Steering lamp control system and method capable of automatically steering

Abstract

The invention relates to the technical field of voice recognition, in particular to a steering lamp control system and method capable of achieving automatic steering, the automatic steering lamp control system remarkably improves driving safety and efficiency through an accurate advanced prediction and timely response mechanism, the system utilizes an advanced sensor technology and an artificial intelligence algorithm, and the driving safety and efficiency are improved. According to the invention, the intention of a driver can be recognized in advance, the behavior of the driver can be predicted and the corresponding steering lamp can be automatically turned on in advance no matter under the conditions of turning, lane changing, overtaking, side parking and the like, the prompting effect on surrounding traffic participants is improved, traffic accidents caused by improper use of the steering lamp are effectively prevented, and meanwhile, the driving safety is improved. The system can respond to changes of traffic conditions and road environments in real time, use of the steering lamps is optimized, it is ensured that intentions of drivers can be timely and accurately conveyed under various driving situations, and therefore the overall traffic smoothness and road safety are improved.

Description

Technical Field

[0001] The invention relates to the technical field of speech recognition, and more specifically to a turn signal light control system and method capable of automatic turning. Background Art

[0002] At present, when a vehicle is driving, it is mainly up to the driver to actively turn on the turn signal. However, when an emergency occurs, the driver may not be able to turn on the turn signal in time and start turning. And for some experienced drivers, they often do not turn on the turn signal when turning or changing lanes, and lack the awareness of turning on the turn signal. This will result in: the driver of the following vehicle may lack the steering information related to the front vehicle and cannot predict the driving situation of the front vehicle, which will lead to traffic accidents and other problems.

[0003] According to Article 57 of the Regulations for the Implementation of the Road Traffic Safety Law, the use of motor vehicle turn signals is clearly stipulated: 1) When turning left, changing lanes to the left, preparing to overtake, leaving a parking place or making a U-turn, the left turn signal should be turned on in advance; 2) When turning right, changing lanes to the right, returning to the original lane after overtaking, or parking by the roadside, the right turn signal should be turned on in advance. It should be noted that the turn signal activation methods stipulated in the Regulations for the Implementation of the Road Traffic Safety Law all need to be turned on in advance. That is to say, at this stage, many drivers' turn signal activation methods are illegal. For this reason, a solution for automatically controlling turn signals has appeared on the market, such as the turn signal control method, device, terminal equipment and medium with patent publication number CN202210059379.8. The method obtains the steering wheel rotation angle and current vehicle speed of the vehicle; calculates the predicted track direction of the vehicle according to the steering wheel rotation angle and the current vehicle speed; determines the position of the rear of the preceding vehicle adjacent to the vehicle; determines the intersection between the predicted track direction and the position of the rear of the preceding vehicle and the lane edge position where the vehicle is currently traveling, and obtains the corresponding first intersection and second intersection; when the distance between the first intersection and the second intersection exceeds the preset driving distance, the target turn signal corresponding to the steering wheel rotation angle is controlled to be turned on according to the steering wheel rotation angle. The present invention can solve the technical problems in the prior art such as the inability to predict the following vehicle and the occurrence of traffic accidents due to the driver not turning on the turn signal.

[0004] However, the ultimate implementation of this method is to feedback and control the method through the driver's operation, and ultimately achieve control of the turn signal. This method is lagging and does not comply with the provisions of the Road Traffic Law. For this reason, we disclose a turn signal control system and method that can automatically predict the driver's driving intention and automatically turn. Summary of the invention

[0005] The object of the present invention is to provide a turn signal control system and method capable of automatic steering, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: a turn signal control system capable of automatic steering, the control system comprising a user interaction module, a navigation and route planning module, a global intention prediction module, a fixed strategy module, a turn signal control module, a sensor integration and data processing module, an instant intention prediction module, an instant 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 back the interaction information 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 to optimize route planning, obtain and plan the best route, and select the navigation mode; The global intention prediction module is used to predict the driver's fixed intention according to the optimal route, and feed the fixed intention 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 obtain vehicle and road information on the optimal route in real time according to the optimal route, and feed the vehicle and road information back to the immediate intention prediction module; The immediate intention prediction module is used to predict the driver's immediate intention based on the vehicle and road information, and feed it back to the immediate strategy module; The real-time strategy module is used to select an immediate control strategy according to the predicted immediate intention and feed it back to the turn signal control module.

[0007] A further technical solution of the present application is: the navigation mode is divided into a pilot mode and a path-finding mode, and the pilot mode is used to provide navigation for the driver and obtain the optimal route; The path-finding mode is used to obtain the optimal route.

