep learning algorithms, the module analyzes this data to identify current traffic conditions and driving behavior patterns, such as smooth driving, acceleration, deceleration, and lane-changing tendencies.

[0142] Next, the module combines traffic rules, historical driving data, and real-time data analysis to predict the driver's likely immediate behavior. This step involves learning the driver's habits, possibly by analyzing past behavioral patterns, while also considering current traffic conditions and road conditions, such as congestion levels and road surface slipperiness. Predicted behaviors may include lane changes, overtaking, and slowing down to avoid collisions.

[0143] Based on behavior prediction and probability assessment, the module determines the driver's most likely immediate intention. This step may employ probabilistic models or decision tree algorithms to evaluate the probability of different behaviors occurring and select the most likely intention as the prediction result.

[0144] The module then sends the predicted real-time intentions to the real-time strategy module and formulates specific control strategies based on these intentions. Control strategies may include automatically turning the turn signals on or off under specific conditions, such as turning them on in advance when the driver is detected to be intending to change lanes.

[0145] Finally, the system continuously monitors changes in vehicle and road information, updating real-time intent predictions. If the predicted behavior does not match the actual behavior or anomalies are detected, the system adjusts the prediction model and re-predicts. This may involve online updates or retraining of the machine learning model. Simultaneously, the system records prediction results and actual behavior for subsequent system optimization and learning, improving prediction accuracy and the effectiveness of control strategies. This step may employ big data analytics and machine learning algorithms to continuously learn from real-world driving data and optimize model parameters.

[0146] Through this series of detailed steps, the real-time intention prediction module can accurately predict the driver's real-time intentions and formulate corresponding control strategies in a timely manner, thereby realizing intelligent control of turn signals and improving driving safety and efficiency.

[0147] This invention significantly improves driving safety and efficiency. Utilizing advanced sensor technology and artificial intelligence algorithms, the system can anticipate driver intentions. Whether turning, changing lanes, overtaking, or parking, it can predict driver behavior and automatically activate the corresponding turn signals in advance. This predictive mechanism not only reduces the driver's workload but also enhances the alerting effect on surrounding road users, effectively preventing traffic accidents caused by improper turn signal use. Simultaneously, the system can respond in real-time to changes in traffic conditions and road environment, optimizing turn signal usage to ensure timely and accurate communication of driver intentions in various driving scenarios, thereby improving overall traffic flow and road safety. Furthermore, the system's intelligent learning and optimization functions enable it to continuously adapt to driver habits and improve predictive accuracy, providing users with a more intelligent, safe, and convenient driving experience.

[0148] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention; the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

[0149] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only independent technical solutions. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A turn signal control system capable of automatic steering, characterized in that, The control system includes a user interaction module, a navigation and route planning module, a global intent prediction module, a fixed strategy module, a turn signal control module, a sensor integration and data processing module, a real-time intent prediction module, a real-time strategy module, a situation definition module, a feedback module, and a learning optimization module. The user interaction module is used to communicate with the driver, obtain destination information, and feed the interaction information back to the navigation and route planning module. The navigation and route planning module is used to integrate real-time traffic data based on the destination information provided by the driver in order to optimize route planning, obtain and plan the optimal route, and select the navigation mode. The global intent prediction module is used to predict the driver's fixed intent based on the optimal route and feed the fixed intent back to the fixed strategy module. The fixed strategy module is used to select a fixed control strategy according to the fixed intention and feed it back to the turn signal control module; The sensor integration and data processing module is used to acquire vehicle and road information on the optimal route in real time according to the optimal route, and to feed back the vehicle and road information to the real-time intent prediction module. The real-time intent prediction module is used to predict the driver's real-time intent based on the vehicle and road information, and then feed it back to the real-time strategy module. The instant strategy module is used to select an instant control strategy based on the predicted instant intention and feed it back to the turn signal control module. The navigation modes are divided into pilot mode and route finding mode. The pilot mode is used to provide navigation for the driver and obtain the optimal route. The pathfinding mode is used to obtain the optimal route; The scenario definition module is used to generate driving scenarios and assign the driving scenarios to the global intent prediction module and the real-time intent prediction module respectively. The driving scenarios include turning, changing lanes, overtaking, returning to the original lane after overtaking, leaving the parking area, making a U-turn, and parking on the side of the road; the fixed control strategy includes turning on the turn signal when turning left or right, turning on the left turn signal when leaving the parking area, turning on the left turn signal when making a U-turn, and turning on the right turn signal when parking on the side of the road. The real-time control strategy includes turning on the turn signal when changing lanes to the left or right, turning on the left turn signal when preparing to overtake, and turning on the right turn signal when returning to the original lane after overtaking. The vehicle and road information includes vehicle speed, vehicle position, vehicle direction and heading, vehicle acceleration and deceleration, vehicle steering angle, lane lines, traffic signs and signals, traffic flow and vehicle density, pedestrians and non-motorized vehicles, and weather conditions.