[0008] A further technical solution of the present application is: the situation definition module is used to generate driving situations, and allocate the driving situations to the global intention prediction module and the immediate intention prediction module respectively; The driving situations include turning, changing lanes, overtaking, returning to the original lane after overtaking, leaving a parking space, making a U-turn and pulling over.

[0009] A further technical solution of the present 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 place, turning on the left turn signal when turning around, and turning on the right turn signal when parking by the roadside; The instant 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.

[0010] A further technical solution of the present application is: the vehicle and road information includes the vehicle's speed, the vehicle's position, the vehicle's direction and heading, the vehicle's acceleration and deceleration, the vehicle's steering angle, lane lines, traffic signs and signals, traffic flow and vehicle density, pedestrians and non-motor vehicles, and weather conditions.

[0011] The present application also provides a control method for a turn signal control system capable of automatic steering, the control method comprising the following steps: S1, starting the vehicle to activate the turn signal control system capable of automatic steering, and activating the user interaction module to communicate with the driver to 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 the navigation mode or path-finding mode, and feed back the optimal route information to the global intention prediction module and the sensor integration and data processing module; S3, global intention prediction predicts the driver's fixed intention based on the optimal route, and feeds the fixed intention back to the fixed strategy module; S4, selecting a corresponding fixed control strategy according to the fixed intention, and feeding back the fixed control strategy to the turn signal control module; S5. When the vehicle is driving, the sensor integration and data processing module is started to obtain vehicle and road information in real time, and the vehicle and road information is fed back to the immediate intention prediction module; S6, predicting the driver's immediate intention based on vehicle and road information, and feeding back the immediate intention to the immediate strategy module; S7, selecting a corresponding immediate control strategy according to the predicted immediate intention, and feeding back the immediate control strategy to the turn signal control module; S8. Combining the fixed control strategy and the real-time control strategy, controlling the turning lights on and off, and automatically turning on the corresponding turning lights when needed; S9, using the scenario definition module to generate driving scenarios, and assigning the driving scenarios to the global intention prediction module and the immediate intention 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 the 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.

[0012] A further technical solution of the present application: the step S3 further includes the following steps: S3.1, receiving route information, the global intention 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, analyzing route information, comparing the received route information with built-in map data to identify key nodes of the route, and analyzing key nodes on the route; S3.3, identify fixed intention points, based on traffic rules and driving habits, identify fixed intention points on the route where turn signals need to be used, and determine the specific type of each fixed intention point; S3.4, determine the fixed intention, for each identified fixed intention point, determine the driver's possible intention, and when approaching the turning point, predict whether the driver will turn left or right; S3.5. Calculate the intention time, estimate the expected time to reach each fixed intention point based on the vehicle's current speed, road conditions and distance to the fixed intention point, and determine when the turn signal needs to be turned on to comply with traffic regulations; S3.6, generating fixed intention instructions, generating corresponding control instructions for each predicted fixed intention; S3.7, sending fixed intention information, sending the predicted fixed intention information and corresponding control instructions to the fixed strategy module, ensuring that the information includes the intention type, estimated time and control instructions; S3.8, monitoring and updating and exception handling, continuously monitor the vehicle position and route information to update the fixed intention prediction when the route changes. If the route changes, re-identify and predict the fixed intention point; if the system detects an abnormal situation, trigger the exception handling process, which includes requesting driver confirmation, re-planning the route or temporarily turning off the automatic turn signal function.

[0013] A further technical solution of the present application: the step S5 further includes the following steps: S5.1. Start the sensor integration and data processing module, activate and test all relevant sensors to collect the vehicle's speed, position, direction information and road information in real time; S5.2, data preprocessing and environmental perception, performing denoising, filtering and format conversion preprocessing operations on the collected raw data, and identifying lane lines, traffic signs, signal lights and other road users 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, fuses data from different sensors to generate comprehensive vehicle and road environment information reports, providing accurate data support for real-time intention prediction; S5.5. Send information to the immediate intention prediction module and monitor the sensor status. Send information reports to the immediate intention prediction module and continuously monitor the working status of the sensor to ensure the continuity and accuracy of the data, and update the data and handle exceptions when necessary.

[0014] A further technical solution of the present application: the step S6 further includes the following steps: S6.1, Real-time data analysis and pattern recognition,The immediate intention prediction module first receives and analyzes the 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 likelihood assessment, combining traffic rules, historical data and real-time data analysis to predict the driver's likely immediate behavior, taking into account driver habits, traffic conditions and road conditions; S6.3, generate an immediate intention prediction, and determine the most likely immediate intention of the driver based on the behavior prediction and likelihood assessment; S6.4, real-time intention communication and strategy formulation, sending the predicted real-time intention to the real-time strategy module, and formulating specific control strategies based on these intentions; S6.5, monitor, update predictions and optimize the system. Continuously monitor changes in vehicle and road information, and update immediate intention predictions in real time. If the predicted behavior does not match the actual behavior or an abnormal situation is detected, adjust the prediction model and re-predict. At the same time, record the prediction results and actual behavior for subsequent system optimization and learning.

[0015] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects: The automatic turn signal control system of the present invention significantly improves driving safety and efficiency through precise advance prediction and timely response mechanism. The system uses advanced sensor technology and artificial intelligence algorithms to identify the driver's intentions in advance. Whether in turning, changing lanes, overtaking or parking on the side of the road, the system can predict the driver's behavior and automatically turn on the corresponding turn signal in advance. This prediction mechanism not only reduces the driver's operating burden, but also improves the prompt effect on surrounding traffic participants, and effectively prevents traffic accidents caused by improper use of turn signals. At the same time, the system can respond to changes in traffic conditions and road environment in real time, optimize the use of turn signals, and ensure that the driver's intentions can be conveyed in a timely and accurate manner in various driving scenarios, thereby improving overall traffic fluency and road safety. In addition, the system's intelligent learning and optimization functions enable it to continuously adapt to the driver's habits and improve the accuracy of predictions, providing users with a more intelligent, safe and convenient driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a system block diagram of the present invention; Figure 2 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0017] 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. The present invention is further described below in combination with the embodiments.

[0018] See also Figure 1 and Figure 2 , In an embodiment of the present application, a turn signal control system capable of automatic steering, a turn signal control system capable of automatic steering, the control system comprises a user interaction module, a navigation and route planning module, a global intention prediction module, a fixed strategy module, a turn signal control module, a sensor integration and data processing module, an instant intention prediction module, an instant 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 back the interaction information 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 to optimize route planning, obtain and plan the best route, and select the navigation mode; The global intention prediction module is used to predict the driver's fixed intention according to the optimal route, and feed the fixed intention 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 obtain vehicle and road information on the optimal route in real time according to the optimal route, and feed the vehicle and road information back to the immediate intention prediction module; The immediate intention prediction module is used to predict the driver's immediate intention based on the vehicle and road information, and feed it back to the immediate strategy module; The real-time strategy module is used to select an immediate control strategy according to the predicted immediate intention and feed it back to the turn signal control module.

[0019] 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 through voice or interface, obtains destination information, and passes it to the navigation and route planning module. The module optimizes the route using real-time traffic data, determines the optimal path, and selects the corresponding navigation mode. Then, the global intention prediction module analyzes the route, predicts fixed intentions such as turning or U-turning, and notifies the fixed strategy module. The fixed strategy module selects a preset control strategy based on these intentions and guides the turn signal control module to execute.

[0020] At the same time, 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 immediate intention prediction module. The module analyzes the data, predicts the driver's immediate intention, such as changing lanes or overtaking, and passes the information to the immediate strategy module. The immediate strategy module then selects the corresponding immediate control strategy and sends it to the turn signal control module to automatically adjust the turn signal.

[0021] The scenario definition module is responsible for generating specific driving scenarios and assigning them to the prediction module so that the system can respond to specific situations. Finally, the feedback module collects system performance data and driver feedback, and the learning optimization module uses this data to optimize the prediction algorithm and control strategy to ensure that the system continues to improve and adapt to different driving habits and traffic conditions. In this way, the system realizes automatic control of turn signals and improves driving safety and convenience.

[0022] Furthermore, the navigation mode is divided into a pilot mode and a path-finding mode, and the pilot mode is used to provide navigation for the driver and obtain the optimal route; The path-finding mode is used to obtain the optimal route.

[0023] Further, the situation definition module is used to generate driving situations and assign the driving situations to the global intention prediction module and the immediate intention prediction module respectively; The driving situations include turning, changing lanes, overtaking, returning to the original lane after overtaking, leaving a parking space, making a U-turn and pulling over.

[0024] 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 place, turning on the left turn signal when making a U-turn, and turning on the right turn signal when parking by the roadside; The instant 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.

[0025] Furthermore, 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-motor vehicles, and weather conditions.

[0026] Specifically, in this embodiment, after the system is started, the user interaction module first communicates with the driver through the vehicle interface or voice recognition system to obtain the destination information. This process may use natural language processing technology to understand and process the driver's voice instructions. After obtaining the destination information, the navigation and route planning module immediately works, using GPS technology and real-time traffic data, and using algorithms such as Dijkstra or A* algorithms to calculate and plan the optimal route. This module also determines whether to use the pilot mode or the path finding mode. The pilot mode provides detailed navigation instructions, while the path finding mode only provides route guidance.