2. A control method for an automatic steering turn signal control system, characterized in that, The control method applied to the automatic steering turn signal control system of claim 1 includes the following steps: S1. Start the vehicle to activate the automatic steering turn signal control system and activate the user interaction module to communicate with the driver and obtain destination information; S2. Based on the destination information provided by the driver, integrate real-time traffic data, optimize route planning, obtain and plan the optimal route, select navigation mode or route finding mode, and feed back the optimal route information to the global intent prediction module and sensor integration and data processing module. S3. Global Intent Prediction: Predicts the driver's fixed intent based on the optimal route and feeds the fixed intent back to the fixed strategy module. S4. Select the appropriate fixed control strategy according to the fixed intention, and feed the fixed control strategy back to the turn signal control module; S5. While the vehicle is in motion, the sensor integration and data processing module is activated to acquire vehicle and road information in real time and feed the vehicle and road information back to the instant intent prediction module. S6. Predict the driver's real-time intention based on vehicle and road information, and feed the real-time intention back to the real-time strategy module. S7. Select the appropriate real-time control strategy based on the predicted real-time intention, and feed the real-time control strategy back to the turn signal control module. S8. Combining fixed control strategy and real-time control strategy, control the opening and closing of turn signals, and automatically turn on the corresponding turn signals when needed. S9. The usage scenario definition module generates driving scenarios and assigns them to the global intent prediction module and the real-time intent prediction module respectively. S10. Collect system performance data and driver feedback through the feedback module, analyze the data, optimize the prediction algorithm and control strategy, and update system parameters; continuously monitor vehicle and road information, as well as system performance, and dynamically adjust the control strategy based on real-time data and feedback to ensure that the system always complies with traffic regulations and safety standards. Step S3 also includes the following steps: S3.1 Receive route information. The global intent prediction module waits for the navigation and route planning module to provide the optimal route information and receives route information including route details, estimated travel time, and traffic conditions. S3.2 Analyze route information by comparing the received route information with the built-in map data to identify key nodes on the route and analyze the key nodes on the route. S3.3 Identify fixed intent points. Based on traffic rules and driving habits, identify fixed intent points on the route that require the use of turn signals, and determine the specific type of each fixed intent point. S3.4 Determine fixed intent. For each identified fixed intent point, determine the driver's possible intent. When approaching a turning point, predict whether the driver will turn left or right. S3.5 Calculate the intended time. Based on the vehicle's current speed, road conditions, and distance from the fixed intended point, estimate the estimated time to reach each fixed intended point and determine when the turn signal needs to be activated to comply with traffic rules. S3.6 Generate fixed intent instructions, generating corresponding control instructions for each predicted fixed intent; S3.7 Send fixed intent information: Send the predicted fixed intent information and corresponding control instructions to the fixed policy module, ensuring that the information includes intent type, expected time and control instructions; S3.8 Monitoring, updating, and anomaly handling: Continuously monitor vehicle location and route information to update fixed intent predictions when routes change. If the route changes, re-identify and predict fixed intent points. If the system detects an anomaly, trigger the anomaly handling process, which includes requesting driver confirmation, replanning the route, or temporarily disabling the automatic turn signal function.

3. The control method for the automatic steering turn signal control system according to claim 2, characterized in that, Step S5 also includes the following steps: S5.1 Start the sensor integration and data processing module, activate and test all relevant sensors to collect vehicle speed, position, direction information and road information in real time; S5.2 Data Preprocessing and Environmental Perception: The collected raw data is preprocessed by denoising, filtering and format conversion, and lane lines, traffic signs, traffic lights and other road users are identified through image recognition and sensor data analysis technology. S5.3 Traffic flow and weather condition analysis: Analyze traffic flow and vehicle density, predict possible traffic changes, and monitor weather conditions; S5.4 Data Fusion and Information Report Generation: This feature integrates data from different sensors to generate comprehensive vehicle and road environment information reports, providing accurate data support for real-time intent prediction. S5.5 Send information to the real-time intent prediction module and monitor the sensor status. Send information reports to the real-time intent prediction module and continuously monitor the sensor's working status to ensure data continuity and accuracy, and perform data updates and anomaly handling.

4. The control method for the automatic steering turn signal control system according to claim 3, characterized in that, Step S6 also includes the following steps: S6.1 Real-time data analysis and pattern recognition, the instant intent prediction module first receives and analyzes real-time vehicle information and real-time road information from the sensor integration and data processing module, and uses pattern recognition technology to identify the current traffic conditions and driving behavior patterns. S6.2 Behavior prediction and probability assessment: Combine traffic rules, historical data and real-time data analysis to predict the driver's possible immediate behavior, taking into account driver habits, traffic conditions and road conditions. S6.3 Generate instant intention prediction: Based on behavior prediction and probability assessment, determine the instant intention that the driver is most likely to take. S6.4 Real-time Intent Communication and Strategy Formulation: The predicted real-time intents are sent to the real-time strategy module, and specific control strategies are formulated based on these intents. S6.5 Monitoring, updating predictions and system optimization: Continuously monitor changes in vehicle and road information, update real-time intent predictions, adjust the prediction model and re-predict if the predicted behavior does not match the actual behavior or anomalies are detected, and record the prediction results and actual behavior for subsequent system optimization and learning.

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