[0027] Next, the global intention prediction module analyzes the optimal route, identifies key nodes on the route, such as turning points and intersections, and predicts the driver's fixed intentions, such as turning and U-turns. This module may use machine learning algorithms, such as decision trees or random forests, to identify patterns and predict intentions based on historical driving data.

[0028] The fixed strategy module selects a preset control strategy based on the predicted fixed intention, such as turning on the corresponding turn signal in advance when turning, and sends instructions to the turn signal control module for execution. These control strategies may be based on the knowledge base of the expert system, which contains traffic rules and best practices.

[0029] The sensor integration and data processing module collects real-time information such as the vehicle's speed, position, direction, and road information such as lane lines, traffic signs, and traffic flow. This module may integrate multiple sensors, such as radar, LiDAR, cameras, and accelerometers, and integrate the data from these sensors through data fusion technology to provide comprehensive environmental perception.

[0030] The immediate intention prediction module analyzes real-time vehicle and road information, predicts the driver's immediate intention, such as changing lanes and overtaking, and feeds back to the immediate strategy module. This module may use deep learning technology, especially convolutional neural networks (CNN) to process visual data, and recurrent neural networks (RNN) to process time series data.

[0031] The real-time strategy module selects the corresponding control strategy based on the predicted real-time intention, such as turning on the corresponding turn signal when changing lanes, and sends instructions to the turn signal control module for execution. These control strategies may be based on reinforcement learning algorithms, which learn the optimal behavior strategy through interaction with the environment.

[0032] The scenario definition module generates specific driving scenarios, such as turning, changing lanes, overtaking, etc., and assigns these scenarios to the global intention prediction module and the immediate intention prediction module so that the system can respond to specific situations. This module may use a rule engine to define and handle different driving scenarios.

[0033] The turn signal control module combines fixed control strategies and immediate control strategies to automatically control the on and off of the turn signal to inform other road users of the driver's intention in advance. This module may use electronic control unit (ECU) technology to directly control the vehicle's hardware.

[0034] Finally, the system collects performance data and driver feedback through the feedback module, and the learning optimization module analyzes this data to optimize the prediction algorithm and control strategy to improve system performance. This process may involve big data analysis and machine learning technology to continuously improve the accuracy and response speed of the system.

[0035] Through this implementation method, the system can predict the driver's intention in advance and automatically control the turn signal, reducing the driver's operating burden and improving driving safety, while ensuring that the use of the turn signal complies with traffic regulations and enhances the driving experience.

[0036] The present application provides a control method for a turn signal control system capable of automatic steering, the control method comprising the following steps: S1, starting the vehicle to activate the turn signal control system capable of automatic steering, and activating the user interaction module to communicate with the driver to 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 the navigation mode or path-finding mode, and feed back the optimal route information to the global intention prediction module and the sensor integration and data processing module; S3, global intention prediction predicts the driver's fixed intention based on the optimal route, and feeds the fixed intention back to the fixed strategy module; S4, selecting a corresponding fixed control strategy according to the fixed intention, and feeding back the fixed control strategy to the turn signal control module; S5. When the vehicle is driving, the sensor integration and data processing module is started to obtain vehicle and road information in real time, and the vehicle and road information is fed back to the immediate intention prediction module; S6, predicting the driver's immediate intention based on vehicle and road information, and feeding back the immediate intention to the immediate strategy module; S7, selecting a corresponding immediate control strategy according to the predicted immediate intention, and feeding back the immediate control strategy to the turn signal control module; S8. Combining the fixed control strategy and the real-time control strategy, controlling the turning lights on and off, and automatically turning on the corresponding turning lights when needed; S9, using the scenario definition module to generate driving scenarios, and assigning the driving scenarios to the global intention prediction module and the immediate intention 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 the 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.

[0037] Specifically, after the system is started, the user interaction module is first activated to interact with the driver through the vehicle interface or voice recognition technology to obtain destination information. This step uses natural language processing technology to understand and process the driver's instructions.

[0038] Subsequently, the navigation and route planning module uses the destination information provided by the driver and real-time traffic data to optimize the route planning and determine the optimal route using a path search algorithm such as the A* algorithm. The system selects the pilot mode or the path finding mode based on the complexity of the route and the driver's preference, and feeds the optimal route information back to the global intention prediction module and the sensor integration and data processing module.

[0039] The global intention prediction module analyzes the optimal route, identifies key nodes on the route, such as turning points and intersections, and predicts the driver's fixed intentions, such as turning and U-turns. This module may use machine learning algorithms, such as decision trees or random forests, to identify patterns and predict intentions based on historical driving data.

[0040] The fixed strategy module selects a preset control strategy based on the predicted fixed intention, such as turning on the corresponding turn signal in advance when turning, and sends instructions to the turn signal control module for execution. These control strategies may be based on the knowledge base of the expert system, which contains traffic rules and best practices.

[0041] During the driving process of the vehicle, the sensor integration and data processing module collects real-time information such as the vehicle's speed, position, direction, and road information such as lane lines, traffic signs, and traffic flow. This module may integrate multiple sensors, such as radar, LiDAR, cameras, and accelerometers, and integrate the data of these sensors through data fusion technology to provide comprehensive environmental perception.

[0042] The immediate intention prediction module analyzes real-time vehicle and road information, predicts the driver's immediate intention, such as changing lanes and overtaking, and feeds back to the immediate strategy module. This module may use deep learning technology, especially convolutional neural networks (CNN) to process visual data, and recurrent neural networks (RNN) to process time series data.

[0043] The real-time strategy module selects the corresponding control strategy based on the predicted real-time intention, such as turning on the corresponding turn signal when changing lanes, and sends instructions to the turn signal control module for execution. These control strategies may be based on reinforcement learning algorithms, which learn the optimal behavior strategy through interaction with the environment.

[0044] The scenario definition module generates specific driving scenarios, such as turning, changing lanes, overtaking, etc., and assigns these scenarios to the global intention prediction module and the immediate intention prediction module so that the system can respond to specific situations. This module may use a rule engine to define and handle different driving scenarios.

[0045] The turn signal control module combines fixed control strategies and immediate control strategies to automatically control the on and off of the turn signal to inform other road users of the driver's intention in advance. This module may use electronic control unit (ECU) technology to directly control the vehicle's hardware.

[0046] Finally, the system collects performance data and driver feedback through the feedback module, and the learning optimization module analyzes this data to optimize the prediction algorithm and control strategy to improve system performance. This process may involve big data analysis and machine learning technology to continuously improve the accuracy and response speed of the system. In this way, the system realizes the automatic control of the turn signal, improves the safety and convenience of driving, and ensures that the use of the turn signal complies with traffic regulations, improving the driving experience.

[0047] Furthermore, the step S3 also includes the following steps: S3.1, receiving route information, the global intention 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, analyzing route information, comparing the received route information with built-in map data to identify key nodes of the route, and analyzing key nodes on the route; S3.3, identify fixed intention points, based on traffic rules and driving habits, identify fixed intention points on the route where turn signals need to be used, and determine the specific type of each fixed intention point; S3.4, determine the fixed intention, for each identified fixed intention point, determine the driver's possible intention, and when approaching the turning point, predict whether the driver will turn left or right; S3.5. Calculate the intention time, estimate the expected time to reach each fixed intention point based on the vehicle's current speed, road conditions and distance to the fixed intention point, and determine when the turn signal needs to be turned on to comply with traffic regulations; S3.6, generating fixed intention instructions, generating corresponding control instructions for each predicted fixed intention; S3.7, sending fixed intention information, sending the predicted fixed intention information and corresponding control instructions to the fixed strategy module, ensuring that the information includes the intention type, estimated time and control instructions; S3.8, monitoring and updating and exception handling, continuously monitor the vehicle position and route information to update the fixed intention prediction when the route changes. If the route changes, re-identify and predict the fixed intention point; if the system detects an abnormal situation, trigger the exception handling process, which includes requesting driver confirmation, re-planning the route or temporarily turning off the automatic turn signal function.

[0048] Specifically, in this embodiment, the specific implementation is as follows: The global intention prediction module first waits for the navigation and route planning module to provide the optimal route information, which includes route details, estimated travel time, traffic conditions, etc. Using the vehicle's communication system, this information is transmitted to the global intention prediction module in real time. After receiving the information, the module compares this data with the built-in high-definition map data to accurately identify key nodes on the route, such as intersections, turning points, U-turn areas, etc. This step may involve geographic information system (GIS) technology and the vehicle's GPS positioning system to ensure an accurate match.

[0049] Next, the system identifies fixed-intention points on the route where turn signals are required based on the built-in traffic rules database and driving habits database, and determines the specific type of each fixed-intention point. For example, the system identifies whether an upcoming intersection requires a left turn or a right turn and marks it as a fixed-intention point. This step may be implemented using an expert system or a rule engine.

[0050] For each identified fixed intention point, the system predicts the driver's likely intention. For example, if the vehicle approaches an intersection marked as a right turn, the system predicts that the driver will perform a right turn. This prediction is based on historical data analysis, driving behavior pattern recognition, and may involve machine learning algorithms to improve prediction accuracy.

[0051] The system further calculates the intention time, which is an estimate of the expected time to reach each fixed intention point. This is based on the vehicle's current speed, real-time traffic information, and the distance to the fixed intention point. The system determines when the turn signal needs to be turned on to comply with traffic regulations, such as turning on the turn signal within a certain distance before turning. Based on these calculations, the system generates fixed intention instructions and formulates specific control strategies for each predicted fixed intention. For example, the system may generate an instruction: "Turn on the right turn signal 100 meters away from the next intersection."

[0052] The system then sends the predicted fixed intention information and corresponding control instructions to the fixed strategy module, ensuring that the information includes the intention type, expected time and control instructions. This communication process may be achieved through the vehicle's internal controller area network (CAN) bus or other in-vehicle communication protocols.

[0053] Finally, the system continuously monitors the vehicle's location and route information to update the fixed intent prediction when the route changes. If the route changes, the system will re-identify and predict the fixed intent point. If the system detects an abnormal situation, such as sensor failure or unclear route information, it triggers the exception handling process, which may include requesting driver confirmation, re-planning the route, or temporarily turning off the automatic turn signal function to ensure driving safety.

[0054] Through this series of detailed steps, the global intention prediction module can accurately predict the driver's fixed intention and generate control instructions in a timely manner, thereby realizing automatic control of the turn signal and improving driving safety and efficiency.

[0055] Furthermore, the 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 the vehicle's speed, position, direction information and road information in real time; S5.2, data preprocessing and environmental perception, performing denoising, filtering and format conversion preprocessing operations on the collected raw data, and identifying lane lines, traffic signs, signal lights and other road users 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, fuses data from different sensors to generate comprehensive vehicle and road environment information reports, providing accurate data support for real-time intention prediction; S5.5. Send information to the immediate intention prediction module and monitor the sensor status. Send information reports to the immediate intention prediction module and continuously monitor the working status of the sensor to ensure the continuity and accuracy of the data, and update the data and handle exceptions when necessary.

[0056] Specifically, first, the system starts the sensor integration and data processing module, activates all relevant sensors including GPS, speed sensor, direction sensor, radar, LiDAR, camera, etc., and performs self-tests to ensure that the sensors are working properly. The combination of these sensors can provide the vehicle's full range of perception capabilities, including but not limited to speed, position, direction, distance and speed of surrounding objects.

[0057] Subsequently, the system performs data preprocessing and environmental perception. The raw data undergoes preprocessing operations such as denoising, filtering, and format conversion to improve the accuracy and usability of the data. Image recognition technologies, such as computer vision algorithms, are used to analyze images captured by cameras to identify lane lines, traffic signs, signal lights, and other road users. At the same time, 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.

[0058] Next, the system analyzes traffic flow and vehicle density to predict possible traffic changes. This may involve using statistical analysis methods of historical traffic data and real-time data, as well as machine learning models to predict traffic flow trends. At the same time, the system monitors weather conditions such as rain, snow, fog, etc. This information can be obtained through integrated meteorological sensors or external data services, which has a significant impact on driving conditions and sensor performance.

[0059] In the data fusion and information report generation step, the system fuses the data from different sensors to generate a comprehensive vehicle and road environment information report. 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 the necessary data support for instant intention prediction.

[0060] Finally, the system sends the information report to the immediate intention prediction module and continuously monitors the working status of the sensor. This includes real-time monitoring of sensor parameters such as signal strength, battery life, and error range to ensure data continuity and accuracy. When necessary, the system will perform data updates and exception handling, such as sensor calibration, data re-collection, or switching backup sensors to ensure that the system can operate stably under various conditions. Through this series of detailed steps, the sensor integration and data processing module can provide real-time, accurate, and comprehensive vehicle and road information for the automatic steering light control system, providing a solid data foundation for immediate intention prediction and control decisions.

[0061] Furthermore, the step S6 also includes the following steps: S6.1, Real-time data analysis and pattern recognition,The immediate intention prediction module first receives and analyzes the 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 likelihood assessment, combining traffic rules, historical data and real-time data analysis to predict the driver's likely immediate behavior, taking into account driver habits, traffic conditions and road conditions; S6.3, generate an immediate intention prediction, and determine the most likely immediate intention of the driver based on the behavior prediction and likelihood assessment; S6.4, real-time intention communication and strategy formulation, sending the predicted real-time intention to the real-time strategy module, and formulating specific control strategies based on these intentions; S6.5, monitor, update predictions and optimize the system. Continuously monitor changes in vehicle and road information, and update immediate intention predictions in real time. If the predicted behavior does not match the actual behavior or an abnormal situation is detected, adjust the prediction model and re-predict. At the same time, record the prediction results and actual behavior for subsequent system optimization and learning.

[0062] Specifically, the immediate intention prediction module first receives real-time vehicle information 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 activities, traffic signal status, etc. Using 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, lane change tendencies, etc.

[0063] 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, which may be achieved by analyzing the driver's past behavior patterns while taking into account current traffic conditions and road conditions, such as congestion levels and slippery road conditions. Predicted behaviors may include changing lanes, overtaking, slowing down to avoid, etc.

[0064] Based on the behavior prediction and likelihood assessment, the module determines the most likely immediate intention of the driver. This step may use a probabilistic model or decision tree algorithm to evaluate the likelihood of different behaviors and select the most likely intention as the prediction result.

[0065] The module then sends the predicted immediate intentions to the immediate policy module, which formulates specific control strategies based on these intentions. The control strategies may include automatically turning on or off the turn signal under certain conditions, such as turning on the turn signal in advance when the driver intends to change lanes.

[0066] Finally, the system continuously monitors changes in vehicle and road information and updates the immediate intention prediction in real time. If the predicted behavior does not match the actual behavior or an abnormal situation is detected, the system adjusts the prediction model and re-predicts. This may involve online updating or retraining of the machine learning model. At the same time, the system records the prediction results and actual behavior for subsequent system optimization and learning to improve the accuracy of predictions and the effectiveness of control strategies. This step may use big data analysis technology and machine learning algorithms to continuously learn from actual driving data and optimize model parameters.

[0067] Through this series of detailed steps, the immediate intention prediction module can accurately predict the driver's immediate intention and formulate corresponding control strategies in a timely manner, thereby realizing intelligent control of turn signals and improving driving safety and efficiency.

[0068] The present invention significantly improves driving safety and efficiency. The system uses advanced sensor technology and artificial intelligence algorithms to identify the driver's intentions in advance. Whether in turning, changing lanes, overtaking or pulling over, the system can predict the driver's behavior and automatically turn on the corresponding turn signal in advance. This prediction mechanism not only reduces the driver's operating burden, but also improves the prompt effect on surrounding traffic participants, and effectively prevents traffic accidents caused by improper use of turn signals. At the same time, the system can respond to changes in traffic conditions and road environment in real time, optimize the use of turn signals, and ensure that the driver's intentions can be conveyed in a timely and accurate manner in various driving scenarios, thereby improving overall traffic fluency and road safety. In addition, the system's intelligent learning and optimization functions enable it to continuously adapt to the driver's habits and improve the accuracy of predictions, providing users with a more intelligent, safe and convenient driving experience.

[0069] The above is a schematic description of the present invention and its implementation methods, which is not restrictive. The drawings show only one implementation method of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and creatively designs a structure and an embodiment similar to the technical solution without departing from the purpose of the invention, they should all fall within the protection scope of the present invention.

[0070] In addition, it should be understood that although this specification is described according to implementation modes, not every implementation mode contains only independent technical solutions. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should take the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes 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 intention prediction module, a fixed strategy module, a turn signal control module, a sensor integration and data processing module, an immediate intention prediction module, an immediate 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 back the interaction information 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 to optimize route planning, obtain and plan the best route, and select the navigation mode; The global intention prediction module is used to predict the driver's fixed intention according to the optimal route, and feed the fixed intention 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 obtain vehicle and road information on the optimal route in real time according to the optimal route, and feed the vehicle and road information back to the immediate intention prediction module; The immediate intention prediction module is used to predict the driver's immediate intention based on the vehicle and road information, and feed it back to the immediate strategy module; The real-time strategy module is used to select an immediate control strategy according to the predicted immediate intention and feed it back to the turn signal control module.

2. The automatic steering turn signal control system according to claim 1, characterized in that: The navigation mode is divided into a pilot mode and a path-finding mode. The pilot mode is used to provide navigation for the driver and obtain the optimal route. The path-finding mode is used to obtain the optimal route.

3. The automatic steering turn signal control system according to claim 2, characterized in that: The situation definition module is used to generate driving situations and assign the driving situations to the global intention prediction module and the immediate intention prediction module respectively; The driving situations include turning, changing lanes, overtaking, returning to the original lane after overtaking, leaving a parking space, making a U-turn and pulling over.

4. The automatic steering turn signal control system according to claim 3, characterized in that: 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 place, turning on the left turn signal when making a U-turn, and turning on the right turn signal when parking by the roadside; The instant 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.

5. The automatic steering turn signal control system according to claim 1, characterized in that: 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-motor vehicles, and weather conditions.

6. A control method for a turn signal control system capable of automatic steering, characterized in that: Applied to the turn signal control system capable of automatic steering according to claims 1 to 5, the control method comprises the following steps: S1, starting the vehicle to activate the turn signal control system capable of automatic steering, and activating the user interaction module to communicate with the driver to 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 the navigation mode or path-finding mode, and feed back the optimal route information to the global intention prediction module and the sensor integration and data processing module; S3, global intention prediction predicts the driver's fixed intention based on the optimal route, and feeds the fixed intention back to the fixed strategy module; S4, selecting a corresponding fixed control strategy according to the fixed intention, and feeding back the fixed control strategy to the turn signal control module; S5. When the vehicle is driving, the sensor integration and data processing module is started to obtain vehicle and road information in real time, and the vehicle and road information is fed back to the immediate intention prediction module; S6, predicting the driver's immediate intention based on vehicle and road information, and feeding back the immediate intention to the immediate strategy module; S7, selecting a corresponding immediate control strategy according to the predicted immediate intention, and feeding back the immediate control strategy to the turn signal control module; S8. Combining the fixed control strategy and the real-time control strategy, controlling the turning lights on and off, and automatically turning on the corresponding turning lights when needed; S9, using the scenario definition module to generate driving scenarios, and assigning the driving scenarios to the global intention prediction module and the immediate intention 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 the 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.

7. The control method of the turn signal control system capable of automatic steering according to claim 6, characterized in that: The step S3 also includes the following steps: S3.1, receiving route information, the global intention 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, analyzing route information, comparing the received route information with built-in map data to identify key nodes of the route, and analyzing key nodes on the route; S3.3, identify fixed intention points, based on traffic rules and driving habits, identify fixed intention points on the route where turn signals need to be used, and determine the specific type of each fixed intention point; S3.4, determine the fixed intention, for each identified fixed intention point, determine the driver's possible intention, and when approaching the turning point, predict whether the driver will turn left or right; S3.

5. Calculate the intention time, estimate the expected time to reach each fixed intention point based on the vehicle's current speed, road conditions and distance to the fixed intention point, and determine when the turn signal needs to be turned on to comply with traffic regulations; S3.6, generating fixed intention instructions, generating corresponding control instructions for each predicted fixed intention; S3.7, sending fixed intention information, sending the predicted fixed intention information and corresponding control instructions to the fixed strategy module, ensuring that the information includes the intention type, estimated time and control instructions; S3.8, monitoring and updating and exception handling, continuously monitor the vehicle position and route information to update the fixed intention prediction when the route changes. If the route changes, re-identify and predict the fixed intention point; if the system detects an abnormal situation, trigger the exception handling process, which includes requesting driver confirmation, re-planning the route or temporarily turning off the automatic turn signal function.

8. The control method of the turn signal control system capable of automatic steering according to claim 6, characterized in that: The 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 the vehicle's speed, position, direction information and road information in real time; S5.2, data preprocessing and environmental perception, performing denoising, filtering and format conversion preprocessing operations on the collected raw data, and identifying lane lines, traffic signs, signal lights and other road users 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, fuses data from different sensors to generate comprehensive vehicle and road environment information reports, providing accurate data support for real-time intention prediction; S5.

5. Send information to the immediate intention prediction module and monitor the sensor status. Send information reports to the immediate intention prediction module and continuously monitor the working status of the sensor to ensure the continuity and accuracy of the data, and update the data and handle exceptions when necessary.

9. The control method of the turn signal control system capable of automatic steering according to claim 7, characterized in that: The step S6 also includes the following steps: S6.1, Real-time data analysis and pattern recognition,The immediate intention prediction module first receives and analyzes the 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 likelihood assessment, combining traffic rules, historical data and real-time data analysis to predict the driver's likely immediate behavior, taking into account driver habits, traffic conditions and road conditions; S6.3, generate an immediate intention prediction, and determine the most likely immediate intention of the driver based on the behavior prediction and likelihood assessment; S6.4, real-time intention communication and strategy formulation, sending the predicted real-time intention to the real-time strategy module, and formulating specific control strategies based on these intentions; S6.5, monitor, update predictions and optimize the system. Continuously monitor changes in vehicle and road information, and update immediate intention predictions in real time. If the predicted behavior does not match the actual behavior or an abnormal situation is detected, adjust the prediction model and re-predict. At the same time, record the prediction results and actual behavior for subsequent system optimization and learning.

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